Abstract
Data association and model selection are important factors for tracking multiple targets in a dense clutter environment. In this paper, we provide an effective solution to the tracking of multiple single-pixel maneuvering targets in a sequence of infrared images by developing an algorithm that combines a sequential probabilistic multiple hypothesis tracking (PMHT) and interacting multiple model (IMM). We explicitly model maneuver as a change in the target's motion model and demonstrate its effectiveness in our tracking application discussed in this paper. We show that inclusion of IMM enables tracking of any arbitrary trajectory in a sequence of infrared images without any a priori special information about the target dynamics. IMM allows us to incorporate different dynamic models for the targets and PMHT helps to avoid the uncertainty about the observation origin. It operates in an iterative mode using expectation-maximization (EM) algorithm. The proposed algorithm uses observation association as missing data.
Highlights
Tracking of multiple moving targets in the presence of clutter has significance in surveillance, navigation, and military application
The performance comparison between a Kalman filter and the interacting multiple model estimator is carried out for single target tracking [22], and it is reported that an IMM estimator is preferred over a Kalman filter to track the maneuvering target
Two sets of clips have been generated: (i) the first clip set consisting of maneuvering trajectories is generated using B-splines, and it is quite important to note that these generated trajectories do not follow any specific model; (ii) for the second clip set, mixed trajectories are generated using constant acceleration model for non-maneuvering trajectories and cosine and sine functions for nonlinear trajectories
Summary
Tracking of multiple moving targets in the presence of clutter has significance in surveillance, navigation, and military application. To avoid uncertainty about the origin of observation, joint probabilistic data association filter (JPDAF) and multiple hypothesis tracking (MHT) schemes have been developed [1]. Different versions of PMHT described above do not incorporate changing target dynamic models for an arbitrary target trajectory, whereas the method proposed in this paper explicitly does so. Model selection is another problem with target tracking. It has been well established that in terms of tracking accuracy, the IMM algorithm performs significantly better for maneuvering targets than other types of filters The performance comparison between a Kalman filter and the interacting multiple model estimator is carried out for single target tracking [22], and it is reported that an IMM estimator is preferred over a Kalman filter to track the maneuvering target
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