有源欺騙干擾環(huán)境下的DOA估計
doi: 10.11999/JEIT180488
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西安電子科技大學(xué)雷達信號處理國家重點實驗室 ??西安 ??710071
DOA Estimation Under Active Deception Jamming Environment
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National Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, China
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摘要:
針對有源欺騙干擾環(huán)境下基于小樣本的DOA估計問題,該文提出自適應(yīng)極化濾波(APF)聯(lián)合塊稀疏貝葉斯學(xué)習(xí)(BSBL)算法的DOA估計方法。首先,通過APF抑制干擾能量,提高信干比。然后,建立有源欺騙干擾環(huán)境下的稀疏貝葉斯模型,基于相鄰快拍相關(guān)性,利用BSBL算法進行DOA估計。仿真和實測數(shù)據(jù)處理結(jié)果表明,所提方法降低了干擾對BSBL算法的影響,且與APF聯(lián)合子空間類算法或最大似然算法(ML)相比,具有更高的空間分辨率和DOA估計精度。
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關(guān)鍵詞:
- DOA估計 /
- 抗干擾 /
- 自適應(yīng)極化濾波 /
- 塊稀疏貝葉斯學(xué)習(xí)
Abstract:For the target DOA estimation under active deception jamming environment with limited samples, a novel DOA estimation method based on the combination of Adaptive Polarization Filter(APF) and Block Sparse Bayesian Learning(BSBL) algorithm is proposed. First, the interference energy is suppressed using APF. Then, the proposed method constructs a sparse Bayesian model under active deception jamming environment. The target DOA is estimated using the BSBL algorithm based on the neighbor time sampling correlation. Simulated and measured data processing results prove that the proposed method reduces the influence of interference on the BSBL algorithm, and has higher spatial resolution and higher angle measurement accuracy, comparing with the method based on the combination of APF and subspace-based DOA algorithms or maximum likelihood DOA algorithm.
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Key words:
- DOA estimation /
- Anti-jamming /
- Adaptive polarization filter /
- Sparse Bayesian learning
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