Communication and Energy-Constrained Neighbor Selection for Distributed Cooperative Localization

被引:5
作者
Fan, Chengfei [1 ]
Li, Liyan [1 ]
Zhao, Ming-Min [2 ]
Liu, An [1 ]
Zhao, Min-Jian [1 ]
机构
[1] Zhejiang Univ, Coll Informat Sci & Elect Engn, Hangzhou 310027, Peoples R China
[2] Zhejiang Univ, Coll Informat Sci & Elect Engn, Hangzhou 310027, Peoples R China
基金
美国国家科学基金会;
关键词
Cooperative localization; resource constraints; neighbor selection; SPEB; DUAL DECOMPOSITION METHOD; WIDE-BAND LOCALIZATION; SENSOR SELECTION; NETWORK LOCALIZATION; FUNDAMENTAL LIMITS; ARRAY MANAGEMENT; PART I; ALGORITHM; VEHICLES;
D O I
10.1109/TWC.2022.3223851
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Cooperative localization is a promising technique in wireless networks, and neighbor selection (NS) is essential to limit the degree of cooperation and reduce the amount of data to be exchanged. However, the existing NS algorithms may suffer from major performance loss when applied to networks with limited resources (e.g., bandwidth, time and energy). In this paper, we establish a general optimization framework for the NS problem to minimize the localization error under strict resource constraints. Based on the squared position error bound (SPEB) criterion, we formulate two distributed NS problems under implicit and explicit energy constraints, respectively, to balance the energy consumption of the network, where implicit energy constraints mean that specific energy profiles of the nodes' neighbors are unavailable while explicit energy constraints mean the opposite. Moreover, we propose to jointly optimize the NS and power allocation in the explicit case to further improve the localization performance. The resulting problems are challenging to solve due to the nonlinear objective functions and discrete optimization variables. We first transform them into more tractable forms and then develop novel algorithms based on the penalty dual decomposition method to solve the transformed problems efficiently. Simulation results show that the proposed algorithms can significantly outperform benchmark algorithms. In particular, the proposed algorithm almost achieves the performance lower bound in the implicit case.
引用
收藏
页码:4158 / 4172
页数:15
相关论文
共 43 条
[21]   Active Target Tracking and Cooperative Localization for Teams of Aerial Vehicles [J].
Morbidi, Fabio ;
Mariottini, Gian Luca .
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, 2013, 21 (05) :1694-1707
[22]   HEVA: Cooperative Localization using a Combined Non-Parametric Belief Propagation and Variational Message Passing Approach [J].
Oikonomou-Filandras, Panagiotis-Agis ;
Wong, Kai-Kit .
JOURNAL OF COMMUNICATIONS AND NETWORKS, 2016, 18 (03) :397-410
[23]   Locating the nodes [J].
Patwari, N ;
Ash, JN ;
Kyperountas, S ;
Hero, AO ;
Moses, RL ;
Correal, NS .
IEEE SIGNAL PROCESSING MAGAZINE, 2005, 22 (04) :54-69
[24]   Vehicles on RFID: Error-Cognitive Vehicle Localization in GPS-Less Environments [J].
Qin, Hua ;
Peng, Yang ;
Zhang, Wensheng .
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2017, 66 (11) :9943-9957
[25]   Sensor Scheduling With Time, Energy, and Communication Constraints [J].
Rusu, Cristian ;
Thompson, John ;
Robertson, Neil M. .
IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2018, 66 (02) :528-539
[26]   Fundamental Limits of Wideband Localization - Part II: Cooperative Networks [J].
Shen, Yuan ;
Wymeersch, Henk ;
Win, Moe Z. .
IEEE TRANSACTIONS ON INFORMATION THEORY, 2010, 56 (10) :4981-5000
[27]   Fundamental Limits of Wideband Localization - Part I: A General Framework [J].
Shen, Yuan ;
Win, Moe Z. .
IEEE TRANSACTIONS ON INFORMATION THEORY, 2010, 56 (10) :4956-4980
[28]   Penalty Dual Decomposition Method for Nonsmooth Nonconvex Optimization-Part II: Applications [J].
Shi, Qingjiang ;
Hong, Mingyi ;
Fu, Xiao ;
Chang, Tsung-Hui .
IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2020, 68 :4242-4257
[29]   Penalty Dual Decomposition Method for Nonsmooth Nonconvex Optimization-Part I: Algorithms and Convergence Analysis [J].
Shi, Qingjiang ;
Hong, Mingyi .
IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2020, 68 :4108-4122
[30]   Machine Learning for Wideband Localization [J].
Thang Van Nguyen ;
Jeong, Youngmin ;
Shin, Hyundong ;
Win, Moe Z. .
IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, 2015, 33 (07) :1357-1380