Multiple-model multiple-hypothesis filter for tracking maneuvering targets

Abstract

In this paper a new method is presented to deal with multiple model filtering. The method is the so called Multiple Model Multiple Hypothesis Filter (MMMH filter). For each hypothesis a Kalman filter is running. This hypothesis represents a specific model mode sequence history. The proposed method has a high level of genericity and is highly flexible. The main feature is that the number of hypotheses that are maintained varies with the difficulty of a scenario. It is shown that the MMMH performs better than the widely used Interacting Multiple Model (IMM) filter.