Sensor cooperative scheduling method for low-altitude maneuvering target tracking in complex environment

Sensor Review ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yunpu Zhang ◽  
Gongguo Xu ◽  
Ganlin Shan
Keyword(s):  
Target Tracking ◽  
Sensor Scheduling ◽  
Multiple Model ◽  
Complex Environment ◽  
Tracking Accuracy ◽  
Content Type ◽  
Target State ◽  
Maneuvering Targets ◽  
Scheduling Method ◽  
Low Altitude

Purpose Continuous and stable tracking of the low-altitude maneuvering targets is usually difficult due to terrain occlusion and Doppler blind zone (DBZ). This paper aims to present a non-myopic scheduling method of multiple radar sensors for tracking the low-altitude maneuvering targets. In this scheduling problem, the best sensors are systematically selected to observe targets for getting the best tracking accuracy under maintaining the low intercepted probability of a multi-sensor system. Design/methodology/approach First, the sensor scheduling process is formulated within the partially observable Markov decision process framework. Second, the interacting multiple model algorithm and the cubature Kalman filter algorithm are combined to estimate the target state, and the DBZ information is applied to estimate the target state when the measurement information is missing. Then, an approximate method based on a cubature sampling strategy is put forward to calculate the future expected objective of the multi-step scheduling process. Furthermore, an improved quantum particle swarm optimization (QPSO) algorithm is presented to solve the sensor scheduling action quickly. Optimization problem, an improved QPSO algorithm is presented to solve the sensor scheduling action quickly. Findings Compared with the traditional scheduling methods, the proposed method can maintain higher target tracking accuracy with a low intercepted probability. And the proposed target state estimation method in DBZ has better tracking performance. Originality/value In this paper, DBZ, sensor intercepted probability and complex terrain environment are considered in sensor scheduling, which has good practical application in a complex environment.

Risk decision based sensor scheduling in target tracking

2020 ◽  
Vol 37 (9) ◽  
pp. 3147-3169
Author(s):  
Ce Pang ◽  
Ganlin Shan
Keyword(s):  
Target Tracking ◽  
Chaos Theory ◽  
Sensor Scheduling ◽  
Sensor Management ◽  
Content Type ◽  
Tracking Method ◽  
Motion State ◽  
Lion Algorithm ◽  
Scheduling Method ◽  
Risk Decision

Purpose This paper aims to introduce a new target tracking method based on risk theory in a 2-D discrete environment. After that, the related sensor scheduling method is proposed. This can make up the blank of target tracking and sensor management in the 2-D discrete environment. Design/methodology/approach The definition of risk is proposed based on risk decision theory firstly. Then the target tracking model in a two-dimensional discrete environment is built. The motion state updating and estimation method of target’s motion state based on Bayes theory is given. Thirdly, the method of computing sensor emission interception risk is provided. Afterwards, the optimization rule of obtaining the minimum risk is followed to model the sensor scheduling objective function. The lion algorithm is adjusted and improved combined with Chaos theory to generate the optimal sensor management projects. Findings The risk-based sensor target tracking method and sensor management method are both effective in a 2-D discrete environment. Originality/value To the best of the authors’ knowledge, this paper is the first to study the target tracking method and sensor scheduling method in a 2-D environment. Furthermore, the lion algorithm is improved combined with Chaos theory to show a better optimization performance.

An Improved Joint Target Tracking and Classification Algorithm Based on Data Fusion

2014 ◽  
Vol 904 ◽  
pp. 325-329
Author(s):  
Hong Wei Quan ◽  
Lin Chen ◽  
Dong Liang Peng
Keyword(s):  
Data Fusion ◽  
Target Tracking ◽  
Classification Algorithm ◽  
Target Classification ◽  
Tracking Accuracy ◽  
Traditional Methods ◽  
System Models ◽  
Target State ◽  
Classification Probability

This paper addresses the problem of the joint target tracking and classification based on data fusion. In traditional methods, a separate suite of sensors and system models are used, target tracking and target classification are usually treated as separate problems. In our JTC framework, the link between target state and class is considered and the feasibility of JTC techniques is discussed. The tracking accuracy and classification probability are improved to some extent with the more accurate classification results from classifier based on data fusion feedback to state filter.

scholarly journals Improved Interacting Multiple Model Particle Filter Algorithm

2018 ◽  
Vol 36 (1) ◽  
pp. 169-175 ◽  
Author(s):  
Qiaoran Liu ◽  
Xun Yang
Keyword(s):  
Kalman Filter ◽  
Particle Filter ◽  
Target Tracking ◽  
Unscented Kalman Filter ◽  
Multiple Model ◽  
Tracking Accuracy ◽  
Proposal Distribution ◽  
Model Particle ◽  
Interactive Multiple Model ◽  
Particle Filter Algorithm

