Radar target recognition using dynamic system model

Ajorloo, Abdollah; Hadavi, Mahdi; Bastani, Mohammad Hassan; Nayebi, Mohammad Mahdi

doi:10.1109/radar.2014.6875828

Cited by 2 publications

(4 citation statements)

References 8 publications

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“…In [10–12], according to the physical behaviour of polynomial and rotational movement of the target and taking into account the electromagnetic backscattering considerations, dynamic system is proposed to model the short dependency between consecutive samples of HRRP in a segment of the HRRP sequence.…”

Section: Introductionmentioning

confidence: 99%

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Polynomial segment model for radar target recognition using Gibbs sampling approach

Hadavi

Radmard

Nayebi

2017

IET signal process.

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High resolution range profile (HRRP) is a widely noted tool in radar target recognition. However, its high sensitivity to the target's aspect angle makes it necessary to seek solutions for this problem. One alternative is to assume consecutive samples of HRRP identically and independently distributed in small frames of aspect angles, an assumption which is not true in reality. Based on this simplifying assumption, some models, such as the hidden Markov model, have been developed to characterise the sequential information contained in multi‐aspect radar echoes. As a result, these models consider only the short dependency between consecutive samples. Considering such issues, in this study, the authors propose an alternative polynomial segment model. In addition, using a Markov chain Monte–Carlo based Gibbs sampler as an iterative approach to estimate the parameters of the segment model, the authors will show that the results are quite satisfactory.

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Section: Introductionmentioning

confidence: 99%

“…In all of the aforementioned works (i.e. [6, 7, 10–12]), the extracted feature from HRRP is the signal's amplitude. However in this paper, the locations of dominant peaks are considered as the observed data, which are less sensitive to the target fluctuations and receiver's noise [13].…”

Section: Introductionmentioning

confidence: 99%

Polynomial segment model for radar target recognition using Gibbs sampling approach

Hadavi

Radmard

Nayebi

2017

IET signal process.

Self Cite

View full text Add to dashboard Cite

show abstract

“…In [10,11,12], according to the physical behavior of linear and rotational movement of the target and taking into account the electromagnetic backscattering considerations, Dynamic system (DS) is proposed to model the short dependency between consecutive samples of HRRP in a segment of the HRRP sequence.…”

Section: Introductionmentioning

confidence: 99%

“…Also, it should be noted that in all of the aforementioned works (i.e., [8,9,10,11,12]), the signal's amplitudes are considered as the features extracted from HRRP. However, in this paper, the locations of dominant peaks are considered as the observed data, which are less sensitive to target fluctuations and the receiver noise [13].…”

Section: Introductionmentioning

confidence: 99%

A New MCMC Sampling Based Segment Model for Radar Target Recognition

Hadavi¹,

Radmard²,

Nayebi³

2015

RADIOENGINEERING

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Abstract. One of the main tools in radar target recognition is high resolution range profile (HRRP). However, it is very sensitive to the aspect angle. One solution to this problem is to assume the consecutive samples of HRRP identically independently distributed (IID) in small frames of aspect angles, an assumption which is not true in reality. However, based on this assumption, some models have been developed to characterize the sequential information contained in the multi-aspect radar echoes. Therefore, they only consider the short dependency between consecutive samples. Here, we propose an alternative model, the segment model, to address the shortcomings of these assumptions. In addition, using a Markov chain Monte-Carlo (MCMC) based Gibbs sampler as an iterative approach to estimate the parameters of the segment model, we will show that the proposed method is able to estimate the parameters with quite satisfying accuracy and computational load.

show abstract

Radar target recognition using dynamic system model

Cited by 2 publications

References 8 publications

Polynomial segment model for radar target recognition using Gibbs sampling approach

Polynomial segment model for radar target recognition using Gibbs sampling approach

A New MCMC Sampling Based Segment Model for Radar Target Recognition

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