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Likelihood as a Method of Multi Sensor Data Fusion for Target Tracking

Gallagher, Jonathan G.
http://rave.ohiolink.edu/etdc/view?acc_num=osu1244041862

Abstract Details

2009, Master of Science, Ohio State University, Electrical and Computer Engineering.
This thesis addresses the problem of detecting and tracking objects in a scene, using a distributed set of sensing devices in different locations, and in general use a mix of different sensing modalities. The goal is to combine data in an efficient but statistically principled way to realize optimal or near-optimal detection and tracking performance. Using the Bayesian framework of measurement likelihood, sensor data can be combined in a rigorous manner to produce a concise summary of knowledge of a target’s location in the state-space. This framework allows sensor data to be fused across time, space and sensor modality. When target motion and sensor measurements are modeled correctly, these “likelihood maps” are optimal combinations of sensor data. By combining all data without thresholding for detections, targets with low signal to noise ratio (SNR) can be detected where standard detection algorithms may fail. For estimating the location of multiple targets, the likelihood ratio is used to provide a sub-optimal but useful representation of knowledge of the state space. As the calculation cost of computing likelihood or likelihood ratio maps over the entire state space is prohibitively high for most practical applications, an approximation computed in a distributed fashion is proposed and analyzed. This distributed method is tested in simulation for multiple sensor modalities, displaying cases where it is and is not a good approximation of central calculation. Detection and tracking examples using measured data from multi-modal sensors (Radar, EO, Seismic) are also presented.
Randolph Moses (Advisor)
Emre Ertin (Advisor)
Lee Potter (Committee Member)
Electrical Engineering
target tracking; state estimation; distributed calculation; likelihood maps; likelihood ratio; data fusion; sensor fusion; sensor networks

Recommended Citations

Citations

  • Gallagher, J. G. (2009). Likelihood as a Method of Multi Sensor Data Fusion for Target Tracking [Master's thesis, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1244041862

    APA Style (7th edition)

  • Gallagher, Jonathan. Likelihood as a Method of Multi Sensor Data Fusion for Target Tracking. 2009. Ohio State University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1244041862.

    MLA Style (8th edition)

  • Gallagher, Jonathan. "Likelihood as a Method of Multi Sensor Data Fusion for Target Tracking." Master's thesis, Ohio State University, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=osu1244041862

    Chicago Manual of Style (17th edition)

osu1244041862
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© 2009, all rights reserved.
This open access ETD is published by The Ohio State University and OhioLINK.