KEYWORDS: Target detection, Environmental sensing, Detection and tracking algorithms, Sensors, Data modeling, Statistical analysis, Monte Carlo methods, Optical character recognition, Signal to noise ratio
Constant false alarm rate (CFAR) detectors are widely used in modern radar system to declare the presence of targets. One or more outliers will appear in the reference cell under the multiple strong interferences situation, and the clutter power estimation will increase, which will affect the detection threshold calculation, the detection probability of CFAR detectors decrease and the alarm rates increase significantly. This paper proposes an adaptive weighted truncation statistic CFAR (AWTS-CFAR) algorithm and achieves good performance. By improving the truncation process, the truncated larger value is adaptively weighted with the smaller value in the reference cell. Since AWTS-CFAR makes the larger value in the reference cell also participate in the calculation of the background clutter power estimation, even if the truncation threshold is selected to be smaller, AWTS-CFAR will not cause too much loss of constant false alarm, and will suppress clutter edge effect as much as possible in the clutter edge environment.
KEYWORDS: Digital signal processing, Field programmable gate arrays, Signal processing, Radar, Radar signal processing, Phased arrays, Filtering (signal processing), Electronic filtering
Cylindrical phased array radar has an important role in low-altitude target surveillance, and signal processing is one of the important component modules of the radar system. Cylindrical radar as one kind of phased-array radar has the characteristics of full azimuth range of multi beam and huge data which makes high demands of signal processing. FPGA occupies an important position in radar signal processing because of its characteristics of high-speed and real-time. In this paper, signal processing scheme based on Xilinx's FPGA Kintex-7 and multi-core digital signal processor (DSP) is proposed, which mainly implements functions such as data reception, pulse compression, and moving target detection (MTD) processing etc. By comparing the actual results with the matlab simulation results, it is shown that this scheme has a good performance in stablility with fast processing speed. Moreover, it has obvious advantages in the design and provides great value for engineering.
This paper proposes a low-resolution ground surveillance radar automatic target recognition(ATR) method based on onedimensional convolutional neural network (1D-CNN), which solves the problem of overfitting using complex CNN for data classification. First, the target recognition algorithm combines the time-domain waveform, power spectrum, and power transform spectrum into the three channels of the established 1D-CNN input. After that, the autoencoder is used to reduce the feature dimension and improve the classifier's ability to select parameters autonomously. Finally, the Bayesian hyperparameter optimization method is used to optimize hyperparameters, which not only simplifies the network structure, but also reduces the parameter calculation scale. We tested our method with the collected data to classify people and cars, and the results showed that the recognition accuracy rate has reached 99%. Compared with the traditional artificial feature extraction target recognition method, our model has better recognition performance and adaptability.
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