Comparison of methods for association and fusion of multi sensor data for tracking applications 13; 13;

Girija, G and Raol, JR (2001) Comparison of methods for association and fusion of multi sensor data for tracking applications 13; 13;. In: AIAA Guidance, Navigation, and Control Conference and Exhibit, 6-9 Aug 2001, Montreal, United States.

[img] PDF
Restricted to Registered users only

Download (472kB)


Two commonly used algorithms for association, Nearest Neighbour (NN) and Probabilistic Data Association (PDA,) are applied to the measurement of track association, estimation/filtering and fusion. The algorithms are first validated on simulated data of a target moving with constant velocity and tracked by two sensors with different measurement noise characteristics. Each of the sensors is equipped with a Kalman filter which is essentially used to update the track states after associating the incoming measurement with the existing track. The estimated states are then fused using a hierarchical sensor architecture to generate a single fused track. A comparison of the two methods is made in terms of various statistical performance measures for the filters. For this case of simulated data of a single target, multisensor scenario, the performance of the NN Filter is superior to that of PDA Filter when the measurement noise levels are high. For low measurement noise levels, the performances of the two filters are similar. The algorithms are then applied to generate a fused trajectory from real data of a moving object.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Copyright for this paper belongs to AIAA
Uncontrolled Keywords: Multisensor fusion;Target tracking;Kalman filters;Probability theory;Comparison;Computerized simulation; State estimation; Data acquisition;Noise reduction
Subjects: AERONAUTICS > Aircraft Design, Testing & Performance
Depositing User: M/S ICAST NAL
Date Deposited: 15 Feb 2008
Last Modified: 24 May 2010 04:20

Actions (login required)

View Item View Item