Statistical analysis of the zero-phase method for aligning noisy high-resolution radar signals

Gil-Pita, Roberto; Rosa-Zurera, Manuel; Jarabo-Amores, P.; Ferreras, F.L.

doi:10.1049/iet-rsn:20080056

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A Introduction To Mixture Models1

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2013

2024

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Cited by 2 publications

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“…The most common is the Expectation-Maximization (EM) algorithm, which estimates the maximum likelihood parameters. EM algorithm has attracted a great deal of interest over the past few years in a wide range of application involving tasks with incomplete data sets [16]- [17].…”

Section: A Introduction To Mixture Modelsmentioning

confidence: 99%

The T-Mixture Model Approach for Radar HRRP Target Recognition

Wang¹,

Xie²

2013

IJCEE

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Abstract-This paper addresses the problem of automatic radar target recognition using high-resolution range profile (HRRP). We develop a t-mixture model (TMM) to model radar echoes from each target as the t-distribution forms. Estimation of the model parameters is achieved using a modified expectation-maximization (EM), i.e., fuzzy EM (FEM) algorithm. Numerical simulation results show that the proposed approach can achieve good performance of robustness and higher recognition rate. It is helpful for real-time identification and the engineering implements of automatic target recognition using HRRP.Index Terms-High-resolution range profile (HRRP), radar automatic target recognition (RATR), t-mixture model (TMM), statistical model, expectation-maximization (EM).

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Section: A Introduction To Mixture Modelsmentioning

confidence: 99%