Xiqin Wang

ISAR 2-D Imaging of Uniformly Rotating Targets via Matching Pursuit

An algorithm based on matching pursuit (MP) is proposed for inverse synthetic aperture radar (ISAR) two-dimensional (2-D) imaging of uniformly rotating targets. The ISAR echo is decomposed into many subsignals that are generated by discretizing spatial domain and synthesizing the ISAR data for every discretized spatial position. The subsignals that indeed contribute to the ISAR echo are selected by the MP, and their coefficients represent the superresolution image. The target rotation rate is estimated by combining MP with maximum contrast search.

A novel STAP algorithm using sparse recovery technique

A novel STAP algorithm based on sparse recovery technique, called CS-STAP, were presented. Instead of using conventional maximum likelihood estimation of covariance matrix, our method utilizes the echo statistics on spatial-temporal plane, which is extracted from sample data of only ONE training range cell with Compressed Sensing techniques, to construct a new estimator of covariance matrix, and build the optimal detector based on it. Full description of CS-STAP is given. Numerical result on real data has provided the evidence for great potential of CS-STAP as a effective approach when clutter is non-stationary because it need much less training data compared with common STAP methods.

Adaptive Sparse Recovery by Parametric Weighted L

It has been shown in the literature that, the inverse synthetic aperture radar (ISAR) echo can be seen as sparse and the ISAR imaging can be implemented by sparse recovery approaches. In this paper, we propose a new parametric weighted L1 minimization algorithm for ISAR imaging based on the parametric sparse representation of ISAR signals. Since the basis matrix used for sparse representation of ISAR signals is determined by the unknown rotation parameter of a moving target, we have to estimate both the ISAR image and basis matrix jointly. The proposed algorithm can adaptively refine the basis matrix to achieve the best sparse representation for the ISAR signals. Finally the high-resolution ISAR image is obtained by solving a weighted L1 minimization problem. Both numerical and real experiments are implemented to show the effectiveness of the proposed algorithm.

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Vehicle detection and classification system is an important part of the intelligent transportation systems (ITS). Its function is to measure traffic parameters such as flow-rate, speed, and vehicle types, which are valuable information for applications of road surveillance, traffic signal control, road planning, and so on. This paper presents a novel low-cost vehicle detection and classification system which is based on a K-band unmodulated CW radar. This system utilizes time-frequency analysis, multi-threshold detection, and Hough transform as the major signal processing methods to extract speed and shape information of vehicles from Doppler signature they generate. It can perform vehicle detection, speed measurement, and vehicle classification simultaneously. Experimental results show that the proposed system and algorithms can provide promising performance and accuracy.

Minimization for ISAR Imaging of Uniformly Rotating Targets

In this letter, a novel radial velocity estimation and range shift compensation algorithm is proposed for high-range resolution profiling of moving targets in stepped-frequency (SF) radar. Compared to traditional methods, this algorithm is based on a more precise signal model, and can therefore achieve much higher estimation accuracy. Furthermore, the range shift problem caused by target motion can be resolved without alterations to the radar waveform. The performance of this algorithm is demonstrated using simulated and experimental results.

A Low-cost Vehicle Detection and Classification System based on Unmodulated Continuous-wave Radar

Based on the linear relationship between the chirp rate of cross-range inverse synthetic aperture radar (ISAR) signal and the slant range, a parametric sparse representation method is proposed for ISAR imaging of rotating targets. The ISAR echo is formulated as a parametric joint-sparse signal and the chirp rates at all range bins are estimated by maximizing the contrast of sparse ISAR image. Comparing with homologous algorithms, the computational complexity of the proposed method is significantly reduced.

Velocity Estimation and Range Shift Compensation for High Range Resolution Profiling in Stepped-Frequency Radar

Random stepped frequency (RSF) radar, which transmits random-frequency pulses, can suppress the range ambiguity, improve convert detection, and possess excellent electronic counter-countermeasures (ECCM) ability [1]. In this paper, we apply a sparse recovery method to estimate the range and Doppler of targets. We also propose a cognitive mechanism for RSF radar to further enhance the performance of the sparse recovery method. The carrier frequencies of transmitted pulses are adaptively designed in response to the observed circumstance. We investigate the criterion to design carrier frequencies, and efficient methods are then devised. Simulation results demonstrate that the adaptive frequency-design mechanism significantly improves the performance of target reconstruction in comparison with the nonadaptive mechanism.

Parametric sparse representation method for ISAR imaging of rotating targets

Compressive sensing (CS) can effectively recover a signal when it is sparse in some discrete atoms. However, in some applications, signals are sparse in a continuous parameter space, e.g., frequency space, rather than discrete atoms. Usually, we divide the continuous parameter into finite discrete grid points and build a dictionary from these grid points. However, the actual targets may not exactly lie on the grid points no matter how densely the parameter is grided, which introduces mismatch between the predefined dictionary and the actual one. In this article, a novel method, namely adaptive matching pursuit with constrained total least squares (AMP-CTLS), is proposed to find actual atoms even if they are not included in the initial dictionary. In AMP-CTLS, the grid and the dictionary are adaptively updated to better agree with measurements. The convergence of the algorithm is discussed, and numerical experiments demonstrate the advantages of AMP-CTLS.Compressive sensing (CS) can effectively recover a signal when it is sparse in some discrete atoms. However, in some applications, signals are sparse in a continuous parameter space, e.g., frequency space, rather than discrete atoms. Usually, we divide the continuous parameter into finite discrete grid points and build a dictionary from these grid points. However, the actual targets may not exactly lie on the grid points no matter how densely the parameter is grided, which introduces mismatch between the predefined dictionary and the actual one. In this article, a novel method, namely adaptive matching pursuit with constrained total least squares (AMP-CTLS), is proposed to find actual atoms even if they are not included in the initial dictionary. In AMP-CTLS, the grid and the dictionary are adaptively updated to better agree with measurements. The convergence of the algorithm is discussed, and numerical experiments demonstrate the advantages of AMP-CTLS.

Cognitive random stepped frequency radar with sparse recovery

In this article, we propose a weighted ℓ2,1 minimization algorithm for jointly-sparse signal recovery problem. The proposed algorithm exploits the relationship between the noise subspace and the overcomplete basis matrix for designing weights, i.e., large weights are appointed to the entries, whose indices are more likely to be outside of the row support of the jointly sparse signals, so that their indices are expelled from the row support in the solution, and small weights are appointed to the entries, whose indices correspond to the row support of the jointly sparse signals, so that the solution prefers to reserve their indices. Compared with the regular ℓ2,1 minimization, the proposed algorithm can not only further enhance the sparseness of the solution but also reduce the requirements on both the number of snapshots and the signal-to-noise ratio (SNR) for stable recovery. Both simulations and experiments on real data demonstrate that the proposed algorithm outperforms the ℓ1-SVD algorithm, which exploits straightforwardly ℓ2,1 minimization, for both deterministic basis matrix and random basis matrix.

Adaptive matching pursuit with constrained total least squares

High range resolution (HRR) profiling using stepped-frequency pulse trains suffers from range shift and the attenuation/dispersion of range profiles while the target of interest is moving. To overcome these two drawbacks, a new algorithm based on the maximum likelihood (ML) estimation is proposed in this paper. Without altering the conventional stepped-frequency waveform, this algorithm can estimate the target velocity and thereby compensate the phase errors caused by the targets motion. It is shown that the velocity can be accurately estimated and the range profile can be correctly reconstructed.