Radar Code Design for the Joint Optimization of Detection Performance and Measurement Accuracy in Track Maintenance

IF 5.8 2区 工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Tao Fan;Augusto Aubry;Vincenzo Carotenuto;Antonio De Maio;Xianxiang Yu;Guolong Cui
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引用次数: 0

Abstract

This paper deals with the design of slow-time coded waveforms which jointly optimize the detection probability and the measurements accuracy for track maintenance in the presence of colored Gaussian interference. The output signal-to-interference-plus-noise ratio (SINR) and Cramér Rao bounds (CRBs) on time delay and Doppler shift are used as figures of merit to accomplish reliable detection as well as accurate measurements. The transmitted code is subject to radar power budget requirements and a similarity constraint. To tackle the resulting non-convex multi-objective optimization problem, a polynomial-time algorithm that integrates scalarization and tensor-based relaxation methods is developed. The corresponding relaxed multi-linear problems are solved by means of the maximum block improvement (MBI) framework, where the optimal solution at each iteration is obtained in closed form. Numeral results demonstrate the trade-off between the detection and the estimation performance, along with the acceptable Doppler robustness achieved by the proposed algorithm.
轨道维修中探测性能与测量精度联合优化的雷达代码设计
本文研究了一种慢时编码波形的设计方法,以优化有色高斯干扰下的轨道维修检测概率和测量精度。输出信噪比(SINR)和时延和多普勒频移的cramsamr - Rao界(crb)作为优值,实现可靠的检测和精确的测量。传输码受雷达功率预算要求和相似度约束。为了解决由此产生的非凸多目标优化问题,提出了一种将标量化和基于张量的松弛方法相结合的多项式时间算法。采用最大块改进(MBI)框架求解相应的松弛多线性问题,其中每次迭代的最优解以封闭形式得到。数值结果证明了该算法在检测和估计性能之间的权衡,以及所提出算法所获得的可接受的多普勒鲁棒性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing 工程技术-工程:电子与电气
CiteScore
11.20
自引率
9.30%
发文量
310
审稿时长
3.0 months
期刊介绍: The IEEE Transactions on Signal Processing covers novel theory, algorithms, performance analyses and applications of techniques for the processing, understanding, learning, retrieval, mining, and extraction of information from signals. The term “signal” includes, among others, audio, video, speech, image, communication, geophysical, sonar, radar, medical and musical signals. Examples of topics of interest include, but are not limited to, information processing and the theory and application of filtering, coding, transmitting, estimating, detecting, analyzing, recognizing, synthesizing, recording, and reproducing signals.
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