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The probability hypothesis density(PHD)flter provides an efciently parallel processing method for multi-target tracking.However,measurements have to be gathered for a scan period before the PHD flter can perform a recursion,therefore,signifcant delay may arise if the scan period is long.To reduce the delay in the PHD flter,we propose a sequential PHD flter which updates the posterior intensity whenever a new measurement becomes available.An implementation of the sequential PHD flter for a linear Gaussian system is also developed.The unique characteristic of the proposed flter is that it can retain the useful information of missed targets in the posterior intensity and sequentially handle the received measurements in time.
The probability hypothesis density (PHD) flter provides an eciently parallel processing method for multi-target tracking. Despite, measurements have to be gathered for a scan period before the PHD flter can perform a recursion, therefore, signifcant delay may arise if the scan period is long.To reduce the delay in the PHD flter, we propose a sequential PHD flter which updates the posterior intensity whenever a new measurement is available. An implementation of the sequential PHD flter for a linear Gaussian system is also developed. The unique characteristic of the proposed flter is that it can retain the useful information of missed targets in the posterior intensity and sequentially handle the received measurements in time.