Detalle Publicación

ARTÍCULO
Improved multitarget tracking in clutter using bearings-only measurements
Autores: Shi, Yifang; Xue, Mengfan; Ding, Yuemin (Autor de correspondencia); Peng, Dongliang
Título de la revista: SENSORS
ISSN: 1424-8220
Volumen: 18
Número: 6
Páginas: 1772
Fecha de publicación: 2018
Resumen:
Multitarget tracking in clutter using bearings-only measurements is a challenging problem. In this paper, a performance improved nonlinear filter is proposed on the basis of the Random Finite Set (RFS) theory and is named as Gaussian mixture measurements-based cardinality probability hypothesis density (GMMbCPHD) filter. The GMMbCPHD filter enables to address two main issues: measurement-origin-uncertainty and measurement nonlinearity, which constitutes the key problems in bearings-only multitarget tracking in clutter. For the measurement-origin-uncertainty issue, the proposed filter estimates the intensity of RFS of multiple targets as well as propagates the posterior cardinality distribution. For the measurement-origin-nonlinearity issue, the GMMbCPHD approximates the measurement likelihood function using a Gaussian mixture rather than a single Gaussian distribution as used in extended Kalman filter (EKF). The superiority of the proposed GMMbCPHD are validated by comparing with several state-of-the-art algorithms via intensive simulation studies.