Pub Date : 2022-07-04DOI: 10.23919/fusion49751.2022.9841279
Hosam Alqaderi, F. Govaers, W. Koch
In some defence applications, it is required to identify targets separated by a certain distance as group-targets. This allows the system to use a suitable tracking and mitigation strategy for a group different from what is used for a point-target. A natural choice to identify a group of this type is the density-based spatial clustering of applications with noise (DBSCAN) algorithm. The DBSCAN algorithm uses the available track information to identify the groups/clusters. Information on these tracks are, in the vast majority of tracking systems, based on the Kalman filter estimate. In this work, we present a scenario where the out-group target is inseparable from the group-target using a Kalman filter. Thereafter, we show that the separability could be significantly improved using the estimates of the joint probability density of the kinematic target states accumulated over a certain time window, up to the present time, given the time series of all sensor data. These densities are known as Accumulated State Densities (ASDs).
{"title":"Accumulated State Densities Filter for Better Separability of Group-Targets","authors":"Hosam Alqaderi, F. Govaers, W. Koch","doi":"10.23919/fusion49751.2022.9841279","DOIUrl":"https://doi.org/10.23919/fusion49751.2022.9841279","url":null,"abstract":"In some defence applications, it is required to identify targets separated by a certain distance as group-targets. This allows the system to use a suitable tracking and mitigation strategy for a group different from what is used for a point-target. A natural choice to identify a group of this type is the density-based spatial clustering of applications with noise (DBSCAN) algorithm. The DBSCAN algorithm uses the available track information to identify the groups/clusters. Information on these tracks are, in the vast majority of tracking systems, based on the Kalman filter estimate. In this work, we present a scenario where the out-group target is inseparable from the group-target using a Kalman filter. Thereafter, we show that the separability could be significantly improved using the estimates of the joint probability density of the kinematic target states accumulated over a certain time window, up to the present time, given the time series of all sensor data. These densities are known as Accumulated State Densities (ASDs).","PeriodicalId":176447,"journal":{"name":"2022 25th International Conference on Information Fusion (FUSION)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130778476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-04DOI: 10.23919/fusion49751.2022.9841382
Michele Somero, L. Snidaro, G. Rogova
In this work, we propose a novel multisource deep learning architecture that employs the evidential Transferable Belief Model (TBM) for combining classifiers for Covid diagnosis. Our architecture was used in the difficult task of distinguishing mild cases of Covid versus severe ones that require urgent medical attention. The available datasets comprised radiographic and clinical data of the patients that we classified separately with a Convolutional Neural Network (CNN) and a decision tree respectively. In our approach, TBM was systematically used to fuse both the results of individual layers in the CNN and to combine the outputs of the CNN with the decision tree. We experimented with both feature and decision fusion approaches. The results outperform the individual classifiers and classical fusion methods.
{"title":"Evidential Decision Fusion of Deep Neural Networks for Covid Diagnosis","authors":"Michele Somero, L. Snidaro, G. Rogova","doi":"10.23919/fusion49751.2022.9841382","DOIUrl":"https://doi.org/10.23919/fusion49751.2022.9841382","url":null,"abstract":"In this work, we propose a novel multisource deep learning architecture that employs the evidential Transferable Belief Model (TBM) for combining classifiers for Covid diagnosis. Our architecture was used in the difficult task of distinguishing mild cases of Covid versus severe ones that require urgent medical attention. The available datasets comprised radiographic and clinical data of the patients that we classified separately with a Convolutional Neural Network (CNN) and a decision tree respectively. In our approach, TBM was systematically used to fuse both the results of individual layers in the CNN and to combine the outputs of the CNN with the decision tree. We experimented with both feature and decision fusion approaches. The results outperform the individual classifiers and classical fusion methods.","PeriodicalId":176447,"journal":{"name":"2022 25th International Conference on Information Fusion (FUSION)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125727943","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-04DOI: 10.23919/fusion49751.2022.9841376
Magnus Malmström, I. Skog, Daniel Axehill, F. Gustafsson
Neural Networks (NNS) can solve very hard classification and estimation tasks but are less well suited to solve complex sensor fusion challenges, such as end-to-end control of autonomous vehicles. Nevertheless, NN can still be a powerful tool for particular sub-problems in sensor fusion. This would require a reliable and quantifiable measure of the stochastic uncertainty in the predictions that can be compared to classical sensor measurements. However, current NN'S output some figure of merit, that is only a relative model fit and not a stochastic uncertainty. We propose to embed the NN'S in a proper stochastic system identification framework. In the training phase, the stochastic uncertainty of the parameters in the (last layers of the) NN is quantified. We show that this can be done recursively with very few extra computations. In the classification phase, Monte-Carlo (MC) samples are used to generate a set of classifier outputs. From this set, a distribution of the classifier output is obtained, which represents a proper description of the stochastic uncertainty of the predictions. We also show how to use the calculated uncertainty for outlier detection by including an artificial outlier class. In this way, the NN fits a sensor fusion framework much better. We evaluate the approach on images of handwritten digits. The proposed method is shown to be on par with MC dropout, while having lower computational complexity, and the outlier detection almost completely eliminates false classifications.
