Nonstationary Hidden Markov Models for Multiaspect Discriminative Feature Extraction From Radar Targets

Zhu, Fei; Zhang, Xian-Da; Hu, Yafeng; Xie, Deguang

doi:10.1109/tsp.2007.892708

Cited by 46 publications

(45 citation statements)

References 25 publications

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“…Bi-/multistatic radar has many advantages over monostatic radar: separated transmitter and receiver configurations offer the ability to detect stealthy targets and offer immunity to jamming; multistatic radar achieves spatially diverse geometries to observe threats and countermeasures from multiple viewing angles and provide highdetection performance. So Bi-/multistatic radar regains much more concern [3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Isar Imaging and Scaling Method of Precession Targets in Wideband T/R-R Bistatic Radar

Ai¹,

Zeng²,

Wang³

et al. 2017

PIER M

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Abstract-Imaging and scaling of precession targets are very important in spatial target surveillance. The bistatic wideband radar echo model of the spatial precession cone-shaped target is induced, and bistatic ISAR imaging method based on time-frequency analysis is described. Combined with the monostatic and bistatic scattering characteristics of cone-shaped targets, the cross scaling method is presented through range instantaneous Doppler (RID) image matching using T/R-R bistatic radar observations, and the correct scaled monostatic and bistatic two-dimensional images can be obtained at the same time, which can reflect the actual size of the target. The algorithm is validated by dynamic simulation with electromagnetic computation data and provides a feasible way for the stable recognition of spatial targets.

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Section: Introductionmentioning

confidence: 99%

Isar Imaging and Scaling Method of Precession Targets in Wideband T/R-R Bistatic Radar

Ai¹,

Zeng²,

Wang³

et al. 2017

PIER M

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“…Since it contains the target structure signatures, such as target size and scatterer distribution, radar HRRP target recognition has received intensive attention from the radar automatic target recognition (RATR) community [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

“…Several studies [8][9][10][11][12][13][14][15][16] show that statistical recognition is an efficient method for RATR. Figure 2 shows a typical flow chart of radar HRRP statistical recognition.…”

Section: Introductionmentioning

confidence: 99%

“…Rather than just utilizing the frequency domain feature as in [6], this study exploits the spectrogram feature of HRRP data for combining both time domain and frequency domain features, which is a two-dimensional feature providing the variation of frequency domain feature with time domain feature. Some statistical models [8][9][10][11][12][13][14][15][16] are developed for HRRP-based RATR, of which [14][15][16] successfully utilized the hidden Markov model (HMM) for modeling the feature vectors from the HRRP sequence. Since the HRRP sample is a typical high-dimensional distributed signal, it is computationally prohibitive to build such a high-dimensional HMM for describing HRRP sequences directly.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, to avoid this problem, some dimensionality reduction methods are utilized. For example, in [14,15], the relax algorithm is employed to extract the waveform constituents from the HRRP radar echoes, and then an HMM is utilized to characterize the features; in [16], a nonstationary HMM is utilized to characterize the following two features, i.e., the location information feature of scattering centers which are extracted via the multirelax algorithm, and the moments of HRRP radar echoes. Nevertheless, some information contained in HRRP samples will be inevitably lost during the dimensional reduction procedure.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Multi-task hidden Markov modeling of spectrogram feature from radar high-resolution range profiles

Pan

Wang

et al. 2012

EURASIP J. Adv. Signal Process.

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In radar high-resolution range profile (HRRP)-based statistical target recognition, one of the most challenging task is the feature extraction. This article utilizes spectrogram feature of HRRP data for improving the recognition performance, of which the spectrogram is a two-dimensional feature providing the variation of frequency domain feature with time domain feature. And then, a new radar HRRP target recognition method is presented via a truncated stick-breaking hidden Markov model (TSB-HMM). Moreover, multi-task learning (MTL) is employed, from which a full posterior distribution on the numbers of states associated with the targets can be inferred and the target-dependent states information are shared among multiple target-aspect frames of each target. The framework of TSB-HMM allows efficient variational Bayesian inference, of interest for large-scale problem. Experimental results for measured data show that the spectrogram feature has significant advantages over the time domain sample in both the recognition and rejection performance, and MTL provides a better recognition performance.

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Radar HRRP Target Recognition with Recurrent Convolutional Neural Networks

Shen

Chen

2018

Lecture Notes in Computer Science

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Nonstationary Hidden Markov Models for Multiaspect Discriminative Feature Extraction From Radar Targets

Cited by 46 publications

References 25 publications

Isar Imaging and Scaling Method of Precession Targets in Wideband T/R-R Bistatic Radar

Isar Imaging and Scaling Method of Precession Targets in Wideband T/R-R Bistatic Radar

Multi-task hidden Markov modeling of spectrogram feature from radar high-resolution range profiles

Radar HRRP Target Recognition with Recurrent Convolutional Neural Networks

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