Study on the Characteristics of the Automatic Tracking of Multiple Target Tracking

2014 ◽  
Vol 496-500 ◽  
pp. 1564-1567
Author(s):  
Jing Feng He ◽  
Ming Ji ◽  
Song Cheng ◽  
Ya Nan Wang
Keyword(s):  
Target Tracking ◽  
Multiple Target Tracking ◽  
Multiple Target ◽  
Multiple Targets ◽  
Automatic Tracking ◽  
Single Target ◽  
Development Direction ◽  
The Future ◽  
Single Target Tracking

Based on introducing the traditional scan and single target tracking state, focuses on the automatic tracking characteristics of each stage under the condition of multiple targets. The two form of automatic tracking multiple targets, and the development direction of the future.

scholarly journals Target Tracking Using Kalman Filter Based Algorithms

2021 ◽  
Vol 2078 (1) ◽  
pp. 012020
Author(s):  
Jiankun Ling
Keyword(s):  
Kalman Filter ◽  
Target Tracking ◽  
Information Gathering ◽  
Multiple Target Tracking ◽  
Multiple Target ◽  
Multiple Targets ◽  
Multiple Hypothesis ◽  
Multiple Hypothesis Tracking ◽  
Single Target ◽  
Single Target Tracking

Abstract Kalman filter and its families have played an important role in information gathering, such as target tracking. Data association techniques have also been developed to allow the Kalman filter to track multiple targets simultaneously. This paper revisits the principle and applications of the Kalman filter for single target tracking and multiple hypothesis tracking (MHT) for multiple target tracking. We present the brief review of the Bayes filter family and introduce a brief derivation of the Kalman filter and MHT. We show examples for both single and multiple targets tracking in simulation to illustrate the efficacy of Kalman filter-based algorithms in target tracking scenarios.

Rao-Blackwellized particle filter for multiple target tracking

2007 ◽  
Vol 8 (1) ◽  
pp. 2-15 ◽  
Author(s):  
Simo Särkkä ◽  
Aki Vehtari ◽  
Jouko Lampinen
Keyword(s):  
Particle Filter ◽  
Target Tracking ◽  
Multiple Target Tracking ◽  
Multiple Target
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