Yubing Han

Constant false alarm rate detector based on the maximal reference cell

In order to improve the detection performance of constant false alarm rate (CFAR) detectors in multiple targets situations, a CFAR detector based on the maximal reference cell (MRC) named MRC-CFAR is proposed. In MRC-CFAR, a comparison threshold is generated by multiplying the amplitude of MRC by a scaling factor. The number of the reference cells left, whose amplitudes are smaller than the comparison threshold, is counted and compared with a threshold integer. Based on the comparison result, proper reference cells are selected for detection threshold computation. A closed-form analysis for MRC-CFAR in both homogeneous and non-homogeneous environments is presented. The performance of MRC-CFAR is evaluated and compared with other CFAR detectors. MRC-CFAR exhibits a very low CFAR loss in a homogeneous environment and performs robustly during clutter power transitions. In multiple targets situations, MRC-CFAR achieves a much better detection performance than switching CFAR (S-CFAR) and order-statistic CFAR (OS-CFAR). Experiment results from an X-band linear frequency modulated continuous wave radar system are given to demonstrate the efficiency of MRC-CFAR. Because ranking reference cells is not required for MRC-CFAR, the computation load of MRC-CFAR is low; it is easy to implement the detector in radar system in practice.

Adaptive beamforming with compressed sensing for sparse receiving array

An adaptive digital beamforming technique with compressed sensing (CS) for sparse receiving arrays is proposed. Because of the angle sparseness of arriving signals, CS theory can be adopted to sample receiving signals. Then, receiving signals from absent elements on the antenna aperture can be reconstructed by using CS method. Adaptive digital beamforming algorithms are adopted to form antenna beam, whose main lobe is steered to the desired direction and nulls steered to the directions of interference.

Recursive Bayesian beamforming with uncertain projected steering vector and strong interferences

A recursive Bayesian beamforming is proposed for the steering vector uncertainty and strong interferences. Signal and noise powers are unknown, and beamforming weight is modeled as a complex Gaussian vector that characterizes the level of projected steering vector uncertainty. By applying the Bayesian model, a recursive algorithm is developed to estimate beamforming weight. Numerical simulations of linear and planar arrays demonstrate the effectiveness and robustness of the proposed beamforming algorithm. After convergence, the proposed algorithm exhibits a performance similar to that of the optimal

Clutter map CFAR detector based on maximal resolution cell

\mathrm {MaxSINR}

A novel adaptive phase-only beamforming algorithm based on semidefinite relaxation

MaxSINR beamformer.

Adaptive digital beamforming algorithm of circle array based on compressed sensing

Abstract. We propose an effective physical model based single-image dehazing algorithm under the regularization framework. Using an anisotropic Laplacian term as a constraint, the atmospheric propagation model that encodes the thickness of the haze is estimated by the regularization theory. Employing the physical model, haze-free images are reconstructed via the Beltrami regularization based on the color image manifold theory. Comparative experiments on a variety of test images demonstrate the power and the applicability of the proposed method.

Generalised reduced-rank structure for broadband space-time GSC and its fast algorithm

In order to improve the detection performance of clutter map constant false alarm rate (CFAR) detectors in multiple persisting targets situation, a clutter map CFAR (CM-CFAR) detector based on the maximal resolution cell (CM/MRC-CFAR) is proposed in this paper. In the CM/MRC-CFAR detector, at each scan, a comparison threshold is computed by multiplying the amplitude of the maximal resolution cell (MRC) in the map cell by a scaling factor. Then, the number of the left resolution cells, whose amplitudes are smaller than the comparison threshold, is counted and compared with a threshold integer. Based on the comparison result, proper resolution cells are selected to update the detection threshold. The detection probability of CM/MRC-CFAR in both homogeneous and multiple persisting targets situations is derived in a closed-form expression. The detection performance of CM/MRC-CFAR is evaluated in various environments and compared with other CM-CFAR detectors. CM/MRC-CFAR exhibits a very low CFAR loss in a homogeneous environment and achieves a robust detection performance in multiple persisting targets situations. Since no ranking is required except searching for the MRC, the computation load of CM/MRC-CFAR is low, and it is easy to implement the detector in radar systems in practice.

Range tracking method based on adaptive “current” statistical model with velocity prediction

Compared to conventional phased-array radar, MIMO radar benefiting from its extra degrees of freedom brought by waveform diversity allows to optimize the Cramer-Rao Bound (CRB) for Direction-of-arrival (DOA) estimation more freely. In this paper, under the premise that the general angular directions of targets are known as priori, a new transmit beamforming method for subarray MIMO radar is proposed with the application to improve the performance of DOA estimator for multiple targets. The CRB expression for DOA estimation of subarray MIMO radar is derived firstly. Then, the correlation matrix of the transmitted waveforms is optimized to minimize the CRB for DOA estimation. Once the optimized correlation matrix is determined, eigendecomposition method is applied to calculate the subarray beamforming weights. Meanwhile, fewer orthogonal waveforms are transmitted in the proposed method compared to conventional MIMO radar, which means that less number of subarrays will be used. The reduction in the number of transmitted orthogonal waveforms can effectively reduce the computational complexity. The proposed method obtains the optimized tradeoff between the effective aperture of virtual array and coherent gain, and consequently improves the performance of DOA estimator. Simulation results show that the proposed method has a superior performance compared with the existing methods. The CRBs for DOA estimation of subarray MIMO radar are derived.A new transmit beamforming algorithm for subarray MIMO radar is proposed.Our method can obtain better tradeoff between array aperture and coherent gain.A better DOA estimation performance is obtained while fewer waveforms are emitted.The computational cost of MIMO radar is also reduced in our method.