Work

Fusion Unbiased Pseudo-Linear Kalman Filter-Based Bearings-Only Target Tracking

Year: 2024

Type: article

Abstract: In the realm of bearings-only target tracking, the pseudo-linear Kalman filter (PLKF) has garnered significant interest, due to its low computational demands and robust stability. However, the interrelation between the measurement matrix and noise introduces bias into the PLKF’s target state estimation. To address this issue, we introduce a fusion unbiased PLKF (FUBKF) algorithm. This algorithm initiates with a global pseudo-linear treatment of the measurement equation, subsequently isolating the noise within the measurement matrix. By employing the unscented Kalman filter (UKF), the algorithm achieves precise estimation of the measurement matrix, thereby mitigating the estimation error stemming from the correlation between the measurement matrix and noise. Simulation outcomes demonstrate that the proposed algorithm substantially enhances tracking accuracy and sustains high stability in both 2D and 3D bearings-only target tracking scenarios, encompassing both non-maneuvering and maneuvering conditions.

Source: Remote Sensing

DOI: 10.3390/rs16234536

Authors: Zhihao Cai, Shiqi Xing, Weize Meng, Junpeng Wang, Xinyuan Su, Sinong Quan

Institutions: National University of Defense Technology, State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System

Language: English

Topic: Target Tracking and Data Fusion in Sensor Networks

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,069

Funder: National Natural Science Foundation of China