KEYWORDS: Pulse signals, Digital signal processing, Detection and tracking algorithms, Signal processing, Radar signal processing, Radar, Signal generators, Visualization, Scattering, Real time imaging
The current development of ISAR imaging is towards higher resolution, using a larger transmission signal bandwidth, resulting in a huge scale of echo data. Pulse compression is an important component of ISAR imaging, which is the prerequisite and foundation of imaging. Under the direct acquisition method of intermediate frequency, the sampling data scale of high bandwidth radar is very large, and traditional DSP processing platforms and serial processing methods are difficult to meet real-time imaging requirements. Therefore, this article proposes a parallelized pulse compression algorithm, which runs on a graphics processing unit (GPU) and utilizes the similarity of radar echo processing methods to design a parallelized digital de skewing algorithm while completing pulse compression. This method can greatly improve processing efficiency, quickly obtain a one-dimensional range profile sequence of the target, and effectively reduce the amount of processed data through target area extraction, which can better achieve real-time ISAR imaging. The measured data validation shows that the efficiency of this method can be improved by more than 9 times, which verifies the advantages and effectiveness of the algorithm.
Aiming at the problems of low accuracy of bird and drone about radar samples, lack of relevant data, a doppler spectrum recognition method of bird and drone based on one-dimensional deep neural network is proposed. First, take Fourier transform on measured radar echo to acquire the doppler specturm vector of the target, to construct doppler specturm dataset.Then based on the characteristics of the doppler spectrum of bird and drone, design the network structure for doppler spectrum vector of target. To reduce the influence of target flight direction and SNR on accuracy, speed up the training and feature enhancing, the first two layers of network add modulo layer and normalization layer. Then connect the improved one-dimensional ResNet18 to build the entire networks. By training the target doppler specturm samples to get and optimize the final model. Experimental results show that this method can achieve excellent results on bird and drone doppler dataset, with accuracy over 97%.
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