高斯非平稳机动目标波达角模型及跟踪
收稿日期: 2014-11-26
修回日期: 2015-01-13
网络出版日期: 2015-01-30
基金资助
国家自然科学基金 (61171127)
DOA model and tracking for Gaussian non-stationary maneuvering targets
Received date: 2014-11-26
Revised date: 2015-01-13
Online published: 2015-01-30
Supported by
National Natural Science Foundation of China (61171127)
在实际的跟踪情况中,由于环境条件、目标反射截面等因素的变化,回波信号的功率会随时间变化,即不满足通常阵列信号处理中对高斯信号作平稳性的假设。针对复杂运动条件下高斯非平稳目标的跟踪问题,提出了一种新的机动目标波达角(DOA)模型。该模型全面地刻画了高斯非平稳机动目标的动态,并将目标的DOA和信号功率作为状态变量进行了联合考虑,同时运用虚拟阵列的表示方法构建了相应的观测方程。对于建立的新模型,最后采用无迹卡尔曼滤波(UKF)的框架完成了整个跟踪算法。分析和仿真结果表明,当高斯非平稳机动目标之间存在长时间相互接近的情况时,新方法仍然可以获得较好的跟踪性能。
虞翔 , 张建秋 . 高斯非平稳机动目标波达角模型及跟踪[J]. 航空学报, 2015 , 36(10) : 3430 -3438 . DOI: 10.7527/S1000-6893.2015.0019
In practical applications, due to the variation of environment, radar cross section and other variable factors, the echo power changes over time, which is inconsistent with the stationary assumption of Gaussian signals in the general array signal processing. To address the tracking problem of Gaussian non-stationary maneuvering targets with complex movements, a new direction-of-arrival (DOA) model is proposed. This model captures the dynamic state of Gaussian non-stationary maneuvering targets completely, considering the DOA and signal power as a joint state vector. Meanwhile, this model utilizes virtual array representing method and constructs the observation equation accordingly. Finally, the unscented Kalman filter (UKF) algorithm is used to complete the whole tracking process. Both analyses and simulation results show that the new method is still able to achieve good tracking performance when the Gaussian non-stationary maneuvering targets are close to each other for a long time.
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