Proc. SPIE. 11384, Eleventh International Conference on Signal Processing Systems
KEYWORDS: Target detection, Radar, Edge detection, Statistical analysis, Detection and tracking algorithms, Sensors, Monte Carlo methods, Optical character recognition, Signal detection, Environmental sensing
Constant false alarm rate (CFAR) detectors are widely used in modern radar system to declare the presence of targets. Due to the serious masking effects under the multiple targets situation and the clutter edge, the detection probability of CFAR detectors decrease sharply and the alarm rates increase significantly. To solve these problems, a robust adaptive amplitude iteration CFAR (AAI-CFAR) algorithm is proposed in this paper and obtains good performance. By combining the 2nd-order statistic, variability index, and the 4th-order statistic, kurtosis, a variable scaling factor is designed in the amplitude iteration to adapt different environment. Plenty of Monte Carlo simulations are applied to evaluate the performance of the proposed method under different clutter scenarios compared with existing CFAR detectors, which illustrate the superiority and robustness of AAI-CFAR.
For coherent integration detection of ultrafast maneuvering targets with modern radar, a novel long-time coherent integration algorithm, Polynomial Rotation-Polynomial Fourier Transform (PRPFT), is proposed to compensate across range unit range walk (RW) and Doppler frequency migration (DFM) simultaneously caused by super-high speed and strong maneuvering. First, RW can be corrected by the polynomial rotation transform (PRT) via rotating the coordinate locations of echo data. Then, the polynomial Fourier transform (PFT) can realize the compensation of DFM and coherent integration. To reduce the computational complexity, one decision method is proposed to search the multidimensional parameter space. Finally, numerical experiments are provided to validate the effectiveness of the proposed method.
The performance of radar automatic target recognition (ATR) highly depends on the quality of training database, the extracted features and classification algorithm. Radar target is detected by the Doppler effect in radar echo signal. Through processing the echo signals in different domains, the distinctive characteristic can be obtained intuitively. Furthermore, we can utilize the extracted features to complete radar target classification. This paper proposes a novel target recognition method based on 1D-convolution neural network (CNN) aiming at the ATR of low-resolution ground surveillance radar. The proposed approach uses 1D-CNN as feature extractor and softmax layer as classifier. We tested our method on actual collected database to classify human and car, which reached an accuracy of 98%. Compared with conventional artificial feature extraction approaches, our model shows better performance and adaptability.