KEYWORDS: Target detection, Environmental sensing, Statistical analysis, Detection and tracking algorithms, Signal detection, Radar, Edge detection, Data processing, Monte Carlo methods, Algorithm development
The actual radar exposure area contains different types of landforms, resulting in non-uniform radar clutter, which significantly reduces the target detection performance and makes it difficult to maintain a constant false alarm probability. This paper proposes an enhanced minimum description length CFAR(EMDL-CFAR) based on median absolute deviation. The algorithm has good target detection performance in the clutter edge environment, in addition, also guarantees the detection performance in the multi-target environment. Using the insensitivity of the median absolute deviation to interference, select different reference samples after the clutter edge detection determines the clutter edge position, and then use the median absolute deviation(MAD) hypothesis test to eliminate the interference from the samples. The performance of the algorithm under different clutter backgrounds is evaluated through simulation, and the superiority of the algorithm is explained.
As low-resolution radar is still the main radar in service in China, the ground target classification and recognition technology of low-resolution radar has a wide application prospect in modern military and civil fields. This paper mainly studies and compares two main types of automatic target recognition and classification method for low-resolution ground radar: conventional recognition based on feature extraction and neural networks, and the conclusion is that the latter has better performance and needs less time to train.
The former model in this paper fuses the time domain and frequency domain features of ground target echo, then simulates, compares and analyzes the performance of different classifiers. The classifiers studied include: naive bayes classifier (NBC), decision tree classifier (DT), linear discriminant analysis (LDA) classifier, k nearest neighbors (KNN) classifier and support vector machine (SVM) classifier. Five-fold cross validation is adopted in the experiment to effectively avoid the impact of arbitrariness on the results caused by the random division of the sample set into training sample set and test sample set. Besides, based on conventional convolutional neural networks, a new neural network structure named multi-scale residual neural network (Multi-scale ResNet) is proposed for one-dimensional feature target recognition, which effectively reduces the data dimension through auto-encoder and solves the problem of performance degradation caused by the difficulty in training too many levels of traditional convolutional neural network. The bayesian hyper-parameter optimization method is utilized to optimize the hyper-parameters of different classifiersl. Finally, compared the accuracy of the two types of target recognition, the best performance of the pattern recognition is the support vector machine, which recognition rate is 91.2%, while multi-scale residual neural network recognition rate is up to 99.6%.
KEYWORDS: Digital signal processing, Field programmable gate arrays, Signal processing, Clocks, Phased arrays, Optical fibers, Interfaces, Data transmission, Radar signal processing, Data storage
With the increasing use of digital array radars, radar signal processing systems have higher performance requirements. This article introduces a signal processing hardware design for the two-dimensional phased array digital multi-beam system. Because of its digital multi-beam characteristics, it is very demanding on the radar signal processing system's computing power, processing speed, and data throughput. This paper proposes a design of signal processing hardware based on Xilinx FPGA Virtex-7 and two multi-core digital signal processors (DSP) to meet the requirements of two-dimensional phased array digital multi-beam system. Subsequent experiments and engineering practices show that this design scheme can fully meet the requirements of the system.
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