In wireless sensor networks (WSN), measurements are always corrupted by outliers or impulsive noise. Cubature information filtering (CIF) is founded based on minimum mean square error (MMSE) criterion, which is not applicable to non-Gaussian noise. Hence, a novel robust CIF (RCIF) is derived based on maximum correntropy criterion (MCC) to enhance the robustness of state estimation in the local node. For the information fusion, weighted average consensus (WAC) based distributed RCIF (DRCIF) is founded to improve the stability of sensor networks and the accuracy of state estimation. The estimation error of DRCIF is proved to be bounded in mean square. Numerical simulations are provided to evaluate the effectiveness of proposed algorithms. INDEX TERMS Robust cubature information filtering, maximum correntropy criterion, Non-Gaussian measurement noise, distributed state estimation, weighted average consensus.

Acoustic vector sensor (AVS) is an effective tool to tracking acoustic sources. However, for the problem of tracking multiple wideband sources using distributed AVS array (DAVS), there are still unsolved issues which include measurements-to-targets association and targets tracking under incorrect or unknown statistics of measurement noise. Joint probabilistic data association (JPDA) is an effective algorithm to solve data association between measurements and targets and JPDA based cubature information filter (MTCIF) is designed for nonlinear system. Meanwhile, noise statistics estimator (NSE) based on modified Sage-Husa maximum posterior (SHMP) is constructed to cope with incorrect or unknown statistics of measurement noise. Then, a two-step distributed information fusion based on weighted average consensus (WAC) is built for DAVS to improve the stability and accuracy of state estimator and NSE. Numerical simulations demonstrate the effectiveness of the proposed algorithms.INDEX TERMS Acoustic vector sensor, cubature information filter, joint probabilistic data association, noise statistics estimator, weighted average consensus.

The Gaussian filtering is a commonly used method for nonlinear system state estimation. However, this method requires both system process noise and measurement noise to be white noise sequences with known statistical characteristics. However, it is difficult to satisfy this condition in engineering practice, making the Gaussian filtering solution deviated or diverged. This paper adopts the random weighting concept to address the limitation of the nonlinear Gaussian filtering. It establishes the random weighting estimations of system noise characteristics on the basis of the maximum a-posterior theory, and further develops a new Gaussian filtering method based on the random weighting estimations to restrain system noise influences on system state estimation by adaptively adjusting the random weights of system noise characteristics. Simulation, experimental and comparison analyses prove that the proposed method overcomes the limitation of the traditional Gaussian filtering in requirement of system noise characteristics, leading to improved estimation accuracy. INDEX TERMSNonlinear system state estimation, Gaussian filtering, system noise characteristics, random weighting.

Aiming at the problem of X-ray pulsar navigation system with uncertain measurement noise, an adaptive cubature Kalman filter (CKF) algorithm is proposed based on variational mode decomposition (VMD). Firstly, the multi-step measurements are predicted by CKF to extend the measurement sequence. Then, the high-frequency noise, which is separated from the extended measurement sequence by VMD, is used to reconstruct the measurement noise. Finally, the measurement noise covariance is estimated based on the reconstructed measurement noise to update the CKF parameters. The simulation results show that the proposed method can adaptively track the change of measurement noise and improve the positioning accuracy of X-ray pulsars navigation system (XPNAV).

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