基于波形分集的线性时变系统辨识
Identification of Linear Time-Varying Systems Through Waveform Diversity
摘要：
线性时变（LTV）系统由时移、频移和复振幅缩放组成，是作用于连续有限能量波形的算子。本文提出了一种新的，资源有效的方法，用于确定此类系统的参数描述，即，时移、频移和定标，从采样响应到线性调频（LFM）波形，重点是雷达处理的应用。如果LTV操作者用足够多样化的LFM波形集进行探测，则可以高精度地识别系统。在无噪声测量的情况下，识别是完美的，而在噪声测量的情况下，精度与噪声水平成反比。最近提出的去噪算法的参数估计技术的使用允许以高精度的参数估计。
英文摘要：
Linear, time-varying (LTV) systems composed of time shifts, frequency shifts, and complex amplitude scalings are operators that act on continuous finite-energy waveforms. This paper presents a novel, resource-efficient method for identifying the parametric description of such systems, i.e., the time shifts, frequency shifts, and scalings, from the sampled response to linear frequency modulated (LFM) waveforms, with emphasis on the application to radar processing. If the LTV operator is probed with a sufficiently diverse set of LFM waveforms, then the system can be identified with high accuracy. In the case of noiseless measurements, the identification is perfect, while in the case of noisy measurements, the accuracy is inversely proportional to the noise level. The use of parametric estimation techniques with recently proposed denoising algorithms allows the estimation of the parameters with high accuracy.
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