scholarly journals A Joint Probability Data Association Filter Algorithm for Multiple Robot Tracking Problems

10.5772/6086 ◽  
2008 ◽  
Author(s):  
Aliakbar Gorji ◽  
Vahid Nazari ◽  
Mohammad Bagher ◽  
Saeed Shiry
Keyword(s):  
Data Association ◽  
Joint Probability ◽  
Robot Tracking ◽  
Multiple Robot

Modified Joint Probability Data Association Algorithm Avoiding Track Coalescence

2012 ◽  
Vol 433-440 ◽  
pp. 2298-2303 ◽  
Author(s):  
Song Lin Chen ◽  
Yi Bing Xu
Keyword(s):  
Small Angle ◽  
Data Association ◽  
Joint Probability ◽  
Tracking Performance ◽  
Probabilistic Data ◽  
Multiple Targets ◽  
Probabilistic Data Association ◽  
Simulation Results ◽  
Exclusive Measurement ◽  
Association Algorithm

Joint Probabilistic Data Association has proven to be effective in tracking multiple targets from measurements amidst clutter and missed detections. But the traditional Joint Probabilistic Data Association algorithm will cause track coalescence when the targets are parallel neighboring or small-angle crossing. To avoid track coalescence, a modified Joint Probabilistic Data Association algorithm is proposed in this paper. An exclusive measurement is defined for every target in the new algorithm. The exclusive measurement of a target is one measurement which associates with the target and has the maximum associated probability. The associated events of the exclusive measurement with other targets will be pruned, which resists two or more targets share the same measurement as a key measurement and avoids track coalescence. The simulation results show that the new algorithm can effectively solve track coalescence problem in all kinds of scenarios and keep a high tracking performance.

scholarly journals Multiple Maneuvering Target Tracking by Improved Particle Filter Based on Multiscan JPDA

2012 ◽  
Vol 2012 ◽  
pp. 1-25 ◽  
Author(s):  
Jing Liu ◽  
ChongZhao Han ◽  
Feng Han ◽  
Yu Hu
Keyword(s):  
Particle Filter ◽  
Target Tracking ◽  
Posterior Probability ◽  
Data Association ◽  
Joint Probability ◽  
Nonstationary Process ◽  
Maneuvering Target Tracking ◽  
Process Noise ◽  
Maneuvering Target ◽  
Novel Method

The multiple maneuvering target tracking algorithm based on a particle filter is addressed. The equivalent-noise approach is adopted, which uses a simple dynamic model consisting of target state and equivalent noise which accounts for the combined effects of the process noise and maneuvers. The equivalent-noise approach converts the problem of maneuvering target tracking to that of state estimation in the presence of nonstationary process noise with unknown statistics. A novel method for identifying the nonstationary process noise is proposed in the particle filter framework. Furthermore, a particle filter based multiscan Joint Probability Data Association (JPDA) filter is proposed to deal with the data association problem in a multiple maneuvering target tracking. In the proposed multiscan JPDA algorithm, the distributions of interest are the marginal filtering distributions for each of the targets, and these distributions are approximated with particles. The multiscan JPDA algorithm examines the joint association events in a multiscan sliding window and calculates the marginal posterior probability based on the multiscan joint association events. The proposed algorithm is illustrated via an example involving the tracking of two highly maneuvering, at times closely spaced and crossed, targets, based on resolved measurements.

A Multi-Target Tracking Formulation of SVSF With the Joint Probabilistic Data Association Technique

2014 ◽  
Author(s):  
Mina Attari ◽  
S. Andrew Gadsden ◽  
Saeid R. Habibi
Keyword(s):  
Parameter Estimation ◽  
Target Tracking ◽  
Data Association ◽  
Joint Probability ◽  
Variable Structure ◽  
Tracking Problem ◽  
Proof Of Concept ◽  
Multi Target Tracking ◽  
Smooth Variable Structure Filter ◽  
Parameter Estimation Techniques

Target tracking scenarios offer an interesting challenge for state and parameter estimation techniques. This paper studies a situation with multiple targets in the presence of clutter. In this paper, the relatively new smooth variable structure filter (SVSF) is combined with the joint probability data association (JPDA) technique. This new method, referred to as the JPDA-SVSF, is applied on a simple multi-target tracking problem for a proof of concept. The results are compared with the popular Kalman filter (KF).

scholarly journals A Moving Ship Detection and Tracking Method Based on Optical Remote Sensing Images from the Geostationary Satellite

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7547
Author(s):  
Wei Yu ◽  
Hongjian You ◽  
Peng Lv ◽  
Yuxin Hu ◽  
Bing Han
Keyword(s):  
Remote Sensing ◽  
Data Association ◽  
Joint Probability ◽  
High Temporal Resolution ◽  
Optical Remote Sensing ◽  
Remote Sensing Images ◽  
Multi Scale ◽  
Detection And Tracking ◽  
Contrast Measure ◽  
Shape Characteristics

Geostationary optical remote sensing satellites, such as the GF-4, have a high temporal resolution and wide coverage, which enables the continuous tracking and observation of ship targets over a large range. However, the ship targets in the images are usually small and dim and the images are easily affected by clouds, islands and other factors, which make it difficult to detect the ship targets. This paper proposes a new method for detecting ships moving on the sea surface using GF-4 satellite images. First, the adaptive nonlinear gray stretch (ANGS) method was used to enhance the image and highlight small and dim ship targets. Second, a multi-scale dual-neighbor difference contrast measure (MDDCM) method was designed to enable detection of the position of the candidate ship target. The shape characteristics of each candidate area were analyzed to remove false ship targets. Finally, the joint probability data association (JPDA) method was used for multi-frame data association and tracking. Our results suggest that the proposed method can effectively detect and track moving ship targets in GF-4 satellite optical remote sensing images, with better detection performance than other classical methods.

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