Information theoretic waveform design with applications to adaptive-on-transmit radar

IF 1.4 4区管理学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC

Iet Radar Sonar and Navigation Pub Date : 2023-10-20 DOI:10.1049/rsn2.12478

Daniel B. Herr, Pranav S. Raju, James M. Stiles

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Abstract

The marginal Fisher information (MFI) metric is used to design waveforms for the sake of informationally optimal adaptive-on-transmit radar operation. A framework for MFI waveform design is developed and the Polyphase-Coded FM (PCFM) waveform model is utilised to produce a constant-modulus, spectrally contained signal amenable to transmission with high-power amplifiers. The efficacy of the MFI waveform design and minimum mean square error (MMSE) estimation is experimentally demonstrated and extended into an adaptive and dynamic sensing paradigm. The radar transmit waveform is optimised to maximise the Fisher information with respect to the range profile. Upon observing new information from radar echoes, the iterative MMSE (iMMSE) estimator then minimises the estimation error variance according to prior observations. Sequential information maximisation (via waveform design) and error minimisation (via iMMSE) tends towards the Cramér–Rao lower bound (CRLB) with additional measurements improving radar resolution and accuracy. These concepts maximise the information extracted by a radar operating in a congested spectrum where the available bandwidth is limited.

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应用于自适应发射雷达的信息论波形设计

边际费舍尔信息（MFI）指标用于设计波形，以实现信息最优的自适应发射雷达操作。研究开发了 MFI 波形设计框架，并利用多相编码调频（PCFM）波形模型产生了恒定模数、频谱包含的信号，适合使用大功率放大器进行传输。实验证明了 MFI 波形设计和最小均方误差（MMSE）估计的有效性，并将其扩展到自适应动态传感范例中。雷达发射波形经过优化，与测距剖面相关的费舍尔信息达到最大化。从雷达回波中观察到新信息后，迭代 MMSE（iMMSE）估计器会根据先前的观察结果使估计误差方差最小化。序列信息最大化（通过波形设计）和误差最小化（通过 iMMSE）趋向于克拉梅尔-拉奥下限（CRLB），附加测量可提高雷达分辨率和精度。这些概念可使雷达在可用带宽有限的拥挤频谱中提取的信息最大化。

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

Iet Radar Sonar and Navigation 工程技术-电信学

CiteScore

4.10

自引率

11.80%

发文量

137

审稿时长

3.4 months

期刊介绍： IET Radar, Sonar & Navigation covers the theory and practice of systems and signals for radar, sonar, radiolocation, navigation, and surveillance purposes, in aerospace and terrestrial applications. Examples include advances in waveform design, clutter and detection, electronic warfare, adaptive array and superresolution methods, tracking algorithms, synthetic aperture, and target recognition techniques.