Yang Zhiwei

Sum-difference Reduced-dimensional Processing in SAR Image Domain and Statistical Analysis for Ground Moving Target Indication

In this article, a new reduced-dimensional adaptive processing algorithm based on joint pixels sum-difference data for clutter rejection is proposed. The sum-difference data are obtained by orthogonal projection of the joint pixels data of different synthetic aperture radar (SAR) images generated by a multi-satellite radar system. In the sense of statistical expectation, the sum-differ- ence data contain the common and different information of the SAR images. Therefore, the objective of clutter cancellation can be achieved by adaptive processing. Moreover, based on the residual image after clutter rejection, statistical analysis of constant false-alarm rate (CFAR) detection of moving targets is also presented. Simulation results demonstrate the effectiveness and robustness of the proposed algorithm even with heterogeneous clutter and image co-registration error.

Adaptive reduced-rank beam-forming method using joint iterative optimization

The proposed method realizes adaptive reduced-dimension processing based on joint optimization of both reduced-dimension matrix and beam-forming weight vector. Meanwhile, the spatial spectrum reconstruction technology is used to update the covariance matrix of array data online. The simulation results show that the proposed method is robust to the dimension of the reduced-dimension matrix and significantly improves the output SINR of adaptive beam-former under the condition of few samples.

A modified fast approximated power iteration subspace tracking method for space-time adaptive processing

We propose a subspace-tracking-based space-time adaptive processing technique for airborne radar applications. By applying a modified approximated power iteration subspace tracing algorithm, the principal subspace in which the clutter-plus-interference reside is estimated. Therefore, the moving targets are detected by projecting the data on the minor subspace which is orthogonal to the principal subspace. The proposed approach overcomes the shortcomings of the existing methods and has satisfactory performance. Simulation results confirm that the performance improvement is achieved at very small secondary sample support, a feature that is particularly attractive for applications in heterogeneous environments.We propose a subspace-tracking-based space-time adaptive processing technique for airborne radar applications. By applying a modified approximated power iteration subspace tracing algorithm, the principal subspace in which the clutter-plus-interference reside is estimated. Therefore, the moving targets are detected by projecting the data on the minor subspace which is orthogonal to the principal subspace. The proposed approach overcomes the shortcomings of the existing methods and has satisfactory performance. Simulation results confirm that the performance improvement is achieved at very small secondary sample support, a feature that is particularly attractive for applications in heterogeneous environments.

Adaptive Reduced-Rank Beamforming Method Based on Knowledge-Aided Joint Iterative Optimization

In this letter, a reduced-rank beamforming method is presented based on knowledge-aided joint iterative optimization. The proposed adaptive reduced-rank processing is realized by joint optimization for the reduced-dimension matrix and beamforming weight vector. Meanwhile, recursively updating the prior covariance matrix using the spatial spectrum reconstruction technology and the weighted processing improve the estimation precision of the array covariance matrix. The simulation results show that the proposed method is robust to the dimension of the reduced-dimension matrix and significantly improves the output signal-to-interference-plus-noise ratio (SINR) of the adaptive beamformer under the condition of few samples.

DOA estimation for truncated cone conformal array in the presence of mutual coupling

When mutual coupling error exists in the sensor array, the ideal steering vector differs from the actual steering vector, leading to degradation of the performance of some eigenstructure-based direction-finding methods. According to the truncated cone conformal array (TCCA) and on the premise of mutual coupling matrix (MCM) having a complex symmetric structure in this paper, we deduces the MCM model of TCCA by detailed analysis of its mutual coupling characteristics. We establish two models of mutual coupling error calibration, and realize the DOA estimation in the presence of mutual coupling by applying the multidimensional joint iteration method to these two models respectively. The correlation between the estimated steering vector and the actual steering vector becomes more closely, and these two proposed methods have an advantage of low signal-to-noise ratio (SNR) threshold. Theoretical analysis and extensive simulations verify the effectiveness of the proposed methods, and results of this paper can provide a reference for the engineering applications of conformal arrays.

Decorrelation in InSAR echoes with existence of channel mismatch

The cross-track baseline in InSAR system is very sensitive to terrain fluctuation which is widely used to retrieve the DEM. The cross-track baseline influences the coherence of the radar echoes seriously. In this paper, we analyzed the decorrelation caused by cross-track baseline and the channel mismatch respectively. And a decorrelation reduction method by means of compensating the decorrelation caused by the spatial baseline is given. Finally, simulation results verified the validity.