本文關(guān)鍵詞： 雙基地MIMO雷達(dá) 參數(shù)估計(jì) 分?jǐn)?shù)低階類相關(guān)熵 平行因子分析 Alpha穩(wěn)定分布　出處：《通信學(xué)報(bào)》2016年12期 　論文類型：期刊論文

【摘要】：針對(duì)Alpha穩(wěn)定分布噪聲環(huán)境下參數(shù)估計(jì)性能退化的問題,受類相關(guān)熵概念的啟發(fā),提出分?jǐn)?shù)低階類相關(guān)熵(FCAS)的概念,并采用分?jǐn)?shù)低階類相關(guān)熵準(zhǔn)則對(duì)平行因子分析(PARAFAC)算法中基于三線性最小二乘(TALS)迭代準(zhǔn)則的目標(biāo)函數(shù)進(jìn)行了修正,推導(dǎo)出適用于沖激噪聲環(huán)境的韌性平行因子分析(FCAS-PARAFAC)算法,并將該方法應(yīng)用于雙基地MIMO雷達(dá)系統(tǒng)中目標(biāo)參數(shù)估計(jì)中。FCAS-PARAFAC算法能夠抑制脈沖噪聲的影響,具有較好的估計(jì)性能,并且能夠?qū)崿F(xiàn)自動(dòng)配對(duì),仿真實(shí)驗(yàn)驗(yàn)證了算法的有效性。
[Abstract]:In order to solve the problem of parameter estimation performance degradation in Alpha stable distributed noise environment, the concept of fractional low order class correlation entropy (FCAS) is proposed, which is inspired by the concept of class correlation entropy. The objective function of parallel factor analysis (PARAFAC) algorithm based on trilinear least squares iteration criterion is modified by using fractional low order correlation entropy criterion, and the FCAS-PARAFAC algorithm suitable for impulse noise environment is derived. The method is applied to the estimation of target parameters in bistatic MIMO radar system. FCAS-PARAFAC algorithm can suppress the influence of impulse noise, has better estimation performance, and can realize automatic pairing. The simulation results show that the algorithm is effective.
【作者單位】： 大連大學(xué)信息工程學(xué)院;大連理工大學(xué)電子信息與電氣工程學(xué)部;
【基金】：國(guó)家自然科學(xué)基金資助項(xiàng)目(No.61401055,No.61671105)~~
【分類號(hào)】：TN958
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本文編號(hào)：1542987