Multiple scan joint probabilistic data association

This paper presents a multiple scan or n-scan-back joint probabilistic data association (JPDA) algorithm which addresses the problem of measurement-to-track data association in a multiple target and clutter environment. The standard single scan JPDA algorithm updates a track with weighted sum of the measurements which could have reasonably originated from the target in track. The only information the standard JPDA algorithm uses is the measurements on the present scan and the state vectors and covariance matrices of the present targets. The n-scan-back algorithm presented here uses multiple scans of measurements along with the present target information to produce better weights for data association. The standard JPDA algorithm can utilize a formidable amount of processing power and the n-scan-back version only worsens the problem. Therefore, along with the algorithm presentation, implementations which make this algorithm practical are discussed and referenced. An example is also shown for a few n-scan-back window lengths