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Cooperative detection based on the adaptive interacting multiple model-information filtering algorithm
Abstract
This paper develops a multiaircraft cooperative detection scheme for effectively improving the accuracy when detecting the highly maneuvering target, where the cooperative estimate and observation trajectory planning of multiaircraft are addressed simultaneously. Based on the error compression ratio of the Markov matrix, an adaptive IMM algorithm is proposed for single detection, the convergence characteristic of which is theoretically proven meanwhile. Then, an adaptive IMM-information filtering algorithm is derived by embedding information filtering into the adaptive IMM algorithm for the fusion of the detection information. Next, the coordinated planning of observation trajectories is achieved for multiaircrafts by relying on receding horizon optimization (RHO) and the detection information. The cooperative detection and trajectory planning scheme is illustrated by the simulation results.
... Multi-vehicle cooperative target capture has strategic and quantitative advantages compared to single vehicle [4]. By cooperating with each other [5], the gaming countermeasures and detection and guidance accuracy can be greatly improved, thus successfully achieving cooperative interception of maneuvering targets [6]. ...
... Wang et al. [5] realized target fusion estimation using a distributed unscented information flter. Te detection data was combined using an adaptive IMM-information fltering technique by Zhang et al. [6]. Another important feld of multi-UAVs' coordinated positioning is to enhance the target state estimation by studying multi-UAVs' position confguration. ...
This paper focuses on a coordinated tracking planning method of multiple unmanned aerial vehicles (UAVs), which was deployed in the best positions to better fulfill the marine target localization tasks when approaching the target. The optimal planning of multi-UAVs was implemented using an online centralized nonlinear model predictive control (NMPC) based on the target state’s uncertainty criteria. The penalty function is used to solve UAV platform dynamic performance in the model predictive control method to consider the more realistic situation. The coordinated planning problems of multi-UAVs are numerically simulated and compared with the Lyapunov vector field guidance (LVFG) method under classical mission scenarios. Simulation results demonstrate that the algorithm can maintain the optimal observation configuration of multi-UAVs to improve the marine target positioning accuracy, verifying the feasibility and superiority of this method. Furthermore, the simulation results can provide a useful reference for the flight control law design of multi-UAVs with optimal observation configuration.
... In a previous study [21], the interceptor's sensor is set to only measure the LOS angle information against the target. The method of cooperative detection can be used to estimate the target's state more precisely based on interactive multiple model (IMM) filter and multiple model particle filter (MMPF) [22][23] . Although the results illustrated that the way of cooperative detection could significantly improve the estimation accuracy and interception performance, powerful real-time data processing capabilities were required. ...
For the case that two pursuers intercept an evasive target, the cooperative strategies and state estimation methods taken by pursuers can seriously affect the guidance accuracy for the target, which performs a bang For the case that two pursuers intercept an evasive target, the cooperative strategies and state estimation methods taken by pursuers can seriously affect the guidance accuracy for the target, which performs a bang-bang evasive maneuver with a random switching time. Combined Fast multiple model adaptive estimation (Fast MMAE) algorithm, the cooperative guidance law takes detection configuration affecting the accuracy of interception into consideration. Introduced the detection error model related to the line-of-sight (LOS) separation angle of two interceptors, an optimal cooperative guidance law solving the optimization problem is designed to modulate the LOS separation angle to reduce the estimation error and improve the interception performance. Due to the uncertainty of the target bang-bang maneuver switching time and the effective fitting of its multi-modal motion, Fast MMAE is introduced to identify its maneuver switching time and estimate the acceleration of the target to track and intercept the target accurately. The designed cooperative optimal guidance law with Fast MMAE has better estimation ability and interception performance than the traditional guidance law and estimation method via Monte Carlo simulation.
Machine vision has been extensively researched in the field of unmanned aerial vehicles (UAV) recently. However, the ability of Sense and Avoid (SAA) largely limited by environmental visibility, which brings hazards to flight safety in low illumination or nighttime conditions. In order to solve this critical problem, an approach of image enhancement is proposed in this paper to improve image qualities in low illumination conditions. Considering the complementarity of visible and infrared images, a visible and infrared image fusion method based on convolutional sparse representation (CSR) is a promising solution to improve the SAA ability of UAVs. Firstly, the source image is decomposed into a texture layer and structure layer since infrared images are good at characterising structural information, and visible images have richer texture information. Both the structure and the texture layers are transformed into the sparse convolutional domain through the CSR mechanism, and then CSR coefficient mapping are fused via activity level assessment. Finally, the image is synthesised through the reconstruction results of the fusion texture and structure layers. In the experimental simulation section, a series of visible and infrared registered images including aerial targets are adopted to evaluate the proposed algorithm. Experimental results demonstrates that the proposed method increases image qualities in low illumination conditions effectively and can enhance the object details, which has better performance than traditional methods.
