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Using Multiple Hypotheses to Improve Target Tracking

Using Multiple Hypotheses to Improve Target Tracking

If the measurement of a target's position by a surveillance radar were perfect and precise, tracking would be much easier. Changes in position would be easily converted into speed, course and accelerations, allowing motions and manoeuvres to be followed. The problem of matching tracks with measurements (the association problem) would not be the challenge that accounts for much of the complexity in a practical tracking implementation. In the face of uncertainty and ambiguous measurements, a target tracker can use multiple hypotheses to accommodate different interpretations of the data, effectively allowing decisions to be deferred until additional information arrives. Complexities of target tracking The complexities of target tracking arise from the uncertainties in the measurement process associated with variations in the way radar is reflected from a target and the effects of the environment. The accuracy of the measurement process can be reflected by the weight given to the measurement in updating the estimate of the target. This weight is represented by the filter gains (for example alpha-beta in the alpha-beta tracker, or the Kalman gains of the Kalman filter), so that a...

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