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. of 22tracking accuracy, the survivability of sensor network should also be considered. It requires us to study new sensor scheduling methods considering both target tracking accuracy and battlefield survivability.Currently, there are four existing methods for active sensors to counter anti-radiation weapons: LPI waveform design technology [6][7][8], bistatic and distributed sensor technology [9,10], decoy and jamming technology [11], and sensor management technology [12][13][14]. Among them, the sensor management technology works through the cooperative work of multiple sensors. To realize the low intercepted probability of sensors, the key idea of sensor management technology is dynamically managing the existing sensor resources. It gives anti-radiation weapons difficulty in identifying the sensor or tracking the sensor for a long, continuous time. In this way, the anti-radiation weapon will not be able to position sensors or attack sensors further. This technology can reduce the intercepted probability of active sensors without increasing the hardware cost, which has become a research hotspot in this field. Reference [13] proposed a LPI controlling method based on Bhattacharyya distance and Jeffreys divergence, which can effectively control the intercepted risk of the whole radar network by reasonably allocating the radar working power. In [14], a low interception probability control method for multi-sensor networks based on mutual information entropy was presented. In [15,16], the radiation degree is used to quantify the intercepted risk of sensors. The radiation risk and the tracking accuracy are considered by information fusion method. However, combination of radiation risk and tracking precision is a single index. It is difficult to ensure that both of them can reach the ideal value. Secondly, the selection...
In this paper, we are concerned with the problem of non-myopic sensor scheduling as a partially observable Markov decision process in which available sensors are assigned dynamically to observe targets for the trade-off between the tracking accuracy and the interception risk. Our scheduling problem is difficult to solve using traditional methods due to continuous and high-dimensional state space. However, foresight optimization restricts the sequence of action mappings to be a stationary action sequence and gives rise to the upper bound of the objective function value, leading to an approximate solution. Furthermore, unscented transformation sampling combined with extended Kalman filtering is proposed to provide an approximate evaluation of the upper bound that results from an action sequence. To determine the best action sequence efficiently, a pruning algorithm is incorporated into the tree-search techniques. The feasibility and effectiveness of our non-myopic schemes are verified in a simple simulation experiment that involves multiple radars tracking a single target.
This study addresses the sensor scheduling problem of selecting and assigning sensors dynamically for multi-target tracking. The authors goal is to trade off the tracking accuracy and the interception risk in a period of time. The interception risk is incurred by the fact that the emission energy originating from a sensor can be intercepted by the target during the tracking mission. To react to sensor emission, the targets are able to switch between dynamic models. This non-myopic sensor scheduling problem is formulated as a partially observable Markov decision process, where the one-step reward is constructed by combining the tracking error with the interception probability and the information state is tracked by the interacting multiple model extended Kalman filtering. A novel sampling approach using the unscented transformation is proposed for long-term reward approximation. Numerical simulations illustrate the validity of the proposed scheduling scheme.
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.
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