Bayesian Deghosting Algorithm for Multiple Target Tracking

Kulmon, Pavel

doi:10.1109/mfi49285.2020.9235215

Cited by 3 publications

(2 citation statements)

References 14 publications

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“…To solve the problem of passive multi-target with multiple sensors, a distributed fusion method based on trajectory integral distance and pseudo-measurement (TIDPM) is proposed in this paper. Unlike centralized Fusion method based on cross-location, the proposed algorithm effectively suppresses the growth of false tracks as the number of targets increases [41]. Benefit by the integral characteristic of trace distance computation in the association step, the association correctness rate improves more rapidly to a reliable level.…”

Section: Introductionmentioning

confidence: 99%

Multi-Sensor Distributed Fusion based on cross-location for passive tracking

Xu,

Guo,

Kuang

et al. 2024

Preprint

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To solve the problem of passive multi-sensor tracking in clutter, a distributed fusion algorithm based on trajectory integral distance and pseudo-measurement is proposed. First, a twofold Gaussian mixture model is used to quantize the target measurement signal, and achieve a more accurate estimate of the initial target state. The labelled multi-Bernoulli filter is employed in adjusting the number of tracking targets and getting single-sensor tracks. Subsequently, in the fusion center, a track-to-track association method based on global nearest-neighbour is employed. Trajectory integration distance is utilized to improve association performances. A maximum-likelihood Cross-location track-to-track fusion strategy using pseudo-measurement transformation is proposed to enhance fusion accuracy in passive scenarios. Simulation results verify the performance of the proposed distributed fusion method.

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Section: Introductionmentioning

confidence: 99%

Multi-Sensor Distributed Fusion based on cross-location for passive tracking

Xu,

Guo,

Kuang

et al. 2024

Preprint

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“…Moreover, the simulated data are used in the sampler to produce multiple long runs of the chain which is later analysed in order to assess its convergence. As this is an extended version of [ 14 ], the detailed convergence assessment and the rest of the detailed analysis is the additional contribution. Illustration of chain iterations in the Cartesian space and the target identification space are provided.…”

Section: Introductionmentioning

confidence: 99%

Reversible Jump MCMC for Deghosting in MSPSR Systems

Kulmon

2021

Sensors

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This paper deals with bistatic track association and deghosting in the classical frequency modulation (FM)-based multi-static primary surveillance radar (MSPSR). The main contribution of this paper is a novel algorithm for bistatic track association and deghosting. The proposed algorithm is based on a hierarchical model which uses the Indian buffet process (IBP) as the prior probability distribution for the association matrix. The inference of the association matrix is then performed using the classical reversible jump Markov chain Monte Carlo (RJMCMC) algorithm with the usage of a custom set of the moves proposed by the sampler. A detailed description of the moves together with the underlying theory and the whole model is provided. Using the simulated data, the algorithm is compared with the two alternative ones and the results show the significantly better performance of the proposed algorithm in such a simulated setup. The simulated data are also used for the analysis of the properties of Markov chains produced by the sampler, such as the convergence or the posterior distribution. At the end of the paper, further research on the proposed method is outlined.

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