Weighted Maximum Correntropy Criterion-Based Interacting Multiple-Model Filter for Maneuvering Target Tracking

Abstract

During the process of maneuvering target tracking, the measurement may be disturbed by outliers, which leads to a decrease in the state estimation performance of the classic interacting multiple-model (IMM) filter. To solve this problem, a weighted maximum correntropy criterion (WMCC)-based IMM filter is proposed. In the proposed filter, the fusion state is used as the input of each sub-model to reduce the computational complexity of state interaction and the WMCC is adopted to derive the sub-model state update and state fusion to improve the state estimation performance under outlier interference. Through principal analysis, the superiority of the proposed filter over the classic IMM filter in fusion strategy is revealed. The specific form of the proposed filter in radar maneuvering target tracking is provided. Two experimental cases of maneuvering target tracking are tested to illustrate the effectiveness of the proposed filter.

An Adaptive IMM Algorithm for a PD Radar with Improved Maneuvering Target Tracking Performance

“…The classical interacting multiple model algorithm consists of the following four main parts: (1) Model interaction; (2) The parallel filtering of models; (3) The updating of model probability; and (4) Model estimation fusion [14]. The IMM algorithm assumes that the true motion model of the target is obtained by summing the individual model sets of the respective weights they occupy [15][16][17].…”