  Integrated sensing and communications (ISAC) enabled by unmanned aerial
vehicles (UAVs) is a promising technology to facilitate target tracking
applications. In contrast to conventional UAV-based ISAC system designs that
mainly focus on estimating the target position, the target velocity estimation
also needs to be considered due to its crucial impacts on link maintenance and
real-time response, which requires new designs on resource allocation and
tracking scheme. In this paper, we propose an extended Kalman filtering-based
tracking scheme for a UAV-enabled ISAC system where a UAV tracks a moving
object and also communicates with a device attached to the object.
Specifically, a weighted sum of predicted posterior Cram\'er-Rao bound (PCRB)
for object relative position and velocity estimation is minimized by optimizing
the UAV trajectory, where an efficient solution is obtained based on the
successive convex approximation method. Furthermore, under a special case with
the measurement mean square error (MSE), the optimal relative motion state is
obtained and proved to keep a fixed elevation angle and zero relative velocity.
Numerical results validate that the obtained solution to the predicted PCRB
minimization can be approximated by the optimal relative motion state when
predicted measurement MSE dominates the predicted PCRBs, as well as the
effectiveness of the proposed tracking scheme. Moreover, three interesting
trade-offs on system performance resulted from the fixed elevation angle are
illustrated.
