双功能雷达通信系统的功率资源分配算法

IF 1.1 4区 工程技术 Q4 ENGINEERING, ELECTRICAL & ELECTRONIC
Yue Xiao, Zhenkai Zhang, Xiaoke Shang
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引用次数: 0

摘要

本文提出了一种功率有限的双功能雷达-通信系统功率分配算法,以获得更好的系统整体性能(雷达信号干扰加噪声比(SINR)和通信信道容量的加权和)。首先,建立了一个功率分配模型,以在有限发射功率下实现雷达信噪比和通信信道容量的最大化。然后,分别在只考虑雷达信噪比或通信信道容量的条件下,利用卡鲁什-库恩-塔克(KKT)条件求解最优目标函数。最后,将最优值与原始模型相结合,转化为单目标优化模型,通过迭代优化算法求解模型,得到最优功率。仿真结果表明,与其他功率分配算法相比,所提出的算法能在相同发射功率下实现更好的雷达-通信一体化性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Power Resource Allocation Algorithm for Dual-Function Radar–Communication System
In this paper, a power allocation algorithm of dual-function radar–communication system with limited power is proposed to obtain better overall system performance measured by the weighted summation of radar signal to interference plus noise ratio (SINR) and communication channel capacity. First, a power allocation model is established to maximize the radar SINR and communication channel capacity with limited transmitted power. Then, the Karush–Kuhn–Tucker (KKT) conditions are used to solve the optimal objective function under the condition that only radar SINR or communication channel capacity is considered, respectively. Finally, the optimal value is combined with the original model and transformed into a single objective optimization model, and the optimal power is obtained by solving the model through the iterative optimization algorithm. Simulation results show that, compared with other power allocation algorithms, the proposed algorithm can achieve better radar-communication integration performance under the same transmit power.
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来源期刊
IET Signal Processing
IET Signal Processing 工程技术-工程:电子与电气
CiteScore
3.80
自引率
5.90%
发文量
83
审稿时长
9.5 months
期刊介绍: IET Signal Processing publishes research on a diverse range of signal processing and machine learning topics, covering a variety of applications, disciplines, modalities, and techniques in detection, estimation, inference, and classification problems. The research published includes advances in algorithm design for the analysis of single and high-multi-dimensional data, sparsity, linear and non-linear systems, recursive and non-recursive digital filters and multi-rate filter banks, as well a range of topics that span from sensor array processing, deep convolutional neural network based approaches to the application of chaos theory, and far more. Topics covered by scope include, but are not limited to: advances in single and multi-dimensional filter design and implementation linear and nonlinear, fixed and adaptive digital filters and multirate filter banks statistical signal processing techniques and analysis classical, parametric and higher order spectral analysis signal transformation and compression techniques, including time-frequency analysis system modelling and adaptive identification techniques machine learning based approaches to signal processing Bayesian methods for signal processing, including Monte-Carlo Markov-chain and particle filtering techniques theory and application of blind and semi-blind signal separation techniques signal processing techniques for analysis, enhancement, coding, synthesis and recognition of speech signals direction-finding and beamforming techniques for audio and electromagnetic signals analysis techniques for biomedical signals baseband signal processing techniques for transmission and reception of communication signals signal processing techniques for data hiding and audio watermarking sparse signal processing and compressive sensing Special Issue Call for Papers: Intelligent Deep Fuzzy Model for Signal Processing - https://digital-library.theiet.org/files/IET_SPR_CFP_IDFMSP.pdf
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