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 an...
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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.
In this paper distributedestimation 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....
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ISBN:
(纸本)9781728153681
In this paper distributedestimation 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.
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 e...
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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.
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 an...
详细信息
ISBN:
(纸本)9781467365567
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.
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 multi...
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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 doaestimation can be performed in an energy-efficient way by incorporating a computationally intelligent distributed hybrid approach. (C) 2019 Elsevier Ltd. All rights reserved.
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 di...
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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 doaestimation 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.
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 di...
详细信息
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 doaestimation 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.
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