Our work aims to address the problem of estimating the parameters of constant-amplitude, time-unsynchronized linear frequency-modulated (LFM) signals that have single or multiple components, which is a crucial task in electronic countermeasure techniques. A method for estimating the parameters, center frequency f0, and chirp rate μ of an LFM signal is proposed; the method is referred to as the Wigner–Ville distribution complex-valued convolutional neural network (WVD-CV-CNN). The method can be regarded as an application of neural networks for extracting parameter features from the signal spectrogram, wherein the CV-CNN is the main body of the network, which takes a complex-valued WVD matrix as the input and outputs several sets of estimated parameters. A performance analysis shows that the estimation accuracy and computational efficiency of the proposed method are significantly improved compared with those of the conventional methods. Further, the proposed method shows strong robustness to changes in modulation parameters. We apply the CV-CNN to other spectrograms and prove compatibility of the WVD and CV-CNN by comparison. We also demonstrate that the estimation accuracy of the proposed method is robust against cross interference on the WVD. Our study shows the advantages of using deep learning systems in signal parameter estimation.
In irregular pulse repetition interval (PRI) radar, successive pulses each with different PRIs are used as the transmission waveform. After analyzing the signal model of irregular PRI radar, we propose a coherent integration method based on Radon-iterative adaptive approach (Radon-IAA) to deal with the problems of irregular range cell migration (RCM) and the irregular phase fluctuations among different pulses introduced by the irregular PRI. In our method, the irregular RCM is compensated by searching through the motion parameters, and the irregular phase fluctuations can be resolved by an IAA-based spectral analysis method. The effectiveness of the proposed method is verified by simulation experiments.
Aircraft recognition is of great theoretical and practical significance in fields like remote sensing, navigation and traffic monitoring. It seems difficult to recognize aircraft in low-resolution SAR imagery because of difference between real image and simulated template induced by poor image quality and inherent simulation error. Aiming at this problem, an aircraft recognition method using peak feature matching is proposed. Firstly, the scattering centers of detected target are extracted in low-resolution SAR imagery using an adaptive threshold. Secondly, the extracted peak features are used to estimate the aircraft azimuth angle, which can be used to reduce the searching space in template database dramatically. Finally, a novel peak feature matching method using spatial distribution information of entire peak set is proposed to measure the similarity between detected target and simulated template. Experimental results demonstrate the good performance of the proposed method on a variety of low-resolution SAR imageries.
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