Data fusion for ground moving target tracking

Koller, Jost; Ulmke, Martin

doi:10.1016/j.ast.2006.10.010

Cited by 14 publications

(4 citation statements)

References 12 publications

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“…We model a single moving target system by a linear state space model as follows [1, 18, 23]: where boldxt=][|bmatrixx1,tx2,tx˙1,tx˙2,t⊤ is the state vector which indicates the position and the velocity of a target, respectively, in a 2D Cartesian coordinate system. Gx and Gu are known matrices according to the classical dynamics defined by boldu=][|bmatrixx¨1,tx¨2,t⊤, where u is the Gaussian noise-like acceleration perturbation and Ts is sampling time-period (s).…”

Section: System Modelmentioning

confidence: 99%

See 1 more Smart Citation

Multitarget Tracking by Particle Filtering Based on RSS Measurement in Wireless Sensor Networks

Lim

Chong

2015

International Journal of Distributed Sensor Networks

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We propose an algorithm for multitarget tracking by particle filtering in wireless sensor networks based on received signal strength (RSS) measurement where we also localize a newly appearing target whose location and reference power are unknown. Therefore, the number, the reference power, and the initial locations of targets are unknown in this problem. At the initial localization step, we apply approximate least squares (LS) method to roughly estimate the target location. After the initial location is estimated, we estimate the reference power. This is possible because we can use multiple number of measurements for estimating multiparameters. The proposed approach is particularly emphasized on the initialization step that completes the whole multitarget tracking system by particle filtering in a challenging scenario. The proposed approach is validated by computer simulations for its effectiveness.

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Section: System Modelmentioning

confidence: 99%

“…Once a target is identified, particle filtering can be applied to track the dynamic states of the target. The problem of multitarget tracking is not an easy task while various approaches, for example, extended Kalman filter, data fusion, and joint probabilistic data association, were proposed in the literature [16][17][18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Multitarget Tracking by Particle Filtering Based on RSS Measurement in Wireless Sensor Networks

Lim

Chong

2015

International Journal of Distributed Sensor Networks

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“…A sigma point filter representation allows non-linear modeling, but is not able to update the estimate when the target is not detected. Kalman filter-based approaches are also common in multiple target tracking applications with probabilistic data association, 3 which require extremely efficient representation of a single target track due to the additional complexity of the data association problem.…”

Section: Introductionmentioning

confidence: 99%

Information-Theoretic Tracking Control Based on Particle Filter Estimate

Ryan

2008

AIAA Guidance, Navigation and Control Conference and Exhibit

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“…The target can be masked both by the clutter notch of the sensor and by terrain obstacles. The results for a Gaussian sum filter (see also [32]) and a standard bootstrap particle filter approach are presented.…”

Section: Related Researchmentioning

confidence: 99%

Pedestrian tracking with an infrared sensor using road network information

Skoglar

Orguner

Törnqvist

et al. 2012

EURASIP J. Adv. Signal Process.

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This article presents a pedestrian tracking methodology using an infrared sensor for surveillance applications. A distinctive feature of this study compared to the existing pedestrian tracking approaches is that the road network information is utilized for performance enhancement. A multiple model particle filter, which uses two different motion models, is designed for enabling the tracking of both road-constrained (on-road) and unconstrained (offroad) targets. The lateral position of the pedestrians on the walkways are taken into account by a specific on-road target model. The overall framework seamlessly integrates the negative information of occlusion events into the algorithm for which the required modifications are discussed. The resulting algorithm is illustrated on real data from a field trial for different scenarios.

show abstract

Data fusion for ground moving target tracking

Cited by 14 publications

References 12 publications

Multitarget Tracking by Particle Filtering Based on RSS Measurement in Wireless Sensor Networks

Multitarget Tracking by Particle Filtering Based on RSS Measurement in Wireless Sensor Networks

Information-Theoretic Tracking Control Based on Particle Filter Estimate

Pedestrian tracking with an infrared sensor using road network information

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