Robust GPS/INS/DVL Navigation and Positioning Method Using Adaptive Federated Strong Tracking Filter Based on Weighted Least Square Principle

Abstract

Multi-sensor integrated positioning technique that combines complementary features of the global positioning system (GPS) and inertial navigation system (INS) for navigation in challenging urban environments has been a hot research area. A variety of algorithms have been proposed over the past two decades for this well-studied field. However, with the increasing demands of seamless positioning, traditional GPS/INS integrated technique faces rigorous challenges, especially in GPS-denied environment, where traditional techniques cannot be applied directly. To improve the precision and robustness of the navigation system, a novel hybrid GPS/INS/Doppler velocity log (DVL) positioning method is proposed, which introduces DVL as the reference information to assist the GPS module to correct the divergence error of INS. A new robust adaptive federated strong tracking Kalman filter (RAFSTKF) algorithm is also presented for data fusion, which has the advantage of robustness with respect to the uncertainty of the system model. Meanwhile, we introduce the least square principle and adaptively adjust information sharing factors to obtain the optimal estimation, which can improve the reliability of the overall system. The theoretical analysis and simulation results demonstrate the effectiveness of the proposed hybrid GPS/INS/DVL positioning method based on RAFSTKF. In addition, the tracking performance of the proposed method outperforms that of traditional federated Kalman filter. INDEX TERMS Localization, target tracking, positioning, hybrid navigation, Kalman filter, weighted least square, federated strong tracking filter. I. INTRODUCION Many existing and perspective technologies of navigation and location systems would benefit notably from the ability to position accurately and reliably in challenging environments [1]-[6]. Recently, inertial navigation systems (INS) provide users with position, velocity, and attitude (PVA) information with high resolution independent of the vehicle platform [7], [8]. However the PVA information provided by INS can only maintain reliable precision for short time limited by the fact that the INS navigation error will accumulate The associate editor coordinating the review of this manuscript and approving it for publication was Shuai Han.

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