EM-Based Recursive Tracking Algorithm for Near-Field Moving Sources

Çekli, Serap; Çekli, Erdinç; Kabaoglu, Nihat; Çırpan, Hakan Ali

doi:10.1109/ivs.2007.4290144

Cited by 3 publications

(4 citation statements)

References 14 publications

(19 reference statements)

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“…Also, a rectangular window with N=50 snapshots is considered for the LPA beamformer. The following scenarios are used to clarify the LPA beamformer performance in the near-field accelerated moving sources and to compare it with that of REM1 in [9]. Figure 1 shows the LPA beamformer output for near-field single source case in pairs, where the time-varying DOA parameters are Fig.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…Using the weighted least squares approach to formulate the LPA beamformer for a single source assumption, which can be extended to the multiple source case. The following LPA based function will be minimized as in [10,13] to be, www.ijacsa.thesai.org (9) …”

Section: Lpa Algorithm For Near-field Sources' Parameter Estimationmentioning

confidence: 99%

“…The EM prevents the computational complexity by switching the multidimensional search problem to less dimensional parallel search problems. In [9], the Expectationmaximization (EM) based recursive algorithm was applied with moving sources in the near-field.…”

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confidence: 99%

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LPA Beamformer for Tracking Nonstationary Accelerated Near-Field Sources

Ashour¹

2014

IJACSA

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Abstract-In this paper, a computationally very efficient algorithm for direction of arrival (DOA) as well as range parameter estimation is proposed for near-field narrowband nonstationary accelerated moving sources. The proposed algorithm based on the local polynomial approximation (LPA) beamformer, which proves its efficiency with far-field applications. The LPA estimates the instantaneous values of the direction of arrival, angular velocity, acceleration as well as the range parameters of near-field sources using weighted least squares approach which based on Taylor series. The performance efficiency of the LPA beamformer to estimate the DOAs of near-field sources is evaluated and compared with the Recursive Expectation-Maximization (REM) method. The comparison is done using standard deviation of DOA estimation error as well as for range versus signal to noise ratio (SNR). The simulation results show that LPA beamformer outperform REM1 in signal-to-noise ratio requirements.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Lpa Algorithm For Near-field Sources' Parameter Estimationmentioning

confidence: 99%

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LPA Beamformer for Tracking Nonstationary Accelerated Near-Field Sources

Ashour¹

2014

IJACSA

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“…However, the number of Gaussian mixture model is difficult to choose. Furthermore, parametrical method such as learning Gaussian distribution using EM al− gorithm is very time−consuming, which is not appropriate in real−time tracking tasks [6].…”

Section: Introductionmentioning

confidence: 99%

A coarse-to-fine kernel matching approach for mean-shift based visual tracking

Feng

Chen

et al. 2009

Opto-Electronics Review

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Mean shift is an efficient pattern match algorithm. It is widely used in visual tracking fields since it need not perform whole search in the image space. It employs gradient optimization method to reduce the time of feature matching and realize rapid object localization, and uses Bhattacharyya coefficient as the similarity measure between object template and candidate template. This thesis presents a mean shift algorithm based on coarse-to-fine search for the best kernel matching. This paper researches for object tracking with large motion area based on mean shift. To realize efficient tracking of such an object, we present a kernel matching method from coarseness to fine. If the motion areas of the object between two frames are very large and they are not overlapped in image space, then the traditional mean shift method can only obtain local optimal value by iterative computing in the old object window area, so the real tracking position cannot be obtained and the object tracking will be disabled. Our proposed algorithm can efficiently use a similarity measure function to realize the rough location of motion object, then use mean shift method to obtain the accurate local optimal value by iterative computing, which successfully realizes object tracking with large motion. Experimental results show its good performance in accuracy and speed when compared with background-weighted histogram algorithm in the literature.

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Distributed expectation-maximization algorithm for DOA estimation in wireless sensor networks

Wen

Liu

2015

2015 IEEE International Conference on Digital Signal Processing (DSP)

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EM-Based Recursive Tracking Algorithm for Near-Field Moving Sources

Cited by 3 publications

References 14 publications

LPA Beamformer for Tracking Nonstationary Accelerated Near-Field Sources

LPA Beamformer for Tracking Nonstationary Accelerated Near-Field Sources

A coarse-to-fine kernel matching approach for mean-shift based visual tracking

Distributed expectation-maximization algorithm for DOA estimation in wireless sensor networks

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