A new energy efficient management approach for wireless sensor networks in target tracking

A non-myopic scheduling method of radar sensors for maneuvering target tracking and radiation control

A sensor scheduling approach focusing on managing the operational risk in target tracking is proposed. The scheme selects sensor actions so that the operational risks are the minimum. First, given the definition, existence of risk in sensor scheduling is analyzed. Second, the target missing risk and the sensor radiation interception risk are summed to create the sensor scheduling objective functions, including myopic sensor scheduling and non-myopic sensor scheduling. Furthermore, to obtain sensor management schemes according to the objective functions, the improved bee colony algorithm based on double-roulette and in combination with the particle swarm optimization algorithm is proposed. Finally, simulations are carried out to illustrate the models and algorithms in the paper possess some advantages over existing ones, including being more practical, keeping nearly the same or even better tracking performance with lower risk values, and owing better sensor scheduling solutions.

Sensor Scheduling Based on Risk for Target Tracking

With the development of sensor and network technologies, a type of hybrid sensor network (HSN) has been developed in traffic information acquisition systems. Sensor types in HSNs are diversified, mostly by batteries or solar energy as their energy source. Energy determines their lifetime. If energy consumption of sensors can be reduced from the aspect of communication, their energy consumption can be saved and lifetime can be increased, so as to improve the benefit and efficiency of the whole network. To optimize the efficiency and consumption of HSNs, a graph theory-based model was proposed by considering nodes’ connectivity power and the energy consumption of data transmission. Targeting different data volumes, two undigraph-based algorithms (PMST and DSPA) were introduced to solve the proposed optimization model. For a low data volume, the minimum spanning tree network based on PMST will be the optimal topology of an HSN. For a high data volume, the shortest path network based on DSPA will be the optimal topology. A numerical example of comprehensive traffic information acquisition at a signalized intersection was used to verify the proposed algorithms. The solution process and final optimal topologies based on the PMST and the DSPA were shown. Moreover, the energy parameters of different optimal topologies of HSNs were discussed, and it was proved that the optimal topology of an HSN is dependent on the data volume of each sensor node.

Design and Optimization of a Hybrid Sensor Network for Traffic Information Acquisition

This paper proposed a distributed sensor management method in the constraint of target losing probability for maneuvering multi-target tracking. Three main contributions existed in this paper, including adding the emission interception risk to the sensor management objective function, analyzing actual working modes of radars and regarding the target losing probability as one of constraints in sensor management. Two kind of radar working modes were introduced. They were the single target tracking mode and the multiple target tracking mode, leading to different kinds of sensor management methods. Then two sensor management models, containing sensor management in a large defending scope and in a small defending scope, were built. After that, the distributed sensor management process was introduced. Finally, simulations were conducted to show the effectiveness and efficiency of the proposed sensor management methods. By applying the proposed sensor management method, targets couldn't be easily lost and radar could be well protected at the same time.

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Distributed Sensor Management Based on Target Losing Probability for Maneuvering Multi-Target Tracking

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 Carmer-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.

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

In this paper, sensor management is divided into two processes: sensor deployment and sensor scheduling, after which a multi-mode sensor management approach based on risk theory is proposed. Firstly, the definition of risk is provided, on the basis of which the target detecting risk and the target tracking risk are separately presented, along with their computing methods. Secondly, when deploying sensors, the objective is to obtain the minimum target detecting risk. Similarly, when scheduling sensors, the objective is to obtain the minimal sum of target detecting risk and target tracking risk. Furthermore, to obtain sensor management schemes according to the objective functions, the improved bee colony algorithm based on double-probability and in combination with the particle swarm optimization algorithm is proposed. Finally, simulations are conducted, which indicate that the models and the algorithm in the paper possess some advantages over existing ones.

https://www.mdpi.com/2079-9292/8/1/71/pdf

A Multi-Mode Sensor Management Approach in the Missions of Target Detecting and Tracking

To solve the problem of sensor optimal deployment in the constrained conditions, this paper propose a multi-sensor optimal deployment method based on Genetic Particle Swarm Optimization (GA-PSO). The method firstly meshes the battlefield geography environment and establishes the constraint matrix of deployment. Then, the objective optimization function based on detection coverage is established. Finally, the Genetic Particle Swarm Optimization is used to solve the optimal position of the sensor. Simulation verifies the effectiveness.

Method of multi-sensor optimal deployment for area coverage

To control the sensor system radiation risk and satisfy the maneuvering target tracking requirement, a sensor selection algorithm with improved accuracy constraint for maneuvering target tracking is proposed. Firstly, the posterior Cramr-Rao lower bound (PCRLB) is utilized to assess the tracking performance. Then multiple model PCRLB is presented to calculate the PCRLB of maneuvering target. Secondly, tracking accuracy is treated in a probabilistic manner in the improved accuracy constraint. Then the improved accuracy constraint can be converted to a deterministic constraint by the given desired tracking accuracy and probability. Finally, as the active sensor obtain the target measurement by emitting energy that can betray their existence and location, radiation control policy is presented to minimize the times of active sensor selection. Simulation results prove the effectiveness of the proposed algorithm.

A Sensor Selection Algorithm with Improved Accuracy Constraint for Maneuvering Target Tracking

Multiple optimization objectives are often taken into account during the process of sensor deployment. Aiming at the problem of multi-sensor deployment in complex environment, a novel multi-sensor ...

Xiusheng Duan

Papers

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

A Multi-Mode Sensor Management Approach in the Missions of Target Detecting and Tracking

Method of multi-sensor optimal deployment for area coverage

A Sensor Selection Algorithm with Improved Accuracy Constraint for Maneuvering Target Tracking

Multi-sensor multi-objective optimization deployment on complex terrain based on Pareto optimal theory