基于离格稀疏表示的近场信源定位方法

靳一; 徐常志; 荆涛; 吴晓欢; 颜俊; 李明玉

doi:10.11999/JEIT200784

基于离格稀疏表示的近场信源定位方法

doi: 10.11999/JEIT200784

靳一¹,
徐常志¹,
荆涛¹,
吴晓欢²,
颜俊²,
李明玉^3, ,

1.
中国空间技术研究院西安分院西安 710100
2.
南京邮电大学通信与信息工程学院南京 210003
3.
重庆大学微电子与通信工程学院重庆 400044

基金项目: 国家重点研发计划项目(2019YFB1803102)；国家自然科学基金(61801377, 62171068)

详细信息

作者简介:
靳一：男，1984年生，高级工程师，博士，研究方向为卫星通信与网络

徐常志：男，1985年生，高级工程师，博士，研究方向为卫星通信与网络

荆涛：男，1985年生，工程师，硕士，研究方向为卫星通信与网络

吴晓欢：男，1988年生，讲师，博士，研究方向为阵列信号处理

颜俊：男，1981年生，副教授，博士，研究方向为通信信号处理

李明玉：男，1978年生，副教授，博士，研究方向为射频电路与系统

通讯作者:
李明玉　myli@cqu.edu.cn

中图分类号: TN911.3
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出版历程
- 收稿日期: 2020-09-08
- 修回日期: 2021-10-14
- 网络出版日期: 2021-10-21
- 刊出日期: 2021-11-23

Off-grid Sparse Representation Based Localization Method for Near-field Sources

1.
China Academy of Space Technology-Xi’an, Xi’an 710100, China
2.
School of Communication and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
3.
School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400044, China

Funds: The National Key Research and Development Program (2019YFB1803102); The National Natural Science Foundation of China (61801377, 62171068)

摘要

摘要: 近场信源定位是下一代无线通信中的一个重要研究方向，现有的方法大多集中在传统子空间或在格稀疏方法。针对子空间类方法损失阵列孔径和稀疏表示类方法精度受网格划分效应制约的问题，该文提出了一种基于离格稀疏表示的定位方法。首先通过获得一个高阶累积量矩阵建立基于角度参数的离格信号模型，利用交替迭代优化方法实现角度的估计。然后根据角度估计值建立基于距离参数的离格信号模型，并采用交替迭代方法进行求解。仿真结果表明，所提方法不仅具有较高的估计精度，而且可以实现角度和距离参数的正确配对。
- 近场定位 /
- 稀疏表示 /
- 离格信号模型 /
- 交替迭代
Abstract: Near-field source localization is a potential research topic in next-generation wireless communications. Most existing methods focus on traditional subspace based methods or on-grid sparse methods. For the problem that the accuracy of subspace class method loss array aperture and sparse representation method is restricted by mesh effect, an off-grid sparse representation localization method is proposed in this paper. First, by obtaining a high-order cumulant matrix, an angle based off-grid signal model is constructed and then the alternatively iterating optimization method is employed to estimate the angles. For range estimation, a range parameter based off-grid signal model is constructed by using the angle estimation values and is solved by alternatively iterating method. Simulation results reveal that the proposed method not only possesses high estimation accuracy, but also can realize auto-pairing of angles and ranges.
- Near-field localization /
- Sparse representation /
- Off-grid signal model /
- Alternative iteration

HTML全文

图 1 近场信源估计示意图

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图 2 RMSE和运行时间随信噪比的变化情况

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图 3 RMSE随快拍数的变化情况

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图 4 小快拍场景下RMSE随信噪比的变化情况

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图 5 RMSE随角度间隔的变化情况

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参考文献(15)

