Spatial clutter measurement density estimation in nonhomogeneous measurement spaces

Clutter measurement density (CMD) is one of data association parameters, which indicates the number of clutter measurements per unit volume of the measurement space. In probabilistic data association based algorithms, the association probability between a prior estimate and a measurement is proportional to the ratio of target measurement likelihood and CMD. Also the measurement likelihood is used for obtaining the target existence probability for false track discrimination. Although CMD is an important parameter for state estimation as well as track management, it depends on surveillance environments in which the true CMD is rarely known in advance. A clutter measurement density estimator (CMDE) calculates the spatial density of clutter adaptively using measurement information, and provides its estimated CMD to data association algorithms for adaptive target tracking in clutter. A spatial CMDE (SCMDE) selects the measurement with the N-th smallest 2-norm distance from the measurement of interest and evaluates volume of the hypersphere centered at the measurement of interest and touches the selected measurement. The sparsity (inverse of CMD) is obtained from dividing the hypersphere volume by N. It is only applicable to homogeneous measurement spaces of which coordinates have the same unit such as Cartesian coordinates. An improved version of SCMDE which can be utilized in nonhomogeneous measurement spaces with the different coordinate units such as polar coordinates is proposed. By using weighted normal distance that reflects the volume of the nonhomogeneous measurement space, the proposed SCMDE calculates the ellipsoidal volume for each measurement of interest. Performance of the proposed SCMDE is verified by Monte Carlo simulations for various cases.