  This study proposes an airborne multisensor management algorithm for target
tracking, taking each of multiple unmanned aircraft as a sensor. The purpose of
the algorithm is to determine the configuration of the sensor deployment and to
guide the mobile sensors to track moving targets in an optimal way. The cost
function as a performance metric is defined as a combination of the
D-optimality criterion of the Fisher information matrix. The convexity of the
cost function is proved and the optimal solution for deployment and guidance
problem is derived by the Frank-Wolfe method, also known as the conditional
gradient descent method. An intuitive optimal approach to deal with the problem
is to direct the sensor to the optimal position obtained by solving a nonlinear
optimization problem. On the other hand, the proposed method takes the
conditional gradient of the cost function as the command to the deployed
sensors, so that the sensors are guaranteed to be in the feasible points and
they achieve the current best performance. Simulation results demonstrate that
the proposed algorithm provides better performance than directing each sensor
to its optimal position.
