Waveform Design Based on Mutual Information Upper Bound For Joint Detection and Estimation
Adaptive radar waveform design grounded in information-theoretic principles is critical for advancing cognitive radar performance in complex environments. This paper investigates the optimization of phase-coded waveforms under constant modulus constraints to jointly enhance target detection and parameter estimation. We introduce a unified design framework based on maximizing a Mutual Information Upper Bound (MIUB), which inherently reconciles the trade-off between detection sensitivity and estimation precision without relying on ad hoc weighting schemes. To model realistic, potentially non-Gaussian statistics of target returns and clutter, we adopt Gaussian Mixture Distributions (GMDs), enabling analytically tractable approximations of the MIUB's constituent Kullback-Leibler divergence and mutual information terms. To address the resulting non-convex problem, we propose the Phase-Coded Dream Optimization Algorithm (PC-DOA), a tailored metaheuristic that leverages hybrid initialization and adaptive exploration-exploitation mechanisms specifically designed for phase-variable optimization. Numerical simulations demonstrate the effectiveness of the proposed method in achieving modestly better detection-estimation trade-off.
Code (0)
등록된 구현이 없습니다.
Tasks
parameter estimationRadar waveform designMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
CLUB: A Contrastive Log-ratio Upper Bound of Mutual Information
Mutual information (MI) minimization has gained considerable interests in various machine learning tasks. However, estimating and minimizing MI in high-dimensional spaces remains a challenging problem, especially when on…
Domain AdaptationA Novel Interference Minimizing Waveform for Wireless Channels with Fractional Delay: Inter-block Interference Analysis
In the physical layer (PHY) of modern cellular systems, information is transmitted as a sequence of resource blocks (RBs) across various domains with each resource block limited to a certain time and frequency duration. …
Gaussian Process Optimization with Mutual Information
In this paper, we analyze a generic algorithm scheme for sequential global optimization using Gaussian processes. The upper bounds we derive on the cumulative regret for this generic algorithm improve by an exponential f…
Gaussian Processesglobal-optimizationWaveform Design for Optimal PSL Under Spectral and Unimodular Constraints via Alternating Minimization
In an active sensing system, waveforms with good auto-correlations are preferred for accurate parameter estimation. Furthermore, spectral compatibility is required to avoid mutual interference between devices as the elec…
parameter estimationA unified framework for information-theoretic generalization bounds
This paper presents a general methodology for deriving information-theoretic generalization bounds for learning algorithms. The main technical tool is a probabilistic decorrelation lemma based on a change of measure and …
Generalization BoundsLEMMA