For the issue of limited filtering accuracy of interactive multiple model particle filter algorithm caused by the resampling particles don't contain the latest observation information, we made improvements on interactive multiple model particle filter algorithm in this paper based on mixed kalman particle filter algorithm. Interactive multiple model particle filter algorithm is proposed. In addition, the composed methods influence to tracking accuracy are discussed. In the new algorithm the system state estimation is generated with unscented kalman filter (UKF) first and then use the extended kalman filter (EKF) to get the proposal distribution of the particles, taking advantage of the measure information to update the particles' state. We compare and analyze the target tracking performance of the proposed algorithm of IMM-MKPF in this paper, IMM-UPF and IMM-EPF through the simulation experiment. The results show that the tracking accuracy of the proposed algorithm is superior to other two algorithms. Thus, the new method in this paper is effective. The method is of important to improve tracking accuracy further for maneuvering target tracking under the non-linear and non-Gaussian circumstances.

scholarly journals IMM Fifth-Degree Spherical Simplex-Radial Cubature Filter for Maneuvering Target Tracking

2017 ◽  
Author(s):  
Hua Liu ◽  
Wen Wu
Keyword(s):  
Target Tracking ◽  
Tracking Performance ◽  
Interacting Multiple Model ◽  
Multiple Model ◽  
Scenario Simulation ◽  
Tracking Accuracy ◽  
Maneuvering Target Tracking ◽  
Switching Speed ◽  
Maneuvering Target ◽  
Spherical Simplex

For improving the tracking accuracy and model switching speed of maneuvering target tracking in nonlinear systems, a new algorithm named interacting multiple model fifth-degree spherical simplex-radial cubature filter (IMM5thSSRCKF) is proposed in this paper. The new algorithm is a combination of the interacting multiple model (IMM) filter and fifth-degree spherical simplex-radial cubature filter (5thSSRCKF). The proposed algorithm makes use of Markov process to describe the switching probability among the models, and uses 5thSSRCKF to deal with the state estimation of each model. The 5thSSRCKF is an improved filter algorithm, which utilizes the fifth-degree spherical simplex-radial rule to improve the filtering accuracy. Finally, the tracking performance of the IMM5thSSRCKF is evaluated by simulation in a typical maneuvering target tracking scenario. Simulation results show that the proposed algorithm has better tracking performance and quicker model switching speed when disposing maneuver models compared with IMMUKF, IMMCKF and IMM5thCKF.

Non-myopic sensor scheduling for low radiation risk tracking using mixed POMDP

2016 ◽  
Vol 39 (2) ◽  
pp. 230-243 ◽  
Author(s):  
Gan-lin Shan ◽  
Zi-ning Zhang
Keyword(s):  
Target Tracking ◽  
Radiation Risk ◽  
Problem Solution ◽  
Sensor Scheduling ◽  
Tracking Accuracy ◽  
Active Sensors ◽  
Unscented Transformation ◽  
Discrete State ◽  
Continuous State

This paper addresses a non-myopic sensor-scheduling problem of how to select and assign active sensors for trading off the tracking accuracy and the radiation risk, where the radiation risk is incurred by the fact that the emission energy originating from active sensors for target tracking can be intercepted by the enemy target. This problem is formulated as a mixed partially observable Markov decision process (POMDP) composed of a continuous-state POMDP for target tracking and a discrete-state POMDP for emission control. Based on the idea of foresight optimization, the long-term accuracy reward is evaluated by the combination of unscented transformation sampling and Kalman filtering, whereas the long-term radiation cost is derived from hidden Markov model filter. Because the problem can be converted into a decision tree, a branch and bound algorithm is developed for problem solution. A simulation example illustrates the effectiveness of our approach.

scholarly journals Multi-Sensor Optimization Scheduling for Target Tracking Based on PCRLB and a Novel Intercept Probability Factor

Electronics ◽  
2019 ◽  
Vol 8 (2) ◽  
pp. 140 ◽  
Author(s):  
Gongguo Xu ◽  
Ce Pang ◽  
Xiusheng Duan ◽  
Ganlin Shan
Keyword(s):  
Target Tracking ◽  
Pso Algorithm ◽  
Sensor Scheduling ◽  
Tracking Accuracy ◽  
Active Sensors ◽  
Intercept Probability ◽  
Multi Target Tracking ◽  
Fast Solution ◽  
Tracking Model ◽  
Set Up

In order to improve the survivability of active sensors, the problem of low probability of intercept (LPI) for a multi-sensor network system is studied in this paper. Two kinds of operational requirements are taken into account, the first of which is to ensure the survivability of sensors and the second is to improve the tracking accuracy of targets as much as possible. Firstly, the sensor tracking model and the posterior Carmér-Rao lower bound (PCRLB) of the target are presented to evaluate the sensor tracking benefits in next time. Then, a novel intercept probability factor (IPF) is proposed for multi-sensor multi-target tracking scenarios. At the basis of PCRLB and IPF, a myopic multi-sensor scheduling model for target tracking is set up to control the intercepted probability of sensors and improve the target tracking accuracy. At last, a fast solution algorithm based on an improved particle swarm optimization (PSO) algorithm is given to obtain the optimal scheduling actions. Simulation of experimental results show that the proposed model can effectively control the intercepted risk of every sensor, which can also obtain better target tracking performance than existing multi-sensor scheduling methods.

