A modified multi-target tracking algorithm based on joint probability data association and Gaussian particle filter

2014 ◽  
Author(s):  
Wang Yuhuan ◽  
Wang Jinkuan ◽  
Wang Bin
Keyword(s):  
Particle Filter ◽  
Target Tracking ◽  
Data Association ◽  
Joint Probability ◽  
Tracking Algorithm ◽  
Multi Target Tracking

Linear Multi-Target Integrated Probabilistic Data Association Filter With Automatic Track Management for Autonomous Vehicles

2018 ◽  
Author(s):  
Andinet Hunde ◽  
Beshah Ayalew
Keyword(s):  
Target Tracking ◽  
Tracking System ◽  
Data Association ◽  
Tracking Algorithm ◽  
Probabilistic Data ◽  
Probabilistic Data Association ◽  
Address Data ◽  
Multi Target Tracking ◽  
Track Management ◽  
Track Initiation

Target tracking in public traffic calls for a tracking system with automated track initiation and termination facilities in a randomly evolving driving environment. In addition, the key problem of data association needs to be handled effectively considering the limitations in the computational resources onboard an autonomous car. In this paper, we discuss a multi-target tracking system that addresses target birth/initiation and death/termination processes with automatic track management feature. The tracking system is based on Linear Multi-target Integrated Probabilistic Data Association Filter (LMIPDAF), which is adapted to specifically include algorithms that handle track initiation and termination, clutter density estimation and track management. The performance of the proposed tracking algorithm is compared to other single and multi-target tracking schemes and is shown to have acceptable tracking error. It is further illustrated through multiple traffic simulations that the computational requirement of the tracking algorithm is less than that of optimal multi-target tracking algorithms that explicitly address data association uncertainties.

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.

Adaptive Tracking Algorithm of Multi-Target Based on Fuzzy Clustering

2014 ◽  
Vol 513-517 ◽  
pp. 448-452
Author(s):  
Xiu Hua Hu ◽  
Lei Guo ◽  
Hui Hui Li
Keyword(s):  
State Estimation ◽  
Target Tracking ◽  
Fuzzy Clustering ◽  
Tracking System ◽  
Joint Probability ◽  
Tracking Algorithm ◽  
Adaptive Tracking ◽  
Correlation Criterion ◽  
Multi Target Tracking ◽  
Fuzzy Clustering Theory

For multi-target tracking system, aiming at solving the problem of low precision of state estimation caused by the data correlation ambiguity, the paper presents a novel multi-sensor multi-target adaptive tracking algorithm based on fuzzy clustering theory. Based on the joint probability data association algorithm, the new approach takes account of the case that whether the measure is validated and its possibility of belong to false alarm, and improves the correlation criterion of effective measurement with existing track on the basis of fuzzy clustering theory, which all perfect the update equation of target state estimation and the covariance. Meanwhile, with the adaptive distributed fusion processing structure, it enhance the robustness of the system and without prejudice to the real-time tracking. With the simulation case studies of radar/infrared sensor fusion multi-target tracking system, it verifies the effectiveness of the proposed approach.

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).

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