A Novel Robust MM Filter against Outliers

An Interacting Multiple Model (IMM) filter suppresses outliers by integrating model-conditioned estimation and model mode recognition through adaptive mode switches based on a Bayesian probability update. However, it may face significant outlier-caused peak errors, which are undesirable or even impermissible in many applications, for example, target tracking. This paper considers that, from the view of the Dempster-Shafer Theory, the IMM filter actually fuses the Bayesian beliefs of the model mode with Dempster's Rule of Combination, which can deal with uncertainties instead of the evidence conflicts that may exist as outliers appear. Therefore, we propose the adaptive robust multiple model (RMM) filter through introducing practical expert knowledge in the belief function framework, which runs online to reduce outlier-caused peak errors. In RMM, the Likelihood Temporal Ratio (LTR) is incorporated to provide extra information on the tendency of mode switching. Moreover, expert rules are introduced to construct adaptive belief functions using discount techniques and to select the combination rule that better deals with evidence conflicts when outliers appear. The simulations in target tracking scenarios show that the proposed RMM filter significantly reduces outlier-caused peak errors and hence obtains a lower rate of track loss in the cluttered environment.