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This paper evaluates the performance of an integrity monitoring algorithm of global navigation satellite systems (GNSS) for the Kalman filter (KF), termed KF receiver autonomous integrity monitoring (RAIM). The algorithm checks measurement inconsistencies in the range domain and requires Schmidt KF (SKF) as the navigation processor. First, realisti...
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... 20 MHz IF data are processed by a developed GPS L1-NavIC L5 software receiver. Figure 4 shows the signal simulation and data collection setup. Performance is first analyzed in detail for a segment of an aircraft flight path during descent and approach, which is illustrated in Figure 5 along with GPS-NavIC satellite visibility. ...
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... 20 MHz IF data are processed by a developed GPS L1-NavIC L5 software receiver. Figure 4 shows the signal simulation and data collection setup. Performance is first analyzed in detail for a segment of an aircraft flight path during descent and approach, which is illustrated in Figure 5 along with GPSNavIC satellite visibility. ...
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Integrity monitoring with a Kalman filter (KF) has recently attracted significant attention. In this paper, a computationally efficient architecture of a KF-based receiver autonomous integrity monitoring (RAIM) algorithm is discussed for aviation applications to ensure reliable operations of Global Navigation Satellite Systems (GNSS). It is built o...
Citations
... In order to avoid the architectural complexity with multiple parallel filters, KF integrity monitors in the range domain have been explored, where fault detection tests are designed with measurement innovations/residuals [15][16][17][18][19][20][21]. This method is called range-based integrity monitoring in this paper. ...
... With the first contribution in this regard presented in the late 1980s in [15,16], which discuss fault detection tests with a moving time window, there is renewed interest in range-based methods as well. Bhattacharyya [17] presents a RAIM algorithm based on the Schmidt KF (SKF) navigation processor to enable fault detection as well as PL calculation in the presence of time-correlated measurement errors, and studies its performance in detail. It is shown that the proposed approach can successfully detect faults, whereas a full-order EKF with estimated time-correlated errors misses the detection of slow ramps. ...
... This is important as integrity monitors are required to operate in real time. Bhattacharyya [17] reports the completion of processing of KF RAIM for the entire duration in time (i.e. faster than the total duration), but this does not guarantee satisfactory epoch-wise performance. ...
Integrity monitoring with a Kalman filter (KF) has recently attracted significant attention. In this paper, a computationally efficient architecture of a KF-based receiver autonomous integrity monitoring (RAIM) algorithm is discussed for aviation applications to ensure reliable operations of Global Navigation Satellite Systems (GNSS). It is built on the Schmidt KF navigation processor to model time-correlated measurement errors. Reasons for important design choices are clarified. Different strategies are adopted to efficiently include the contributions of past KF measurements in fault detection as well as protection level (PL) calculations. Module-wise most significant numerical complexity is also analyzed in detail. The algorithm performance is studied with simulated Global Positioning System (GPS) and Navigation with Indian Constellation (NavIC) signals for a number of scenarios. They comprise different configurations related to the number of satellites, geometry, total duration, and aircraft dynamics. Fault detection performance of presented KF RAIM is shown to be superior to another innovation-based test with a moving time window. It is demonstrated that KF RAIM running on a single-core virtual machine can complete processing within a small fraction of each time interval. The performance is also analyzed by restricting CPU usage. The processing time of GPS-NavIC KF RAIM at every interval is shown to be consistently less than that of standalone GPS in all scenarios. Therefore, dual constellations not only result in lower PLs, but also require shorter execution times. An explanation for faster execution times with dual GNSS is provided using the numerical complexity of different modules.
... In parallel, relevant scholars also carried out relevant research on the application of covariance intersection to the above-mentioned filter. At this stage, covariance intersection is mainly applied to the Kalman filter [16][17][18][19][20][21][22][23][24]. Qi, W. [25] (2020) applied BCI fusion and fast SCI fusion to a time-varying Kalman filter in their research and suggested that this method should solve the high-dimensional nonlinear optimization problem; however, the algorithm's operation implies great computational complexity and quantity. ...
... The sequential inverse covariance intersection (SICI) multisensor fusion algorithm consists of a (L − 1) dual-sensor system based on the ICI method. The SICI fusion algorithm is shown in Figure 2 [17]. Moreover, for any ...