This paper mainly studies the problem of using UAVs to provide accurate remote target indication for hypersonic projectiles. Based on the optimal trajectory trends and feedback guidance methods, a new cooperative control algorithm is proposed to optimize trajectories of multi-UAVs for target tracking in approaching stage. Based on UAV kinematics and sensor performance models, optimal trajectory trends of UAVs are analyzed theoretically. Then, feedback guidance methods are proposed under the optimal observation trends of UAVs in the approaching target stage, producing trajectories with far less computational complexity and performance very close to the best-known trajectories. Next, the sufficient condition for the UAV to form the optimal observation configuration by the feedback guidance method is presented, which guarantees that the proposed method can optimize the observation trajectory of the UAV in approaching stage. Finally, the feedback guidance method is numerically simulated. Simulation results demonstrate that the estimation performance of the feedback guidance method is superior to the Lyapunov guidance vector field (LGVF) method and verify the effectiveness of the proposed method. Additionally, compared with the receding horizon optimization (RHO) method, the proposed method has the same optimization ability as the RHO method and better real-time performance.
An algorithm for tracking a maneuvering target under measurement origin uncertainties is derived based on the approximation and propagation of the target state posterior distribution by combining Bayesian decision theory and suitable hypothesis merging procedures. Simulation results show that the main feature of this algorithm is its ability to significantly reduce the estimation error during non-maneuvering periods, making it quite suitable for tracking low maneuvering aircrafts. At the same time, the proposal is able to keep very low levels of track loss rate even for scenarios with high false alarm probability and trajectories with high degree of maneuverability. This proposal presents overall superiority when compared to the Interacting Multiple-Model with Probabilistic Data Filtering (IMMPDAF) solution, with an affordable increase in computational cost.
Budget-constraint admissible output consensus tracking design and analysis for the intermittent-interaction singular multiagent networks with given cost budgets are addressed in this paper. Two critical characteristics of many practical multiagent networks are focused. The first one is that the dynamics of each agent contains dynamical modes, as well as impulsive modes and static modes, and interactions among agents are intermittent. The second one is that the network is constrained by the cost budget, and the weighted design between tracking performances and energy consumptions should be guaranteed under budget constraints. Firstly, in virtue of the decomposition of observability, a new singular consensus tracking protocol with the dynamic output feedback is proposed by intermittent protocol states. Meanwhile, a cost index is constructed by considering both the tracking performance and the energy consumption. Then, criteria of budget-constraint admissible output consensus tracking analysis and design are given. By designing the gain matrices of consensus tracking protocols, not only multiagent networks with dynamical modes and static modes reach output consensus tracking while eliminating impulsive modes, but also the upper bound of the cost index can be kept within the cost budget; that is, the weighted design with the budget constraint is achieved. Finally, theorical results are demonstrated by numerical simulations.
This paper investigates the problem of spacecraft interception game with incomplete-information and proposes switching strategies based on the differential game theory. In the interception process, the target can switch among multiple strategies to evade the interceptor. This leads to the formulation of switching strategies pursuit problem for the interceptor. Firstly, in the complete-information scenario, the game switching strategies are derived to match target different strategies. Then, for the scenario where the target information is not available to the interceptor, the interacting multiple model approach, combined with the mode-matched smooth variable sliding filters, is proposed to estimate target unknown information. After obtaining the incomplete information, the estimation-dependent interception idea is incorporated into the game without the target switching strategies. Finally, simulations are presented to demonstrate the performance of the proposed switching strategies scheme. The numerical simulations indicate that the target information can be estimated effectively using the interacting multiple mode approach, and the well interception performance can be achieved.
A multi-agent engagement scenario is considered in which a high-value aircraft launches two defenders to intercept two homing missiles aimed at the aircraft. Under the assumption that all aircrafts have first-order linear dynamic characteristics, a combined multiple-mode adaptive estimation (MMAE) and a two-way cooperative optimal guidance law are proposed for the target–defenders team. Considering the full cooperation of the target and both the two defenders, the two-way cooperative strategies provide the analytical expressions for their optimal control input, enabling the target–defenders team to intercept the missiles with minimal control effort. To successfully intercept the missiles, MMAE is used to identify the guidance laws adopted by the missiles and estimate their states. The simulation results show that the target cooperating with the defenders to perform lure manoeuvres for the missiles can improve the guidance performance of the defenders as well as reduce the control effort of the defenders for intercepting the missiles.
