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IEEE International Conference on Ubiquitous Wireless Broadband (ICUWB)

作者： Zhang, Li Xie, Ning Wang, Hui Shenzhen Univ CIE Shenzhen 518060 Guangdong Peoples R China Shenzhen Key Lab Adv Commun & Informat Proc Shenzhen 518060 Peoples R China

ISBN: (纸本)9781467365550

In this paper, we consider the problem of direction of arrival (doa) estimation achieved by a distributed way in wireless sensors networks. The goal for each node is to detect targets based on its local information and that of its neighbors through some iteration. Classic estimation methods, such as maximum likelihood (ML) algorithm, are not suitable here because there is often a requirement of a central unit to obtain the optimal solution. We propose a new distributed doa estimation algorithms based on the randomized Gossip method, the goal of which is to realize the conventional Capon method by a distributed way. The proposed algorithm does not require any constraint on the network geometries, thereby making it suitable for distributed signal processing in large wireless sensor networks. The given simulation results illustrate the main characteristics of the proposed algorithm, including doa resolution and mean square error (MSE) performance.

关键词： distributed doa estimation Wireless sensors networks Gossip algorithm

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Sparsity-based distributed doa estimation for Radar Networks

Sparsity-based Distributed DoA Estimation for Radar Networks

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IEEE Radar Conference (RadarConf)

作者： Chalise, Batu K. Wong, Daniel M. Amin, Moeness G. Buehrer, R. Michael Martone, Anthony Kirk, Benjamin Sherbondy, Kelly New York Inst Technol ECE Dept Old Westbury NY 11568 USA Villanova Univ Ctr Adv Commun Villanova PA 19085 USA Virginia Tech Bradley Dept Elect & Comp Engn Blacksburg VA USA Army Res Lab Adelphi MD USA

ISBN: (纸本)9781728153681

In this paper distributed estimation of direction of arrival (doa) is proposed for a network of radar nodes, in which nodes share their limited information related to their decisions with only their neighboring nodes. The sparsity of target scenario is exploited and distributed doa estimation is formulated as the estimation of sparse vectors. The neighboring nodes aim to achieve a consensus on their estimation of these sparse vectors. The estimation problem is solved iteratively with alternating direction of method of multipliers (ADMM) method. Each node leverages co-prime array configuration, a type of structured sparse array, to enable direction finding for more sources than the number of node antennas. Numerical simulations show that the proposed distributed method converges within few iterations and provides much improved spatial spectra than local (nondistributed) estimations.

关键词： distributed doa estimation compressed sensing ADMM coordinate descent

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Effect of Communications Link Impairments on the Sparsity-based distributed doa estimation 56

Effect of Communications Link Impairments on the Sparsity-ba...

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56th Asilomar Conference on Signals, Systems, and Computers

作者： Chalise, Batu K. Amin, Moeness G. Martone, Anthony F. Kirk, Benjamin H. New York Inst Technol ECE Dept Old Westbury NY 11568 USA Villanova Univ Ctr Adv Commun Villanova PA 19085 USA Army Res Lab Adelphi MD USA

ISBN: (纸本)9781665459068

In this paper, distributed direction of arrival (doa) estimation is proposed for a network of monostatic radar nodes, in which the radar nodes gather target reflected signals during the radar sensing phase, and then efficiently share this data with the central coordinator, during the communications phase. This is achieved by only using one-bit quantized versions of the node signals. We consider a two-node network, each having a uniform linear array (ULA). When viewed from a common reference node, the positions of node antennas form a co-prime array. The central coordinator builds sample covariance matrix using estimated signals and the knowledge of antenna element locations of the two nodes. We assume that the communications channel estimation errors can be modeled as additive Gaussian noise. It is shown that the distributed doa estimation can be formulated as a sparse-recovery problem and solved within a compressed-sensing framework. Computer simulations show that the proposed distributed doa estimation can effectively estimate more targets than the total number of available antenna elements at all nodes.

