基于復數因子分析模型的步進頻數據壓縮感知
doi: 10.11999/JEIT140407
基金項目:
國家自然科學基金(61271024, 61201296, 61322103)和全國優(yōu)秀博士學位論文作者專項資金(FANEDD-201156)資助課題
Compressive Sensing Using Complex Factor Analysis for Stepped-frequency Data
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摘要: 認知雷達發(fā)射高距離分辨率步進頻信號通常需要較長的觀測時間。為了節(jié)省時間資源，該文提出一種貝葉斯重構算法，用較少的步進頻信號脈沖得到的頻點缺失頻域數據，重構出相應的全帶寬頻域數據。首先利用復數貝塔過程因子分析(Complex Beta Process Factor Analysis, CBPFA)模型對一組全帶寬頻域數據進行統(tǒng)計建模，求解得到其概率密度函數；然后在目標被跟蹤且姿態(tài)變化不大的情況下，只發(fā)射步進頻信號的部分脈沖，根據先前CBPFA模型得到的概率密度函數，對頻點缺失的頻域數據利用壓縮感知理論和貝葉斯準則解析地重構出相應的全帶寬頻域數據?；趯崪y1維高分辨距離(High Range Resolution, HRR)數據的重構實驗，證明了該文提出方法的性能。Abstract: It usually takes a long observing time when a cognitive radar transmits the High-Range-Resolution (HRR) stepped-frequency signal. To save time, partial pulses of the stepped-frequency signal are transmitted to obtain the incomplete frequency data, and a Bayesian reconstruction algorithm is proposed to reconstruct the corresponding full-band frequency data. Firstly, the Complex Beta Process Factor Analysis (CBPFA) model is utilized to statistically model a set of full-band frequency data, whose probability density function (pdf) can be learned from this CBPFA model. Secondly, when the target is tracked and its attitude changes not much, the cognitive radar can just transmit the partial pulses of the stepped-frequency signal, and the corresponding full-band frequency data can be analytically reconstructed from the incomplete frequency data via the Compressive Sensing (CS) method and Bayesian criterion based on the previous pdf learned with CBPFA model. The reconstruction experiments of the measured HRR data demonstrate the performance of the proposed method.
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