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Waveform design based on mutual information upper bound for joint detection and estimation
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Ruofeng YU1, Caiguang ZHANG2, Chenyang LUO1, Mengdi BAI1,Shangqu YAN1, Wei YANG1, Yaowen FU1. Waveform design based on mutual information upper bound for joint detection and estimation[J]. Frontiers of Information Technology & Electronic Engineering, 1998, -1(-1): .
@article{title="Waveform design based on mutual information upper bound for joint detection and estimation",
author="Ruofeng YU1, Caiguang ZHANG2, Chenyang LUO1, Mengdi BAI1,Shangqu YAN1, Wei YANG1, Yaowen FU1",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="-1",
number="-1",
pages="",
year="1998",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2500276"
}
%0 Journal Article
%T Waveform design based on mutual information upper bound for joint detection and estimation
%A Ruofeng YU1
%A Caiguang ZHANG2
%A Chenyang LUO1
%A Mengdi BAI1
%A Shangqu YAN1
%A Wei YANG1
%A Yaowen FU1
%J Journal of Zhejiang University SCIENCE C
%V -1
%N -1
%P
%@ 2095-9184
%D 1998
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2500276
TY - JOUR
T1 - Waveform design based on mutual information upper bound for joint detection and estimation
A1 - Ruofeng YU1
A1 - Caiguang ZHANG2
A1 - Chenyang LUO1
A1 - Mengdi BAI1
A1 - Shangqu YAN1
A1 - Wei YANG1
A1 - Yaowen FU1
J0 - Journal of Zhejiang University Science C
VL - -1
IS - -1
SP -
EP -
%@ 2095-9184
Y1 - 1998
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2500276
Abstract: Information-theoretic principles provide a rigorous foundation for adaptive radar waveform design in contested and dynamically varying environments. This paper addresses the joint optimization of constant-modulus waveforms to enhance both target detection and parameter estimation concurrently. A unified design framework is developed by maximizing a mutual information upper bound (MIUB), which intrinsically reconciles the tradeoff between detection sensitivity and estimation accuracy without heuristic weighting. Realistic, potentially non-Gaussian statistics of target and clutter returns are modeled using Gaussian mixture distributions (GMDs), enabling tractable closed-form approximations of the MIUBs Kullback-Leibler(KL) divergence and mutual information components. To tackle the ensuing non-convex optimization, a tailored metaheuristic phase-coded dream optimization algorithm (PC-DOA) is proposed, incorporating hybrid initialization and adaptive exploration-exploitation mechanisms for efficient phase-space search. Numerical results substantiate the proposed approach's superiority in achieving favorable detection estimation trade-offs over existing benchmarks.
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