基于循環(huán)匹配追蹤的稀疏重構(gòu)時延估計算法
doi: 10.11999/JEIT180460
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信息工程大學(xué)信息系統(tǒng)工程學(xué)院 ??鄭州 ??450001
Time of Arrival Estimation Based on Sparse Reconstruction Loop Matching Pursuit Algorithm
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Institute of Information System Engineering, The Information Engineering University, Zhengzhou 450001, China
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摘要:
在單樣本(SMV)、低信噪比條件下，稀疏重構(gòu)方法可提升時延估計精度，但現(xiàn)有的重構(gòu)算法在支撐集元素的選擇中存在錯選和漏選的情況，從而導(dǎo)致估計精度受限。針對上述問題，該文提出一種基于循環(huán)匹配追蹤(LMP)的稀疏重構(gòu)時延估計算法。該方法引入了“循環(huán)刪除，匹配添加”的思想，有效提升了直達(dá)徑的估計精度。算法首先建立信道沖激響應(yīng)稀疏表示模型；然后在獲得初始支撐集的前提下，先循環(huán)刪除支撐集內(nèi)的元素，再從支撐集補(bǔ)集中依據(jù)與當(dāng)前殘差內(nèi)積值最大來匹配添加新元素，直至殘差內(nèi)積基本不變；最后利用時延值與稀疏支撐集的關(guān)系得到了時延的估計值。仿真結(jié)果表明，所提算法相比于傳統(tǒng)稀疏重構(gòu)時延估計算法具有更高的估計精度。同時基于USRP平臺，利用實(shí)際信號對所提算法進(jìn)行了有效性驗證。
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關(guān)鍵詞:
- 時延估計 /
- 稀疏重構(gòu) /
- 循環(huán)匹配追蹤 /
- 支撐集 /
- USRP平臺
Abstract:Under Single Measurement Vector (SMV) and low Signal-to-Noise Ratio (SNR) conditions, the sparse reconstruction method can improve the estimation accuracy of Time Of Arrival (TOA). However, the existing reconstruction algorithms have some mistakes and missing in the selection of sparse support set elements, which leads to limited estimation accuracy. In order to solve this problem, this paper proposes an algorithm based on sparse reconstruction Loop Matching Pursuit (LMP), which improves the estimation accuracy of the direct path. The algorithm first establishes a sparse representation model of channel impulse response. Then, under the premise of having obtained initial support set, the elements in the support set are removed cyclically. In addition, according to the maximum value of the current residual within the product, the remaining elements are used to match and add the new elements until the residual product is the same. Finally, the estimate of the TOA is obtained using the relationship between the time delay value and the sparse support set. The simulation results show that the proposed algorithm has higher estimation accuracy than the traditional sparse reconstruction time delay estimation algorithm. At the same time, based on the USRP platform, the effectiveness of the proposed algorithm is verified by the actual signal.
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表 1 OFDM系統(tǒng)參數(shù)設(shè)置
參數(shù) 數(shù)值 FFT周期${T_{{\rm{FFT}}}}$ 3.2 μs 系統(tǒng)帶寬$B$ 20 MHz 子載波數(shù) 64個 載波頻率${f_c}$ 2.4 GHz 
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表 2 計算復(fù)雜度對比
算法 復(fù)雜度 Root-Music $O\left( {{M^3}{\rm{ + 2}}{{\rm{M}}^2}{\rm{ + }}5M{\rm{ - }}LM} \right)$ OMP $O\left( {{M^2}{L^2} + MNL} \right)$ LMP $O\left( {2{M^2}{L^2} + 2MNL} \right)$ CoSaMP $O\left( {3{M^2}{L^2} + MNL} \right)$ NS $O\left( {{N^2} + ({M^2} + {L^2})N} \right)$
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表 3 5種算法多徑估計結(jié)果比較
算法 第1條徑 第2條徑 第3條徑 估計均值(m) 標(biāo)準(zhǔn)差 RMSE 估計均值(m) 標(biāo)準(zhǔn)差 估計均值(m) 標(biāo)準(zhǔn)差 LMP 50.0100 0.3015 0.3017 57.0900 0.3803 62.7900 0.3562 OMP 48.9000 1.4387 1.8111 56.8400 2.2037 61.5600 1.3537 CoSaMP 47.5500 2.5989 3.5717 58.4800 1.4648 59.8200 1.3563 NS 49.7600 0.6556 0.6742 55.8900 2.3162 60.5100 1.1623 Root-Music 49.8250 0.7915 0.7925 57.6600 0.9468 62.2200 0.9119
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