【摘要】：為了降低分布式協(xié)同估計(jì)算法的計(jì)算量并改善其收斂性能,提出了基于壓縮感知(CS)和遞歸最小二乘(RLS)的分布式協(xié)同估計(jì)算法。該算法在傳統(tǒng)RLS分布式協(xié)同估計(jì)算法的基礎(chǔ)上引入壓縮感知技術(shù),首先在壓縮域中進(jìn)行遞歸最小二乘運(yùn)算,然后利用壓縮感知重構(gòu)算法得到未知參數(shù)向量的估計(jì)值。提出的算法能夠在增量式策略和兩種模式的擴(kuò)散式策略下實(shí)現(xiàn)對(duì)未知向量的有效估計(jì)。理論分析和仿真結(jié)果表明,該算法一方面降低了RLS分布式協(xié)同估計(jì)算法的計(jì)算量,另一方面保持較快的收斂速度與良好的均方誤差性能。
[Abstract]:In order to reduce the computational complexity and improve the convergence performance of the distributed cooperative estimation algorithm, a distributed cooperative estimation algorithm based on compression-aware (CS) and recursive least square (RLS) is proposed. Based on the traditional RLS distributed cooperative estimation algorithm, the compressed sensing technique is introduced. Firstly, the recursive least square operation is performed in the compressed domain, and then the estimated value of unknown parameter vector is obtained by using the compressed perceptual reconstruction algorithm. The proposed algorithm can effectively estimate unknown vectors under incremental strategy and diffusion strategy of two modes. Theoretical analysis and simulation results show that the proposed algorithm can reduce the computational complexity of the RLS distributed cooperative estimation algorithm on the one hand and maintain a faster convergence rate and good mean square error performance on the other hand.
【作者單位】： 北京信息科技大學(xué)信息與通信工程學(xué)院;
【基金】：國(guó)家自然科學(xué)基金資助項(xiàng)目(61302073) 北京市自然科學(xué)基金面上項(xiàng)目(4172021);北京市自然科學(xué)基金資助項(xiàng)目(Z160002) 北京市教委面上項(xiàng)目(KM201711232010)
【分類號(hào)】：TN911.7

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