SwePub - sökning: WFRF:(Garcia Angel 1984)

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1.	Fatemi, Maryam, 1982, et al. (författare) Road Geometry Estimation Using a Precise Clothoid Road Model and Observations of Moving Vehicles 2014 Ingår i: 17th International IEEE Conference on Intelligent Transportation Systems October 8-11, 2014, Qingdao, China. - 9781479960781 ; , s. 238-244 Konferensbidrag (refereegranskat)abstract An important part of any advanced driver assistancesystem is road geometry estimation. In this paper, wedevelop a Bayesian estimation algorithm using lane markingmeasurements received from a camera and measurements of theleading vehicles received from a radar-camera fusion system, to estimate the road up to 200 meters ahead in highway scenarios. The filtering algorithm uses a segmented clothoid-based road model. In order to use the heading of leading vehicles we need to detect if each vehicle is keeping lane or changing lane. Hence, we propose to jointly detect the motion state of the leading vehicles and estimate the road geometry using a multiple model filter. Finally the proposed algorithm is compared to an existingmethod using real data collected from highways. The resultsindicate that it provides a more accurate road estimation insome scenarios.
2.	Garcia, Angel, 1984, et al. (författare) A comparison between PMBM Bayesian track initiation and labelled RFS adaptive birth 2022 Ingår i: 2022 25th International Conference on Information Fusion, FUSION 2022. ; , s. 1143-1150 Konferensbidrag (refereegranskat)abstract This paper provides a comparative analysis between the adaptive birth model used in the labelled random finite set literature and the track initiation in the Poisson multi-Bernoulli mixture (PMBM) filter, with point-target models. The PMBM track initiation is obtained via Bayes' rule applied on the pre-dicted PMBM density, and creates one Bernoulli component for each received measurement, representing that this measurement may be clutter or a detection from a new target. Adaptive birth mimics this procedure by creating a Bernoulli component for each measurement using a different rule to determine the probability of existence and a user-defined single-target density. This paper first provides an analysis of the differences that arise in track initiation based on isolated measurements. Then, it shows that adaptive birth underestimates the number of objects present in the surveillance area under common modelling assumptions. Finally, we provide numerical simulations to further illustrate the differences.
3.	Garcia, Angel, 1984, et al. (författare) A Metric on the Space of Finite Sets of Trajectories for Evaluation of Multi-Target Tracking Algorithms 2020 Ingår i: IEEE Transactions on Signal Processing. - 1941-0476 .- 1053-587X. ; 68, s. 3917-3928 Tidskriftsartikel (refereegranskat)abstract In this article, we propose a metric on the space of finite sets of trajectories for assessing multi-target tracking algorithms in a mathematically sound way. The main use of the metric is to compare estimates of trajectories from different algorithms with the ground truth of trajectories. The proposed metric includes intuitive costs associated to localization error for properly detected targets, missed and false targets and track switches at each time step. The metric computation is based on solving a multi-dimensional assignment problem. We also propose a lower bound for the metric, which is also a metric for sets of trajectories and is computable in polynomial time using linear programming. We also extend the proposed metrics on sets of trajectories to random finite sets of trajectories.
4.	Garcia, Angel, 1984, et al. (författare) A Poisson multi-Bernoulli mixture filter for coexisting point and extended targets 2021 Ingår i: IEEE Transactions on Signal Processing. - 1941-0476 .- 1053-587X. ; 69, s. 2600-2610 Tidskriftsartikel (refereegranskat)abstract This paper proposes a Poisson multi-Bernoulli mixture (PMBM) filter for coexisting point and extended targets, i.e., for scenarios where there may be simultaneous point and extended targets. The PMBM filter provides a recursion to compute the multi-target filtering posterior based on probabilistic information on data associations, and single-target predictions and updates. In this paper, we first derive the PMBM filter update for a generalised measurement model, which can include measurements originated from point and extended targets. Second, we propose a single-target space that accommodates both point and extended targets and derive the filtering recursion that propagates Gaussian densities for single targets and gamma Gaussian inverse Wishart densities for extended targets. As a computationally efficient approximation of the PMBM filter, we also develop a Poisson multi-Bernoulli (PMB) filter for coexisting point and extended targets. The resulting filters are analysed via numerical simulations.
