Adaptive statistical model for radar HRRP recognition

Hou, Qingwen; Liu, Hongfei; Chen, F.; Bao, Zhenjie

doi:10.1049/cp.2009.0358

Cited by 2 publications

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“…Thus, HRRP is usually normalised before performing the learning task. In addition to the three sensitivities, the applications of the classic feature extraction and classification algorithms, such as kernel principle component analysis, dictionary learning and factor analysis, are widely studied in the field of RATR [15][16][17][18][19][20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Radar high‐resolution range profile recognition via geodesic weighted sparse representation

Wang

Chen

2015

IET Radar, Sonar & Navigation

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One of the radar high-resolution range profile (HRRP) recognition issues is the target-aspect sensitivity. Both theoretical analysis and real-world data show that the HRRP shows a high correlation only in a very small aspect region. To overcome this problem, in traditional methods, the scattering centre model and averaged range profile are utilised. In this study, the authors present a graph-based semi-supervised method, called geodesic weighted sparse representation (GWSR), to overcome the target-aspect sensitivity problem. It is assumed that HRRP from different targets is located on different manifolds and the correlation information is utilised to separate these manifolds. In GWSR, the geodesic distance is calculated firstly and then the labelled HRRP is reconstructed by the geodesic weight. The nonlinear structure of HRRP can be transformed into a linear one through the reconstruction process. Then, the unlabelled HRRP is sparsely reconstructed and the sparse reconstruction weight can be utilised to estimate the label of the unlabelled HRRP from the given labels. Experiments on three kinds of ground target HRRPs with different backgrounds demonstrate the effectiveness of the authors' method.

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Section: Introductionmentioning

confidence: 99%

Radar high‐resolution range profile recognition via geodesic weighted sparse representation

Wang

Chen

2015

IET Radar, Sonar & Navigation

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“…In order to avoid the huge computation burden in bispectra method, some dimensionality reduction methods were proposed in [6], [11], [15]. Hidden Markov model was used in radar HRRP target recognition and studied in [19]; some time-shift invariant features of radar HRRP target were proposed in [5], [7]- [11]; the statistical recognition model was studied in [12], [14], [17]; many classifiers were proposed in [13], [15], [16]. there has been little work, however, in the field of radar HRRP target based fuzzy clustering algorithm.…”

Section: Introductionmentioning

confidence: 99%

Radar HRRP Target Recognition Based on the Improved Kernel Distance Fuzzy C-Means Clustering Method

Chen

2015

IEICE Trans. Inf. & Syst.

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SUMMARYTo overcome the target-aspect sensitivity in radar high resolution range profile (HRRP) recognition, a novel method called Improved Kernel Distance Fuzzy C-means Clustering Method (IKDFCM) is proposed in this paper, which introduces kernel function into fuzzy cmeans clustering and relaxes the constraint in the membership matrix. The new method finds the underlying geometric structure information hiding in HRRP target and uses it to overcome the HRRP target-aspect sensitivity. The relaxing of constraint in the membership matrix improves anti-noise performance and robustness of the algorithm. Finally, experiments on three kinds of ground HRRP target under different SNRs and four UCI datasets demonstrate the proposed method not only has better recognition accuracy but also more robust than the other three comparison methods. key words: fuzzy c-means clustering method (FCM), high-resolution range profile (HRRP), radar automatic target recognition (RATR), kernel function

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Adaptive statistical model for radar HRRP recognition

Cited by 2 publications

References 0 publications

Radar high‐resolution range profile recognition via geodesic weighted sparse representation

Radar high‐resolution range profile recognition via geodesic weighted sparse representation

Radar HRRP Target Recognition Based on the Improved Kernel Distance Fuzzy C-Means Clustering Method

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