关键词： distributed doa estimation compressed sensing sparse recovery communications link impairments

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distributed doa estimation in Wireless Sensor Networks Using Randomized Gossip Method

Distributed DOA Estimation in Wireless Sensor Networks Using...

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IEEE International Conference on Ubiquitous Wireless Broadband

作者： Li Zhang Ning Xie Hui Wang CIE Shenzhen University

ISBN: (纸本)9781467365567

关键词： distributed doa estimation Wireless sensors networks Gossip algorithm

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distributed version of hybrid swarm intelligence-Nelder Mead algorithm for doa estimation in WSN

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EXPERT SYSTEMS WITH APPLICATIONS 2020年 144卷

作者： Maruthi, Shree Prasad Panigrahi, Trilochan Jagannath, Ravi Prasad K. NIT Goa Dept Elect & Commun Engn Ponda 403401 India NIT Goa Dept Humanities & Sci Ponda 403401 India

distributed direction of arrival (doa) estimation based on maximum likelihood (ML) is an energy-efficient source localization technique that is vital to systems such as wireless sensor networks (WSN). Due to the multimodal nature of the ML function, the distributed approach uses swarm intelligence (SI) algorithms. However, this approach has slow convergence and incurs significant communication overhead for more than two sources. Hence, to obtain accurate doa estimates with faster convergence, this paper proposes a distributed hybrid version of SI and Nelder-Mead (NM) simplex algorithm. In this approach, a modified evolutionary population dynamics based distributed Grey Wolf optimization SI algorithm is proposed to obtain initial doa estimates. Then NM provides accurate doa estimates with faster convergence and thereby reduces the communication overhead. Detailed simulation analysis shows that by using Quasi-Opposition based population initialization and sensor node degree combiner coefficients, the proposed distributed hybrid algorithm converges to theoretical Cramer-Rao lower bound with small communication overhead. Thus doa estimation can be performed in an energy-efficient way by incorporating a computationally intelligent distributed hybrid approach. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Swarm intelligence Grey wolf optimization Nelder-Mead simplex search method distributed doa estimation Wireless sensor networks

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DECENTRALIZED DIRECTION FINDING USING PARTLY CALIBRATED ARRAYS

DECENTRALIZED DIRECTION FINDING USING PARTLY CALIBRATED ARRA...

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21st European Signal Processing Conference (EUSIPCO)

作者： Suleiman, Wassim Pesavento, Marius Zoubir, Abdelhak Tech Univ Darmstadt Inst Telecommun Darmstadt Germany

ISBN: (纸本)9781479936878

We consider direction-finding in partly calibrated arrays composed of multiple identically oriented subarrays. The subarrays are assumed to possess a shift invariance structure that can be exploited for search free direction of arrival (doa) estimation. We propose a fully distributed doa estimation scheme that is based on the averaging consensus algorithm, in which the subarrays communicate only locally with their neighboring subarrays to exchange local averages of their measurements and received signal to iteratively compute global estimates in the network. The proposed scheme eliminates communication bottlenecks and the need for a centralized computation center. Our algorithm is based on subspace methods which is originally devised for doa estimation in centralized systems. We show that in our fully distributed doa estimation scheme, the number of jointly estimated directions can be larger than the number identifiable by each individual subarray.

关键词： Averaging consensus algorithm subarray systems ESPRIT distributed doa estimation

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DECENTRALIZED DIRECTION FINDING USING PARTLY CALIBRATED ARRAYS

DECENTRALIZED DIRECTION FINDING USING PARTLY CALIBRATED ARRA...

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European Signal Processing Conference

作者： Wassim Suleiman Marius Pesavento Abdelhak Zoubir Institute of Telecommunications Technische Universit?t Darmstadt

ISBN: (纸本)9781479936878

关键词： Averaging consensus algorithm subarray systems ESPRIT distributed doa estimation direction finding shift invariant Esapent Direction of arrival estimation Decentralized subarray received signal arrays

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