A Risk-Based Sensor Management Method for Target Detection in the Presence of Suppressive Jamming

In this paper, a risk-based sensor management method for target detection in the presence of suppressive jamming is proposed, in which available sensors are dynamically scheduled to control the risk in the execution of target detection task. Firstly, the sensor detection models in the absence/ presence of jamming are established, and the calculation method of target detection risk is presented based on the target detection probability. Secondly, the sensor radiation model is established, and the calculation method of radiation risk is given by using hidden Markov filter. Then, a non-myopic objective function is constructed to minimize the sum of detection risk and radiation risk. Furthermore, in order to obtain the optimal solution of objective function quickly, a decision tree search algorithm combining with branch and bound theory and greedy search is proposed. Finally, simulations are conducted, and the results show that the proposed algorithm and sensor management method are effective and advanced compared with the existing algorithms and methods

Threat-based sensor management for target tracking

sensor management scheme that focuses on managing the uncertainty in the threat level of targets is proposed. The scheme selects the best sensing mode such that the uncertainty in the threat level of targets is minimized. The main advantage of the proposed scheme is that it opens the possibility for incorporation of the operational context when performing Bayes-optimal sensor management. Different aspects of threat can be meaningfully aggregated making this flexible approach a favorite choice for multifunctional systems. The proposed scheme is demonstrated in simulated scenarios, both simple and advanced, where the data association problem is taken into account. In the multitarget example, the proposed scheme outperforms the other schemes considered in this manuscript, both naive and adaptive. The proposed scheme can be used in target tracking applications, such as air traffic management or area surveillance