【摘要】：在波達(dá)方向估計(jì)中,陣列接收數(shù)據(jù)(觀測(cè)矩陣)的秩小于信源數(shù)時(shí)會(huì)導(dǎo)致經(jīng)典的基于子空間分解類的超分辨算法失效,基于壓縮感知理論的超分辨方法雖可解決算法失效問(wèn)題,但隨著觀測(cè)矩陣秩的增加,壓縮感知方法大多只是進(jìn)行簡(jiǎn)單的向量范數(shù)合成,即將多測(cè)量向量問(wèn)題轉(zhuǎn)變到單次測(cè)量向量問(wèn)題來(lái)解決,并沒(méi)有充分利用多余的采樣信息.在研究秩缺失條件下信號(hào)重構(gòu)機(jī)理的基礎(chǔ)上,提出了一種自適應(yīng)加權(quán)遞歸算法,能夠利用額外的采樣信息通過(guò)空間投影構(gòu)造出相對(duì)正確的信號(hào)子空間,彌補(bǔ)了在秩缺失情況下估計(jì)精度差的問(wèn)題,并且在采樣數(shù)逐漸增加的基礎(chǔ)上,可以實(shí)現(xiàn)對(duì)信號(hào)的無(wú)偏估計(jì).
[Abstract]:In DOA estimation, when the rank of array received data (observation matrix) is less than the number of sources, the classical super-resolution algorithm based on subspace decomposition class will fail, and the super-resolution method based on compressed sensing theory can solve the problem of algorithm failure. However, with the increase of the rank of observation matrix, compression sensing methods are mostly used in simple vector norm synthesis, that is, the multi-measurement vector problem is transformed to the single-measurement vector problem to solve the problem, and the redundant sampling information is not fully utilized. On the basis of studying the mechanism of signal reconstruction under the condition of lack of rank, an adaptive weighted recursive algorithm is proposed, which can make use of extra sampling information to construct a relatively correct signal subspace by spatial projection. It makes up for the problem of poor estimation accuracy under the condition of lack of rank, and the unbiased estimation of signal can be realized on the basis of increasing the number of samples.
【作者單位】： 海軍工程大學(xué)電子工程學(xué)院;哈爾濱工業(yè)大學(xué)電子與信息工程學(xué)院;
【分類號(hào)】：TN911.23

【參考文獻(xiàn)】

相關(guān)期刊論文 前2條

1 丁君,王s，

本文編號(hào)：2161034