Distributed Multi-Target Tracking Based on the K-MTSCF Algorithm in Camera Networks

Chen, Yanming; Zhao, Qingjie; An, Zhulin; Lv, Peng; Zhao, Liang

doi:10.1109/jsen.2016.2565263

Cited by 30 publications

(12 citation statements)

References 29 publications

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“…ICF was combined with joint probabilistic data association (JPDA) to address multitarget tracking in visual networks. 12 Based on ICF algorithm framework and experimental setup similar to the work of Kamal et al, 9 a square-root cubature ICF was proposed for single-target tracking 13 and multitarget tracking. 11 Combining ICF with K-best JPDA, a square-root cubature ICF for multitarget tracking was proposed.…”

Section: Related Workmentioning

confidence: 99%

“…11 Combining ICF with K-best JPDA, a square-root cubature ICF for multitarget tracking was proposed. 12 Based on ICF algorithm framework and experimental setup similar to the work of Kamal et al, 9 a square-root cubature ICF was proposed for single-target tracking 13 and multitarget tracking. 14 In the work of Katragadda et al, 15 several consensus-based filters were compared, and the conclusion was that the original ICF was the best one given target model similar to the work of Kamal et al 9 In order to realize the approximation between the centralized solution and the distributed solution, two respective scaling relationships for the estimation-error covariance and the process-noise covariance are given in the work of Olfati-Saber et al 16 The aforementioned relationships are two mandatory definitions due to the lack of a build process.…”

Section: Related Workmentioning

confidence: 99%

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Unscented summation information‐weighted consensus filter for distributed sensor networks with incomplete information

Yao

Liu

et al. 2019

Adaptive Control & Signal

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In order to improve network scalability and fault tolerance, the distributed sensor networks are desirable. However, the distributed state estimation becomes challenging when some sensors have insufficient information due to restricted observability, and/or have imparity information due to unequal measurement-noise covariances. Centralized summation information-fusion (CSI) model is presented which performs weighted least-squares estimation for all measurement information to achieve the optimal centralized state estimation. The CSI model revises the initialization and covariance propagation in the original information-weighted consensus filter (ICF). Since centralized information fusion is a summation mode and is approached by the average consensus protocol, all the covariances involved in the CSI model contain the information regarding the total number of nodes. The artificially preset initial values are considered as measurement information and fused in accordance with the CSI model. By combining the CSI model with unscented transform, distributed unscented summation information-weighted consensus filter (USICF) is proposed. USICF realizes the nonlinear estimation in the context of highly incomplete information. Theoretical analysis and experimental verification showed that USICF achieves better performance than UICF that is based on ICF.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Unscented summation information‐weighted consensus filter for distributed sensor networks with incomplete information

Yao

Liu

et al. 2019

Adaptive Control & Signal

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“…Theorem 1. Given any initial feasible joint event, the distribution of Gibbs samples (18) asymptotically converges to the target distribution (17) with an exponential rate as…”

Section: Gibbs-mht Algorithmmentioning

confidence: 99%

Track-Oriented Multiple Hypothesis Tracking Based on Tabu Search and Gibbs Sampling

Shin

Tsourdos

2018

IEEE Sensors J.

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In order to circumvent the curse of dimensionality in multiple hypothesis tracking (MHT) data association, this paper proposes two efficient implementation algorithms using Tabu search and Gibbs sampling. As the first step, we formulate the problem of generating the best global hypothesis in MHT as the problem of finding a maximum weighted independent set of a weighted undirected graph. Then, the metaheuristic Tabu search with two basic movements is designed to find the global optimal solution of the problem formulated. To improve the computational efficiency, this paper also develops a sampling based algorithm based on Gibbs sampling. The problem formulated for the Tabu search based algorithm is reformulated as a max product problem to enable implementation of Gibbs sampling. The detailed algorithm is then designed and the convergence is also theoretically analysed. The performance of the two algorithms proposed are verified through numerical simulations and compared with that of a mainstream MDA implementation algorithm. The simulation results confirm that the proposed algorithms significantly improve the computational efficiency while maintaining or even enhancing the tracking performance.

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“…Consequently, the results of the control system under the algorithm give good performances. Since the CKF algorithm is designed for the nonlinear system, it has been widely used in control applications [19][20][21]. More recent learning approaches have been published in control system applications, known as the actor-critic reinforcement algorithm [22][23][24].…”

Section: Introductionmentioning

confidence: 99%

A Neural Network PID-Like Controller Using a Hybrid of Online Actor-Critic Reinforcement Algorithm with the Square Root Cubature Kalman Filter

Sento¹,

Kitjaidure²

2018

IJIES

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This paper presents a new model of the Neural Network PID-Like controller using an Actor-Critic reinforcement algorithm, called the Neural Network PID-Like controller using an Actor-Critic reinforcement algorithm (NNPID-AC). The proposed NNPID-AC controller is designed to develop the performances and the speed of calculation under the iterative learning algorithm. In the learning algorithm, the critic algorithm receives the reward value and control input to criticize the current state using the action-state value function approximation. Furthermore, instead of applying every available action to predict the local successor state, the algorithm only uses one-step estimation using the fifth degree spherical-radial cubature rule algorithm. To evaluate the proposed NNPID-AC controller, the robot arm MATLAB simulations have been implemented and provide the control system with the load and noise to prove the robustness and fault tolerance, respectively. From the results, the robot arm control system simulation under the control of the proposed NNPID-AC controller can potentially track the error and gives the best responses compared with the other conventional controller either with or without the load and the noise disturbance.

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Distributed Multi-Target Tracking Based on the K-MTSCF Algorithm in Camera Networks

Cited by 30 publications

References 29 publications

Unscented summation information‐weighted consensus filter for distributed sensor networks with incomplete information

Unscented summation information‐weighted consensus filter for distributed sensor networks with incomplete information

Track-Oriented Multiple Hypothesis Tracking Based on Tabu Search and Gibbs Sampling

A Neural Network PID-Like Controller Using a Hybrid of Online Actor-Critic Reinforcement Algorithm with the Square Root Cubature Kalman Filter

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