基于復(fù)數(shù)因子分析模型的步進(jìn)頻數(shù)據(jù)壓縮感知
doi: 10.11999/JEIT140407 cstr: 32379.14.JEIT140407
基金項(xiàng)目:
國家自然科學(xué)基金(61271024, 61201296, 61322103)和全國優(yōu)秀博士學(xué)位論文作者專項(xiàng)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(rèn)知雷達(dá)發(fā)射高距離分辨率步進(jìn)頻信號(hào)通常需要較長的觀測時(shí)間。為了節(jié)省時(shí)間資源，該文提出一種貝葉斯重構(gòu)算法，用較少的步進(jìn)頻信號(hào)脈沖得到的頻點(diǎn)缺失頻域數(shù)據(jù)，重構(gòu)出相應(yīng)的全帶寬頻域數(shù)據(jù)。首先利用復(fù)數(shù)貝塔過程因子分析(Complex Beta Process Factor Analysis, CBPFA)模型對(duì)一組全帶寬頻域數(shù)據(jù)進(jìn)行統(tǒng)計(jì)建模，求解得到其概率密度函數(shù)；然后在目標(biāo)被跟蹤且姿態(tài)變化不大的情況下，只發(fā)射步進(jìn)頻信號(hào)的部分脈沖，根據(jù)先前CBPFA模型得到的概率密度函數(shù)，對(duì)頻點(diǎn)缺失的頻域數(shù)據(jù)利用壓縮感知理論和貝葉斯準(zhǔn)則解析地重構(gòu)出相應(yīng)的全帶寬頻域數(shù)據(jù)?；趯?shí)測1維高分辨距離(High Range Resolution, HRR)數(shù)據(jù)的重構(gòu)實(shí)驗(yàn)，證明了該文提出方法的性能。
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關(guān)鍵詞:
- 認(rèn)知雷達(dá) /
- 步進(jìn)頻 /
- 貝葉斯重構(gòu)算法 /
- 壓縮感知 /
- 因子分析模型
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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