A Random Maximum Likelihood Algorithm Based on Limited PSO Initial Space

doi:10.3969/j.issn.1006-2475.2019.02.011

Abstract

Abstract: Aiming at the problem of large computational complexity due to multidimensional nonlinear optimization in the DOA estimation of the random maximum likelihood algorithm, a SML algorithm is proposed to limit the search space of particle swarm optimization algorithm. This algorithm overcomes a defect that when the ESPRIT algorithm defines the initial space of PSO, the signal cannot be directly processed by ESPRIT algorithm when the array structure is a non-uniform linear array and the signal is a coherent signal, and we need to adopt a set of preprocessing techniques, which increases the complexity of algorithm calculation. The key point of the proposed algorithm is to use the hypothesis technique to determine the initialization point instead of the solution of the ESPRIT algorithm, the initial solution space of the PSO algorithm is determined by combining the CRRAM. This method eliminates the need for preprocessing techniques, and the algorithm that defines the PSO initialization space greatly reduces the computational complexity of the SML algorithm. Experimental results show that the proposed algorithm provides fairly good initial values for both coherent and incoherent cases. Finally, the proposed algorithm is compared with many existing algorithms, and the validity and accuracy of the proposed algorithm are verified.

Key words: direction-of-arrival estimation, particle swarm optimization algorithm, stochastic maximum likelihood algorithm, computational complexity, hypothesis technique

CLC Number:

TP18

CAI Li-ping, TIAN Hui, CHEN Hai-hua, HU Jia-liang. A Random Maximum Likelihood Algorithm Based on Limited PSO Initial Space[J]. Computer and Modernization, doi: 10.3969/j.issn.1006-2475.2019.02.011.

References

［1］蒋柏峰,吕晓德,向茂生. 一种基于阵列接收信号重排的单快拍DOA估计方法［J］. 电子与信息学报, 2014,36(6):1334-1339.
［2］ DAI J S, SO H C. Sparse Bayesian learning approach for outlier-resistant direction-of-arrival estimation［J］. IEEE Transactions on Signal Processing, 2018,66(3):744-756.
［3］ LI Y X, GU S N, ZHENG N E. MIMO Radar Transmit Beampattern Design for DOA Estimation with Sidelobe Suppression［J/OL］. (2016-02-16). https://www.hindawi.com/journals/ijap/2016/1512843/.〖HJ1.55mm〗
［4］黄中瑞,张剑云,周青松. 双基地MIMO雷达发射功率聚焦的角度估计算法研究［J］. 电子与信息学报, 2015,37(10):2314-2320.
［5］ CHEN H H, SUZUKI M. Exact formulation for stochastic ML estimation of DOA［J］. IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, 2010,E93-A(11):2141-2152.
［6］ WU R Y, MA W K, FU X. A stochastic maximum-likelihood framework for simplex structured matrix factorization［C］// Proceedings of the 2017 IEEE International Conference on Acoustics, Speech and Signal Processing. 2017:2557-2561.
［7］ KUCHAR A, TANGEMANN M, BONEK E. A real-time DOA-based smart antenna processor［J］. IEEE Transactions on Vehicular Technology, 2002,51(6):1279-1293.
［8］ LAVATE T B, KOKATE V K, SAPKAL A M. Performance analysis of MUSIC and ESPRIT DOA estimation algorithms for adaptive array smart antenna in mobile communication［C］// Proceedings of the 2nd International Conference on Computer and Network Technology. 2010:308-311.
［9］ ZHANG X, BANAVAR M K, WILLERTON M, et al. Performance comparison of localization techniques for sequential WSN discovery［C］// Sensor Signal Processing for Defence. 2012, doi: 10.1049/ic.2012.0120.
［10］ALHAJRI M I, GOIAN A, DARWEESH M, et al. Hybrid RSS-DOA technique for enhanced WSN localization in a correlated environment［C］// Proceedings of the 2015 International Conference on Information and Communication Technology Research. 2015:238-241.

