Sensor Alignment for Ballistic Target Tracking Based on Sparse Representation

• Published in: 2024 Second International Conference on Cyber-Energy Systems and Intelligent Energy (ICCSIE) pp. 1 - 6
• Main Authors: Li, Dong, Wei, Chao, Zhao, Shuyuan
• Format: Conference Proceeding
• Language: English
• Published: IEEE 17.05.2024
• Subjects: Accuracy; ballistic target tracking; Estimation; Measurement uncertainty; Optimization models; particle filter; Particle filters; sensor alignment; Simulation; Sparse approximation; sparse representation; Systematics; Target tracking; Trajectory
• DOI: 10.1109/ICCSIE61360.2024.10698270

Sensor alignment for tracking of ballistic target is an important research topic in the field of aerospace. A particle filter based on sparse representation is proposed to achieve sensor alignment in this paper. The system state is established by combining the trajectory of ballistic target and the systematic errors of measurements. The sparse representation model of the system state is constructed by using the sparse characteristic of systematic errors. The posterior distribution of the system state is recursively calculated by using particle filter. Then, the trajectory and systematic errors are estimated simultaneously. To improve the filtering performance, a constrained optimization model is constructed and solved to estimate the support set of the system state. Then, the support set is tuned to be adapted for the existence of systematic errors. Simulation results show that the proposed filter can improve the estimation accuracy of trajectory over the existing filters. The estimates of systematic errors are close to the true values, indicating that the on-line sensor alignment is achieved successfully.