ABSTRACT
In multiple target tracking (MTT) systems that track targets with less-than-unity probability
of detection in the presence of false alarms (FA), data association is very important. Data
association is responsible for deciding which of the received multiple measurements should
update which track. Some data association techniques use a unique pairing to update a track;
i.e. at most one observation is used to update a track. An alternative approach is to use all of
the validated measurements with different weights (probabilities), known as probabilistic data
association (PDA). Due to the increase in the FA rate or low probability of target detection,
most of the data association algorithms begin to fail. In this paper, we introduce a new
suboptimal PDA technique for MTT in dense clutter environment. The proposed technique is
based on merging the probabilistic nearest-neighbor filter (PNNF) with the PDA algorithm.
The main idea is based on high-weighting the measurements that has minimum statistical
distance from the predicted position of the target. The state updating equation in Kalman filter
uses the combined innovation as in Joint Probabilistic Data Association method which is
defined as the weighted sum of the residuals associated with many observations. Due to its
simplicity in calculations and robustness, this technique can be used for real-time applications
even though in dense clutter environments. We applied the proposed algorithm in tracking
multiple targets in presence of various clutter densities. Results showed better performance
when compared to Nearest-Neighbor and All-Neighbors approaches in different clutter
densities and noise measurements.