Advantages and challenges of power spectral density estimation methods for scanning radar angular superresolution

The angular superresolution is of great significance for scanning radar in forward-looking imaging. There are many techniques documented in literature to enhance the angular resolution, of which deconvolution method and power spectral density(PSD)methods are favored and attain many interests. In this paper, we focus on analyzing the advantages and challenges of PSD methods in comparison with the deconvolution method. Firstly, three typical PSD estimation approaches are introduced, followed with the comparison with deconvo-lution method that summarizes the advantages and challenges of PSD methods in theory. Simulations are provided in terms of coherence and number of snapshots, which presents the performance of different PSD methods and Lucy-Richardson deconvolution method, better demonstrating the advantages and challenges of PSD methods.

[1]  Jianyu Yang,et al.  Augmented Lagrangian method for angular super-resolution imaging in forward-looking scanning radar , 2015 .

[2]  N. Goodman,et al.  Superresolution of Coherent Sources in Real-Beam Data , 2010, IEEE Transactions on Aerospace and Electronic Systems.

[3]  Yue Wang,et al.  Bayesian Deconvolution for Angular Super-Resolution in Forward-Looking Scanning Radar , 2015, Sensors.

[4]  Jianyu Yang,et al.  Angular super-resolution algorithm based on maximum entropy for scanning radar imaging , 2014, 2014 IEEE Geoscience and Remote Sensing Symposium.

[5]  Jianyu Yang,et al.  Divide and conquer: A fast matrix inverse method of iterative adaptive approach for real beam superresolution , 2014, 2014 IEEE Geoscience and Remote Sensing Symposium.

[6]  Jianyu Yang,et al.  Maximum a posteriori–based angular superresolution for scanning radar imaging , 2014, IEEE Transactions on Aerospace and Electronic Systems.

[7]  Jianyu Yang,et al.  Angular superresolution for real beam radar with iterative adaptive approach , 2013, 2013 IEEE International Geoscience and Remote Sensing Symposium - IGARSS.