[1]	SCHMIDT R. Multiple emitter location and signal parameter estimation[J]. IEEE Transactions on Antennas and Propagation, 1986, 34(3): 276–280. doi: 10.1109/TAP.1986.1143830
[2]	ROY R and KAILATH T. ESPRIT-estimation of signal parameters via rotational invariance techniques[J]. IEEE Transactions on Acoustics, Speech, and Signal Processing, 1989, 37(7): 984–995. doi: 10.1109/29.32276
[3]	MALIOUTOV D, CETIN M, and WILLSKY A S. A sparse signal reconstruction perspective for source localization with sensor arrays[J]. IEEE Transactions on Signal Processing, 2005, 53(8): 3010–3022. doi: 10.1109/TSP.2005.850882
[4]	蒋莹, 王冰切, 韩俊, 等. 基于分布式压缩感知的宽带欠定信号DOA估计[J]. 电子与信息学报, 2019, 41(7): 1690–1697. doi: 10.11999/JEIT180723 JIANG Ying, WANG Bingqie, HAN Jun, et al. Underdetermined wideband DOA estimation based on distributed compressive sensing[J]. Journal of Electronics &Information Technology, 2019, 41(7): 1690–1697. doi: 10.11999/JEIT180723
[5]	WU Xiaohuan, ZHU Weiping, and YAN Jun. Direction of arrival estimation for off-grid signals based on sparse bayesian learning[J]. IEEE Sensors Journal, 2016, 16(7): 2004–2016. doi: 10.1109/JSEN.2015.2508059
[6]	CHEN Peng, CAO Zhenxin, CHEN Zhimin, et al. Sparse off-grid DOA estimation method with unknown mutual coupling effect[J]. Digital Signal Processing, 2019, 90: 1–9. doi: 10.1016/j.dsp.2019.04.001
[7]	CHEN Peng, CHEN Zhimin, CAO Zhenxin, et al. A new atomic norm for DOA estimation with gain-phase errors[J]. IEEE Transactions on Signal Processing, 2020, 68: 4293–4306. doi: 10.1109/TSP.2020.3010749
[8]	ZHU Hao, LEUS G, and GIANNAKIS G. Sparsity-cognizant total least-squares for perturbed compressive sampling[J]. IEEE Transactions on Signal Processing, 2011, 59(5): 2002–2016. doi: 10.1109/TSP.2011.2109956
[9]	YANG Zai, ZHANG Cishen, and XIE Lihua. Robustly stable signal recovery in compressed sensing with structured matrix perturbation[J]. IEEE Transactions on Signal Processing, 2012, 60(9): 4658–4671. doi: 10.1109/TSP.2012.2201152
[10]	YANG Zai, XIE Lihua, and ZHANG Cishen. Off-grid direction of arrival estimation using sparse Bayesian inference[J]. IEEE Transactions on Signal Processing, 2013, 61(1): 38–43. doi: 10.1109/TSP.2012.2222378
[11]	WU Xiaohuan, ZHU Weiping, YAN Jun, et al. Two sparse-based methods for off-grid direction-of-arrival estimation[J]. Signal Processing, 2018, 142: 87–95. doi: 10.1016/j.sigpro.2017.07.004
[12]	BJÖRNSON E, SANGUINETTI L, WYMEERSCH H, et al. Massive MIMO is a reality - what is next?: five promising research directions for antenna arrays[J]. Digital Signal Processing, 2019, 94: 3–20. doi: 10.1016/j.dsp.2019.06.007
[13]	HE J, SWAMY M N S, and AHMAD M. Efficient application of MUSIC algorithm under the coexistence of far-field and near-field sources[J]. IEEE Transactions on Signal Processing, 2012, 60(4): 2066–2070. doi: 10.1109/TSP.2011.2180902
[14]	LIANG Junli and LIU Ding. Passive localization of mixed near-field and far-field sources using two-stage MUSIC algorithm[J]. IEEE Transactions on Signal Processing, 2010, 58(1): 108–120. doi: 10.1109/TSP.2009.2029723
[15]	WANG Bo, LIU Juanjuan, and SUN Xiaoying. Mixed sources localization based on sparse signal reconstruction[J]. IEEE Signal Processing Letters, 2012, 19(8): 487–490. doi: 10.1109/lsp.2012.2204248