Performance Analysis of Radar Target Recognition Using Natural Frequency: Frequency Domain Approach

Lee, Joon-Ho; Cho, Sung‐Woo; Park, Sang-Hong; Kim, Kyung‐Tae

doi:10.2528/pier12071107

Cited by 17 publications

(14 citation statements)

References 96 publications

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“…Since the input signal is given in discrete form, a discrete version of Fourier transform is used. Fourier transform is a fundamental mathematical tool for signal processing and matching and finds an enormous amount of applications such as radar data processing [54], polynomial construction [55], design of devices [56], target recognition [57] and landmine detection [58], to name a few.…”

Section: Discrete Fourier Transform (Dft)mentioning

confidence: 99%

Multi-Feature Based Detection of Landmines Using Ground Penetrating Radar

Park¹,

Park²,

Kim³

et al. 2013

PIER

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Abstract-In this paper, we present a method for detecting antitank or anti-personnel landmines buried in the ground. A set of data generated by a ground penetrating radar is processed to remove the surface reflection and clutter, yielding signals for possible landmines. In order to detect landmines in the signals, features are computed and compared against a database, which contains those of various landmines. Three features are proposed to use; principal components from principal component analysis, Fourier coefficients and singular values from singular value decomposition method, each of which is chosen to represent each landmine uniquely. Detection is performed using Mahalanobis distance-based method. Examples show that the proposed method can effectively detect landmines in various burial condition.

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Section: Discrete Fourier Transform (Dft)mentioning

confidence: 99%

Multi-Feature Based Detection of Landmines Using Ground Penetrating Radar

Park¹,

Park²,

Kim³

et al. 2013

PIER

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“…In the research area of state estimation and target tracking [1][2][3][4][5][6], the technique of multiple target tracking (MTT) can estimate the target number as well as each target's state in the scene based on a sequence of uncertain measurements, where the "uncertain" mainly comes from detection uncertainty, association uncertainty and clutters in radar systems [7,8]. Traditional MTT filters, such as multiple hypothesis tracking (MHT) filters, Markov chain Monte Carlo (MCMC) filters and joint probabilistic data association (JPDA) [9], mostly adopt the strategy of measurement-data association to existing tracks and then track each target with a separate filter based on the assumption that each target moves independently.…”

Section: Introductionmentioning

confidence: 99%

Threshold-Based Resampling for High-Speed Particle PHD Filter

Shi¹,

Zheng²,

Bian³

et al. 2013

PIER

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Abstract-In recent years, particle probability hypothesis density (PHD) filtering has become an active research topic for multiple targets tracking in dense clutter scenarios. However, it is highly required to improve the real-time performance of particle PHD filtering because it is a kind of Monte Carlo approach and the computational complexity is very high. One of major difficulties to improve the realtime performance of particle PHD filtering lies in that, resampling, which is usually a sequential process, is crucial to the fully-parallel implementation of particle PHD filter. To overcome this difficulty, this paper presents a novel threshold-based resampling scheme for the particle PHD filter, in which the particle weights are all set below a proper threshold. This specific threshold is determined using a distinguishing feature of the particle PHD filters: The weight sum of all particles in weight update is equal to the total target number in the current iteration. This proposed resampling scheme allows the use of fully-pipelined architecture in the hardware design of particle PHD filter. Theoretical analysis indicates that the particle PHD filter employing the proposed resampling technique can reduce the time complexity by 33% around in a typical multi-target tracking (MTT) scenario compared with that employing the traditional systematic resampling technique, while simulation results show that it can maintain the almost same performance of estimation accuracy.

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“…Nowadays, Synthetic Aperture Radar (SAR) is playing an important role in the field of military remote sensing because of its ability to imaging with high resolution under all-weather and all-time condition [1][2][3][4][5]. As a key application of SAR, automatic target recognition (ATR) based on SAR has a great value both in military and commercial applications.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Scale Local Phase Quantization Plus Biomimetic Pattern Recognition Method for Sar Automatic Target Recognition

Zhai¹,

Li²,

Gan³

et al. 2013

PIER

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Abstract-Synthetic aperture radar (SAR) automatic target recognition (ATR) has been receiving more and more attention in the past two decades. But the problem of how to overcome SAR target ambiguities and azimuth angle variations has still left unsolved. In this paper, a multi-scale local phase quantization plus biomimetic pattern recognition (BPR) method is presented to solve these two difficulties. By applying multiple scales local phase quantization (LPQ) on the observed SAR images, the blur and azimuth invariant features can be extracted, and these features are fusion at consecutive multiple scales to achieve better results. Then PCA method is applied to further reduce the feature dimension and achieve its efficiency. Finally, high dimensional space geometry covering method based on BPR theory is adopted to construct hyper sausage neuron links for target recognition. Experiments on the MSTAR database show that the proposed method can achieve satisfying recognition accuracy compared with other stateof-the-art methods.

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Performance Analysis of Radar Target Recognition Using Natural Frequency: Frequency Domain Approach

Cited by 17 publications

References 96 publications

Multi-Feature Based Detection of Landmines Using Ground Penetrating Radar

Multi-Feature Based Detection of Landmines Using Ground Penetrating Radar

Threshold-Based Resampling for High-Speed Particle PHD Filter

A Multi-Scale Local Phase Quantization Plus Biomimetic Pattern Recognition Method for Sar Automatic Target Recognition

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