Abstract
Splitting and combination are two important events of group target motion. However, the existing tracking approaches for group target splitting and combination events suffer the problems of high-computational cost and low accuracy. Under the random finite set framework, with target extent modeled by random matrix, the algorithms for group target splitting and combination tracking based on δ-generalized labeled multi-Bernoulli filter are researched. Three classical splitting modes of group target are discussed. With appropriate splitting criteria developed, e.g., the setting of the splitting gate, the chosen of the splitting dimension, the compensation of the subgroup's centroid position, and so on. According to the characteristics of each mode, the efficiency and the accuracy of the algorithm for group target splitting event are improved. The group combination approach is derived, where the representation of labels under the tack complicatedly changed condition, e.g., the group splitting and combination events jointly exist are given. With the velocity combination criterion established according to the target motion trend, a decreased sensitivity of the algorithm for target splitting event is avoided. The results show that the proposed algorithms have improved the tracking performance for group target splitting and combination events.
Highlights
Multi-target tracking refers to the estimation of the target state, target number and track from the sensor data corrupted by noise, missed detection, and false alarms [1]–[4], and is widely applied in military and civilian fields e.g., air traffic control, satellite reconnaissance, automatic drive and biological research etc. [5]–[8]
The gamma Gaussian inverse Wishart (GGIW) approach, with the measurement rates modeled as gamma distribution, target extent modeled as random matrix and kinematic states modeled as Gaussian distribution was proposed in [45], an implementation of the model based on CPHD filter was developed in [46]
SIMULATION RESULTS To illustrate the effectiveness of each part of the proposed approaches (GG-DCPC-G123-com and GG-PC-G123-abcom) and the comprehensive performances of them, 6 scenarios are considered
Summary
Multi-target tracking refers to the estimation of the target state, target number and track from the sensor data corrupted by noise, missed detection, and false alarms [1]–[4], and is widely applied in military and civilian fields e.g., air traffic control, satellite reconnaissance, automatic drive and biological research etc. [5]–[8]. The gamma Gaussian inverse Wishart (GGIW) approach, with the measurement rates modeled as gamma distribution, target extent modeled as random matrix and kinematic states modeled as Gaussian distribution was proposed in [45], an implementation of the model based on CPHD filter was developed in [46]. An δ-GLMB filter based implementation of the splitting model proposed in [50] was developed in [51], where the tracks of targets are estimated as well as targets states, the split tracks and the VOLUME 7, 2019 newly born tracks are discriminated by the track labels, kept the continuity of split tracks.
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