In this paper, two cooperative guidance schemes with impact angle and time constraints are proposed for multiple missiles cooperatively intercepting a maneuvering target in the presence or absence of a leader missile. First, cooperative interception with impact angle constraint is formulated based on the relative motion equation between the interceptors. Subsequently, the design of the guidance law is divided into two stages: In the first stage, based on finite-time consensus theory and the super-twisting control algorithm, the cooperative guidance law in the direction of the line of sight (LOS) is derived to control the impact time so that all missiles can simultaneously intercept the target in finite time. Then, based on finite-time sliding mode control and the super-twisting control algorithm, a finite-time convergence cooperative guidance law in the normal direction to the LOS is developed to control the intercept angle and the LOS angle rate so that each missile can accurately hit the target at a desired angle. Finally, by controlling the consensus error of flight time in the presence of a leader missile, a finite-time cooperative guidance method in the “leader-follower” scenario is derived for simultaneous arrival at the target. The proposed methods can effectively suppress chattering and ensure fast convergence in finite time. Simulation experiments are conducted to verify the effectiveness of the proposed cooperative guidance schemes.
When threatened by a pursuer, an evading aircraft launches two defenders to accomplish a cooperative evasion, constituting a four-aircraft interception engagement. Under the assumption that the pursuing aircraft adopts the augmented proportional guidance law and first-order dynamics, a cooperative intercept mathematical model with an intercept angle constraint is established, allowing for the cooperative maneuvering of the evader. Based on the differential linear matrix inequality (DLMI), a controller design method of input and output finite time stability (IO-FTS) is proposed and applied to the aircraft’s cooperative intercept scenario. A cooperation performance analysis is carried out for two cases: (1) two defenders intercept the pursuer with various intercept angle constraints, and (2) the evader acts in a lure role to cooperate with two defenders. The simulation results indicate that the proposed method of controller design has the ability to guarantee that the two defenders intercept the pursuer at the preassigned intercept angles. The cooperative intercept scenario with a lure role is shown to be a very effective method for reducing the maximum required acceleration for defenders, which confirms the availability and advantage of cooperation. The strong adaptability and robustness of the cooperative guidance law with respect to various initial launch conditions is also verified.
As one of the most critical issues for target track, α-jerk model is an effective maneuver target track model. Non-Gaussian noises always exist in the track process, which usually lead to inconsistency and divergence of the track filter. A novel Kalman filter is derived and applied on α-jerk tracking model to handle non-Gaussian noise. The weighted least square solution is presented and the standard Kalman filter is deduced firstly. A novel Kalman filter with the weighted least square based on the maximum correntropy criterion is deduced. The robustness of the maximum correntropy criterion is also analyzed with the influence function and compared with the Huber-based filter, and, moreover, the kernel size of Gaussian kernel plays an important role in the filter algorithm. A new adaptive kernel method is proposed in this paper to adjust the parameter in real time. Finally, simulation results indicate the validity and the efficiency of the proposed filter. The comparison study shows that the proposed filter can significantly reduce the noise influence for α-jerk model.
This paper proposes a novel method to cope with local measurement ambiguity problem in particle filtering. The ambiguity of the measurement has been attributed as a crucial cause of filter degradation and divergence. Given the observation that the ambiguous measurement update is contributed by not only the shape of the measurement model but also the prior distribution of the filter estimate, we adopt a solution to the out-of-sequence measurement (OOSM) problem on the framework of the particle filter with sequential importance resampling (SIR). Once an ambiguous measurement update is detected, the proposed method skips the measurement update at the time step and utilizes the measurement later when the particle distribution becomes adequate for the measurement update. This strategy provides a remedy to the ambiguity problem to obtain accurate current position estimate with lower covariance. Numerical simulation is presented to demonstrate effectiveness and performance of the proposed method. Compared to other methods such as the standard particle filter, the auxiliary particle filter, the mixture particle filter, and the receding-horizon Kalman filter, the proposed method shows better performance in terms of root-mean-square error and estimated covariance.
Cooperative estimation/guidance for a team of missiles is the topic of this paper. An example scenario is considered where an aircraft simultaneously launches several cooperative defending missiles as a countermeasure against an attacking homing missile. A new reduced-order estimation scheme based on information sharing to cooperatively estimate the relative states and the unknown parameters of the attacking missile is proposed. Each defending missile shares its own noise-corrupted line-of-sight angle measurement with the rest of the team. The observability of this multi-line-of-sight measuring environment is enhanced by cooperatively imposing nonnegative relative intercept angles between consecutive defenders. The ability of the proposed strategy to protect the targeted aircraft is studied for a two-defender case via extensive Monte Carlo simulations. The effect of different values of the commanded relative intercept angle on the pure estimation as well as on the intertwined guidance-estimation performance is carefully analyzed for various considerations of the defenders’ maneuverability.
