Multi‐model particle cardinality‐balanced multi‐target multi‐Bernoulli algorithm for multiple manoeuvring target tracking

Abstract

Multiple manoeuvring target tracking is an extremely difficult problem in the target tracking field, especially under the non-linear systems. The probability hypothesis density (PHD) and cardinalised PHD (CPHD) algorithms based on the particle filter have proved to be promising algorithms for multi-target tracking. However, they have a heavy computational burden because of the particle clustering in the stage of state extraction. Especially, the additional calculation is added to the CPHD algorithm because of the estimation of the cardinality distribution. To solve the problem, the authors propose a novel multiple manoeuvring target tracking algorithm by extending the multi-model method to the cardinality-balanced multi-target multiBernoulli filter and then using the sequential Monte Carlo implementation. Moreover, in order to obtain the individual target tracks, the particle labelling technique is introduced in the proposed algorithm. Simulation results show that the proposed algorithm can effectively achieve the track continuity for the multiple manoeuvring target tracking and has a higher accuracy of state estimates than the multiple model particle PHD and CPHD algorithm with a better computational efficiency.

The structure and evolution of the major capsid protein of a large, lipid-containing DNA virus

“…C hlorella viruses, isolated from freshwater sources throughout the world, are among the largest and most complex known icosahedral viruses. These viruses have a layered structure consisting of a double-stranded DNA (dsDNA) genome, surrounded by a protein core, a lipid membrane, and an outer icosahedral capsid shell (1,2). They have amino acid sequence similarities in the major capsid protein, as well as similarities in the virion architecture, to African swine fever virus and to iridoviruses (3,4) such as Chilo iridescent virus (1).…”

“…A cryoelectron microscopy (cryo-EM) reconstruction of PBCV-1 (1) showed that the outer glycoprotein capsid is icosa-hedral and surrounds a lipid bilayer membrane. The external layer is assembled from 20 triangular units (''trisymmetrons''; refs.…”

Adaptive parameter particle CBMeMBer tracker for multiple maneuvering target tracking

“…The benchmark technique is the MMP-CBMeMBer algorithms [29]. In the considered scenario, the measurements are obtained at four stationary sensors located at (0, 0) m, (0, 1 × 10 4 ) m, (1 × 10 4 , 0) m, and (1 × 10 4 , 1 × 10 4 ) m. At time k, each sensor outputs the measured bearing of the received signal, which is given by…”

“…The multiple-model PHD (MM-PHD) filter and the MM-CPHD filter implemented using the sequential Monte Carlo (SMC) method were presented in [26,27], and a corrected version, also known as the jump Markov multi-target Bayes filter, was later proposed in [28]. However, the MMP-CBMeMBer filter [29] has a higher accuracy than the MM-PHD filter due to the fact that the multi-Bernoulli-based method propagates the parameterized approximation to the posterior cardinality distribution. Most of the MM-based filters track multiple maneuvering targets through the interaction of multiple models, which is realized via combining estimates from different models according to their respective model likelihoods.…”

Regional variance in target number: Analysis and application for multi-Bernoulli point processes

“…The extension to multiple objects, known as the multi-Bernoulli process, has been developed for multi-object filtering [7,18,19]. It has been applied for visual tracking in images [20][21][22][23][24][25], or audiovideo fusion [26], and inspired numerous implementations (see [27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42][43][44][45] for recent examples). In a more general context, (multi-)Bernoulli filters have also been developed for scenarios with unknown clutter intensity [46], sensor control problems [15,47], tracking in sensor networks [48][49][50], superpositional sensors [51,52], distributed data fusion [53][54][55], and tracking with road constraints [56].…”