［11］BOCCATO L, KRUMMENAUER R, ATTUX R, et al. Application of natural computing algorithms to maximum likelihood estimation of direction of arrival［J］. Signal Processing, 2012,92(5):1338-1352.
［12］CHEN H H, LI S B, LIU J H, et al. A JPSO algorithm for SML estimation of DOA［C］// Proceedings of the 2016 16th International Symposium on Communications and Information Technologies. 2016:457-460.
［13］WANG J, ZHAO Y J, WANG Z G. Low complexity subspace fitting method for wideband signal location［C］// Proceedings of the 2008 5th IFIP International Conference on Wireless and Optical Communications Networks. 2008, doi: 10.1109/WOCN.2008.4542533.
［14］张水平,王碧. 动态搜索空间的粒子群算法［J］. 计算机应用研究, 2016,33(7):2047-2050.
［15］冯舒,张志成,石要武. 引入蝙蝠算法的最大似然DOA估计［J］. 现代电子技术, 2016,39(8):26-29.
［16］张义元,张志成,石要武,等. 基于布谷鸟搜索算法的最大似然DOA估计［J］. 吉林大学学报(信息科学版), 2015,33(3):235-240.
［17］CHEN H H, LI S B, LIU J H, et al. A novel modification of PSO algorithm for SML estimation of DOA［J］. Sensors, 2016,16(12), doi: 10.3390/s16122188.
［18］WANG Y L, CHEN H, WAN S H. An effective DOA method via virtual array transformation［J］. Science in China, 2001,44(1):75-82.
［19］WILLIAMS R T, PRASAD S, MAHALANABIS A K, et al. An improved spatial smoothing technique for bearing estimation in a multipath environment［J］. IEEE Transactions on Acoustics, Speech, and Signal Processing, 1988,36(4):425-432.
［20］HARDT M. Understanding alternating minimization for matrix completion［C］// Proceedings of the 2014 Annual IEEE Symposium on Foundations of Computer Science. 2014:651-660.
［21］马永杰,云文霞. 遗传算法研究进展［J］. 计算机应用研究, 2012,29(4):1201-1206.
［22］STOICA P, NEHORAI A. Performance study of conditional and unconditional direction-of-arrival estimation［J］. IEEE Transactions on Acoustics, Speech, and Signal Processing, 1990,38(10): 1783-1795.
［23］OTTERSTEN B, VIBERG M, KAILATH T, et al. Analysis of subspace fitting and ML techniques for parameter estimation from sensor array data［J］. IEEE Transactions on Signal Processing, 1992,40(3):590-600.
［24］VAN TREES H L. Detection, Estimation, and Modulation Theory, Part IV: Optimum Array Processing［M］. John Wiley and Sons, New York, 2002.

[1]	ZENG Yi-pu, DAI Yi-ru, CHEN Yu-tian. Dynamic Particle Swarm Optimization without Velocity Based on Opposition-based Learning and Elite Promotion [J]. Computer and Modernization, 2023, 0(03): 113-120.
[2]	LIANG Zheng-you , , WANG Lu , LI Xuan-ang , YANG Feng , . A Point Cloud Registration Algorithm Combining Improved PSO Algorithm and TrICP Algorithm [J]. Computer and Modernization, 2022, 0(05): 90-95.
[3]	LI Ling, CHEN Xi, SHEN Wei-jie, XIONG Han-wu, CAI Ran-ran. An Edge Caching Strategy for Intelligent Manufacturing Supervision of Electrical Equipment [J]. Computer and Modernization, 2022, 0(05): 61-67.
[4]	LIU Zhi-feng, SHU Zhi-hao, XU Yue-feng, YANG Shu-yi, SHEN Wen-long. Artificial Fish Swarm Algorithm Based on PSO Adaptive Dual Strategy [J]. Computer and Modernization, 2022, 0(05): 46-53.
[5]	ZHANG Li-juan1， QIU Jian-wei1， DU Deng-chong2， WANG Xin1. Research on Military Logistics Distribution Routing Optimization Problem #br# Based on Spark and PSO Algorithm [J]. Computer and Modernization, 2018, 0(11): 65-.
[6]	GU Quan-yu1， ZHANG Meng-ting2. BP Neural Network Algorithm Based on Frog Leaping Particle Swarm Optimization [J]. Computer and Modernization, 2015, 0(9): 57-59,65.
[7]	LU Jian1, ZHANG Ke2. PID Controller of Permanent Magnet Synchronous Motor Based #br# on Improved Particle Swarm Optimization Algorithm [J]. Computer and Modernization, 2015, 0(7): 123-.
[8]	PEI Xiang， LI Qiaojun. Localization for Wireless Sensor Network Based on Modified Particle #br# Swarm Optimization Algorithm [J]. Computer and Modernization, 2015, 0(7): 81-.
[9]	LIU Chun. Network Intrusion Detection Model Based on Combination Algorithm Selecting Features [J]. Computer and Modernization, 2014, 0(8): 75-80.
[10]	YU Zhi-hui;YU Xu-jin;SONG Ye;WU Lie-yang. A New Evolution Algorithm by Stages for Particle Swarm Optimization [J]. Computer and Modernization, 2012, 1(200): 152-03.
[11]	GAO Yan-xia;HU Chang-biao. Inspection of Heat-damaged Soybean Based on Machine Vision [J]. Computer and Modernization, 2011, 7(7): 88-91.
[12]	GENG Jun-cheng;NIU Shuang-xia;ZHANG Cai-jun. Text Automatic Categorization with Evolutionary Neural Network [J]. Computer and Modernization, 2011, 1(11): 59-5.
[13]	LI Xin-ran. Research on Particle Swarm Optimization Algorithm [J]. Computer and Modernization, 2010, 1(6): 6-0008.
[14]	LIU Hongwen;ZENG Min. Study on Service Grid Task Scheduling Model Based on Particle Swarm Optimization Algorithm  [J]. Computer and Modernization, 2010, 1(12): 8-11.