The micro-motion characteristics of warheads have been utilized to discriminate false warheads from true ones. To obtain accurate dimensional measurements, a multiple-input multiple-output (MIMO) radar is adopted to o...
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ISBN:
(纸本)9781479919482
The micro-motion characteristics of warheads have been utilized to discriminate false warheads from true ones. To obtain accurate dimensional measurements, a multiple-input multiple-output (MIMO) radar is adopted to observe the kinetic information of the warhead. A distributed state space model (SSM) is built and the differences between the true and false warheads are characterized as different system parameters of the SSM. In this paper, we extend the locally optimal unknown direction (LOUD) detector, which has shown its effectiveness for hypothesis testing, to the underlying distributed detection problem, and a novel consensus-based LOUD detector is proposed. The superior detection performance of the proposed detection algorithm in identifying the true and false warheads is verified using simulation results.
The broadcast-based consensus algorithm is one special type of randomized consensusalgorithm, and is amenable to practical implementation in wireless networks. This paper focuses on the performance analysis of this b...
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ISBN:
(纸本)9781424493326
The broadcast-based consensus algorithm is one special type of randomized consensusalgorithm, and is amenable to practical implementation in wireless networks. This paper focuses on the performance analysis of this broadcast-based consensus algorithm in the presence of non-zero-mean stochastic perturbations. It is demonstrated that as the algorithm proceeds, the deviation of the node states from their average will converge, in expectation, to a fixed value, which is determined by the Laplacian matrix of the network, the mixing parameter, and the mean of the stochastic perturbations. Asymptotic upper and lower bounds on the total mean-square deviation are derived, which describe the range of distances over which the node states deviate from consensus. These bounds can facilitate evaluation of the applicability of this algorithm in practice. In addition, performance of the broadcast-based consensus algorithm under zero-mean stochastic disturbances is also analyzed, and results regarding its convergence and mean-square deviation are given. The theoretical results presented in this work hold true regardless of the statistics of the stochastic disturbances, and are valid for arbitrary network topology as long as the topology is connected.
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