This paper proposes a ground vehicle tracking method using an airborne ground moving target indicator radar where the surrounding geographic information is considered to determine vehicle's movement type as well as constrain its positions. Multiple state models corresponding to different movement modes are applied to represent the vehicle's behaviour in different terrain conditions. Based on geographic conditions and multiple state models, a constrained variable structure multiple model particle filter algorithm is proposed. Compared with the traditional multiple model particle filtering schemes, the proposed algorithm utilises a particle swarm optimisation technique which generates more effective particles and generated particles are constrained into the feasible geographic region. Numerical simulation results in a realistic environment show that the proposed method achieves better tracking performance compared with current state-of-the-art ones for manoeuvring vehicle tracking.
This paper investigates the problem of fusion filtering for a class of networked multisensor fusion systems with multiple uncertainties, including sensor failures, stochastic parameter uncertainties, random observation delays, and packet dropouts. A novel model is proposed to describe the random observation delays and packet dropouts, and a robust optimal fusion filter for the addressed networked multisensor fusion systems is designed using the innovation analysis method. The dimension of the designed filter is the same as that of the original system, which helps to reduce computation cost compared with the augmentation method. Moreover, robust reduced-dimension observation-fusion Kalman filters are proposed to further reduce the computation burden. Note that the designed fusion filter gain matrices can be computed off-line, as they depend only on the upper bounds of random delays and on the occurrence probabilities of delays and sensor failures. Some sufficient conditions are presented for stability and optimality of the designed fusion filters, and a steady-state fusion filter is also given for the networked multisensor fusion systems. Simulations show the effectiveness of the proposed fusion filters.
This paper addresses the problem of modeling maneuvering target motion in tracking applications. A target trajectory can typically be divided into segments with different dynamic motion modes, such as a constant velocity motion, a constant acceleration motion or a constant turn rate motion. To integrate the different motion modes into a uniform model, a Constant Speed Changing Rate and Constant Turn Rate (CSCRCTR) model is proposed. A new state vector is defined, and the state transition function is derived. Based on the CSCRCTR model, we present a tracking algorithm using a particle filter. The performances of the CSCRCTR model, the uniform model (UM) and the interacting multiple model (IMM) for tracking a simulated maneuvering target are compared and show that the CSCRCTR model maintains a good consistency for different types of motions and achieves better accuracy than UM and IMM when maneuvers occur.
Radar tracking of a ballistic target is a topic presenting very complex issues especially when the characteristics of the objects flying into the Earth's atmosphere are poorly known. As the target identification is required to obtain good performance, a technique frequently employed in the practical applications is the multiple model filter. This study, thought the increasing interest for this class of estimators, aims to identify the key parameters of the problem by making recognisable their effects on the solution. Thus – along with the ideal operative conditions – the relative geometry target-sensor, the presence of missing plots, the measurement uncertainties and the credibility of filter predictions are investigated. In order to have a strict benchmark of evaluation the multiple model approach performance is compared with the optimal estimate given by posterior Cramér–Rao lower bounds.
The terminal phase (end game) of an encounter between an air-to-air missile equipped with an active monopulse radar seeker and an evading fighter aircraft, possibly employing electronic countermeasures in the form of electronic jinking, is addressed. The missile uses a guidance law derived from linear differential game theory, which is implemented by using a multiple-model adaptive estimator (MMAE). The MMAE identifies the evasion strategy of the aircraft, which consists of the combination of evasion maneuver and electronic jinking. An extensive numerical study is used to demonstrate the viability of the concept. In comparison with a previously proposed mixed strategy guidance methodology, the new MMAE-based approach leads to a substantial improvement in the guaranteed single-shot kill probability for the missile.
This paper proposes UAV rendezvous and standoff tracking guidance laws against a moving target using differential geometry. Searching and subsequent tracking of moving ground based target is one of the primary capabilities of cooperative UAVs. In performing such missions, UAVs are to approach a target and keep a certain distance, known as a standoff distance. This allows target tracking without being noticed and acquisition of accurate target information. In this study, standoff target tracking is proposed using the solution of differential geometry between the UAV and the target. The proposed algorithm brings several advantages along with its inherent simplicity: rigorous stability, explicit use of a target velocity, and tuning parameter reduction. The feasibility and performance of the proposed approach is not only mathematically analysed, but also verified through realistic scenarios.
http://link.springer.com/article/10.1007%2Fs10846-012-9751-0
This paper is concerned with distributed Kalman filtering for linear time-varying systems over multiagent sensor networks. We propose a diffusion Kalman filtering algorithm based on the covariance intersection method, where local estimates are fused by incorporating the covariance information of local Kalman filters. Our algorithm leads to a stable estimate for each agent regardless of whether the system is uniformly observable locally by the measurements of its neighbors which include the measurements of itself as long as the system is uniformly observable by the measurements of all the agents and the communication is sufficiently fast compared to the sampling. Simulation results validate the effectiveness of the proposed distributed Kalman filtering algorithm.