... The sequential inverse covariance intersection (SICI) multisensor fusion algorithm consists of a (L − 1) dual-sensor system based on the ICI method. The SICI fusion algorithm is shown in Figure 2 [17]. When there are multiple sensors in the system, the SICI fusion algorithm needs to perform L − 1 double fusion, which results in a significant amount of running time. ...
A distributed GM-CPHD filter based on parallel inverse covariance crossover is designed to attenuate the local filtering and uncertain time-varying noise affecting the accuracy of sensor signals. First, the GM-CPHD filter is identified as the module for subsystem filtering and estimation due to its high stability under Gaussian distribution. Second, the signals of each subsystem are fused by invoking the inverse covariance cross-fusion algorithm, and the convex optimization problem with high-dimensional weight coefficients is solved. At the same time, the algorithm reduces the burden of data computation, and data fusion time is saved. Finally, the GM-CPHD filter is added to the conventional ICI structure, and the generalization capability of the parallel inverse covariance intersection Gaussian mixture cardinalized probability hypothesis density (PICI-GM-CPHD) algorithm reduces the nonlinear complexity of the system. An experiment on the stability of Gaussian fusion models is organized and linear and nonlinear signals are compared by simulating the metrics of different algorithms, and the results show that the improved algorithm has a smaller metric OSPA error than other mainstream algorithms. Compared with other algorithms, the improved algorithm improves the signal processing accuracy and reduces the running time. The improved algorithm is practical and advanced in terms of multisensor data processing.
The collaboration among swarmed aircraft can provide additional observation to improve the integrity of its onboard navigation systems. But due to the limitation on relative communicating and measuring burdens, adopting all the observations between aircraft in the swarm is inefficient. The geometry of the collaborated partners is a key factor that influences the effectiveness of the navigation integrity augmentation, which needs to be optimized in collaborative integrity augmented navigation. In this paper, the integrity augmented navigation method for aerial swarm based on collaborative partner optimization is proposed. The geometry constructed by the GNSS satellites and the cooperative partners in the aerial swarm are analyzed dynamically, and the augmented integrity protection levels with different collaborative relationships are predicted to distinguish the partner essential for navigation integrity augmentation. Then the collaborative partner makes a key contribution to integrity augmentation adopted in collaborative navigation integrity monitoring, improving the efficiency of the collaborative navigation. The simulation results indicate the effectiveness of the proposed cooperative partnership optimization strategy, as well as the superiority of the proposed method compared with the traditional independent integrity framework in improving the integrity protection level and fault detection capacity.
Reliable urban navigation using global navigation satellite system requires accurately estimating vehicle position despite measurement faults and monitoring the trustworthiness (or integrity) of the estimated location. However, reflected signals in urban areas introduce biases (or faults) in multiple measurements, while blocked signals reduce the number of available measurements, hindering robust localization and integrity monitoring. This paper presents a novel particle filter framework to address these challenges. First, a Bayesian fault-robust optimization task, formulated through a Gaussian mixture model (GMM) measurement likelihood, is integrated into the particle filter to mitigate faults in multiple measurement for enhanced positioning accuracy. Building on this, a novel test statistic leveraging the particle filter distribution and the GMM likelihood is devised to monitor the integrity of the localization by detecting errors exceeding a safe threshold. The performance of the proposed framework is demonstrated on real-world and simulated urban driving data. Our localization algorithm consistently achieves smaller positioning errors compared to existing filters under multiple faults. Furthermore, the proposed integrity monitor exhibits fewer missed and false alarms in detecting unsafe large localization errors.
Location-based service (LBS) has been playing an essential role in various sectors of society. As the most important solution in LBS, the Global Navigation Satellite System (GNSS) has been limited in availability in areas such as city and canyon. With the development of wireless communication technology, the widely distributed mobile networks can provide numerous quality line-of-sight path signals in GNSS-denied environments. Therefore, this paper develops a machine learning location (MLLoc) system based on receiver autonomous integrity monitoring (RAIM) to fuse mobile network signals with GNSS, in which a machine learning (ML) method is used to obtain stable time of arrival (TOA) estimation from the downlink broadcast signal of mobile networks. The field tests carried out in Long Term Evolution networks have verified the performance of the method. Specifically, the range accuracy of the TOA estimation based on ML and the positioning performance of the MLLoc system were evaluated through field tests. Our results verified that the developed MLLoc system is highly available in GNSS-denied environments and achieves meter-level positioning accuracy.
