Maneuvering target tracking under mixed line-of-sight/non-line-of-sight (LOS/NLOS) conditions has received considerable interest in the last decades. In this paper, a hierarchical interacting multiple model (HIMM) method is proposed for estimating target position under mixed LOS/NLOS conditions. The proposed HIMM is composed of two layers with Markov switching model. The purpose of the upper layer, which is composed of two interacting multiple model (IMM) filters in parallel, is to handle the switching between the LOS and the NLOS environments. To estimate the target kinetic variables (position, speed and acceleration), the unscented Kalman filter (UKF) with the current statistical (CS) model is used in the lower-layer. Simulation results demonstrate the effectiveness and superiority of the proposed method, which obtains better tracking accuracy than the traditional IMM.

In this letter, we present a real-time orientation estimation and motion tracking scheme using interacting multiple model (IMM) based Kalman filtering method. Two nonlinear filters, quaternion-based extended Kalman filter (QBEKF) and gyroscope-based extended Kalman filter (GBEKF) are utilized in the proposed IMM-based orientation estimator for sensor motion state estimation. In the QBEKF, measurements from gyroscope, accelerometer and magnetometer are processed; while in the GBEKF, sole measurements from gyroscope are processed. The interacting multiple model algorithm is used for fusing the estimated states via adaptive model weighting. Simulation results validate the proposed design concept, and the scheme is capable of reducing overall estimation errors in sensor motion tracking.

This paper presents a 3D feature-based binocular tracking algorithm for tracking crowded people indoors. The algorithm consists of a two stage 3D feature points grouping method and a robust 3D feature-based tracking method. The two stage 3D feature points grouping method can use kernel-based ISODATA method to detect people accurately even though the part or almost full occlusion occurs among people in surveillance area. The robust 3D feature-based Tracking method combines interacting multiple model (IMM) method with a cascade multiple feature data association method. The robust 3D feature-based tracking method not only manages the generation and disappearance of a trajectory, but also can deal with the interaction of people and track people maneuvering. Experimental results demonstrate the robustness and efficiency of the proposed framework. It is real-time and not sensitive to the variable frame to frame interval time. It also can deal with the occlusion of people and do well in those cases that people rotate and wriggle.

A new interacting multiple model (IMM) algorithm using intelligent input estimation (IIE) is proposed for maneuvering target tracking. In the proposed method, the acceleration level for each sub-model is determined by IIE-the estimation of the unknown target acceleration by a fuzzy system using the relation between the residuals of the maneuvering filter and the non-maneuvering filter. The genetic algorithm (GA) is utilized to optimize a fuzzy system for a sub-model within a fixed range of target acceleration. Then, multiple models are represented as the acceleration levels estimated by these fuzzy systems, which are optimized for different ranges of target acceleration. In computer simulation for an incoming anti-ship missile, it is shown that the proposed method has better tracking performance compared with the adaptive interacting multiple model (AIMM) algorithm.

This paper proposes a maneuvering target tracking algorithm using multiple model filters. This filtering algorithm is discussed in terms of tracking performance, tracking success rate and tracking accuracies for short sampling interval as compared with other conventional methodology. Through several simulations, validity of this algorithm has been confirmed.

We have developed an efficient recursive algorithm based on the interacting multiple model (IMM) for enhancing speech degraded by additive white noise. The clean speech is modeled by the hidden filter model (HFM). The simulation results shows that the proposed method offers performance gains relative to the previous one with slightly increased complexity.