The Interacting Multiple Model (IMM) estimator is a suboptimal
hybrid filter that has been shown to be one of the most cost-effective
hybrid state estimation schemes. The main feature of this algorithm is
its ability to estimate the state of a dynamic system with several
behavior modes which can “switch” from one to another. In
particular, the IMM estimator can be a self-adjusting variable-bandwidth
filter, which makes it natural for tracking maneuvering targets. The
importance of this approach is that it is the best compromise available
currently-between complexity and performance: its computational
requirements are nearly linear in the size of the problem (number of
models) while its performance is almost the same as that of an algorithm
with quadratic complexity. The objective of this work is to survey and
put in perspective the existing IMM methods for target tracking
problems. Special attention is given to the assumptions underlying each
algorithm and its applicability to various situations
A model of target motion in three-dimensional space that includes position derivatives up to the third order is developed. Compared with available models, which include terms at the most up to the second derivative, the model introduced in this work, called the jerk model, can more accurately describe agile target maneuvers which are likely to contain significant higher order derivatives. A compatible 4-state Kalman filter to perform tracking in conjunction with the jerk model is also presented, and an initialization procedure for the filter is provided. The improved performance of the jerk model over a lower order model is illustrated through numerical simulation.
In this paper, a fuzzy multiobjective path planning method based on distributed predictive control is proposed to deal with the problem of cooperative searching and tracking of unknown ground moving target by multiple unmanned aerial vehicles (UAVs) in urban environment. Firstly, extended Kalman filter (EKF) is combined with probability estimation to predict the states of the unknown target. Secondly, the line of sight occlusion of buildings, and energy consumptions of UAVs and sensors are considered in path planning. The objective functions are designed as target coverage degree, control input cost of UAV and sensor energy consumption respectively. The cooperative surveillance path planning problem is transformed into multiobjective optimization with different importance levels. Thirdly, distributed predictive control is used to obtain the local optimal path of each UAV. The predictive states of UAVs in finite horizon are exchanged to build the collision avoidance constraint, and the minimum turning radius constraint is also addressed. Then, all the objectives are fuzzified to handle the different importance level requirement. The sensor energy consumption function with switch value is equivalently converted using Sigmoid function and sign function. According to the principle that the objective with higher priority has higher satisfactory degree, preemptive priorities are transformed into the relaxed order of satisfactory degrees. The best path satisfying the requirement of multiobjective optimization and importance levels can be obtained. Finally, the simulation results show the effectiveness of the proposed method by comparing with traditional multiobjective weighted algorithm.
This work investigates the challenging problem of fast robust fault tolerant attitude control for spacecraft to handle external disturbances, actuator failures and misalignments. More specially, a novel nonsingular terminal sliding mode based finite-time extended state observer is first designed to estimate and compensate for the lumped system faults or uncertainties. And the proposed extended state observer is analysed and proved to be stable in the sense of fast finite-time uniformly ultimately bounded stability. Then, utilizing the techniques of super-twisting and terminal sliding mode control synthetically, a novel continuous attitude control algorithm is developed. The finite-time stability of the closed-loop attitude control system is proved by using a continuously-differentiable, homogeneous and strict Lyapunov function. And also the proposed control scheme is continuous with the property of chattering restraining. Finally, some numerical simulation results are shown to verify the effectiveness and superior performances of the spacecraft attitude stabilization control system driven by the proposed fast robust fault tolerant attitude control scheme.
A spacecraft performing unknown maneuvers significantly increases the probability of a collision between spacecraft with neighboring orbits. Traditional orbit determination filters cannot robustly cope with unknown maneuvers. To track non-cooperative, unknowingly maneuvering spacecraft, a novel variable structure estimator is proposed based on the input compensation to a normal filter. We develop an observer to estimate the unknown maneuver and then analyze the estimated maneuver performance based on error propagation. Once the maneuver detector declares the occurrence of an unknown maneuver, the estimated maneuver is fed to an extended Kalman filter as compensation, which enables the estimator to work adaptively. Finally, a series of numerical simulations are carried out to demonstrate the effectiveness of the proposed variable structure estimator, and the estimation performances in the cases of constant and time-varying maneuvers are analyzed based on the simulation results.
The Position and Orientation System (POS) serves as a key component for the airborne remote sensing system, which integrates Strapdown Inertial Navigation System (SINS) and Global Position System (GPS) to provide the reliable and continuous motion compensation using Kalman Filter (KF). However, the high-order position errors resulting from C/A (Coarse/Acquisition) Code GPS cannot be effectively compensated or estimated by the traditional KF, which severely weakens the imaging quality. In this paper, we propose a Dual-rate Hybrid Filter (DHF) to deal with the high-order position errors based on Least Squares Support Vector Machine (LSSVM) and Kalman Filter. DHF builds a low update rate filter by integrating high-precision SINS and online LSSVM to isolate the high-order position errors. Meanwhile, the high update rate filter of DHF maintains the advantages of traditional SINS/GPS integrated navigation system to restrain the accumulation errors of system. The experimental results show that the proposed method significantly reduces the high-order position errors by 84.6% at each sampling period comparing with the conventional single KF based SINS/GPS integrated navigation system.