5.	Garcia, Angel, 1984, et al. (författare) A time-weighted metric for sets of trajectories to assess multi-object tracking algorithms 2021 Ingår i: Proceedings of 2021 IEEE 24th International Conference on Information Fusion, FUSION 2021. ; , s. 363-370 Konferensbidrag (refereegranskat)abstract This paper proposes a metric for sets of trajectories to evaluate multi-object tracking algorithms that includes time-weighted costs for localisation errors of properly detected targets, for false targets, missed targets and track switches. The proposed metric extends the metric in [1] by including weights to the costs associated to different time steps. The time-weighted costs increase the flexibility of the metric [1] to fit more applications and user preferences. We first introduce a metric based on multi-dimensional assignments, and then its linear programming relaxation, which is computable in polynomial time and is also a metric. The metrics can also be extended to metrics on random finite sets of trajectories to evaluate and rank algorithms across different scenarios, each with a ground truth set of trajectories.
6.	Garcia, Angel, 1984, et al. (författare) Adaptive unscented Gaussian likelihood approximation filter 2015 Ingår i: Automatica. - : Elsevier BV. - 0005-1098. ; 54, s. 166-175 Tidskriftsartikel (refereegranskat)abstract This paper focuses on the update step of Bayesian nonlinear filtering. We first derive the unscented Gaussian likelihood approximation filter (UGLAF), which provides a Gaussian approximation to the likelihood by applying the unscented transformation to the inverse of the measurement function. The UGLAF approximation is accurate in the cases where the unscented Kalman filter (UKF) is not and the other way round. As a result, we propose the adaptive UGLAF (AUGLAF), which selects the best approximation to the posterior (UKF or UGLAF) based on the Kullback-Leibler divergence. This enables AUGLAF to outperform both the UKF and UGLAF.
7.	Garcia, Angel, 1984, et al. (författare) Bayesian Road Estimation Using Onboard Sensors 2014 Ingår i: IEEE Transactions on Intelligent Transportation Systems. - 1524-9050 .- 1558-0016. ; PP:99, s. 1-14 Tidskriftsartikel (refereegranskat)abstract This paper describes an algorithm for estimating the road ahead of a host vehicle based on the measurements from several onboard sensors: a camera, a radar, wheel speed sensors, and an inertial measurement unit. We propose a novel road model that is able to describe the road ahead with higher accuracy than the usual polynomial model. We also develop a Bayesian fusion system that uses the following information from the surroundings: lane marking measurements obtained by the camera and leading vehicle and stationary object measurements obtained by a radar–camera fusion system. The performance of our fusion algorithm is evaluated in several drive tests. As expected, the more information we use, the better the performance is.
8.	Garcia, Angel, 1984, et al. (författare) Cooperative Localization Using Posterior Linearization Belief Propagation 2018 Ingår i: IEEE Transactions on Vehicular Technology. - 0018-9545 .- 1939-9359. ; 67:1, s. 832-836 Tidskriftsartikel (refereegranskat)abstract This paper presents the posterior linearization belief propagation (PLBP) algorithm for cooperative localization in wireless sensor networks with nonlinear measurements. PLBP performs two steps iteratively: linearization and belief propagation. At the linearization step, the nonlinear functions are linearized using statistical linear regression with respect to the current beliefs. This SLR is performed in practice by using sigma-points drawn from the beliefs. In the second step, belief propagation is run on the linearized model. We show by numerical simulations how PLBP can outperform other algorithms in the literature.
9.	Garcia, Angel, 1984, et al. (författare) Gaussian MAP Filtering Using Kalman Optimization 2015 Ingår i: IEEE Transactions on Automatic Control. - : Institute of Electrical and Electronics Engineers (IEEE). - 0018-9286 .- 1558-2523. ; 60:5, s. 1336-1349 Tidskriftsartikel (refereegranskat)abstract This paper deals with the update step of Gaussian MAP filtering. In this framework, we seek a Gaussian approximation to the posterior probability density function (PDF) whose mean is given by the maximum a posteriori (MAP) estimator. We propose two novel optimization algorithms which are quite suitable for finding the MAP estimate although they can also be used to solve general optimization problems. These are based on the design of a sequence of PDFs that become increasingly concentrated around the MAP estimate. The resulting algorithms are referred to as Kalman optimization (KO) methods. We also provide the important relations between these KO methods and their conventional optimization algorithms (COAs) counterparts, i.e., Newton's and Levenberg-Marquardt algorithms. Our simulations indicate that KO methods are more robust than their COA equivalents.
