Joint transmitter selection and resource management strategy based on low probability of intercept optimization for distributed radar networks

In this paper, a joint transmitter selection and resource management (JTSRM) strategy based on low probability of intercept (LPI) is proposed for target tracking in distributed radar network system. The basis of the JTSRM strategy is to utilize the optimization technique to control transmitting resources of radar networks in order to improve the LPI performance, while guaranteeing a specified target tracking accuracy. The weighted intercept probability and transmit power of radar networks is defined and subsequently employed as the optimization criterion for the JTSRM strategy. The resulting optimization problem is to minimize the LPI performance criterion of radar networks by optimizing the revisit interval, dwell time, transmitter selection, and transmit power subject to a desired target tracking performance and some resource constraints. An efficient and fast three‐step solution technique is also developed to solve this problem. The presented mechanism implements the optimal working parameters based on the feedback information in the tracking recursion cycle in order to improve the LPI performance for radar networks. Numerical simulations are provided to verify the superior performance of the proposed JTSRM strategy

Controlling Target Estimate Covariance in Centralized Multisensor Systems

Current multisensor fusion tracking systems can be easily overwhelmed by incoming data, especially as the number of targets and sensors increases. Sensor management schemes have been proposed to reduce the computational demand of these systems while minimizing the loss of tracking performance. This paper presents a sys- tem that will maintain a desired covariance level for each target while reducing the resource demands on the tracking system. Other functions performed by a sensor manager like prioritizing and scheduling are assumed to be done elsewhere, but result in delays in the execution of sensing requests made by the system. Three sensor selection algorithms are presented based on different resource and performance metrics and show a dramatic improvement over "dumb" sensing systems in simulation. Execution delay is shown to have a deleterious effect on the tracking performance of the system, but most of that performance can be restored when a prediction algorithm is used to model the delay

The Effects Of Delayed Sensor Requests On Sensor Manager Systems

Current multisensor tracking systems can be easily overwhelmed by incoming data, especially as the number of targets and sensors increases. A sensor management scheme has been proposed in previous work to reduce the computational demand of these systems while minimizing the loss of tracking performance by selecting only enough sensing resources to maintain a desired covariance level for each target, reducing the resource demands on the tracking system. However, the proposed system is sensitive to delays in the execution of sensor assignments. This paper analyzes the effect of that delay and examines methods of eliminating that effect. Because of the lack of a closed form solution for the covariance matrix of the discrete-time Kalman fil- ter, the analysis centers on the performance of the continuous-time scalar Kalman-Bucy filter and then extends those results to the discrete-time case. The analysis shows that for all stable systems and unstable systems under certain conditions, the sensitivity of the covariance estimate to delays of sensing actions decreases steadily with time. Furthermore, when attempting to estimate unknown delays, overestimating the delay will produce smaller covariance prediction errors than underestimating the delay by a similar amount