本文選題：到達(dá)角估計(jì)　切入點(diǎn)：協(xié)方差擬合　出處：《上海交通大學(xué)學(xué)報(bào)》2017年09期 　論文類型：期刊論文

【摘要】：為進(jìn)行高分辨到達(dá)角(DOA)估計(jì)的同時(shí)避免稀疏類算法的不足,提出了協(xié)方差擬合旋轉(zhuǎn)不變子空間信號(hào)參數(shù)估計(jì)(ESPRIT)算法.首先將協(xié)方差擬合準(zhǔn)則轉(zhuǎn)換成半正定規(guī)劃問(wèn)題,利用凸優(yōu)化進(jìn)行求解,得到更接近理論值的信號(hào)協(xié)方差矩陣;然后對(duì)估計(jì)的信號(hào)協(xié)方差矩陣進(jìn)行特征分解,利用信號(hào)子空間和噪聲子空間特征值的差異估計(jì)信源個(gè)數(shù);最后利用子空間旋轉(zhuǎn)不變性反解出未知DOA.仿真實(shí)驗(yàn)從DOA估計(jì)精度、分辨率等方面驗(yàn)證了該算法的有效性,較傳統(tǒng)ESPRIT算法具有更高的DOA估計(jì)分辨率并且受相干信源影響小;與稀疏類算法相比,不依賴先驗(yàn)信息以及避免了網(wǎng)格失配問(wèn)題.
[Abstract]:In order to carry out high resolution DOA estimation and avoid the shortage of sparse algorithms, a covariance fitting rotation invariant subspace signal parameter estimation algorithm is proposed. The covariance fitting criterion is first converted into a positive semidefinite programming problem. The signal covariance matrix, which is closer to the theoretical value, is solved by convex optimization, then the estimated signal covariance matrix is decomposed and the number of information sources is estimated by using the difference between the eigenvalues of the signal subspace and the noise subspace. Finally, the unknown DOA is solved by subspace rotation invariance. The simulation results show that the proposed algorithm is effective in terms of DOA estimation accuracy and resolution. Compared with the traditional ESPRIT algorithm, the proposed algorithm has higher DOA resolution and is less affected by coherent sources. Compared with sparse class algorithm, it does not rely on prior information and avoids mesh mismatch problem.
【作者單位】： 空軍預(yù)警學(xué)院;
【基金】：國(guó)家自然科學(xué)基金(61401504) 軍內(nèi)計(jì)劃科研項(xiàng)目(2015×××) 湖北省自然科學(xué)基金(2016CFB288)資助
【分類號(hào)】：TN911.7
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本文編號(hào)：1593330