Scheduling of Multifunction Multistatic Sensor

The concept of multistatic sensor scheduling is of paramount importance in modern surveillance and tracking systems. It is a complex undertaking that requires careful consideration of the multiple objectives involved. This paper presents a multi-objective integer nonlinear optimization model of a multistatic passive sensor that has been scalarized using the goal programming method. This approach is designed to yield an optimal solution that facilitates a balanced schedule for tuning multiple receivers to fulfill a multi-functional role, namely to survey the frequency spectrum and track targets effectively. The paper presents a mathematical model for each functionality in the form of an objective function. In this study, we investigate the probability density function of the first passage time (FPT) in a Markov chain, which we approximate by an exponential distribution. Monte Carlo simulation demonstrates that our approximation is an effective means of minimizing the mean of the FPT random vector. Based on this approximation, an objective function for surveying the frequency spectrum with a multistatic passive sensor is provided. In contrast to existing works that employ information-driven scheduling and utilize expected information gain derived from Rényi divergence, we propose an information-driven objective function derived from Kullback-Leibler divergence. This is subject to the constraint that position measurement is obtained only if all sensors of a multistatic system receive in the same frequency band. To our best knowledge, this work provides the most balanced multi-objective optimization model for scheduling to date, as no weights are incorporated in the resulting model. Instead, the objective functions are normalized.