{"title":"Detection of outliers in classification by using quantified uncertainty in neural networks","authors":"Magnus Malmström, I. Skog, Daniel Axehill, F. Gustafsson","doi":"10.23919/fusion49751.2022.9841376","DOIUrl":"https://doi.org/10.23919/fusion49751.2022.9841376","url":null,"abstract":"Neural Networks (NNS) can solve very hard classification and estimation tasks but are less well suited to solve complex sensor fusion challenges, such as end-to-end control of autonomous vehicles. Nevertheless, NN can still be a powerful tool for particular sub-problems in sensor fusion. This would require a reliable and quantifiable measure of the stochastic uncertainty in the predictions that can be compared to classical sensor measurements. However, current NN'S output some figure of merit, that is only a relative model fit and not a stochastic uncertainty. We propose to embed the NN'S in a proper stochastic system identification framework. In the training phase, the stochastic uncertainty of the parameters in the (last layers of the) NN is quantified. We show that this can be done recursively with very few extra computations. In the classification phase, Monte-Carlo (MC) samples are used to generate a set of classifier outputs. From this set, a distribution of the classifier output is obtained, which represents a proper description of the stochastic uncertainty of the predictions. We also show how to use the calculated uncertainty for outlier detection by including an artificial outlier class. In this way, the NN fits a sensor fusion framework much better. We evaluate the approach on images of handwritten digits. The proposed method is shown to be on par with MC dropout, while having lower computational complexity, and the outlier detection almost completely eliminates false classifications.","PeriodicalId":176447,"journal":{"name":"2022 25th International Conference on Information Fusion (FUSION)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121375244","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-04DOI: 10.23919/fusion49751.2022.9841271
Xile Li, Yangming Lai, Shixing Yang, Wei Yi
This paper considers the integrated localization for the mixed near-field (NF) and far-field (FF) sources using the uniform linear array (ULA). With the help of the polynomial rooting methods and the propagator, an efficient algorithm is proposed to provide an integrated estimation of the direction of arrival (DOA) and the ranges of the sources. It takes low computational burden without the requirements that separating the DOA and range information or pre-classification of the sources. We first construct two special fourth-order cumulant matrices using the received array data, then extract the prior-electrical parameters related to the array elements by its steering matrix, and finally carry out parameter matching and classification. Besides, the proposed algorithm eliminates the need for tedious eigenvalue decomposition and spectral search steps, and has almost no aperture loss. Eventually, several simulation results show that the proposed algorithm has lower computational complexity under an acceptable accuracy, compared to the state-of-the-art methods.