Aiming at the trajectory optimization of the solar-powered UAVs (SUAVs) cooperative target tracking in urban environment, the distributed model predictive control (DMPC) method based on Adaptive Grasshopper Optimization Algorithm (AGOA) is proposed in this paper. Firstly, the cooperative target tracking problem in urban environment is modeled by formulating the SUAVs kinematic and target models, the urban environment constraints, solar power harvesting and consumption models for SUAV, and sight occlusions by constructions. Especially, the sight occlusions in urban environment for SUAV are taken into consideration for the first time in this paper. A judgment method of sight occlusions for SUAV is proposed to make the calculation of the energy index more precise. Second, based on the precise modeling, the DMPC method is adopted as the framework for trajectory optimization in real time. Third, AGOA, a novel intelligent algorithm to mimic the behaviors of grasshoppers, is proposed to be the DMPC solver. The proposed AGOA has a better searching ability than the traditional GOA and some other intelligent algorithms by introducing some improvement measures e.g. the natural selection strategy, the democratic decision-making mechanism, and the dynamic feedback mechanism based on the 1/5 Principle. Finally, the effectiveness of the proposed method is demonstrated by the simulations.
In this paper, a novel second-order sliding mode control (NSOSMC) scheme with finite time convergence is proposed for the autopilot design of skid-to-turn (STT) missiles considering coupling effects to track the desired maneuver commands. Firstly, the control system formulation is developed based on the mathematical model for nonlinear missile dynamics with system uncertainties and unknown multiple disturbances. Secondly, the NSOSMC scheme is developed by using a PID sliding mode surface with twisting control algorithm, which is to suppress the system uncertainties and unknown external disturbances, and to eliminate the chattering phenomenon. Then, the proposed NSOSMC law is proved to be finite time convergent to the origin. Finally, the effectiveness and robustness of the proposed NSOSMC scheme is verified through the numerical simulations with comparisons.
The traditional multiple model cardinalized probability hypothesis density (MMCPHD) filter uses a fixed model set for all targets. It is clearly inefficient and may cause the decrease of performance in the situa- tions where the size of the model set is large and the scene is complicated. This paper presents a variable structure multiple model (VSMM) version of the Gaussian mixture cardinalized probability hypothesis density (GMCPHD) filter based on the expected mode augmentation (EMA) for the multiple maneuver- ing target tracking. The GMCPHD filter which is suitable for the VSMM is derived, thus different model sets can be used for different tar gets. Then EMA algorithm which is an efficient model set adaptation method is introduced and applied to the VSMM version of the GMCPHD (VSMM-GMCPHD) filter. This method costs less computational time and has better estimate precision than that of the traditional mul- tiple model version of GMCPHD (MM-GMCPHD) filter. The gating technique is utilized to further improve the computational efficiency. Simulation results verify the performance of the proposed methods. The es- timate precision of VSMM-GMCPHD filters with and without the gate are almost the same, however the former one is more cost-effective. Both of the two VSMM-GMCPHD filters outperform the MM-GMCPHD filter.
This paper investigates a velocity-free finite-time attitude control scheme for a rigid spacecraft with actuator saturation and external disturbances. Initially, a finite-time observer is designed to compensate the unknown angular velocity information. With the estimated values, a finite-time controller is further developed under which the time-varying reference attitude trajectory can be tracked precisely. Moreover, input saturation problem is solved by adaptive method. Rigorous Lyapunov-based analysis shows that the states of the closed-loop system converge to a small neighborhood of the origin in finite time. Finally, the dynamic performance of attitude control system is presented by numerical simulation examples and the superiority of the finite-time control scheme is verified.
An optimal path synthesis problem for a moving observer that performs angular observations over a target moving uniformly along a straight line on a plane is solved. It is supposed that elevation and azimuth angles can be observed when the observer moves in space and only the azimuth angle can be observed when the observer moves on a plane. Observer’s trajectories are obtained with the help of Pontryagin’smaximum principle as numerical solutions of an optimal control problem. As a performance criterion the trace of covariance matrix of the target motion elements estimate is used. A possibility of solving the problem in real time on board for unmanned aerial vehicle is investigated. A comparison with the scenario of two unmanned aerial vehicles using is given.
