Maneuvering multi-target tracking based on variable structure multiple model GMCPHD filter

2017 ◽  
Vol 141 ◽  
pp. 158-167 ◽  
Author(s):  
Peng Dong ◽  
Zhongliang Jing ◽  
Deren Gong ◽  
Baitao Tang
Electronics ◽  
2021 ◽  
Vol 10 (16) ◽  
pp. 1969
Author(s):  
Saima Ishtiaq ◽  
Xiangrong Wang ◽  
Shahid Hassan

Multi-target tracking (MTT) generally requires either a network of Doppler radar receivers distributed at different locations or a phased array radar. The targets moving with small/no radial velocity or angular velocity only cannot be detected and localized completely by deploying Doppler radar without antenna arrays or multiple receivers. To resolve this issue, we present a new MTT algorithm based on 2-D velocity measurements, namely, radial and angular velocities, using dual-frequency interferometric radar. The contributions of the proposed research are twofold: First, we introduce the mathematical model and implementation of the proposed algorithm by explicitly establishing the relationship between 2-D velocity measurements and kinematic state of the target in terms of Cartesian coordinates. Based on 2-D velocity measurement function, the proposed MTT algorithm comprises the following steps: (i) data association using global nearest neighbor (GNN) method (ii) target state estimation using interacting multiple model (IMM) estimator combined with square-root cubature Kalman filter (SCKF) (iii) track management using rule-based M/N logic. Second, performance of the proposed algorithm is evaluated in terms of tracking accuracy, computational complexity and IMM mean model probabilities. Simulation results for different scenarios with multiple targets moving in different tracks have been presented to verify the effectiveness of the proposed algorithm.


Author(s):  
Mina Attari ◽  
S. Andrew Gadsden ◽  
Saeid R. Habibi

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).


2019 ◽  
Vol 2019 (13) ◽  
pp. 127-1-127-7
Author(s):  
Benjamin J. Foster ◽  
Dong Hye Ye ◽  
Charles A. Bouman

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