{"title":"An Integrated Localization Method for Mixed Near-Field and Far-Field Sources Based on Mixed-order Statistic","authors":"Xile Li, Yangming Lai, Shixing Yang, Wei Yi","doi":"10.23919/fusion49751.2022.9841271","DOIUrl":"https://doi.org/10.23919/fusion49751.2022.9841271","url":null,"abstract":"This paper considers the integrated localization for the mixed near-field (NF) and far-field (FF) sources using the uniform linear array (ULA). With the help of the polynomial rooting methods and the propagator, an efficient algorithm is proposed to provide an integrated estimation of the direction of arrival (DOA) and the ranges of the sources. It takes low computational burden without the requirements that separating the DOA and range information or pre-classification of the sources. We first construct two special fourth-order cumulant matrices using the received array data, then extract the prior-electrical parameters related to the array elements by its steering matrix, and finally carry out parameter matching and classification. Besides, the proposed algorithm eliminates the need for tedious eigenvalue decomposition and spectral search steps, and has almost no aperture loss. Eventually, several simulation results show that the proposed algorithm has lower computational complexity under an acceptable accuracy, compared to the state-of-the-art methods.","PeriodicalId":176447,"journal":{"name":"2022 25th International Conference on Information Fusion (FUSION)","volume":"51 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113938661","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-04DOI: 10.23919/fusion49751.2022.9841362
M. Emzir, Niki A. Loppi, Zheng Zhao, S. S. Hassan, S. Särkkä
Multi-layered Gaussian process (field) priors are non-Gaussian priors, which offer a capability to handle Bayesian inference on both smooth and discontinuous functions. Previ-ously, performing Bayesian inference using these priors required the construction of a Markov chain Monte Carlo sampler. To converge to the stationary distribution, this sampling technique is computationally inefficient and hence the utility of the approach has only been demonstrated for small canonical test problems. Furthermore, in numerous Bayesian inference applications, such as Bayesian inverse problems, the uncertainty quantification of the hyper-prior layers is of less interest, since the main concern is to quantify the randomness of the process/field of interest. In this article, we propose an alternative approach, where we optimize the hyper-prior layers, while inference is performed only for the lowest layer. Specifically, we use the Galerkin approximation with automatic differentiation to accelerate optimization. We validate the proposed approach against several existing non-stationary Gaussian process methods and demonstrate that it can significantly decrease the execution time while maintaining comparable accuracy. We also apply the method to an X-ray tomography inverse problem. Due to its improved performance and robustness, this new approach opens up the possibility for applying the multi-layer Gaussian field priors to more complex problems.
{"title":"Fast optimize-and-sample method for differentiable Galerkin approximations of multi-layered Gaussian process priors","authors":"M. Emzir, Niki A. Loppi, Zheng Zhao, S. S. Hassan, S. Särkkä","doi":"10.23919/fusion49751.2022.9841362","DOIUrl":"https://doi.org/10.23919/fusion49751.2022.9841362","url":null,"abstract":"Multi-layered Gaussian process (field) priors are non-Gaussian priors, which offer a capability to handle Bayesian inference on both smooth and discontinuous functions. Previ-ously, performing Bayesian inference using these priors required the construction of a Markov chain Monte Carlo sampler. To converge to the stationary distribution, this sampling technique is computationally inefficient and hence the utility of the approach has only been demonstrated for small canonical test problems. Furthermore, in numerous Bayesian inference applications, such as Bayesian inverse problems, the uncertainty quantification of the hyper-prior layers is of less interest, since the main concern is to quantify the randomness of the process/field of interest. In this article, we propose an alternative approach, where we optimize the hyper-prior layers, while inference is performed only for the lowest layer. Specifically, we use the Galerkin approximation with automatic differentiation to accelerate optimization. We validate the proposed approach against several existing non-stationary Gaussian process methods and demonstrate that it can significantly decrease the execution time while maintaining comparable accuracy. We also apply the method to an X-ray tomography inverse problem. Due to its improved performance and robustness, this new approach opens up the possibility for applying the multi-layer Gaussian field priors to more complex problems.","PeriodicalId":176447,"journal":{"name":"2022 25th International Conference on Information Fusion (FUSION)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134053511","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-04DOI: 10.23919/fusion49751.2022.9841355
Liang Xu, R. Niu, Erik Blasch
In this paper, an extended Kalman filter (EKF) framework, called uncertainty aware EKF (UA-EKF), is developed by utilizing contextual knowledge to improve the vehicle tracking accuracy for autonomous vehicles. The proposed framework can learn and estimate the uncertainty associated with the measurements provided by a LiDAR-based vehicle detection algorithm. The UA-EKF has two major parts: one has the ability to estimate the state-dependent measurement noise's statistics for LiDAR object detections, and the other is to create multiple-hypothesis measurements based on the detected vehicle's heading. The measurement uncertainties are learned based on the EKFNet, which is an algorithm that can learn the system noise covariance from measurement data. Both the learned noise statistics and multiple-hypothesis estimators are used to compensate the physical limitations of the LiDAR measurements. A detailed analysis of the measurement uncertainty and the methods to improve tracking performance during filtering are provided for the UA-EKF. The obtained results by using the nuScenes datasets show that estimating the measurement uncertainty is an efficient solution for tracking the vehicle based on LiDAR detections.