As distributed parameter systems, dynamics of freeway traffic are dominated by the current traffic parameter and boundary fluxes from upstream/downstream sections or on/off ramps. The difference between traffic demand-supply and boundary fluxes actually reflects the congestion level of freeway travel. This study investigates simultaneous traffic density and boundary flux estimation with data extracted from on-road detectors. The existing studies for traffic estimation mainly focus on the traffic parameters (density, velocity etc.) of mainline traffic and ignore flux fluctuations at boundary sections of the freeway. The authors propose a stochastic hybrid traffic flow model by extending the cell transmission model with Markovian multi-mode switching. A novel interacting multiple model filtering for simultaneous input and state estimation is developed for discrete-time Markovian switching systems with unknown input. A freeway segment of Interstate 80 East (I-80E) in Berkeley, Northern California, is chosen to investigate the performance of the developed approach. Traffic data is obtained from the performance measurement system.
This study is devoted to the problem of state estimate of discrete-time stochastic systems with Markov jump parameters. A robust algorithm. diagonal interacting multiple model algorithm based on H∞ filtering is presented for manoeuvering target tracking when noise of measurement is of unknown statistics. Extensive Monte Carlo simulations show the effectiveness and superiority of the proposed algorithm.
We present an information theoretic approach to develop an interacting multiple model (IMM) estimator. In the mixing and output steps of the proposed estimator, the weighted Kullback-Leibler (KL) divergence is used to derive the fusion of conditional probability density functions. A lower bound and an upper bound are derived for the error covariance of controllable and observable Markov jump linear systems. Simulation results are provided to verify the effectiveness of the proposed estimator.
A manoeuvring target tracking algorithm based on the autoregressive (AR) model is proposed. First, the AR model is incorporated into the Kalman filter (KF) for target tracking. The closed-form solution of the AR model coefficients is obtained by minimising the mean-square tracking error, and subject to the polynomial constraint of target motion. Then, based on the AR model, the proposed algorithm is constructed by combining the KF with the interacting multiple model (IMM) filter, coupled with the proposed detection schemes for manoeuvre occurrence and termination, as well as for switching initialisation. Simulations are performed to demonstrate the effectiveness of the AR model, and the proposed algorithm is compared with the IMM filter and variable-dimension filter in the manoeuvring scenario.
The paper presents linear system Input-Output Finite-Time Stabilization (IO-FTS) method under Finite-Time Boundedness (FTB) constraint. A state feedback controller is designed, via Linear Matrix Inequalities (LMIs), to guarantee the system both IO-FTS and FTB. The proposed methods are applied to the guidance design of a class of terminal guidance systems to suppress disturbances with IO-FTS method and FTB constraints simultaneously satisfied. The simulation results illustrate the effectiveness of the proposed methods.
A study was conducted to investigate the ballistic missile (BM) maneuver-strategy-design problem in which the maneuver-strategy design required to take account of the energy cost that would affect the payload of the BM directly. It was demonstrated that a periodic maneuver involving weaving and barrel roll was implemented easily without causing large miss distance to its designated target. The ballistic maneuver was considered in the study to alter the ballistic trajectory of a BM instead of evading from an interceptor directly. Methods were proposed to evaluate the maneuver consequence quantitatively the maneuver consequences were analyzed and the optimal maneuver was designed considering the defense system required to meet these objectives. A BM anti-interception scenario was also consideration, in which there were two opposite sides, such as a BM and a BM defense system.
This technical note studies the distributed estimation problem for a continuous-time moving target under switching interconnection topologies. A recursive distributed estimation algorithm is proposed by using state-consensus strategy, where a common gain is assigned to adjust the innovative and state-consensus information for each sensor in the network. Under mild conditions on observability and connectivity, the stability of the distributed estimation algorithm is analyzed. An upper bound and lower bound for the total mean square estimation error (TMSEE) are obtained by virtue of the common Lyapunov method and Kalman-Bucy filtering theory, respectively. Then a numerical simulation is given to verify the effectiveness of the proposed algorithm.
A multiple-model algorithm for maneuvering target tracking is proposed. It is referred to as a second-order Markov chain (SOMC)-based interacting multiple-model (SIMM) algorithm. The target maneuver process is modeled by a SOMC to incorporate more information. SIMM adopts a merging strategy similar to that of the interacting multiple-model (IMM) algorithm, except that the one-step model transition probabilities are updated based on the SOMC. A scheme is proposed to design the transition probabilities of the SOMC for target tracking. The performance of the proposed SIMM algorithm is evaluated via several scenarios for maneuvering target tracking. Simulation results demonstrate the effectiveness of SIMM compared with IMM, the second-order IMM (IMM2) algorithm, and the likely-model set (LMS) algorithm. It is shown that SIMM performs about the same as IMM2 but requires only n filters versus n2 filters in IMM2 for n models. The effectiveness and efficiency of combining SIMM and LMS for state estimation are also demonstrated in the simulation.
