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.
Proc. SPIE. 11719, Twelfth International Conference on Signal Processing Systems
KEYWORDS: Target detection, Signal to noise ratio, Statistical analysis, Detection and tracking algorithms, Data modeling, Sensors, Monte Carlo methods, Optical character recognition, Environmental sensing
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.
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.
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.
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.
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.