scholarly journals Application of Adaptive UKF Algorithm in Multi-target Tracking and Positioning System

2022 ◽  
Vol 2146 (1) ◽  
pp. 012005
Author(s):  
Guofang Liu ◽  
Xiong Wang
Keyword(s):  
Target Tracking ◽  
Adaptive Filtering ◽  
Design Method ◽  
Tracking System ◽  
Distribution Model ◽  
Probability Density Distribution ◽  
Target Movement ◽  
Filtering Algorithm ◽  
Time Performance ◽  
Multi Target Tracking

Abstract Adaptive filtering algorithm (FIR) is a design method of adaptive variable target tracking system based on probability density distribution model. The algorithm realizes the target movement in the global range by estimating the parameters of different regions in the image, which improves the real-time performance and effectiveness.

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.

Adaptive robust control of moving-target tracking for marching tank based on constraint following

2021 ◽  
pp. 095440702110518
Author(s):  
Yuze Ma ◽  
Guolai Yang ◽  
Qinqin Sun ◽  
Xiuye Wang ◽  
Quanzhao Sun
Keyword(s):  
Target Tracking ◽  
Tracking System ◽  
Adaptive Robust Control ◽  
Moving Target ◽  
Three Dimensional Model ◽  
Target Movement ◽  
Movement Model ◽  
Moving Target Tracking ◽  
Target Time ◽  
Highly Nonlinear

This paper is devoted to the constraint-following scheme for the moving-target tracking control problem of tank on move. The mission of tank on the battlefield is to find and shoot the armored vehicle, both conditions are required to accomplish this task: complete the process from finding a moving target (time-varying constraints) to pointing to it; keep the barrel stable under highly nonlinear disturbance (which is caused by the battlefield environment). Considering modeling uncertainty and initial condition deviation, an adaptive robust strategy based on Udwadia-Kalaba scheme is presented to solve the matters of target tracking and stable following. Considering the limitation of the analytical model, a tracking system model and a target movement model are built in virtual prototyping environment, complicated road condition, and real target motion state are restored by this method. The model-based control system and the three-dimensional model are combined to verify the feasibility of the control algorithm by the method of RecurDyn/Matlab. By this way, the barrel responds and follows the movement of the target stably within [Formula: see text] s under the action of the stabilization system, and the constraints are approximately satisfied under complex perturbations.

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.

scholarly journals Multisensor Multi-Target Tracking Based on GM-PHD Using Out-Of-Sequence Measurements

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4315 ◽  
Author(s):  
Meiqin Liu ◽  
Tianyi Huai ◽  
Ronghao Zheng ◽  
Senlin Zhang
Keyword(s):  
Target Tracking ◽  
Tracking System ◽  
Gaussian Mixture ◽  
Tracking Performance ◽  
Probability Hypothesis Density ◽  
Single Target ◽  
Multi Target Tracking ◽  
Finite Set ◽  
Single Target Tracking ◽  
Better Than

In this paper, we study the issue of out-of-sequence measurement (OOSM) in a multi-target scenario to improve tracking performance. The OOSM is very common in tracking systems, and it would result in performance degradation if we used it inappropriately. Thus, OOSM should be fully utilized as far as possible. To improve the performance of the tracking system and use OOSM sufficiently, firstly, the problem of OOSM is formulated. Then the classical B1 algorithm for OOSM problem of single target tracking is given. Next, the random finite set (RFS)-based Gaussian mixture probability hypothesis density (GM-PHD) is introduced. Consequently, we derived the equation for re-updating of posterior intensity with OOSM. Implementation of GM-PHD using OOSM is also given. Finally, several simulations are given, and results show that tracking performance of GM-PHD using OOSM is better than GM-PHD using in-sequence measurement (ISM), which can strongly demonstrate the effectiveness of our proposed algorithm.

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