基于道路信息的知識輔助空時自適應處理
doi: 10.11999/JEIT140626 cstr: 32379.14.JEIT140626
基金項目:
國家自然科學基金(61372133, 61471285, 61401500)和國家留學基金資助課題
A Knowledge Aided Space Time Adaptive Processing Based on Road Network Data
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摘要: 主波束中的車輛回波信號會污染空時自適應處理(STAP)的訓練樣本，導致空時自適應處理時的目標自相消，引起漏警。針對這一問題，該文提出一種基于道路信息的知識輔助(KA)空時自適應處理方法。該方法首先根據(jù)主波束中道路相對于雷達的位置估計道路上車輛相對于雷達的徑向速度，然后得到可能含有主波束車輛回波信號的距離-多普勒單元，接著根據(jù)訓練樣本與雜波導向矢量和主波束導向矢量的匹配程度判斷這些訓練樣本是否包含主波束車輛回波信號，最后在進行空時自適應處理估計雜波協(xié)方差矩陣時剔除被主波束車輛回波信號污染的訓練樣本。理論分析及實驗結(jié)果表明該方法可以提高道路密集環(huán)境中空時自適應處理的信雜噪比輸出，改善空時自適應處理雷達的性能。Abstract: The echo of the vehicle from the main lobe may contaminate the training samples of Space Time Adaptive Processing (STAP), which results in target self nulling effect, and therefore degrades the probability of detection. To mitigate this problem, this paper proposes a Knowledge Aided (KA) STAP which is based on the road network data to select the training samples. This study firstly estimates the radial velocity of vehicle to the radar; then the range-Doppler cells which may contain vehicle echo are obtained according to the velocity; in the following, this study distinguish whether the training samples contain vehicle echo according to the matching degree of the training samples with the steering vector of the main lobe and the clutter; finally, the samples containing vehicle echo are discarded when the covariance matrix for the STAP is estimated. The theory analysis and experimental results illustrate that the proposed method advances the output of signal to clutter plus noise ratio, and improves the performance of STAP in the road network environments.
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