10.	Garcia, Angel, 1984, et al. (författare) Iterated Posterior Linearization Smoother 2017 Ingår i: IEEE Transactions on Automatic Control. - 0018-9286 .- 1558-2523. ; 62:4, s. 2056-2063 Tidskriftsartikel (refereegranskat)abstract This note considers the problem of Bayesian smoothing in nonlinear state-space models with additive noise using Gaussian approximations. Sigma-point approximations to the general Gaussian Rauch-Tung-Striebel smoother are widely used methods to tackle this problem. These algorithms perform statistical linear regression (SLR) of the nonlinear functions considering only the previous measurements. We argue that SLR should be done taking all measurements into account. We propose the iterated posterior linearization smoother (IPLS), which is an iterated algorithm that performs SLR of the nonlinear functions with respect to the current posterior approximation. The algorithm is demonstrated to outperform conventional Gaussian nonlinear smoothers in two numerical examples.
11.	Garcia, Angel, 1984, et al. (författare) Iterated statistical linear regression for Bayesian updates 2014 Ingår i: 17th International Conference on Information Fusion, FUSION 2014; Salamanca; Spain; 7 July 2014 through 10 July 2014. - 9788490123553 ; , s. Art. no. 6916133- Konferensbidrag (refereegranskat)abstract This paper deals with Gaussian approximations to the posterior probability density function (PDF) in Bayesian nonlinear filtering. In this setting, using sigma-point based approximations to the Kalman filter (KF) recursion is a prominent approach. In the update step, the sigma-point KF approximations are equivalent to performing the statistical linear regression (SLR) of the (nonlinear) measurement function with respect to the prior PDF. In this paper, we indicate that the SLR of the measurement function with respect to the posterior is expected to provide better results than the SLR with respect to the prior. The resulting filter is referred to as the posterior linearisation filter (PLF). In practice, the exact PLF update is intractable but can be efficiently approximated by carrying out iterated SLRs based on sigma-point approximations. On the whole, the resulting filter, the iterated PLF (IPLF), is expected to outperform all sigma-point KF approximations as demonstrated by numerical simulations.
12.	Garcia, Angel, 1984, et al. (författare) Poisson Multi-Bernoulli Mixture Filter: Direct Derivation and Implementation 2018 Ingår i: IEEE Transactions on Aerospace and Electronic Systems. - 1557-9603 .- 0018-9251. ; 54:4, s. 1883-1901 Tidskriftsartikel (refereegranskat)abstract We provide a derivation of the Poisson multi-Bernoulli mixture (PMBM) filter for multitarget tracking with the standard point target measurements without using probability generating functionals or functional derivatives. We also establish the connection with the δ-generalized labeled multi-Bernoulli (δ -GLMB) filter, showing that a δ-GLMB density represents a multi-Bernoulli mixture with labeled targets so it can be seen as a special case of PMBM. In addition, we propose an implementation for linear/Gaussian dynamic and measurement models and how to efficiently obtain typical estimators in the literature from the PMBM. The PMBM filter is shown to outperform other filters in the literature in a challenging scenario.
13.	Garcia, Angel, 1984, et al. (författare) Poisson multi-Bernoulli mixture filter with general target-generated measurements and arbitrary clutter 2023 Ingår i: IEEE Transactions on Signal Processing. - 1941-0476 .- 1053-587X. ; 71, s. 1895-1906 Tidskriftsartikel (refereegranskat)abstract This paper shows that the Poisson multi-Bernoulli mixture (PMBM) density is a multi-target conjugate prior for general target-generated measurement distributions and arbitrary clutter distributions. That is, for this multi-target measurement model and the standard multi-target dynamic model with Poisson birth model, the predicted and filtering densities are PMBMs. We derive the corresponding PMBM filtering recursion. Based on this result, we implement a PMBM filter for point-target measurement models and negative binomial clutter density in which data association hypotheses with high weights are chosen via Gibbs sampling. We also implement an extended target PMBM filter with clutter that is the union of Poisson-distributed clutter and a finite number of independent clutter sources. Simulation results show the benefits of the proposed filters to deal with non-standard clutter.
