scholarly journals Distributed Multiagent Control Approach for Multitarget Tracking

2015 ◽  
Vol 2015 ◽  
pp. 1-10
Author(s):  
Liang Ma ◽  
Kai Xue ◽  
Ping Wang
Keyword(s):  
Information Fusion ◽  
Multitarget Tracking ◽  
Control Approach ◽  
Numerical Studies ◽  
Utility Optimization ◽  
Partially Observed ◽  
Markov Decision ◽  
Multiagent Control ◽  
Bernoulli Filter ◽  
Effectiveness And Efficiency

In multiagent systems, tracking multiple targets is challenging for two reasons: firstly, it is nontrivial to dynamically deploy networked agents of different types for utility optimization; secondly, information fusion for multitarget tracking is difficult in the presence of uncertainties, such as data association, noise, and clutter. In this paper, we present a novel control approach in distributed manner for multitarget tracking. The control problem is modelled as a partially observed Markov decision process, which is a NP-hard combinatorial optimization problem, by seeking all possible combinations of control commands. To solve this problem efficiently, we assume that the measurement of each agent is independent of other agents’ behavior and provide a suboptimal multiagent control solution by maximizing the local Rényi divergence. In addition, we also provide the SMC implementation of the sequential multi-Bernoulli filter so that each agent can utilize the measurements from neighbouring agents to perform information fusion for accurate multitarget tracking. Numerical studies validate the effectiveness and efficiency of our multiagent control approach for multitarget tracking.

scholarly journals Multitarget Tracking with Spatial Nonmaximum Suppressed Sensor Selection

2015 ◽  
Vol 2015 ◽  
pp. 1-10
Author(s):  
Liang Ma ◽  
Kai Xue ◽  
Ping Wang
Keyword(s):  
Information Gain ◽  
Sensor Selection ◽  
Multitarget Tracking ◽  
Sensor Management ◽  
Information Quantity ◽  
Partially Observed ◽  
Markov Decision ◽  
Finite Set ◽  
Bernoulli Filter ◽  
Fusion Scheme

Multitarget tracking is one of the most important applications of sensor networks, yet it is an extremely challenging problem since multisensor multitarget tracking itself is nontrivial and the difficulty is further compounded by sensor management. Recently, random finite set based Bayesian framework has opened doors for multitarget tracking with sensor management, which is modelled in the framework of partially observed Markov decision process (POMDP). However, sensor management posed as a POMDP is in essence a combinatorial optimization problem which is NP-hard and computationally unacceptable. In this paper, we propose a novel sensor selection method for multitarget tracking. We first present the sequential multi-Bernoulli filter as a centralized multisensor fusion scheme for multitarget tracking. In order to perform sensor selection, we define the hypothesis information gain (HIG) of a sensor to measure its information quantity when the sensor is selected alone. Then, we propose spatial nonmaximum suppression approach to select sensors with respect to their locations and HIGs. Two distinguished implementations have been provided using the greedy spatial nonmaximum suppression. Simulation results verify the effectiveness of proposed sensor selection approach for multitarget tracking.

scholarly journals Learning Feedforward Control Using Multiagent Control Approach for Motion Control Systems

Energies ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 420
Author(s):  
Phong B. Dao
Keyword(s):  
Real Time ◽  
Feedforward Control ◽  
Design Method ◽  
Mechatronic Systems ◽  
Control Approach ◽  
Runtime Environment ◽  
Multiagent Control ◽  
On Line ◽  
Promising Solution ◽  
Function Approximator

Multiagent control system (MACS) has become a promising solution for solving complex control problems. Using the advantages of MACS-based design approaches, a novel solution for advanced control of mechatronic systems has been developed in this paper. The study has aimed at integrating learning control into MACS. Specifically, learning feedforward control (LFFC) is implemented as a pattern for incorporation in MACS. The major novelty of this work is that the feedback control part is realized in a real-time periodic MACS, while the LFFC algorithm is done on-line, asynchronously, and in a separate non-real-time aperiodic MACS. As a result, a MACS-based LFFC design method has been developed. A second-order B-spline neural network (BSN) is used as a function approximator for LFFC whose input-output mapping can be adapted during control and is intended to become equal to the inverse model of the plant. To provide real-time features for the MACS-based LFFC system, the open robot control software (OROCOS) has been employed as development and runtime environment. A case study using a simulated linear motor in the presence of nonlinear cogging and friction force as well as mass variations is used to illustrate the proposed method. A MACS-based LFFC system has been designed and implemented for the simulated plant. The system consists of a setpoint generator, a feedback controller, and a time-index LFFC that can learn on-line. Simulation results have demonstrated the applicability of the design method.

