This paper presents a new and simple approach to the problem of multiple sensor data fusion. We introduce an efficient algorithm that can fuse multiple sensor measurements to track an arbitrary number of objects in a cluttered environment. The algorithm combines conventional Kalman filtering techniques with probabilistic data association methods. A Gauss Markov process model is assumed to handle sensor outputs at various sampling frequencies and random nonequidistant time intervals. We applied the algorithm to post-process the digital range returns of radar sensors to improve their quality. Since the static noise returns have near-zero velocity, the algorithm associates a certain track with each digital return, and estimates the track velocity, thereby allowing for removal of false returns originating from static pattern noise.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Radar detection improvement by integration of multi-object tracking


    Contributors:
    Lingmin Meng, (author) / Grimm, W. (author) / Donne, J. (author)


    Publication date :

    2002-01-01


    Size :

    434976 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Radar Detection Improvement by Integration of Multi-Object Tracking

    Meng, L. / Grimm, W. / Donne, J. et al. | British Library Conference Proceedings | 2002


    Multi-target multi-object radar tracking

    Mobus, R. / Joos, A. / Kolbe, U. | IEEE | 2003


    RADAR EXTENDED OBJECT TRACKING USING RADAR

    European Patent Office | 2024

    Free access

    RADAR EXTENDED OBJECT TRACKING USING RADAR

    GOSALA NIKHIL BHARADWAJ / MENG XIAOLI | European Patent Office | 2023

    Free access