14.	Garcia, Angel, 1984, et al. (författare) Posterior Linearization Filter: Principles and Implementation Using Sigma Points 2015 Ingår i: IEEE Transactions on Signal Processing. - : Institute of Electrical and Electronics Engineers (IEEE). - 1941-0476 .- 1053-587X. ; 63:20, s. 5561-5573 Tidskriftsartikel (refereegranskat)abstract This paper is concerned with Gaussian approximations to the posterior probability density function (PDF) in the update step of Bayesian filtering with nonlinear measurements. In this setting, sigma-point approximations to the Kalman filter (KF) recursion are widely used due to their ease of implementation and relatively good performance. In the update step, these sigma-point KFs are equivalent to linearizing the nonlinear measurement function by statistical linear regression (SLR) with respect to the prior PDF. In this paper, we argue that the measurement function should be linearized using SLR with respect to the posterior rather than the prior to take into account the information provided by the measurement. The resulting filter is referred to as the posterior linearization filter (PLF). In practice, the exact PLF update is intractable but can be approximated by the iterated PLF (IPLF), which carries out iterated SLRs with respect to the best available approximation to the posterior. The IPLF can be seen as an approximate recursive Kullback-Leibler divergence minimization procedure. We demonstrate the high performance of the IPLF in relation to other Gaussian filters in two numerical examples.
15.	Garcia, Angel, 1984, et al. (författare) Trajectory multi-Bernoulli filters for multi-target tracking based on sets of trajectories 2020 Ingår i: Proceedings of 2020 23rd International Conference on Information Fusion, FUSION 2020. ; , s. 313-320 Konferensbidrag (refereegranskat)abstract This paper presents two multi-Bernoulli filters on sets of trajectories for multiple target tracking. The first filter provides a multi-Bernoulli approximation of the posterior density over the set of alive trajectories at the current time step. The second filter provides a multi-Bernoulli approximation of the posterior density over the set of all trajectories (alive and dead) up to the current time. We also explain the Gaussian implementation of the filters and compare them with other multiple target tracking algorithms in a simulated scenario.
16.	Garcia, Angel, 1984, et al. (författare) Trajectory PHD and CPHD filters 2019 Ingår i: IEEE Transactions on Signal Processing. - 1941-0476 .- 1053-587X. ; 67:22, s. 5702-5714 Tidskriftsartikel (refereegranskat)abstract This paper presents the probability hypothesis density filter (PHD) and the cardinality PHD (CPHD) filter for sets of trajectories, which are referred to as the trajectory PHD (TPHD) and trajectory CPHD (TCPHD) filters. Contrary to the PHD/CPHD filters, the TPHD/TCPHD filters are able to produce trajectory estimates from first principles. The TPHD filter is derived by recursively obtaining the best Poisson multitrajectory density approximation to the posterior density over the alive trajectories by minimising the Kullback-Leibler divergence. The TCPHD is derived in the same way but propagating an independent identically distributed (IID) cluster multitrajectory density approximation. We also propose the Gaussian mixture implementations of the TPHD and TCPHD recursions, the Gaussian mixture TPHD (GMTPHD) and the Gaussian mixture TCPHD (GMTCPHD), and the L-scan computationally efficient implementations, which only update the density of the trajectory states of the last L time steps.
17.	Garcia, Angel, 1984, et al. (författare) Trajectory Poisson Multi-Bernoulli Filters 2020 Ingår i: IEEE Transactions on Signal Processing. - 1941-0476 .- 1053-587X. ; 68, s. 4933-4945 Tidskriftsartikel (refereegranskat)abstract This paper presents two trajectory Poisson multi-Bernoulli (TPMB) filters for multi-target tracking: one to estimate the set of alive trajectories at each time step and another to estimate the set of all trajectories, which includes alive and dead trajectories, at each time step. The filters are based on propagating a Poisson multi-Bernoulli (PMB) density on the corresponding set of trajectories through the filtering recursion. After the update step, the posterior is a PMB mixture (PMBM) so, in order to obtain a PMB density, a Kullback-Leibler divergence minimisation on an augmented space is performed. The developed filters are computationally lighter alternatives to the trajectory PMBM filters, which provide the closed-form recursion for sets of trajectories with Poisson birth model, and are shown to outperform previous multi-target tracking algorithms.
