KEYWORDS: Detection and tracking algorithms, Image processing, Infrared search and track, Infrared radiation, Kinematics, Logic, Data modeling, Infrared imaging, Infrared sensors, Personal digital assistants
The interacting multiple model probability data association (IMMPDA) algorithm is widely used to target tracking in
clutter. However, it is difficult for IMMPDA to get high precision track when measurements of kinematics state is inaccurate,
because it only considers kinematics feature of targets. To overcome the disadvantage, this paper presents an IMMPDA algorithm based on multi-feature fusion that utilizes multiple features of infrared targets such as kinematics state, size and gray. Association probabilities for targets position are calculated based on IMMPDA algorithm in the polar coordinates. Then the statistic distances of the size and gray are calculated according to state predictions and measurements. After that, statistic distances are further used to compute related association probabilities of targets that are in
the validation region. The decision of synthetic data association of all targets in the validation region is made based on
the information fusion, which uses fuzzy logic to get different weights of each feature. Experiments indicate that the
proposed algorithm has high quality tracking performance. Compared with conventional IMMPDA algorithm, the new algorithm cannot only get higher accurate target association but also improve the stability of the infrared target tracking system.
The infrared (IR) sensor provides the azimuth and elevation angle measurements of the target. For 3-D tracking, at least
two IR sensors are needed. The conventional method puts two sensors in good observation position to gain high precision
target track. However, the performance of the method is limited because of instability and observable limit of IR
sensors. Therefore, more IR sensors are required to improve efficiency of the tracking system. Multisensor data fusion
algorithm proposed in this paper is a novel approach to handle measurements from multiple IR sensors. Measurements
extracted from every IR sensor by image processing are put into the extended Kalman filter. Then intersection results of
measurements from two sensors in acceptable geometrical position are computed. Every intersection result is assigned a
weight factor that represents the performance of intersection using fuzzy logic techniques. The fused estimate of the target
is obtained by using a weighted average method to all the intersection results. The simulations with Monte Carlo
methods show that the proposed algorithm can fuse the target tracks effectively and accurately. Compared with conventional
algorithm, the new algorithm can provide higher precision and more robust estimate of the target.
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