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Great interest has been generated in low-cost inertial navigation systems (INS) in the last few years. The development of Micro-Electro-Mechanical Systems (MEMS) in the last decade has permitted mass production of devices, thereby reducing the cost of previously expensive sensors. Simulation is part of the design process of an INS. However, if we e...
Citations
... Additionally, it employs the trajectory and curvilinear motion theory to simulate accelerometer and gyro measurements. Giroux et al. [16] introduce a modular, Simulink-based INS simulator for rapid prototyping and realtime testing. The simulator's validation and performance are evaluated, suggesting its utility in designing inertial navigation algorithms and confirming its effectiveness compared to existing integration schemes. ...
Inertial navigation systems have a long history of providing kinematic information for various applications, ranging from aviation and maritime navigation to guidance systems for space exploration and military operations. An inertial navigation system includes two essential sensor triads: accelerometers and gyroscopes. Their outputs are integrated to deliver high-frequency, real-time data on attitude, velocity, and position. However, inherent errors in these sensors, such as bias, drift, and noise, lead to degradation in the navigation solution over time. This problem is effectively mitigated by the utilization of global navigation satellite systems, which provide global absolute positioning data with a comparably lower error range that remains bounded over time. Consequently, combining the solutions of these systems has become standard practice on nearly all platforms. However, signals transmitted from satellites are vulnerable to interference such as jamming and spoofing, and can also be obstructed or degraded. This susceptibility has spurred research into leveraging alternative systems to reduce the errors in inertial navigation systems. This thesis primarily focuses on terrain-aided and vision-aided navigation techniques, as well as synthetic inertial measurement unit data generation for testing and validating integrated navigation solutions in a simulation environment. A novel multi hypothesis filter, designed specifically for terrain-aided navigation, is introduced, offering robustness against diverse terrain shapes and significant initialization errors. In addition, a method for generating synthetic inertial measurement unit data is also proposed that incorporates various sensor errors and vibrational effects. Finally, the direct linear transform-based absolute visual positioning method is tested with various visual feature extraction methods to evaluate their positioning accuracy using a real orthophoto dataset.
... In this regard, there have been comparisons of simulation languages and environments including Simulink in various engineering fields. Giroux et al. [1] did research on the performance of Simulink for an inertial navigation system simulator. Hödlmoser and Kitzler [2] researched into a performance comparison between Simulink and other software in the implementation of fuzzy control of a two tank system. ...
In the present paper, a comparison between the simulation performance of a highly nonlinear model in MATLAB/Simulink and in a compiled language has been drawn. A complete powertrain layout was drawn in Simulink and the same model was developed from scratch in Fortran 2003 which led to creating a complete simulation software program named Powertrain Simulator. The results show that for a system with not many details and phase changes, both of the simulation environments offer acceptable performance. However, when the modeling layout is overly complicated, developing the model in a compiled language is a smarter choice.
... In our case, the development of a simulation tool capable to cost-effectively and safely represent realistic collaboration scenarios allows us to evaluate the performance of different sensor combinations and processing parameters [13]. Compelling simulation approaches for single-vehicle positioning and inertial measurements are presented in [19][20][21], however, with no consideration of the environment characteristics. If collaborative scenarios are the target of the study, [22] compares two commercial simulation engines, PreScan and ITS Modeller. ...
... 1. Starting from a time series of the velocity in the forward direction of the body frame and attitude [19]. ...
... In the case of collaborative scenarios, the development of a simulation tool helps to cost-effectively and safely reproduce a wide spectrum of realistic situations in order to evaluate the performance of different sensor setups and configurations under different environmental scenarios. Simulation environments as the ones designed by Giroux et al. (2003), Jwo et al. (2014) or Gade (2005) represent valid simulation and post-processing methodologies for positioning and inertial measurements, although no environmental characteristics are considered. Zhang et al. (2018) use 3D city models in order to overcome the GNSS limitations in urban canyons using a collaborative approach. ...
