Gongguo Xu

Bio: Gongguo Xu is an academic researcher. The author has contributed to research in topics: Wireless sensor network & Particle swarm optimization. The author has an hindex of 3, co-authored 6 publications receiving 22 citations.

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

Abstract: 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.

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

Abstract: 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.

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

Abstract: 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 ...

An Improved Approach towards Multi-Agent Pursuit–Evasion Game Decision-Making Using Deep Reinforcement Learning

Abstract: A pursuit–evasion game is a classical maneuver confrontation problem in the multi-agent systems (MASs) domain. An online decision technique based on deep reinforcement learning (DRL) was developed in this paper to address the problem of environment sensing and decision-making in pursuit–evasion games. A control-oriented framework developed from the DRL-based multi-agent deep deterministic policy gradient (MADDPG) algorithm was built to implement multi-agent cooperative decision-making to overcome the limitation of the tedious state variables required for the traditionally complicated modeling process. To address the effects of errors between a model and a real scenario, this paper introduces adversarial disturbances. It also proposes a novel adversarial attack trick and adversarial learning MADDPG (A2-MADDPG) algorithm. By introducing an adversarial attack trick for the agents themselves, uncertainties of the real world are modeled, thereby optimizing robust training. During the training process, adversarial learning was incorporated into our algorithm to preprocess the actions of multiple agents, which enabled them to properly respond to uncertain dynamic changes in MASs. Experimental results verified that the proposed approach provides superior performance and effectiveness for pursuers and evaders, and both can learn the corresponding confrontational strategy during training.

Sensor Scheduling Based on Risk for Target Tracking

Abstract: 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.