Comparative analysis of alternative ground target tracking techniques

Ke, Chih-Chung; Herrero, Jesús García; Llinas, James

doi:10.1109/ific.2000.859832

Cited by 9 publications

(8 citation statements)

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“…The first is to tune the measurement s such as Chih-Chung et.al. [3] and LIU Gang et.al. [4]which propose to project the estimation location and velocity of the filter onto the road and formate confidence of the statistics distance to examine whether adjust the estimation location onto the road using Chi square test or not.…”

Section: Introductionmentioning

confidence: 99%

“…The second category is to tune the dynamic model of the target, which includes the single model tracker ,the multi-model tracker, the Hidden Markov Model (HMM) -based tracker and the Particle Filter(PF).For the single model tracker, Chih-Chung et.al. [3] used a approach which adjust the variance of the process noise to matching the actual moving of the target. In [5] the circular dynamics are defined by the continuous model.…”

Section: Introductionmentioning

confidence: 99%

“…[6] proposed an IMM (Intercating Multiple Model) filter with a variable structure (VS-IMM) where the models set adapts sequentially according to the target position and to the road network configuration. Also in [3] ,another tracker using the HMM with transition array was also developed. Table Ⅰ provides a summary and comparison of various algorithms for ground tracking of vehicles [7].…”

Section: Introductionmentioning

confidence: 99%

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Ground Target Tracking Using Aerial Images

Wang

Shi

2013

AMR

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The tracking of ground targets using aerial images was studied. A improved Kalman filter was derived for the tracking of ground targets. The novel feature of this improved filter were that the grey prediction equations and the road information have been incorporated to improve the accuracy of state estimates.The GM(1,1) (Grey Model) was introduced into Kalman prediction equations.The next value was forecasted by using few forward estimated values with a grey differential equation,which was baesd on the correction of mesurement covariance matrix. The tracking of the targets shows a satisfactory result.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Ground Target Tracking Using Aerial Images

Wang

Shi

2013

AMR

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“…Graphical techniques such as HMMs [17,18] have been investigated and widely applied in target tracking domain. In these approaches, targets move in a rectangular area at each discrete time interval.…”

Section: Related Workmentioning

confidence: 99%

“…HMM has been used for solving the 2D plane target tracking problem in [17,18]. A discrete HMM can be applied when the division of the region is finite.…”

Section: Hidden Markov Modelmentioning

confidence: 99%

Model-based Tracking for Mobile Ad Hoc Networks

Demirbaş

2011

NPA

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We consider the problem of tracking of a mobile target node in a mobile ad hoc network (MANET) set-up. We find a Gradient model alone is usually not very efficient, whereas a precise Markov model which includes transition probabilities is too hard to achieve. We propose a generic tracking framework for online tracking applications, by integrating a Gradient model of the target's proximity and an online statistically estimated Markov model of the target's likely direction. We show PMBT achieves a short catching path with a high success rate. PMBT is a probabilistic online tracking algorithm that computes information utilities at each step, and then chooses the next step toward the target based on the maximum expected utility. Our algorithm avoids the need to maintain a tracking data structure (such as a hierarchical directory look-up structure) and the need to send periodic update messages about the target's location. Simulation results show, by taking a hybrid approach that integrates a gradient model and a Markov model, our algorithm significantly outperforms both gradient-based and Markov approaches alone.

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