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Entropy 2017, 19(8), 397; https://doi.org/10.3390/e19080397

LPI Optimization Framework for Radar Network Based on Minimum Mean-Square Error Estimation

Key Laboratory of Radar Imaging and Microwave Photonics, Ministry of Education, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
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Received: 14 June 2017 / Revised: 28 July 2017 / Accepted: 31 July 2017 / Published: 2 August 2017
(This article belongs to the Section Information Theory)
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Abstract

This paper presents a novel low probability of intercept (LPI) optimization framework in radar network by minimizing the Schleher intercept factor based on minimum mean-square error (MMSE) estimation. MMSE of the estimate of the target scatterer matrix is presented as a metric for the ability to estimate the target scattering characteristic. The LPI optimization problem, which is developed on the basis of a predetermined MMSE threshold, has two variables, including transmitted power and target assignment index. We separated power allocation from target assignment through two sub-problems. First, the optimum power allocation is obtained for each target assignment scheme. Second, target assignment schemes are selected based on the results of power allocation. The main problem of this paper can be considered in the point of views based on two cases, including single radar assigned to each target and two radars assigned to each target. According to simulation results, the proposed algorithm can effectively reduce the total Schleher intercept factor of a radar network, which can make a great contribution to improve the LPI performance of a radar network. View Full-Text
Keywords: low probability of intercept (LPI); Schleher intercept factor; radar network; target assignment; power allocation; minimum mean-square error (MMSE) low probability of intercept (LPI); Schleher intercept factor; radar network; target assignment; power allocation; minimum mean-square error (MMSE)
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She, J.; Zhou, J.; Wang, F.; Li, H. LPI Optimization Framework for Radar Network Based on Minimum Mean-Square Error Estimation. Entropy 2017, 19, 397.

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