基于張量分解的互質(zhì)陣MIMO雷達(dá)目標(biāo)多參數(shù)估計(jì)方法
doi: 10.11999/JEIT140826
基金項(xiàng)目:
國(guó)家部委基金，教育部博士點(diǎn)基金(20113219110018)，中國(guó)航天科技集團(tuán)公司航天科技創(chuàng)新基金(CASC04-02)，國(guó)家自然科學(xué)基金(61302188)和江蘇省自然科學(xué)基金(BK20131005)資助課題
Co-prime MIMO Radar Multi-parameter Estimation Based on Tensor Decomposition
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摘要: 該文提出了一種基于雙基地互質(zhì)陣列(CPA)多輸入多輸出(MIMO)雷達(dá)的多目標(biāo)波離方向角(DOD)、波達(dá)方向角(DOA)和多普勒頻率估計(jì)算法。收發(fā)陣列各由兩個(gè)滿(mǎn)足互質(zhì)結(jié)構(gòu)的稀疏均勻子線陣組成。時(shí)域的快拍序列同樣由兩個(gè)互質(zhì)的稀疏均勻采樣構(gòu)成。算法利用張量因子分解得到分別包含DOD, DOA和多普勒頻率信息的3個(gè)流形矩陣，再?gòu)闹袠?gòu)造出具有范德蒙德矩陣結(jié)構(gòu)的虛擬流形矩陣。為了提高估計(jì)精度，還提出了一種基于特征值分解的誤差抑制算法，并通過(guò)旋轉(zhuǎn)不變子空間算法(ESPRIT)求取各目標(biāo)的3個(gè)待估參數(shù)。與傳統(tǒng)算法相比，該算法通過(guò)構(gòu)造均勻虛擬陣列和虛擬快拍提高參數(shù)估計(jì)性能，且不會(huì)產(chǎn)生模糊，避免了譜峰搜索和額外的配對(duì)過(guò)程。仿真實(shí)驗(yàn)驗(yàn)證了該算法有效性。
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關(guān)鍵詞:
- 雙基地MIMO雷達(dá) /
- 互質(zhì)數(shù)對(duì) /
- 張量分解 /
- Swerling-I模型
Abstract: A novel algorithm for estimation of Direction Of Departure (DOD), Direction Of Arrival (DOA), and Doppler frequency based on bistatic MIMO radar with Co-Prime Array (CPA) is presented. The transmit and receive arrays are both composed of a pair of sparse uniform subarrays. Similarly, a pair of snapshot sequences with co-prime intervals constitutes the sampling of temporal. Three manifold matrices which contain multi-targets DODs, DOAs and Doppler frequencies respectively are estimated through tensor decomposition. From which a group of Vandermonde matrices of virtual manifold are constructed. To improve the estimation accuracy, an error depressing algorithm based on eigenvalue decomposition is proposed. Finally, the above three parameters are estimated by an Estimation of Signal Parameters via Rotation Invariant Techniques (ESPRIT) algorithm. The proposed algorithm offers better performance through virtual array and virtual snapshot without parameter ambiguous. It requires neither peak searching nor pairing processes, and the simulation results are presented to verify the effectiveness of the proposed algorithm.-
Key words:
- Bistatic MIMO radar /
- Co-prime pair /
- Tensor decomposition /
- Swerling-I model
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