Robust Partially Strong Tracking Extended Consider Kalman Filtering for INS/GNSS Integrated Navigation

Lou, Taishan; Chen, Nanhua; Chen, Zhiwu; Wang, Xiaolei

doi:10.1109/access.2019.2948229

Cited by 11 publications

(8 citation statements)

References 22 publications

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“…The detection quantity v i of the innovation rate was derived in [14]. Assuming that spoofing does not exist, the null hypothesis is H 0 : v i ∼ N (0, 1), and the alternative hypothesis is 1), where v i follows a normal distribution, and δ is a noncentral parameter. According to the integrity requirements of the navigation system [25], if the false alarm probability is set to P fa , the corresponding false alarm probability of the i-th measurement value a 0 is as follows [26]:…”

Section: Tightly Coupled Gnss/ins Integration Spoofing Detection Algo...mentioning

confidence: 99%

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Tightly Coupled GNSS/INS Integration Spoofing Detection Algorithm Based on Innovation Rate Optimization and Robust Estimation

Gao

Zhang

et al. 2022

IEEE Access

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The spoofing detection algorithm for a global navigation satellite system/inertial navigation system (GNSS/INS) integrated navigation system based on the innovation rate and robust estimation has limitations such as extensive or invalid detection times, high missed detection rates, and false alarm rates. This study addresses these limitations by proposing a tightly coupled GNSS/INS integration spoofing detection algorithm based on innovation rate optimization and robust estimation. The proposed algorithm improved the normalized innovation of a small step or slow-growing ramp, thereby optimizing its innovation rate test statistics. The proposed approach also reduces the spoofing effect on the innovation rate by adaptively adjusting a gain matrix using robust estimation, thus improving the detection ability further. The simulation results show that the detection time of the proposed algorithm is reduced by 51.9% on average when dealing with small step or slow-growing ramp spoofing. Moreover, the missed detection rate decreases by 58% on average, and the false alarm rate remains at approximately zero. The proposed algorithm is suitable for spoofing detection in unmanned aerial vehicle applications of GNSS/INS integrated navigation systems with the advantages of fast detection and good performance.INDEX TERMS Innovation rate optimization, robust estimation, spoofing detection, tightly coupled GNSS/INS integration.

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Section: Tightly Coupled Gnss/ins Integration Spoofing Detection Algo...mentioning

confidence: 99%

“…A global navigation satellite system (GNSS) and an inertial navigation system (INS) have complementary error characteristics [1]. A GNSS can provide global all-weather continuous position, velocity, and time services [2].…”

Section: Introductionmentioning

confidence: 99%

Tightly Coupled GNSS/INS Integration Spoofing Detection Algorithm Based on Innovation Rate Optimization and Robust Estimation

Gao

Zhang

et al. 2022

IEEE Access

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“…However, inaccurate statistics of the measurement information, such as means and covariances, and improper information fusion methods can lead to unpredictable or undesired navigation errors. Therefore, it is necessary to consider the most probable biases, such as drifts of the inertial measurement unit (IMU), receiver clock biases, multi-path biases of the GNSS, temperature biases of barometers, and other biases of the navigation system [4][5][6], when designing information fusion methods for a multisensor integrated navigation system. Among the information fusion algorithms, Kalman filter and its variants are the most successful and widely used in the integrated navigation field [7].…”

Section: Introductionmentioning

confidence: 99%

A consider unscented particle filter with genetic algorithm for UAV multi-source integrated navigation

Lou

Chen

Jiao

et al. 2023

Meas. Sci. Technol.

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The unscented particle filter (UPF) combines the advantages of both the unscented Kalman filter (UKF) and particle filter (PF) algorithms. However, during the fusion of multi-source information for UAV integrated navigation, the UPF may encounter problems related to measurement biases and particle depletion. These issues can lead to algorithm results that are unsatisfactory or divergent. To address these challenges, a novel approach called the consider unscented particle filter with genetic algorithm (GA-CUPF) is proposed. The GA-CUPF algorithm incorporates covariance and co-covariance of biases into the state estimation covariance through the "consider" method employed by the CUPF, and it should be noted that the biases are not estimated in this process. Moreover, the GA-CUPF algorithm enhances the importance probability density of particles to avoid the degeneracy of PF algorithm and improves particle diversity through the application of selection, crossover, and mutation methods in GA. Simulation results demonstrate that the proposed GA-CUPF algorithm can effectively mitigate the adverse effects of measurement biases and particle depletion that the UPF algorithm faces in the UAV navigation system. Additionally, it significantly improves the accuracy of UAV integrated navigation.

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“…be applied to linear systems, is no longer suitable for nonlinear systems. For nonlinear system filtering, Quinchia and Falco choose an extended Kalman filter (EKF) to linearize nonlinear systems [3], [4]. EKF linearizes the nonlinear system model via the first-order Taylor expansion.…”

Section: Introductionmentioning

confidence: 99%

Interacting Multiple Model UAV Navigation Algorithm Based on a Robust Cubature Kalman Filter

Liu

Zhang

et al. 2020

IEEE Access

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To improve the precision and robustness of Unmanned Aerial Vehicle (UAV) integrated navigation systems, this paper presents an Interacting Multiple Model (IMM) navigation algorithm based on a Robust Cubature Kalman Filter (RCKF) with modified Zero Velocity Update (ZUPT) method assistance. This algorithm has a two-level fusion structure. At the bottom level, the Global Positioning System/Inertial Navigation System (GPS/INS) integrated navigation model and the Dynamic Zero Velocity Update/Inertial Navigation System (DZUPT/INS) integrated navigation model are established by modifying the Zero Velocity Update (ZUPT) method. Subsequently, the RCKF algorithm adopts a robust factor to weaken the influence of measurement outliers on the filter solution. At the top level, the estimation results of the GPS/INS integrated navigation model and the DZUPT/INS integrated navigation model are fused by the IMM algorithm. In addition to enhancing the robustness of filter estimation in the presence of measurement outliers, the proposed navigation algorithm also corrects navigation errors with ZUPT method assistance. Simulation and experimental analyses demonstrate the performance of the proposed navigation algorithm for UAVs. INDEX TERMSInteracting Multiple Model, Robust Cubature Kalman Filter, Dynamic Zero Velocity Update, integrated navigation.

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Robust Partially Strong Tracking Extended Consider Kalman Filtering for INS/GNSS Integrated Navigation

Cited by 11 publications

References 22 publications

Tightly Coupled GNSS/INS Integration Spoofing Detection Algorithm Based on Innovation Rate Optimization and Robust Estimation

Tightly Coupled GNSS/INS Integration Spoofing Detection Algorithm Based on Innovation Rate Optimization and Robust Estimation

A consider unscented particle filter with genetic algorithm for UAV multi-source integrated navigation

Interacting Multiple Model UAV Navigation Algorithm Based on a Robust Cubature Kalman Filter

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