On adaptive probability hypothesis density filter for multi-target tracking
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Cites background from "On adaptive probability hypothesis ..."
...However, the filters have unsatisfactory tracking performance when the target has nonlinear motion state and the clutter occurs in the surveillance region [6,11]....
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"On adaptive probability hypothesis ..." refers background or methods in this paper
...Further, the point spread function in (10) is given by [1,11]:...
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...After defining the true set =1 g i i X x and the estimated track set respectively, we write the OSPA distance as [1,4,11]: =1 ˆ ˆ e i i X x...
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461 citations
"On adaptive probability hypothesis ..." refers background or methods in this paper
...Further, the point spread function in (10) is given by [1,11]:...
[...]
...where , n k I is the source intensity, 2 is the variance of measurement error, and is the time–updated position coordinate [1]....
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...After defining the true set =1 g i i X x and the estimated track set respectively, we write the OSPA distance as [1,4,11]: =1 ˆ ˆ e i i X x...
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...1 Introduction According to the stochastic–geometry formulation of random finite set (RFS) theory, the probability hypothesis density (PHD) filter has been applied in the target state and measurement spaces [1]....
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...2 Problem Statements Considering the 2–dimension (2D) state vector with the position coordinate T , , , , k k k k k k x x y y x , k k x y , the velocity coordinate , k k x y , and the turn rate k , where T denotes the transpose of matrix, we have the stochastically dynamic equations at time k [1,4,8]:...
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23 citations
"On adaptive probability hypothesis ..." refers background in this paper
...By propagating the PHD of multi–target posterior density, the scalar product of the PHD in state space represents the estimated cardinality of targets, and the associated peaks define the state estimates [2]....
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