scholarly journals UAV Formation Shape Control via Decentralized Markov Decision Processes

Algorithms ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 91
Author(s):  
Md Ali Azam ◽  
Hans D. Mittelmann ◽  
Shankarachary Ragi
Keyword(s):  
Control Problem ◽  
Shape Control ◽  
Formation Control ◽  
Three Dimensional ◽  
Geographical Region ◽  
Dynamic Programming Method ◽  
Theoretic Approach ◽  
Control Approach ◽  
Uav Swarm ◽  
Markov Decision

In this paper, we present a decentralized unmanned aerial vehicle (UAV) swarm formation control approach based on a decision theoretic approach. Specifically, we pose the UAV swarm motion control problem as a decentralized Markov decision process (Dec-MDP). Here, the goal is to drive the UAV swarm from an initial geographical region to another geographical region where the swarm must form a three-dimensional shape (e.g., surface of a sphere). As most decision-theoretic formulations suffer from the curse of dimensionality, we adapt an existing fast approximate dynamic programming method called nominal belief-state optimization (NBO) to approximately solve the formation control problem. We perform numerical studies in MATLAB to validate the performance of the above control algorithms.

scholarly journals On the complexity of partially observed Markov decision processes

1996 ◽  
Vol 157 (2) ◽  
pp. 161-183 ◽  
Author(s):  
Dima Burago ◽  
Michel de Rougemont ◽  
Anatol Slissenko
Keyword(s):  
Markov Decision Processes ◽  
Decision Processes ◽  
Partially Observed ◽  
Markov Decision

scholarly journals Labeled Multi-Bernoulli Filter Joint Detection and Tracking of Radar Targets

2019 ◽  
Vol 9 (19) ◽  
pp. 4187 ◽  
Author(s):  
Rang Liu ◽  
Hongqi Fan ◽  
Huaitie Xiao
Keyword(s):  
Likelihood Function ◽  
Signal To Noise Ratio ◽  
Mutual Interference ◽  
Joint Detection ◽  
Detection And Tracking ◽  
Point Spread ◽  
Spread Function ◽  
Bernoulli Filter ◽  
Effectiveness And Efficiency ◽  
Radar Targets

A labeled multi-Bernoulli (LMB) filter is presented to jointly detect and track radar targets. A relevant LMB filter is recently proposed by Rathnayake which assumes that the measurements of different targets do not overlap, leading to the favorable separable likelihood assumption. However, new or close tracks often violate the assumption and lead to a bias in the cardinality estimate. To address this problem, a one-to-one association method between measurements and tracks is proposed. In our method, any target only corresponds to its associated measurements and different tracks have little mutual interference. In addition, an approximate method for calculating the point spread function of radar is developed to improve the computational efficiency of likelihood function. The simulation under low signal-to-noise ratio scenario with closely spaced targets have demonstrated the effectiveness and efficiency of the proposed algorithm.

scholarly journals “Statistics 103” for Multitarget Tracking

Sensors ◽  
2019 ◽  
Vol 19 (1) ◽  
pp. 202 ◽  
Author(s):  
Ronald Mahler
Keyword(s):  
Point Process ◽  
Information Fusion ◽  
Theoretical Foundation ◽  
Measure Theory ◽  
Multitarget Tracking ◽  
Mathematical Abstraction ◽  
Finite Set ◽  
Theoretic Point

The finite-set statistics (FISST) foundational approach to multitarget tracking and information fusion was introduced in the mid-1990s and extended in 2001. FISST was devised to be as “engineering-friendly” as possible by avoiding avoidable mathematical abstraction and complexity—and, especially, by avoiding measure theory and measure-theoretic point process (p.p.) theory. Recently, however, an allegedly more general theoretical foundation for multitarget tracking has been proposed. In it, the constituent components of FISST have been systematically replaced by mathematically more complicated concepts—and, especially, by the very measure theory and measure-theoretic p.p.’s that FISST eschews. It is shown that this proposed alternative is actually a mathematical paraphrase of part of FISST that does not correctly address the technical idiosyncrasies of the multitarget tracking application.

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