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Relationships Between Generalized Bernoulli Numbers and Polynomials and Generalized Euler Numbers and Polynomials [PDF]
, 2002 In this paper, concepts of the generalized Bernoulli and Euler numbers and polynomials are introduced, and some relationships between them are ...
Qi, Feng, Luo, Qiu-Ming
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Robust Poisson Multi-Bernoulli Filter With Unknown Clutter Rate
IEEE Access, 2019 In Bayesian multi-target tracking (MTT), knowledge of clutter intensity is required for effective multi-target state estimation. In this paper, we propose an online multi-target filter that can operate under background with unknown clutter intensity. Our
Weijian Si, Hongfan Zhu, Zhiyu Qu
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Bernoulli Particle/Box-Particle Filters for Detection and Tracking in the Presence of Triple Measurement Uncertainty [PDF]
, 2012 This work presents sequential Bayesian detection and estimation methods for nonlinear dynamic stochastic systems using measurements affected by three sources of uncertainty: stochastic, set-theoretic and data association uncertainty.
Gning, Amadou +5 more
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Bernoulli track‐before‐detect smoothing for maritime radar
IET Radar, Sonar & Navigation, 2022 Detection and tracking of small targets in sea clutter using a high‐resolution radar is a challenging problem as the sea surface constantly moves in a complex manner, with rough seas creating strong target‐like returns. Recently, a Bernoulli track‐before‐
Branko Ristic +3 more
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Particle filter implementation of the multi-Bernoulli filter for superpositional sensors [PDF]
2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2013 The multi-Bernoulli filter is a promising method for computationally efficient and accurate multi-target tracking. Computationally tractable approximations of the multi-Bernoulli filter equations for superpositional sensors were recently derived. In this paper we present a particle filter implementation of these approximate update filter equations.
Santosh Nannuru, Mark Coates
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Image-Based Multi-Target Tracking through Multi-Bernoulli Filtering with Interactive Likelihoods
Sensors, 2017 We develop an interactive likelihood (ILH) for sequential Monte Carlo (SMC) methods for image-based multiple target tracking applications. The purpose of the ILH is to improve tracking accuracy by reducing the need for data association.
Anthony Hoak +2 more
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Remote Sensing, 2022 In this paper, we propose the specific recursion formula for the generalized labeled multi-Bernoulli filter based on the track-before-detect strategy (GLMB-TBD) using a belief propagation algorithm. The proposed method aims to track multiple weak targets
Chenghu Cao, Yongbo Zhao
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Digit frequencies and Bernoulli convolutions [PDF]
, 2014 This work was supported partly by the Dutch Organisation for Scientific Research (NWO) grant number 613.001.022 and partly by the Engineering and Physical Sciences Research Council grant number EP/K029061/1.It is well known that when β is a Pisot number,
Kempton, Thomas Michael William
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Distributed Bernoulli Filtering Using Likelihood Consensus [PDF]
IEEE Transactions on Signal and Information Processing over Networks, 2019 We consider the detection and tracking of a target in a decentralized sensor network. The presence of the target is uncertain, and the sensor measurements are affected by clutter and missed detections. The state-evolution model and the measurement model may be nonlinear and non-Gaussian.
Papa, Giuseppe +4 more
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Polish Maritime Research, 2023 Underwater localization and tracking is a challenging problem and Time-of-Arrival and Time-Difference-of-Arrival approaches are commonly used. However, the performance difference between these approaches is not well understood or analysed adequately ...
Gunes Ahmet
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