{"title":"Uncertainty Aware EKF: a Tracking Filter Learning LiDAR Measurement Uncertainty","authors":"Liang Xu, R. Niu, Erik Blasch","doi":"10.23919/fusion49751.2022.9841355","DOIUrl":"https://doi.org/10.23919/fusion49751.2022.9841355","url":null,"abstract":"In this paper, an extended Kalman filter (EKF) framework, called uncertainty aware EKF (UA-EKF), is developed by utilizing contextual knowledge to improve the vehicle tracking accuracy for autonomous vehicles. The proposed framework can learn and estimate the uncertainty associated with the measurements provided by a LiDAR-based vehicle detection algorithm. The UA-EKF has two major parts: one has the ability to estimate the state-dependent measurement noise's statistics for LiDAR object detections, and the other is to create multiple-hypothesis measurements based on the detected vehicle's heading. The measurement uncertainties are learned based on the EKFNet, which is an algorithm that can learn the system noise covariance from measurement data. Both the learned noise statistics and multiple-hypothesis estimators are used to compensate the physical limitations of the LiDAR measurements. A detailed analysis of the measurement uncertainty and the methods to improve tracking performance during filtering are provided for the UA-EKF. The obtained results by using the nuScenes datasets show that estimating the measurement uncertainty is an efficient solution for tracking the vehicle based on LiDAR detections.","PeriodicalId":176447,"journal":{"name":"2022 25th International Conference on Information Fusion (FUSION)","volume":"93 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134324024","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-04DOI: 10.23919/fusion49751.2022.9841374
P. Miceli, W. Blair, P. Willett
When a target is not maneuvering, the residuals from the nearly constant velocity (NCV) Kalman filter are inde-pendent, zero mean, and Gaussian. When the target maneuvers, the residuals are no longer independent, unbiased or Gaussian. A chi-squared (χ2) test is a common method to detect maneuvering targets, however, the correlation between consecutive residuals is lost by the calculation of the χ2 statistic. In this work, two methods that utilize the correlation between consecutive residuals are explored for the purpose of detecting weak maneuvers. First, a recursion is developed to compute the full cross correlation for an arbitrary number of residuals under the hypothesis that the target is maneuvering. This result is compared in a hypothesis test to the null hypothesis that the target is not maneuvering. Second, an arbitrary size window of residual errors is used to form a least squares estimate of the bias (i.e. acceleration), and the significance of that estimate is tested against the corresponding covariance. Both methods are shown to be an improvement over a standard χ2 test.
