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).
Proc. SPIE. 11384, Eleventh International Conference on Signal Processing Systems
KEYWORDS: Sensors, Target detection, Monte Carlo methods, Environmental sensing, Optical character recognition, Detection and tracking algorithms, Radar, Signal detection, Edge detection, Statistical analysis
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.
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: Signal detection, Signal attenuation, Signal processing, Protactinium, Detection and tracking algorithms, Digital filtering, Statistical modeling, Fourier transforms, Filtering (signal processing), Electronics engineering
This paper proposes a note segmentation method combining music score, aiming at the problem that cannot segment multi-tone music with more changes in intensity accurately. By extracting the envelope peak value of music signal and matching it with note values and pitch information of musical score, the segmentation of notes is completed.Simulation results show that the method based on the prior knowledge of value and pitch information of music score can not only realize the segmentation of notes of continuous single tone music, but also be suitable for multi-tone music with strong and weak variations.
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.
In the process of extracting rotor features using time-frequency analysis, clutter suppression and improving time-frequency resolution have always been problems that need to be solved and improved. The paper proposes a rotor feature extraction method with high time-frequency resolution that can suppress clutter. Firstly, the separation of the micro-motion target and the clutter is realized by the complex empirical mode decomposition (CEMD). The high-resolution time-frequency diagram of the rotor is obtained by the synchrosqueezing improved S transform (SIST) proposed in the paper. The features extracted from the diagram are of high accuracy. The simulation results show that this method (CEMD-SIST) has better clutter suppression performance and higher time-frequency resolution than other rotor feature extraction methods.
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.
Proc. SPIE. 11071, Tenth International Conference on Signal Processing Systems
KEYWORDS: Pulse filters, Convex optimization, Electronic filtering, Signal to noise ratio, Signal attenuation, Radar, MATLAB, Frequency modulation, Signal processing, Detection theory
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.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print format on
SPIE.org.