A New Adaptive H-Infinity Filtering Algorithm for the GPS/INS Integrated Navigation

Jiang, Chung-Hsiang; Zhang, Shubi; Zhang, Qiuzhao

doi:10.3390/s16122127

Cited by 36 publications

(20 citation statements)

References 30 publications

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“…In this part, the CSTRKF is compared with the H-infinity-based robust filter (HI-RF) [ 22 ] and the traditional KF under the condition of measurements with outliers in the INS/SRS/CNS integrated navigation system.…”

Section: Simulation and Resultsmentioning

confidence: 99%

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A Robust INS/SRS/CNS Integrated Navigation System with the Chi-Square Test-Based Robust Kalman Filter

Gao

Hong

et al. 2020

Sensors

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In order to achieve a highly autonomous and reliable navigation system for aerial vehicles that involves the spectral redshift navigation system (SRS), the inertial navigation (INS)/spectral redshift navigation (SRS)/celestial navigation (CNS) integrated system is designed and the spectral-redshift-based velocity measurement equation in the INS/SRS/CNS system is derived. Furthermore, a new chi-square test-based robust Kalman filter (CSTRKF) is also proposed in order to improve the robustness of the INS/SRS/CNS navigation system. In the CSTRKF, the chi-square test (CST) not only detects measurements with outliers and in non-Gaussian distributions, but also estimates the statistical characteristics of measurement noise. Finally, the results of our simulations indicate that the INS/SRS/CNS integrated navigation system with the CSTRKF possesses strong robustness and high reliability.

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Section: Simulation and Resultsmentioning

confidence: 99%

“…However, the forgetting factors used in these filters are determined empirically. In [ 22 , 23 ], an H-infinity strategy was used to handle the uncertainties in observation noise. However, this method may only work under the condition of randomly occurring outliers.…”

Section: Introductionmentioning

confidence: 99%

A Robust INS/SRS/CNS Integrated Navigation System with the Chi-Square Test-Based Robust Kalman Filter

Gao

Hong

et al. 2020

Sensors

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“…The SVF(state-vector fusion) is a simplest process to combine the predicted states with state error covariance matrices achieved from fuzzy H-infinity and H-infinity. Hinfinity filter's presence depends on the presence of conditions in [19]. The state vector and covariance are fused together to predict state vectors and covariance matrices of every sensor.…”

Section: Target State Vector Fusion Model Using H-infinity and Fuzmentioning

confidence: 99%

Energy Efficient Target Tracking Method for Multi-Sensory scheduling in Wireless Sensor Networks

Lokesh¹,

Reddy²

2020

IJITEE

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Data collection utilizing wireless sensors networks (WSNs) has been utilized for surveillance, monitoring environment, animal etc. Target tracking of maneuvering objects is an essential need of modern life. Nonetheless, because of diverse nature of sensor and complex environment, sensors measurement errors need to be minimized considering diverse motion states in process of tracking (sensing) operation. Enhancing network lifetime (i.e., reducing energy dissipation of sensor nodes) and improving tracking quality are major concern of target tracking using WSN. Form improving network energy efficiency, multi-sensory target tracking method has been modelled using Kalman Filter (KF) by existing target tracking method. The KF based model are affected due to presence of noise or missing data. For overcoming research issues this paper present an H-infinity filter (HF) to evaluate fusion for maneuvering target tracking in WSN. Further, to minimize the estimation errors and reduces/controlling the effects of outliers fuzzy H-infinity (FHF) filter for target tracking WSN is presented. Experiment outcome shows proposed HF and FHF fusion model attain better performance than existing KF based method for clustered based WSN in terms of positional and velocity root mean square error and energy dissipation.

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“…The H-infinity filter can obtain the optimal estimation of state variables under the condition of noise with unknown statistics [ 33 , 34 ]. According to the characteristics of the near space environment, the H-infinity filter can be improved based on the prior information of

.…”

Section: Second-order State Augmented H-infinity Filtermentioning

confidence: 99%

INS/CNS Deeply Integrated Navigation Method of Near Space Vehicles

Sun

et al. 2020

Sensors

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Celestial navigation is required to improve the long-term accuracy preservation capability of near space vehicles. However, it takes a long time for traditional celestial navigation methods to identify the star map, which limits the improvement of the dynamic response ability. Meanwhile, the aero-optical effects caused by the near space environment can lead to the colorization of measurement noise, which affects the accuracy of the integrated navigation filter. In this paper, an INS/CNS deeply integrated navigation method, which includes a deeply integrated model and a second-order state augmented H-infinity filter, is proposed to solve these problems. The INS/CNS deeply integrated navigation model optimizes the attitude based on the gray image error function, which can estimate the attitude without star identification. The second-order state augmented H-infinity filter uses the state augmentation algorithm to whiten the measurement noise caused by the aero-optical effect, which can effectively improve the estimation accuracy of the H-infinity filter in the near space environment. Simulation results show that the proposed INS/CNS deeply integrated navigation method can reduce the computational cost by 50%, while the attitude accuracy is kept within 10” (3 ). The attitude root mean square of the second-order state augmented H-infinity filter does not exceed 5”, even when the parameter error increases to 50%, in the near space environment. Therefore, the INS/CNS deeply integrated navigation method can effectively improve the rapid response ability of the navigation system and the filtering accuracy in the near space environment, providing a reference for the future design of near space vehicle navigation systems.

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A New Adaptive H-Infinity Filtering Algorithm for the GPS/INS Integrated Navigation

Cited by 36 publications

References 30 publications

A Robust INS/SRS/CNS Integrated Navigation System with the Chi-Square Test-Based Robust Kalman Filter

A Robust INS/SRS/CNS Integrated Navigation System with the Chi-Square Test-Based Robust Kalman Filter

Energy Efficient Target Tracking Method for Multi-Sensory scheduling in Wireless Sensor Networks

INS/CNS Deeply Integrated Navigation Method of Near Space Vehicles

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