The Statistical Wide-Band Feature Method of HRRP Based on Optimal Path Means

Zhao, Tao; Dong, Chunzhu

doi:10.1109/iiki.2016.38

Cited by 1 publication

(2 citation statements)

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“…Hand-crafted features are widely used for HRRP recognition in the pre-deep learning era [13,[60][61][62][63][64][65][66][67][68][69]. The commonly applied features can be divided into 3 categories: geometric, structural, and power features.…”

Section: ) Hand-crafted Featuresmentioning

confidence: 99%

“…In our experiment, a total of 8 features are selected for evaluation. In the geometric category, there are 3 features: the number of scattering centers [60], the length of the target [61], and the number of half-power scattering centers which counts the scattering centers with the power above the half of the strongest one [62,63]. The structural category contains 2 features: the symmetry that measures the ratio of the two halves [64] and the dispersibility that calculates the average distance between two adjacent scattering centers [65].…”

Section: ) Hand-crafted Featuresmentioning

confidence: 99%

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Multi-View HRRP Generation With Aspect-Directed Attention GAN

Song

Zhou

Yang

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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In radar automatic target recognition (RATR), highresolution range profile (HRRP) has received intensive attention due to its low computational cost. As HRRP is sensitive to the aspect of the target, a training set covering sufficient aspects is essential to the success of a RATR model, which is however intractable in complex environment with noncooperative targets. In this paper, an aspect-directed attention generative adversarial network (ADA-GAN) is proposed to generate multi-view HRRPs using real samples from few aspects. The key is that the HRRPs from the similar targets share the same aspect variation pattern. Hence, an HRRP is decomposed into its identity and aspect features via an aspect-directed disentangled representation network with self-attention modules. In the training stage, the decomposition network and the aspect variation pattern are learned from full aspect samples of cooperative targets. When generation, the desired multi-view HRRPs of the noncooperative target are synthesized by its identity features extracted from few aspect samples and the learned aspect variation pattern. Three types of experiments on the simulated and measured datasets demonstrate the generation performances of our method. First, the generated HRRPs are visually compared with the truth. Second, the similarity of the scattering center power and handcrafted feature distributions are quantitatively evaluated. Finally, recognition experiments verify the feasibility of data augmentation with the generated HRRPs. Extensive results show the superior performance of our method over other state-of-theart methods.

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