Radar Target Recognition Based on Sparse Representation of Dictionary Optimized

Pei Pei Duan; Hui Li; Qi Li

doi:10.4028/www.scientific.net/AMM.701-702.433

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 701-702Radar Target Recognition Based on Sparse...

Radar Target Recognition Based on Sparse Representation of Dictionary Optimized

Abstract:

The high range resolution profile samples are numerous and sparse. But less radar target recognition algorithms based on high range resolution profiles (HRRP) employed the sparseness of HRRP samples. A new radar target recognition algorithm using a fast sparse decomposition method is presented here. This algorithm was to be carried out in three major steps. First, the Gabor redundant dictionary was partitioned according to its atom characteristics to decrease the atoms storage. Then, the matching pursuit algorithm was improved by the genetic algorithm and the fast cross-correlations calculation to accelerate training samples decomposition and generate the taxonomic dictionaries. Finally, the reconstruction errors of testing samples were used to recognize different radar targets. The simulations show that this method can resist noise disturbs and its recognition rate is high.

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Periodical:

Applied Mechanics and Materials (Volumes 701-702)

Pages:

433-436

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.701-702.433

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Online since:

December 2014

Authors:

Pei Pei Duan*, Hui Li, Qi Li

Keywords:

High Range Resolution Profile, Radar Target Recognition, Redundant Dictionary, Sparse Decomposition

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References

[1] Zeng.J. Y, Gan.J. Y, Zhai Y.K. Face recognition based on fast sparse representation of Gabor dictionary and l0 norm[J]. Journal of Signal Processing. Vol. 29(2)(2013), pp.256-261. (in Chinese).

Google Scholar

[2] Mashud. H, Kaushik.M. An Improved Smoothed l0 Approximation Algorithm for Sparse Representation. IEEE Transactions on Signal Processing. Vol. 58(4)(2010), pp.2194-2205.

DOI: 10.1109/tsp.2009.2040018

Google Scholar

[3] Zhang.Z. A Fast Computation Method for Sparse Decomposition Based on the Cross-Correlation Calculation. Software Guide. Vol. 10(7) (2011), pp.45-47. (in Chinese).

Google Scholar

[4] Feng. B, Du. L, Liu.H. W, et al．Radar HRRP target recognition based on K-SVD algorithm. 2011 IEEE CIE International Conference on Radar. Chengdu: [s. n. ](2011), pp.642-645.

DOI: 10.1109/cie-radar.2011.6159622

Google Scholar

[5] ELAD . M, AHARON.M. Image denoising via sparse and redundant representations over learned dictionaries. IEEE Transactions on Image Process. Vol. 15(12)(2006), pp.3736-3745.

DOI: 10.1109/tip.2006.881969

Google Scholar