... Given that the tool provides different possibilities to simulate the vehicle dynamics and environmental characteristics, it is possible to analyze the performance of different sensor fusion strategies in any desired situation. This improves simulation approaches such as those implemented by Giroux et al. (2003), Jwo et al. (2014) or Gade (2005) -which only consider the vehicle dynamics' simulation-, or filter approaches such as Vogel et al. (2018), where only one vehicle is considered. In addition, the collaborative navigation approach using GNSS baselines to link the vehicles allows us to estimate the state parameters from each vehicle simultaneously in the same coordinate frame (unlike the approach presented by de Ponte-Müller et al. (2016), where the relative position and velocity of the observed vehicles are estimated in the ego-vehicle navigation frame). ...
... 1. Starting from velocity and attitude time series (Giroux et al. (2003) or Jwo et al. (2014)) 2. Starting from waypoints (coordinate time series) that are interpolated using cubic spline in order to achieve a smooth trajectory and velocity information, (Garcia-Fernandez and Schön (2017)). The remaining kinematic variables can be obtained following the approach of Giroux et al. (2003). ...
Collaborative Positioning (CP) is a networked positioning technique in which different multi-sensor systems (nodes) enhance the accuracy and precision of these navigation solutions by performing measurements or by sharing information (links) between each other. The wide spectrum of available sensors that are used in these complex scenarios bring the necessity to analyze the sensibility of the system to different configurations in order to find optimal solutions. In this paper, we discuss the implementation and evaluation of a simulation tool that allows us to study these questions. The simulation tool is successfully implemented as a plane based localization problem, in which the sensor measurements are fused in a Collaborative Extended Kalman Filter (C-EKF) algorithm with implicit constraints. Using a real urban scenario with three vehicles equipped with various positioning sensors, the impact of the sensor configuration is investigated and discussed by intensive Monte Carlo simulations. The results show the influence of the laser scanner measurements on the accuracy and precision of the estimation, and the increased performance of the collaborative navigation techniques with respect to the single vehicle method.
... 1) Trajectory from Velocity and Attitude: In [14], the authors describe a methodology to generate a trajectory from a set of values of the velocity in the forward direction of the body frame (b-frame) and attitude (Euler angles) at distinct times. A spline interpolation is used to connect the way-points. ...
... For the trajectory generation, we use the approach of Giroux [11] which is based on a given velocity in the forward direction of the body frame and attitude (Euler angles) at distinct times. Using spline interpolation the fine resolution of the velocity and angle time series is obtained. ...
... Similar to the principle of optical interferometry, such as Ring Laser Gyros [5] [9], an atom interferometer is realized by applying a sequence of coherent beam-splitting processes, instead of mirrors, to an ensemble of atoms (cf. Figure 1). These beam-splitters are usually separated by a specific time interval T which is followed by detection of the fluorescence or absorption signal in each of the two output channels to estimate the [4] interferometer phase shift ∆Φ Therefore, the traveled trajectory of the two momentum states in the coherent superposition represents the two arms of a conventional Mach-Zehnder interferometer [10]. In a preparation step an atomic wave packet, e.g. the atoms are trapped and cooled in a 3D magneto-optical trap (3D-MOT), and subsequently further cooled in an optical molasses to a temperature at the K. ...
... Since the expected position error (RMS) of such sensor, given precise knowledge of the gravity field, of sub 10 m/h navigation capability [4], the introduced numerical error of the simulation should be as small as possible, e.g. below the 1m level after one hour of integration. ...
... This assumes that the gyro observation remains constant, in other words the angular rate vector does not change direction during the update interval Δ . Consequently, such approximation contains third order algorithms where the corresponding error observed in our simulation was similar to [4]. ...
... In spite of the fact that some few another INS/GNSS data processing software have been proposed in the literature (Giroux et al., 2003;Niu et al., 2015), these tools are neither open source nor freely available. It is the authors' believe that NaveGo is the first coordinated academic effort to develop an open-source INS/GNSS processing framework for both navigation and GIS communities. ...
