Nonlinear filtering for tracking low elevation targets in multipath

Monopulse radar tracking of target elevation for objects flying close to a reflecting surface is difficult due to interference between the direct echo and surface-reflected target echoes. Ideally, target height could be estimated directly from the probability density for monopulse measurements given target range and height. This direct approach is usually unfeasible because the density generally has many false peaks so there are multiple solutions for target height. However, as target range varies the locations of false peaks fluctuate rapidly while the true peak tracks target height. This paper describes a nonlinear filter that exploits this behavior to estimate target height. The filter recursively computes the probability density for height and vertical velocity conditioned on the monopulse measurement sequence. The time evolution of this density between measurements is determined by a Fokker-Planck partial differential equation. This is solved in real-time using a finite difference scheme. The monopulse measurement probability density is computed from a physical model and used to update the conditional target state density using Bayes' rule. In simulation testing for a generic C-band shipboard radar the filter is able to reliably acquire and track transonic targets through mild maneuvers with about 12 m root-mean-square height accuracy.