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为增强被动协同跟踪系统对复杂环境的适应性，提高跟踪精度和系统鲁棒性，提出了一种基于自适应滤波技术的目标跟踪算法。该算法通过基于Sage-Husa自适应噪声估计的无迹卡尔曼滤波对多站被动协同定位结果进行状态估计，并利用残差量的局部动态统计对噪声估计器进行改进，以提高噪声估计的准确性和稳定性。同时，引入协方差自相关量匹配判据来保证噪声方差阵的正定性，防止滤波发散。仿真结果表明，该方法可有效提高噪声估计精度，增强目标跟踪系统对环境的适应性，大幅提升跟踪性能。
Abstract：In order to enhance the adaptability of passive co-tracking system in complex environment and improve the tracking accuracy, an adaptive filter algorithm for target tracking is proposed. In the proposed framework, a UKF algorithm based on Sage-Husa filtering technique with improved noise estimator is applied to estimate the state result of passive co-tracking. To get better estimation accuracy, the noise estimator is improved by using the statistics of residual vectors instead of the single residual. In addition, a matching strategy based on self-correlation portion of the observation covariance is implemented, which can prevent the filtering divergence. The simulation results show that the proposed algorithm can improve the tracking accuracy and robustness effectively.
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Basic Information:
DOI：10.16358/j.issn.1009-1300.20220198
China Classification Code:TN713;TJ765
Citation Information:
[1]杨月霜,田锦昌,池庆玺,等.基于自适应滤波技术的被动协同跟踪算法[J].战术导弹技术,2023,No.221(05):83-88+96.DOI:10.16358/j.issn.1009-1300.20220198.
Fund Information:
国家自然科学基金（U2141230）