Maneuvering Target Tracking Algorithm Based on Particle PHD Filtering

2014 ◽  
Vol 989-994 ◽  
pp. 2212-2215
Author(s):  
Song Gao ◽  
Chao Bo Chen ◽  
Qian Gong
Keyword(s):  
Particle Filter ◽  
Target Tracking ◽  
Tracking Accuracy ◽  
Maneuvering Target Tracking ◽  
Probability Hypothesis Density ◽  
Filtering Algorithm ◽  
Maneuvering Target ◽  
Maneuvering Targets ◽  
The Status ◽  
Simulation Results

As for the problem of maneuvering target tracking in the clutter environment, this paper combines IMM with PHD and realizes it through approach of particle filter. This algorithm avoids the troublesome problem of data association, and takes advantage of probability hypothesis density (PHD) filter in tracking maneuvering targets and interacting multi-model (IMM) algorithm in the field of model switching effectively, in the clutter environment, the status of the targets can be estimated precisely and steadily. This paper compares the proposed filtering algorithm with the classical IMM algorithm in performance, and the simulation results show that, the improved filtering algorithm has good tracking performance and tracking accuracy.

An Adaptive Interacting Multiple Model for Vehicle Target Tracking Method

2013 ◽  
Vol 718-720 ◽  
pp. 1286-1289 ◽  
Author(s):  
Jin Song Du ◽  
Xin Bi
Keyword(s):  
Target Tracking ◽  
Traffic Safety ◽  
Information Structure ◽  
Likelihood Function ◽  
Interacting Multiple Model ◽  
Multiple Model ◽  
Tracking Accuracy ◽  
Target Movement ◽  
Movement Model ◽  
Accelerated Motion

In the field of traffic safety vehicle target tracking prediction as the background, this paper proposes an adaptive interacting multiple model tracking algorithm. According to the field of transportation vehicle movement state characteristics, based on the uniform (CV) and uniformly accelerated motion (CA) model, based on new information structure model of motion of the likelihood function, online adaptive adjustment model of the noise variance and the Markov matrix, realization of maneuvering target movement model and model set adaptation, not only improved IMM algorithm for tracking accuracy, and enhances the real-time performance of system, the simulation results show that, the algorithm for tracking precision compared to the traditional IMM method has bigger improvement.

scholarly journals Adaptive Dynamic Programming-Based Multi-Sensor Scheduling for Collaborative Target Tracking in Energy Harvesting Wireless Sensor Networks

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4090 ◽  
Author(s):  
Fen Liu ◽  
Wendong Xiao ◽  
Shuai Chen ◽  
Chengpeng Jiang
Keyword(s):  
Wireless Sensor Networks ◽  
Dynamic Programming ◽  
Energy Harvesting ◽  
Target Tracking ◽  
Infinite Horizon ◽  
Adaptive Dynamic Programming ◽  
Wireless Sensor ◽  
Sensor Scheduling ◽  
Tracking Accuracy ◽  
Scheduling Scheme

Collaborative target tracking is one of the most important applications of wireless sensor networks (WSNs), in which the network must rely on sensor scheduling to balance the tracking accuracy and energy consumption, due to the limited network resources for sensing, communication, and computation. With the recent development of energy acquisition technologies, the building of WSNs based on energy harvesting has become possible to overcome the limitation of battery energy in WSNs, where theoretically the lifetime of the network could be extended to infinite. However, energy-harvesting WSNs pose new technical challenges for collaborative target tracking on how to schedule sensors over the infinite horizon under the restriction on limited sensor energy harvesting capabilities. In this paper, we propose a novel adaptive dynamic programming (ADP)-based multi-sensor scheduling algorithm (ADP-MSS) for collaborative target tracking for energy-harvesting WSNs. ADP-MSS can schedule multiple sensors for each time step over an infinite horizon to achieve high tracking accuracy, based on the extended Kalman filter (EKF) for target state prediction and estimation. Theoretical analysis shows the optimality of ADP-MSS, and simulation results demonstrate its superior tracking accuracy compared with an ADP-based single-sensor scheduling scheme and a simulated-annealing based multi-sensor scheduling scheme.

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