To solve the problem of interception avoidance of flight vehicle, this paper comprehensively considers miss distance, energy consumption and prediction uncertainty on the final time of interception by a finite-time H∞ performance measure, and designs optimal energy consumption maneuver. An optimal interception against optimal evader maneuver of flight vehicle is also presented. The results show that the best evader effect will be achieved under condition of a large amplitude evader maneuver at the end of interception if the flight vehicle can predict the final time of interception accurately, and a large interception miss distance will be gained at a relatively small energy consumption.
This paper considers cooperative path planning for aerial munitions during the attack phase of a mission against ground targets. It is assumed that sensor information from multiple munitions is available to refine an estimate of the target location. Based on models of the munition dynamics and sensor performance, munition trajectories are designed that enhance the ability to cooperatively estimate the target location. The problem is posed as an optimal control problem using a cost function based on the variances in the target-location estimate. These variances are computed by fusing the individual munition measurements in a weighted least-squares estimate. Solutions to the problem are found using a direct-shooting method. These solutions are compared with trajectories developed by an alternative suboptimal feedback-guidance law. This feedback law produces solutions with far less numerical expense and with a performance very close to the best known solutions. The reduction in target-location uncertainty associated with these trajectories could enable the attack of targets with greater precision using smaller, cheaper munitions.
The application of mobile robotic sensor networks has been widely studied in target localization and pursuit. Conventional target tracking methods always require an explicit system observation model of the target positions, which, however, would fail if such model is not available. In this paper, a distributed target localization and pursuit scheme is proposed based on discrete measurements of the energy intensity field produced by mobile targets. The accurate observation model of such field is not available except some critical bounds. By our control strategy, all robots are categorized into two groups: the leaders, responsible for the target pursuit, and the followers, responsible for the formation and connectivity maintenance. The influence of the system parameters on the convergence of leaders to the local maximum points is analyzed. Finally, the proposed scheme is demonstrated by simulation.
For maneuvering target tracking, the interacting multiple model (IMM) algorithm employs a fixed model set. The performance of this algorithm depends on the model set adopted. The result of using too many models is as bad as the case of too few models. Therefore, a variable structure IMM (VSIMM) was presented and applied to ground target tracking. This algorithm improves performance and reduces computational load with using auxiliary information. But it is difficult to extend the VSIMM to other scenario (for example, aerial target), where there is not auxiliary information such as a map. A novel interacting multiple model (Novel-IMM) algorithm was presented to solve the problem of model set adaptation without auxiliary information. The Novel-IMM algorithm consists of N independent IMM filters operating in parallel, and each independent IMM filter also consists of multiple sub-filters, which operate interactively. In every time index, only one IMM output of a certain model set is used; but for a long time, the algorithm will alternatively choose an output of the model set to be the optimum final output. The Novel-IMM approach was illustrated in detail with an aerial complex maneuvering target tracking example.
This paper presents a new algorithm for tracking a maneuvering target modeled as a class of Markov jump linear systems. The proposed algorithm consists of two interacting multiple model-extended Viterbi (IMM-EV) algorithms, coupled with proposed detection schemes for maneuver occurrences and terminations as well as for switching initializations. Combined performance strengths of the two IMM-EV algorithms are utilized via switching from one IMM-EV algorithm to the other. Appropriate design parameter values are derived for the detection schemes of maneuver occurrences and terminations. The results demonstrate that the proposed algorithm can be a viable alternative to several well-known tracking methods.
Mehrotra developed a jerk model for tracking highly maneuvering targets in 1997, which include terms at the most up to the third order derivatives of target position. The model is investigated in this paper. By theoretical analysis, it is shown that the filter, which based on the jerk model, may suffer from deterministic steady state estimation deviations. To find a way out of this question, a current statistic jerk model, for short cs-jerk, is developed, in which the jerk maneuvering is assumed to be an exponential correlated stochastic process with non-zero mean. It consists of the cs-jerk model of target motion, and a tracking filter with compatible order. The stable performance of the cs-jerk model is also analyzed and the result indicates that the cs-jerk model eliminates performance limitation of the jerk model. The improved performance of the cs-jerk model over the jerk model is illustrated through simulation.
This is the fifth part of a series of papers that provide a comprehensive survey of techniques for tracking maneuvering targets without addressing the so-called measurement-origin uncertainty. Part I and Part II deal with target motion models. Part III covers measurement models and associated techniques. Part IV is concerned with tracking techniques that are based on decisions regarding target maneuvers. This part surveys the multiple-model methods $the use of multiple models (and filters) simultaneously - which is the prevailing approach to maneuvering target tracking in recent years. The survey is presented in a structured way, centered around three generations of algorithms: autonomous, cooperating, and variable structure. It emphasizes the underpinning of each algorithm and covers various issues in algorithm design, application, and performance.
Event-triggered cooperative target tracking in wireless sensor networks
- Lu
Coordinated standoff tracking of moving target groups using multiple UAVs
- Oh