18.	Garcia, Angel, 1984, et al. (författare) Trajectory probability hypothesis density filter 2018 Ingår i: 2018 21st International Conference on Information Fusion, FUSION 2018. - 9780996452762 ; , s. 1430-1437 Konferensbidrag (refereegranskat)abstract This paper presents the probability hypothesis density (PHD) filter for sets of trajectories: the trajectory probability density (TPHD) filter. The TPHD filter is capable of estimating trajectories in a principled way without requiring to evaluate all measurement-to-target association hypotheses. The TPHD filter is based on recursively obtaining the best Poisson approximation to the multitrajectory filtering density in the sense of minimising the Kullback-Leibler divergence. We also propose a Gaussian mixture implementation of the TPHD recursion. Finally, we include simulation results to show the performance of the proposed algorithm.
19.	Garcia-Porta, Joan, et al. (författare) Environmental temperatures shape thermal physiology as well as diversification and genome-wide substitution rates in lizards 2019 Ingår i: Nature Communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 10 Tidskriftsartikel (refereegranskat)abstract Climatic conditions changing over time and space shape the evolution of organisms at multiple levels, including temperate lizards in the family Lacertidae. Here we reconstruct a dated phylogenetic tree of 262 lacertid species based on a supermatrix relying on novel phylogenomic datasets and fossil calibrations. Diversification of lacertids was accompanied by an increasing disparity among occupied bioclimatic niches, especially in the last 10 Ma, during a period of progressive global cooling. Temperate species also underwent a genomewide slowdown in molecular substitution rates compared to tropical and desert-adapted lacertids. Evaporative water loss and preferred temperature are correlated with bioclimatic parameters, indicating physiological adaptations to climate. Tropical, but also some populations of cool-adapted species experience maximum temperatures close to their preferred temperatures. We hypothesize these species-specific physiological preferences may constitute a handicap to prevail under rapid global warming, and contribute to explaining local lizard extinctions in cool and humid climates.
20.	Granström, Karl, 1981, et al. (författare) Spatiotemporal Constraints for Sets of Trajectories with Applications to PMBM Densities 2020 Ingår i: Proceedings of 2020 23rd International Conference on Information Fusion, FUSION 2020. ; , s. 343-350 Konferensbidrag (refereegranskat)abstract In this paper we introduce spatiotemporal constraints for trajectories, i.e., restrictions that the trajectory must be in some part of the state space (spatial constraint) at some point in time (temporal constraint). Spatiotemporal contraints on trajectories can be used to answer a range of important questions, including, e.g., “where did the person that were in area A at time t, go afterwards?”. We discuss how multiple constraints can be combined into sets of constraints, and we then apply sets of constraints to set of trajectories densities, specifically Poisson Multi-Bernoulli Mixture (PMBM) densities. For Poisson target birth, the exact posterior density is PMBM for both point targets and extended targets. In the paper we show that if the unconstrained set of trajectories density is PMBM, then the constrained density is also PMBM. Examples of constrained trajectory densities motivate and illustrate the key results.
21.	Hammarstrand, Lars, 1979, et al. (författare) Long-range road geometry estimation using moving vehicles and road-side observations 2016 Ingår i: IEEE Transactions on Intelligent Transportation Systems. - 1524-9050 .- 1558-0016. ; 17:8, s. 2144-2158 Tidskriftsartikel (refereegranskat)abstract This paper presents an algorithm for estimating the shape of the road ahead of a host vehicle equipped with the following onboard sensors: a camera, a radar and vehicle internal sensors. The aim is to accurately describe the road geometry up to 200 m ahead in highway scenarios. This purpose is accomplished by deriving a precise clothoid-based road model for which we design a Bayesian fusion framework. Using this framework the road geometry is estimated using sensor observations on the shape of the lane markings, the heading of leading vehicles and the position of road side radar reflectors. The evaluation on sensor data shows that the proposed algorithm is capable of capturing the shape of the road well, even in challenging mountainous highways.