The development of new approaches in the GIS research community generally requires the use of a computational tool to post-process GNSS and inertial sensors data in order to get more accurate position, velocity, and orientation angles (attitude) information. An open-source framework for simulating integrated navigation systems (INS/GNSS) called NaveGo has been developed using MATLAB/GNU Octave and is freely available online. Although preliminary tests have shown that NaveGo appears to work properly, a deep examination must be carried out to confirm that this framework is an adequate tool for post-processing INS/GNSS information. The main goal of this work is to produce a methodology to validate that NaveGo mathematical model works within its specifications. Firstly, static measurements from inertial sensors are processed and analysed by NaveGo applying the Allan variance for profiling typical errors. Some details of Allan variance procedure are exhibited. Then, performances of NaveGo and Inertial Explorer, a closed-source commercial package software for INS/GNSS integration, are compared for a real-world trajectory. It is statistically concluded that NaveGo presents close accuracy to Inertial Explorer for attitude and position. Consequently, it is confirmed that NaveGo is an useful INS/GNSS post-processing framework that can be used in GIS applications.
... In the reviewed literature few papers have points in common with this work, and none is so comprehensive. In (Giroux et al., 2003), (Wenling et al., 2010), (Liansheng and Tao, 2011), and (Zhang et al., 2012) only mathematical models of a SINS are shown. Works from (Esposito et al., 2007) and (Lijun et al., 2008) exhibit an INS simulator, but none of them expose complete mathematical models. ...
... Works from (Esposito et al., 2007) and (Lijun et al., 2008) exhibit an INS simulator, but none of them expose complete mathematical models. Although simulation results are shown in all these works, most do not expose a validation method to assess the proposed simulator, except in (Giroux et al., 2003). Thus, to the best of the authors' knowledge, NaveGo is the most complete work about a simulation environment of low-cost integrated navigation systems. ...
In this work, a simulation framework for low-cost integrated navigation systems called NaveGo is presented. NaveGo simulates measurements from inertial sensors and a GPS receiver. It also executes a loosely-coupled navigation algorithm. Complete mathematical models are shown. As example, simulation results for a particular scenario are exhibited. NaveGo is developed in MATLAB and freely distributed under an open source license. It is the authors' believe that this article is the most complete work about a simulation environment for low-cost integrated navigation systems. NaveGo is validated by using a practical approach, which main goal is to evaluate how close in performance is a simulated navigation system to a real one. Thus, simulated sensors are generated based upon a real trajectory, during which real sensors were logged. Then, simulated sensors are corrupted with noises according to real sensors error profiles. Both simulated and real navigation systems are processed. It is found that absolute differences between real and simulated systems are under 0.6 degrees for attitude, under 23 centimetres for 2D position, and under 10 centimetres for vertical position. As a result, it is verified that NaveGo is a suitable simulation tool for the design and analysis of low-cost integrated navigation systems.
... The results coming from this experiment are provided in the table 1 [2]. This experiment has been performed several times and it has been possible to show the repeatability of the results. ...
Aerospace specialized in control, at ESTACA University, Paris (France) With the state of the art innovations of navigation systems, GPS is becoming a very important part of daily life. Indeed, GPS is a very helpful and precise navigation system however its performance is fairly vulnerable due to the environment noise, which may cause GPS signals loss and attenuation. Hence this is hard to rely much on this technology. Compared with GPS, the INS is a self-contained navigation system, which has jamproof performances but the sensors induce errors which are growing with time. These two navigation systems integrated together can compensate each other's weakness providing a continuous data acquisition using an adapted filter. This integrated solution of GPS/INS gives an accurate solution which is low-cost and jamproof. This paper presents a low-cost GPS/INS platform developed with Micro Electro-Mechanical System (MEMS). Fast prototyping tools such as Matlab/Simulink are utilized to implement/validate the algorithm. This platform is consist of an assembly of different PC104 cards such as acquisition card, GPS card, Ethernet card, mother board... stacked up together providing the link between the sensors, the algorithm and the host computer. The software including INS, GPS data acquisition and the Kalman filter is loaded and processed on the PC104 based computer. This platform is totally self-embeddable and .can be used independently without any external hardware/software supports. The algorithm utilized can be adapted to different grade of inertial sensors by changing few parameters.
