I want to fuse objects coming from several sensors, with different (sometimes overlapping!) fields of view. Having object lists, how can I determine whether some objects observed by different sensors are in fact the same object? Only then I can truly write an algorithm to predict future state of such an object.

From literature I read those 4 steps:

1. Plot to track association (first update tracks estimates and then associate by "acceptance gate" or by statistical approach PDAF or JPDAF)
2. Track smoothing (lots of algorithms for generating new improved estimate, e.g.: EKF, UKF, PF)
3. Track initiation (create new tracks from unassociated plots)
4. Track maintenance (delete a track if was not associated for last M turns. also: predict those tracks that were associated, their new location based on previous heading and speed)

So basically I am questioning point 1, acceptance gate. For a single sensor I can imagine it can be just a comparison of xy position of object and sensor measurement, velocity with heading eventually. My case is however, I have already ready object lists from each sensor in every cycle, there are some algorithms how to merge informations about an object collected by different sensors (great source is e.g. here: http://www.mathworks.de/matlabcentral/fileexchange/37807-measurement-fusion-state-vector-fusion), but question is how to decide which objects should be fused, and which left as they were? Fields of view overlap partly, not totally. I think is called high level fusion.

I want to fuse objects coming from several sensors, with different (sometimes overlapping!) fields of view. Having object lists, how can I determine whether some objects observed by different sensors are in fact the same object? Only then I can truly write an algorithm to predict future state of such an object.

From literature I read those 4 steps:

1. Plot to track association (first update tracks estimates and then associate by "acceptance gate" or by statistical approach PDAF or JPDAF)
2. Track smoothing (lots of algorithms for generating new improved estimate, e.g.: EKF, UKF, PF)
3. Track initiation (create new tracks from unassociated plots)
4. Track maintenance (delete a track if was not associated for last M turns. also: predict those tracks that were associated, their new location based on previous heading and speed)

So basically I am questioning point 1, acceptance gate. For a single sensor I can imagine it can be just a comparison of xy position of object and sensor measurement, velocity with heading eventually. My case is however, I have already ready object lists from each sensor in every cycle, there are some algorithms how to merge informations about an object collected by different sensors (great source is e.g. here: http://www.mathworks.de/matlabcentral/fileexchange/37807-measurement-fusion-state-vector-fusion), but question is how to decide which objects should be fused, and which left as they were? Fields of view may overlap partly, not totally.

I want to fuse objects coming from several sensors, with different (sometimes overlapping!) fields of view. Having object lists, how can I determine whether some objects observed by different sensors are in fact the same object? Only then I can truly write an algorithm to predict future state of such an object. In other words, I want to associate properly those lists.

I want to fuse objects coming from several sensors, with different (sometimes overlapping!) fields of view. Having object lists, how can I determine whether some objects observed by different sensors are in fact the same object? Only then I can truly write an algorithm to predict future state of such an object.

From literature I read those 4 steps:

1. Plot to track association (first update tracks estimates and then associate by "acceptance gate" or by statistical approach PDAF or JPDAF)
2. Track smoothing (lots of algorithms for generating new improved estimate, e.g.: EKF, UKF, PF)
3. Track initiation (create new tracks from unassociated plots)
4. Track maintenance (delete a track if was not associated for last M turns. also: predict those tracks that were associated, their new location based on previous heading and speed)

So basically I am questioning point 1, acceptance gate. For a single sensor I can imagine it can be just a comparison of xy position of object and sensor measurement, velocity with heading eventually. My case is however, I have already ready object lists from each sensor in every cycle, there are some algorithms how to merge informations about an object collected by different sensors (great source is e.g. here: http://www.mathworks.de/matlabcentral/fileexchange/37807-measurement-fusion-state-vector-fusion), but question is how to decide which objects should be fused, and which left as they were? Fields of view overlap partly, not totally. I think is called high level fusion.