22.	Miras, Marina, et al. (författare) Analytical Tool for Quality Control of Irrigation Waters via a Potentiometric Electronic Tongue 2023 Ingår i: Chemosensors. - : MDPI AG. - 2227-9040. ; 11:7 Tidskriftsartikel (refereegranskat)abstract A potentiometric electronic tongue (ET) for the analysis of well and ditch irrigation water samples is herein proposed. The sensors' array is composed of six ion-selective electrodes based on plasticized polymeric membranes with low selectivity profiles, i.e., the membranes do not contain any selective receptor. The sensors differ between them in the type of ion-exchanger (sensors for cations or anions) and the plasticizer used in the membrane composition, while the polymeric matrix and the preparation protocol were maintained. The potentiometric response of each sensor towards the main cations (Na+, K+, Ca2+, Mg2+) and anions (HCO3-, Cl-, SO42-, NO3-) expected in irrigation water samples was characterized, revealing a fast response time (<50 s). A total of 19 samples were analyzed with the sensor array at optimized experimental conditions, but, also, a series of complementary analytical techniques were applied to obtain the exact ion composition and conductivity to develop a trustable ET. The principal component analysis of the final potential values of the dynamic response observed with each sensor in the array allows for the differentiation between most of the samples in terms of quality. Furthermore, the ET was treated with a linear multivariate regression method for the quantitative determination of the mentioned ions in the irrigation water samples, revealing rather good prediction of Mg2+, Na+, and Cl- concentrations and acceptable results for the rest of ions. Overall, the ET is a promising analytical tool for irrigation water quality, exceeding traditional characterization approaches (conductivity, salinity, pH, cations, anions, etc.) in terms of overhead costs, versatility, simplicity, and total time for data provision.
23.	Rahmathullah, Abu Sajana, 1986, et al. (författare) Generalized optimal sub-pattern assignment metric 2017 Ingår i: 20th International Conference on Information Fusion, Fusion 2017, Xian, China, 10-13 July 2017. - 9780996452700 ; , s. 182-189 Konferensbidrag (refereegranskat)abstract This paper presents the generalized optimal sub-pattern assignment (GOSPA) metric on the space of finite sets of targets. Compared to the well-established optimal sub-pattern assignment (OSPA) metric, GOSPA is not normalised by the cardinality of the largest set and it penalizes cardinality errors differently, which enables us to express it as an optimisation over assignments instead of permutations. An important consequence of this is that GOSPA allows us to penalize localization errors for detected targets and the errors due to missed and false targets, as indicated by traditional multiple target tracking (MTT) performance measures, in a sound manner. In addition, we extend the GOSPA metric to the space of random finite sets, which is important to evaluate MTT algorithms via simulations in a rigorous way.
24.	Ristić, Branko, et al. (författare) Performance evaluation of random set based pedestrian tracking algorithms 2013 Ingår i: 2013 IEEE 8th International Conference on Intelligent Sensors, Sensor Networks and Information Processing: Sensing the Future, ISSNIP 2013. - 9781467355001 ; 1, s. 300-305 Konferensbidrag (refereegranskat)abstract The paper evaluates the error performance of three random finite set based multi-object trackers in the context of pedestrian video tracking. The evaluation is carried out using a publicly available video dataset of 4500 frames (town centre street) for which the ground truth is available. The input to all pedestrian tracking algorithms is an identical set of head and body detections, obtained using the Histogram of Oriented Gradients (HOG) detector. Head and body detections are unreliable in the sense that the probability of detection is low and false detections are non-uniformly distributed. The tracking error is measured using the recently proposed OSPA metric for tracks (OSPA-T), adopted as the only known mathematically rigorous metric for measuring the distance between two sets of tracks. The paper presents the correct proof of the triangle inequality for the OSPA-T. A comparative analysis is presented under various conditions.
