This paper considers the general problem of tracking a noncooperative target in the presence of multipath. The multipath effect occurs intermittently, according to a discrete-time Markov chain, and exerts an additional unknown measurement error (i.e, a bias), with biases autocorrelated if they occur across successive sampling times. We calculate the posterior Cramér-Rao bound (PCRB) for this problem by augmenting the target state with the multipath bias. An established, efficient Riccati recursion is then used to determine the PCRB, thereby providing mean squared error performance bounds for both the estimation of the target state and the multipath bias. The approach is demonstrated in a simulated scenario in which an airborne radar tracks a low altitude airborne target that is moving in a horizontal plane with nearly constant velocity, using measurements of azimuth. elevation and range. The measurements are intermittently corrupted by multipath bias resulting from specular reflection at the surface boundary. It is shown that the PCRB increases rapidly when the multipath effect occurs, indicating that optimal target tracking performance is significantly degraded at such times. Future work will compare the PCRB developed herein with alternative PCRB methodologies that do not implicitly condition on the multipath effects, and also compare the bound to the performance of a tracking algorithm that is designed to identify and adjust for the multipath effects.

In this paper, we are concerned with the problem of utilizing a large network of sensors in order to track multiple targets. Large-scale sensor array management has applications in a number of target tracking domains. For example, in ground target tracking, hundreds or even thousands of unattended ground sensors may be dropped over a large surveillance area. At any one time, it may then only be possible to utilize a very small number of the available sensors at the fusion center because of physical limitations, such as available communications bandwidth. A similar situation may arise in tracking sea-surface or underwater targets using a large network of sonobuoys. The general problem is then to select a small subset of the available sensors in order to optimize tracking performance. In a practical scenario with hundreds of sensors, the number of possible sensor combinations would make it infeasible to use enumeration in order to find the optimal solution. Motivated by this consideration, in this paper we use an efficient search technique in order to determine near-optimal sensor utilization strategies in real-time. This search technique consists of convex optimization followed by greedy local search. We consider several problem formulations and the posterior Cramer-Rao lower bound is used as the basis for network management. Simulation results illustrate the performance of the algorithms, both in terms of their real-time capability and the resulting estimation accuracy. Furthermore, in comparisons it can also be seen that the proposed solutions are near-optimal.

The paper presents an analysis of the phased array radar allocation demands, when tracking highly maneuverable anti-ship missiles (ASM) using a collocated radar/IRST sensor combination. The motion of the ASM is modeled using the quantized acceleration levels. The principal aim of this analysis is to determine an upper bound on the average radar update time. This bound follows from a Cramer-Rao type error bound for the estimation of linear jump Markov dynamic systems. Given a dynamic motion model of an ASM, the IRST/radar sensor characteristics and a tolerable level of target state estimation error, we can theoretically predict the maximum average update time required for the phased-array radar. The presented analysis allows us to quantify the IRST benefits in ASM defence, without a need for extensive Monte Carlo simulations