Skip to main content

Table 3 Algorithm 3

From: A general cardinalized probability hypothesis density filter

The general partitioning algorithm

1. Identify the potential target measurements

2. Obtain the general original partition

(1) Calculate the multiple-detection distance \(d({{{\mathbf {z}}}_{i}},{{{\mathbf {z}}}_{j}})\) between any two measurements

(2) Obtain the original partition \({{\wp }_{o}}=\left\{ {{O}_{d}} \right\} _{d=1}^{D}\) of measurement set

(3) Reserve the target cells and merge the clutter measurements into the clutter cell

(4) Obtain the general original partition \({{\wp }_{c}}=\left( \bigcup \nolimits _{d=1:{\hat{D}}}{{{O}_{d}}} \right) \bigcup C\)

3. Obtain the general partitions

(1) Calculate all partitions of each target cell in the general original partition

(2) Obtain the 1st type of partition

(3) Obtain the 2nd type of partition

(4) Obtain the 3rd type of partition