25.	Xia, Yuxuan, 1993, et al. (författare) An Efficient Implementation of the Extended Object Trajectory PMB Filter Using Blocked Gibbs Sampling 2023 Ingår i: 2023 26th International Conference on Information Fusion, FUSION 2023. Konferensbidrag (refereegranskat)abstract This paper presents an efficient implementation of the trajectory Poisson multi-Bernoulli (PMB) filter for multiple extended object tracking (EOT), which directly estimates a set of object trajectories. The trajectory PMB filter propagates a PMB density on the posterior of sets of trajectories through the filtering recursions over time, where the multi-Bernoulli (MB) mixture in the PMB mixture (PMBM) posterior after each update step is approximated as a single MB. The efficient MB approximation is achieved by first running a blocked Gibbs sampler on the joint posterior of the set of trajectories and the measurement association variables. The single-object measurement model is assumed to be a Poisson point process which enables us to parallelize the sampling across all objects and association variables, respectively. Then, samples of object states are utilized to form the approximate MB density via Kullback-Leibler divergence minimization. Simulation results on EOT with known and constant elliptical shapes show that the TPMB implementation using blocked Gibbs sampling outperforms the state-of-the-art TPMB implementation using loopy belief propagation with significantly reduced runtime.
26.	Xia, Yuxuan, et al. (författare) An Implementation of the Poisson Multi-Bernoulli Mixture Trajectory Filter via Dual Decomposition 2018 Ingår i: 2018 21st International Conference on Information Fusion, FUSION 2018. ; , s. 2453-2460 Konferensbidrag (refereegranskat)abstract This paper proposes an efficient implementation of the Poisson multi-Bernoulli mixture (PMBM) trajectory filter. The proposed implementation performs track-oriented N-scan pruning to limit complexity, and uses dual decomposition to solve the involved multi-frame assignment problem. In contrast to the existing PMBM filter for sets of targets, the PMBM trajectory filter is based on sets of trajectories which ensures that track continuity is formally maintained. The resulting filter is an efficient and scalable approximation to a Bayes optimal multi-target tracking algorithm, and its performance is compared, in a simulation study, to the PMBM target filter, and the delta generalized labelled multi-Bernoulli filter, in terms of state/trajectory estimation error and computational time.
27.	Xia, Yuxuan, 1993, et al. (författare) Backward simulation for sets of trajectories 2020 Ingår i: Proceedings of 2020 23rd International Conference on Information Fusion, FUSION 2020. Konferensbidrag (refereegranskat)abstract This paper presents a solution for recovering full trajectory information, via the calculation of the posterior of the set of trajectories, from a sequence of multitarget (unlabelled) filtering densities and the multitarget dynamic model. Importantly, the proposed solution opens an avenue of trajectory estimation possibilities for multitarget filters that do not explicitly estimate trajectories. In this paper, we first derive a general multitrajectory forward-backward smoothing equation based on sets of trajectories and the random finite set framework. Then we show how to sample sets of trajectories using backward simulation when the multitarget filtering densities are multi-Bernoulli processes. The proposed approach is demonstrated in a simulation study.
28.	Xia, Yuxuan, 1993, et al. (författare) Extended target Poisson multi-Bernoulli mixture trackers based on sets of trajectories 2019 Ingår i: FUSION 2019 - 22nd International Conference on Information Fusion. Konferensbidrag (refereegranskat)abstract The Poisson multi-Bernoulli mixture (PMBM) is a multi-target distribution for which the prediction and update are closed. By applying the random finite set (RFS) framework to multi-target tracking with sets of trajectories as the variable of interest, the PMBM trackers can efficiently estimate the set of target trajectories. This paper derives two trajectory RFS filters for extended target tracking, called extended target PMBM trackers. Compared to the extended target PMBM filter based on sets on targets, explicit track continuity between time steps is provided in the extended target PMBM trackers.
29.	Xia, Yuxuan, et al. (författare) Markov Chain Monte Carlo Multi-Scan Data Association for Sets of Trajectories 2024 Ingår i: IEEE Transactions on Aerospace and Electronic Systems. - 1557-9603 .- 0018-9251. ; In Press Tidskriftsartikel (refereegranskat)abstract This paper considers a batch solution to the multi-object tracking problem based on sets of trajectories. Specifically, we present two offline implementations of the trajectory Poisson multi-Bernoulli mixture (TPMBM) filter for batch data based on Markov chain Monte Carlo (MCMC) sampling of the data association hypotheses. In contrast to online TPMBM implementations, the proposed offline implementations solve a large-scale, multi-scan data association problem across the entire time interval of interest, and therefore they can fully exploit all the measurement information available. Furthermore, by leveraging the efficient hypothesis structure of TPMBM filters, the proposed implementations compare favorably with other MCMC-based multi-object tracking algorithms. Simulation results show that the TPMBM implementation using the Metropolis-Hastings algorithm presents state-of-the-art multiple trajectory estimation performance.
