A Novel FEM Based T-S Fuzzy Particle Filtering for Bearings-Only Maneuvering Target Tracking

Abstract

In this paper, we propose a novel fuzzy expectation maximization (FEM) based Takagi-Sugeno (T-S) fuzzy particle filtering (FEMTS-PF) algorithm for a passive sensor system. In order to incorporate target spatial-temporal information into particle filtering, we introduce a T-S fuzzy modeling algorithm, in which an improved FEM approach is proposed to adaptively identify the premise parameters, and the model probability is adjusted by the premise membership functions. In the proposed FEM, the fuzzy parameter is derived by the fuzzy C-regressive model clustering method based on entropy and spatial-temporal characteristics, which can avoid the subjective influence caused by the artificial setting of the initial value when compared to the traditional FEM. Furthermore, using the proposed T-S fuzzy model, the algorithm samples particles, which can effectively reduce the particle degradation phenomenon and the parallel filtering, can realize the real-time performance of the algorithm. Finally, the results of the proposed algorithm are evaluated and compared to several existing filtering algorithms through a series of Monte Carlo simulations. The simulation results demonstrate that the proposed algorithm is more precise, robust and that it even has a faster convergence rate than the interacting multiple model unscented Kalman filter (IMMUKF), interacting multiple model extended Kalman filter (IMMEKF) and interacting multiple model Rao-Blackwellized particle filter (IMMRBPF).

Labeled Multi‐Bernoulli Maneuvering Target Tracking Algorithm via TSK Iterative Regression Model

“…For the identification of model parameters, please refer to the Refs. [26,27], the Ref. [26] used the extended forgetting factor least square method (EFFLSM) to estimate the consequent parameter, and Wang et al .…”

“…[26] used the extended forgetting factor least square method (EFFLSM) to estimate the consequent parameter, and Wang et al . [ 27 ] used the fuzzy expectation maximization (EM) method to identify the premise parameter; 2) Gibbs sampling; 3) Track management.…”

“… is calculated by the entropy regression clustering method via the fuzzy EM, and the specific process is referred to Ref. [27]. are calculated by the EFFLSM, the implementation process is as follows: …”

“…The TSK fuzzy model [ 23–25 ] is the key technology to solve such problem. In the case of single target strong maneuver, we have proposed TSK iterative regression multiple model [ 26–29 ] , which effectively solved the problem of uncertainty of target motion in nonlinear systems. Therefore, we extend this algorithm to the multi‐target tracking, and effectively integrate with the LMB algorithm proposed by Vo et al .…”

“…The detailed TSK fuzzy model can be found in Refs. [26–29]. This section only briefly describes the TSK fuzzy model.…”