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.
As an effective method in signal reconstruction model, compressed sensing (CS) has achieved excellent performance in sparse array reconstruction. However, it is necessary to set the penalization factor before iterative calculation, which will increase the difficulty to convergence the result to the global optimal solution. In this paper, we remove the process of choosing penalization factor and reconstruction error by modifying the iterative expression as well as alternating direction method of multipliers (ADMM) algorithm respectively. In addition, the improved model is shown to be convex and thus can be solved using the CVX toolbox. Simulation result shows that the reference pattern could be reconstructed with minimum number of antenna elements by the proposed algorithms. Moreover, the proposed methods have significant performance improvement in main sidelobe level (MSL).
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.
Acquisition of Direct Sequence Spread Spectrum-Minimum Shift Keying (DSSS-MSK) signal in low signal to noise (SNR) and high dynamic environment will impact the overall performance of the receiving system seriously. The proposed all-digital IF receiver has a serial structure, transforming the DSSS-MSK signal into approximating DSSSBPSK signal using the matched filter. The matched filter is designed according to the known frequency response based on convex optimization. Then, the signals are regrouped by spreading code period. Finally, combining Doppler frequency shift compensation with the parallel code acquisition algorithm based on FFT, the PN code phase difference and Doppler frequency shift are captured simultaneously. Simulation results show that the proposed algorithm has 7dB and 8dB SNR improvement than delay correlation method and ML-FFT method respectively. Furthermore, the proposed algorithm has quick acquisition rate, wide acquisition range, high acquisition accuracy, low complexity and is suitable for low SNR environment.
In this paper, a newly-designed method of ultra-low sidelobe pulse compression filter for linear frequency modulation (LFM) signal is proposed. In the conventional processing of pulse compression, there exists the problem that the ratio of mainlobe to sidelobe is too low. In order to solve this problem, the convex optimization method is used to design the coefficient of the pulse compression filter, and the ratio of mainlobe to sidelobe of the pulse compression output could achieve 60dB or more to be applied in specific engineering applications.