30.	Xia, Yuxuan, 1993, et al. (författare) Multiscan implementation of the trajectory poisson multi-Bernoulli mixture filter 2019 Ingår i: Journal of Advances in Information Fusion. - 1557-6418. ; 14:2, s. 213-235 Tidskriftsartikel (refereegranskat)abstract The Poisson multi-Bernoulli mixture (PMBM) and the multi-Bernoulli mixture (MBM) are two multitarget distributions for which closed-form filtering recursions exist. The PMBM has a Poisson birth process, whereas the MBM has a multi-Bernoulli birth process. This paper considers a recently developed formulation of the multitarget tracking problem using a random finite set of trajectories, through which the track continuity is explicitly established. A multiscan trajectory PMBM filter and a multiscan trajectory MBM filter, with the ability to correct past data association decisions to improve current decisions, are presented. In addition, a multiscan trajectory MBM01 filter, in which the existence probabilities of all Bernoulli components are either 0 or 1, is presented. This paper proposes an efficient implementation that performs track-oriented N-scan pruning to limit computational complexity, and uses dual decomposition to solve the involved multiframe assignment problem. The performance of the presented multitarget trackers, applied with an efficient fixed-lag smoothing method, is evaluated in a simulation study.
31.	Xia, Yuxuan, et al. (författare) Performance evaluation of multi-bernoulli conjugate priors for multi-target filtering 2017 Ingår i: 20th International Conference on Information Fusion, Fusion 2017, Xian, China, 10-13 July 2017. - 9780996452700 ; , s. 644-651 Konferensbidrag (refereegranskat)abstract In this paper, we evaluate the performance of labelled and unlabelled multi-Bernoulli conjugate priors for multi-target filtering. Filters are compared in two different scenarios with performance assessed using the generalised optimal sub-pattern assignment (GOSPA) metric. The first scenario under consideration is tracking of well-spaced targets. The second scenario is more challenging and considers targets in close proximity, for which filters may suffer from coalescence. We analyse various aspects of the filters in these two scenarios. Though all filters have pros and cons, the Poisson multi-Bernoulli filters arguably provide the best overall performance concerning GOSPA and computational time.
32.	Xia, Yuxuan, 1993, et al. (författare) Poisson Multi-Bernoulli Approximations for Multiple Extended Object Filtering 2022 Ingår i: IEEE Transactions on Aerospace and Electronic Systems. - 1557-9603 .- 0018-9251. ; 58:2, s. 890-906 Tidskriftsartikel (refereegranskat)
33.	Xia, Yuxuan, 1993, et al. (författare) Trajectory PMB Filters for Extended Object Tracking Using Belief Propagation 2023 Ingår i: IEEE Transactions on Aerospace and Electronic Systems. - 1557-9603 .- 0018-9251. ; 59:6, s. 9312-9331 Tidskriftsartikel (refereegranskat)abstract In this paper, we propose a Poisson multi-Bernoulli (PMB) filter for extended object tracking (EOT), which directly estimates the set of object trajectories, using belief propagation (BP). The proposed filter propagates a PMB density on the posterior of sets of trajectories through the filtering recursions over time, where the PMB mixture (PMBM) posterior after the update step is approximated as a PMB. The efficient PMB approximation relies on several important theoretical contributions. First, we present a PMBM conjugate prior on the posterior of sets of trajectories for a generalized measurement model, in which each object generates an independent set of measurements. The PMBM density is a conjugate prior in the sense that both the prediction and the update steps preserve the PMBM form of the density. Second, we present a factor graph representation of the joint posterior of the PMBM set of trajectories and association variables for the Poisson spatial measurement model. Importantly, leveraging the PMBM conjugacy and the factor graph formulation enables an elegant treatment on undetected objects via a Poisson point process and efficient inference on sets of trajectories using BP, where the approximate marginal densities in the PMB approximation can be obtained without enumeration of different data association hypotheses. To achieve this, we present a particle-based implementation of the proposed filter, where smoothed trajectory estimates, if desired, can be obtained via single-object particle smoothing methods, and its performance for EOT with ellipsoidal shapes is evaluated in a simulation study.

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