Radar HRRP Statistical Recognition: Parametric Model and Model Selection

Du, Lan; Liu, Hongwei; Bao, Zheng

doi:10.1109/tsp.2007.912283

Cited by 137 publications

(176 citation statements)

References 21 publications

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“…In scrupulous, it used two procedures of coherence [4] to present an in-depth examination of an easy one-step threshold (OST) algorithm for model collection. An iterated algorithm for model collection is proposed in [5], which can routinely present the best form of clusters and expected [6].…”

Section: Literature Reviewmentioning

confidence: 99%

The Context of Knowledge and Data Discovery in Highly Dense Data Points Using Heuristic Approach

Sasireka¹,

Raviraj²

2013

JSIP

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In data mining framework, for proficient data examination recent researchers utilized branch-and-bound methods such as seriation, clustering, and feature selection. Conventional cluster search was completed with diverse partitioning schemes to optimize the cluster pattern. Considering image data, partitioning approaches seems to be computationally complex due to large data size, and uncertainty of number of clusters. Recent work presented a new version of branch and bound model called model selection problem, handles the clustering issues more efficiently. The existing work deployed spatially coherent sampling for generating cluster parameter candidates. But if the problem-specific bounds and/or added heuristics in the data points of the domain area get surmounted, memory overheads, specific model selection, and uncertain data points cause various clustering abnormalities. To overcome the above mentioned issues, we plan to present an Optimal Model-Selection Clustering for image data point analysis in the context of knowledge and data discovery in highly dense data points with more uncertainty. In this work, we are going to analyze the model selection clustering which is first initiated through the process of heuristic training sequences on image data points and appropriates the problem-specific characteristics. Heuristic training sequences will generate and test a set of models to determine whether the model is matched with the characteristics of the problem or not. Through the process of heuristic training sequences, we efficiently perform the model selection criteria. An experimental evaluation is conducted on the proposed model selection clustering for image data point using heuristic approach (MSCHA) with real and synthetic data sets extracted from research repositories (UCI) and performance of the proposed MSCHA is measured in terms of Data point density, Model-Selection Criteria, Cluster validity.

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Section: Literature Reviewmentioning

confidence: 99%

The Context of Knowledge and Data Discovery in Highly Dense Data Points Using Heuristic Approach

Sasireka¹,

Raviraj²

2013

JSIP

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“…M. Li et al [16] propose a sparse representation-based denoizing method for improving recognition performance using HRRP. L. Du et al [17] apply the statistical model for recognizing the radar HRRP. L. Shi et al [18] use the local factor analysis to model the nonGaussianity of the radar HRRP data.…”

Section: Introductionmentioning

confidence: 99%

Orthogonal maximum margin projection subspace for radar target HRRP recognition

Zhou

2016

J Wireless Com Network

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In this paper, a novel target recognition method, namely orthogonal maximum margin projection subspace (OMMPS), is proposed for radar target recognition using high-resolution range profile (HRRP). The core of OMMPS is to maximize the between-class margin by increasing the between-class scatter distance and reducing the within-class scatter distance simultaneously. By introducing the nonlinear mapping function, we also derive the kernel version of OMMPS, namely orthogonal kernel maximum margin projection subspace (OKMMPS). Compared with maximum margin criterion (MMC) method, OMMPS are optimal in meaning of maximum margin due that the coordinate axes of OMMPS are obtained sequentially by solving the constrained optimization problem, thus improves the recognition performance. In addition, the number of efficient features for OMMPS is not limited by the number of pattern classes, and the appropriate features can still be obtained for separating the classes, even in high-dimensional space with only a few classes. Moreover, the coordinate axes of OMMPS are mutually orthogonal, and the features extracted by OMMPS reduce the redundancy. The extensive experimental results show that the proposed method has better recognition performance than the other methods such as MMC and LDA.

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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%

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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 Statistical Recognition: Parametric Model and Model Selection

Cited by 137 publications

References 21 publications

The Context of Knowledge and Data Discovery in Highly Dense Data Points Using Heuristic Approach

The Context of Knowledge and Data Discovery in Highly Dense Data Points Using Heuristic Approach

Orthogonal maximum margin projection subspace for radar target HRRP recognition

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

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