Performance analysis of an automated E-pulse target discrimination scheme

Mooney, J.E.; Ding, Zhi; Riggs, L.S.

doi:10.1109/8.843677

Cited by 20 publications

(16 citation statements)

References 10 publications

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“…The incident angle is arbitrarily chosen to be 60 • . The natural frequencies of the targets in Figure 1 can be found in [5].…”

Section: Numerical Resultsmentioning

confidence: 99%

“…[1] The angle-of-arrival estimation and beamforming have been the main research topics of the array signal processing, and radar [2][3][4][5][6] and sonar have been classical applications of array signal processing.…”

Section: Introductionmentioning

confidence: 99%

“…Much research has been conducted on radar target detection [2][3][4][5] and recognition. [6] The performance of the natural frequency-based radar target detection scheme has been analyzed in the time domain [3] and in the frequency domain.…”

Section: Introductionmentioning

confidence: 99%

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Recursive implementation of GLRT-based radar target detection

Lee

Jeong

2014

Journal of Electromagnetic Waves and Applications

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We consider recursive implementation of the natural frequency-based radar target detection for an augmented data vector. In the previous study, it was shown that, the probability of detection can be calculated using the probability density function (PDF) of the non-central chi-square distribution and that non-centrality of the chi-square distribution is dependent on the eigenvectors of a matrix. The essential idea in this paper is that the eigenvectors of the augmented matrix can be recursively calculated without computationally intensive eigendecomposition. To do that, we make use of how the QR factorization of the row-augmented matrix can be updated from the QR factorization of the original matrix to get the probability of detection recursively. The recursive formulation is validated by comparing the detection performance using the recursive method with that using non-recursive method.

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“…The incident angle is arbitrarily chosen to be 60 • . The natural frequencies of the targets in Figure 1 can be found in [5].…”

Section: Numerical Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Recursive implementation of GLRT-based radar target detection

Lee

Jeong

2014

Journal of Electromagnetic Waves and Applications

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“…This technique has been analyzed in several subsequent works [2]- [4], and several authors have proposed changes and studied its performance when the target response is contaminated by noise; see for example [5], [6], and references therein. Moreover, the applicability and feasibility of this technique has been studied in several published articles [7]- [9].…”

Section: Introductionmentioning

confidence: 99%

An Asymptotically Unbiased E-Pulse-Based Scheme for Radar Target Discrimination

Blanco

Ruíz

Alameda

et al. 2004

IEEE Trans. Antennas Propagat.

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Abstract-This communication proposes an E-pulse-based scheme for radar target discrimination that provides asymptotically correct results for any level of additive white noise contaminating the radar signal. After multiple sampling of the signal dispersed by the target, it is analytically shown that the cross correlation between the output signals of the E-pulse designed for the standard target, corresponding to two different sampling periods, is asymptotically null, regardless of the amount of contaminating noise. The results obtained by simulation have allowed us to propose a discrimination criterion that produces better results than the original E-pulse technique at very low signal-to-noise ratio (SNR) levels.

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“…Target identification based on resonance excitation (e.g. the Extinction pulse technique of [3][4][5]), uses baseband pulse excitation to reveal the target natural resonance modes. The mode resonant frequencies are target dependent, have enhanced returns and independent of aspect; thus making them a prime candidate for inclusion in any target feature set description.…”

Section: Introductionmentioning

confidence: 99%

Euclidean Feature Set Based on Modified Characteristic Polarization States

2016

IJSR

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This paper presents a feature set selection procedure, which is implemented in the frequency domain and involves developing a modified CPS feature set that incorporates power information. Metric distances between the respective modified CPS at several target resonant frequencies are computed and are used to form the final feature set. Stationary individual targets in free space were used to display the theoretical feasibility of this feature selection algorithm.

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Performance analysis of an automated E-pulse target discrimination scheme

Cited by 20 publications

References 10 publications

Recursive implementation of GLRT-based radar target detection

Recursive implementation of GLRT-based radar target detection

An Asymptotically Unbiased E-Pulse-Based Scheme for Radar Target Discrimination

Euclidean Feature Set Based on Modified Characteristic Polarization States

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