Computer Science ›› 2026, Vol. 53 ›› Issue (6A): 250300058-7.doi: 10.11896/jsjkx.250300058

• Image Processing & Multimedia Technology • Previous Articles     Next Articles

Improved Method for Radar Target Tracking Under Time-varying Non-Gaussian Observation Noise

YANG Hankun1, ZHU Bowei2, WANG Zuoshuai3, XU Yidong1   

  1. 1 Yantai Research Institute of Harbin Engineering University,Yantai 265500,China
    2 Beijing Institute of Astronautical Systems Engineering,Beijing 100076,China
    3 Hubei Key Laboratory of Marine Electromagnetic Detection and Control of Wuhan Second Ship Design and Research Institute,Wuhan 430064, China
  • Online:2026-06-16 Published:2026-06-12
  • About author:YANG Hankun,born in 2001,postgra-duate.His main research interest is radar signal processing.
    WANG Zuoshuai,born in 1990,Ph.D,senior engineer.His main research interest is the application and protection of electromagnetic fields in ships.
  • Supported by:
    National Natural Science Foundation of China(52101383).

Abstract: This paper proposes an improved Minimum Error Entropy Kalman Filter algorithm aimed at addressing the radar target tracking problem in complex time-varying non-Gaussian observation noise environments.By introducing an adaptive noise covariance adjustment strategy and observation data smoothing techniques,the algorithm can dynamically and effectively adapt to sea surface noise characteristics.Through simulation experiments,it is found that in time-varying heavy-tailed noise environments,the improved algorithm reduces the average absolute error distribution in the x and y directions by approximately 67.44% and 69.09%,respectively.In time-varying skewed noise environments,the reductions are approximately 71.99% and 70.90%,respectively.These results demonstrate that the improved MEEKF algorithm has significant advantages in handling time-varying non-Gaussian observation noise environments,providing an effective solution.

Key words: Radar target tracking, Time-varying non-Gaussian noise, MEEKF, Adaptive strategy, Data smoothing techniques

CLC Number: 

  • TN953
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