Aiming at the performance evaluation of joint detection and estimation(JDE)for multiple unresolved target-groups in the presence of clutters and missed detections
an error(lower)bound is proposed using information inequality and optimal sub-pattern assignment distance within the random finite set(RFS)framework. Firstly
the states of multiple unresolved target-groups are modeled as a multi-Bernoulli RFS
and the group observation likelihood is modeled based on the assumption of the continuous individual target number proposed by Mahler. Then
the maximum posterior probability detection criteria and unbiased estimation criteria are used in deriving the proposed bound. Experimental results show that the proposed bound can effectively indicate the performance limits of JDE algorithms for the existing unresolved target-groups
and the average relative error is less than 7% for various clutter density and detection probability.
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references
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