{"title":"Note on Autocorrelation of the Residuals of the NCV Kalman Filter Tracking a Maneuvering Target - Part 2","authors":"P. Miceli, W. Blair, P. Willett","doi":"10.23919/fusion49751.2022.9841374","DOIUrl":"https://doi.org/10.23919/fusion49751.2022.9841374","url":null,"abstract":"When a target is not maneuvering, the residuals from the nearly constant velocity (NCV) Kalman filter are inde-pendent, zero mean, and Gaussian. When the target maneuvers, the residuals are no longer independent, unbiased or Gaussian. A chi-squared (χ2) test is a common method to detect maneuvering targets, however, the correlation between consecutive residuals is lost by the calculation of the χ2 statistic. In this work, two methods that utilize the correlation between consecutive residuals are explored for the purpose of detecting weak maneuvers. First, a recursion is developed to compute the full cross correlation for an arbitrary number of residuals under the hypothesis that the target is maneuvering. This result is compared in a hypothesis test to the null hypothesis that the target is not maneuvering. Second, an arbitrary size window of residual errors is used to form a least squares estimate of the bias (i.e. acceleration), and the significance of that estimate is tested against the corresponding covariance. Both methods are shown to be an improvement over a standard χ2 test.","PeriodicalId":176447,"journal":{"name":"2022 25th International Conference on Information Fusion (FUSION)","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133782758","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-04DOI: 10.23919/fusion49751.2022.9841243
Andreas Serov, J. Clemens, K. Schill
Visual-inertial navigations systems (VINS) can be an essential building block for autonomous systems, that are equipped with a camera and an inertial measurement unit (IMU), especially in indoor or GNSS-denied environments. One prevalent visual-inertial odometry (VIO) algorithm is the multi-state constraint Kalman Filter (MSCKF). OpenVINS is an open source project that performs visual-inertial state estimation using the core functionality of the MSCKF. However, it offers several extensions. It is designed for general purpose, but in certain cases it is beneficial to use domain knowledge in order to increase accuracy and robustness in state estimation. In this paper, we propose using vehicle speed, steering angle, and wheel speeds measurements in conjunction with OpenVINS by performing additional filter updates in the context of autonomous driving. The updates are conducted using a classical Kalman filtering approach, where speed and steering measurements are processed at their respective sensor frequency. Additionally, all measurements between camera frames, which have the slowest measurement frequency, are gathered and a 3 degrees of freedom (DOF) planar motion update is performed in a preintegrated fashion. In contrast to handheld devices and drones commonly used as visual-inertial platforms, the movement of automotive vehicles is constrained in a nonholonomic way which is reflected in the updates. These extensions are evaluated on real-world datasets of typical urban driving. The results show that state estimation with the additional updates is smoother and leads to lower translation errors, where a preintegrated vehicle update using a single-track model offers the best overall performance. The code is provided in a fork of the original project: https://github.com/aserbremen/open_vins
{"title":"Visual-Inertial Odometry aided by Speed and Steering Angle Measurements","authors":"Andreas Serov, J. Clemens, K. Schill","doi":"10.23919/fusion49751.2022.9841243","DOIUrl":"https://doi.org/10.23919/fusion49751.2022.9841243","url":null,"abstract":"Visual-inertial navigations systems (VINS) can be an essential building block for autonomous systems, that are equipped with a camera and an inertial measurement unit (IMU), especially in indoor or GNSS-denied environments. One prevalent visual-inertial odometry (VIO) algorithm is the multi-state constraint Kalman Filter (MSCKF). OpenVINS is an open source project that performs visual-inertial state estimation using the core functionality of the MSCKF. However, it offers several extensions. It is designed for general purpose, but in certain cases it is beneficial to use domain knowledge in order to increase accuracy and robustness in state estimation. In this paper, we propose using vehicle speed, steering angle, and wheel speeds measurements in conjunction with OpenVINS by performing additional filter updates in the context of autonomous driving. The updates are conducted using a classical Kalman filtering approach, where speed and steering measurements are processed at their respective sensor frequency. Additionally, all measurements between camera frames, which have the slowest measurement frequency, are gathered and a 3 degrees of freedom (DOF) planar motion update is performed in a preintegrated fashion. In contrast to handheld devices and drones commonly used as visual-inertial platforms, the movement of automotive vehicles is constrained in a nonholonomic way which is reflected in the updates. These extensions are evaluated on real-world datasets of typical urban driving. The results show that state estimation with the additional updates is smoother and leads to lower translation errors, where a preintegrated vehicle update using a single-track model offers the best overall performance. The code is provided in a fork of the original project: https://github.com/aserbremen/open_vins","PeriodicalId":176447,"journal":{"name":"2022 25th International Conference on Information Fusion (FUSION)","volume":"13 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114010677","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-04DOI: 10.23919/fusion49751.2022.9841248
Yiru Lian, Feng Lian, Liming Hou
In this paper, a robust labeled multi-Bernoulli (RLMB) filter for the multi-target tracking (MTT) scenarios with inaccurate and time-varying process and measurement noise covariances is proposed. The process noise covariance and measurement noise covariance are modeled as inverse Wishart (IW) distributions, respectively. The state together with the predicted error and measurement noise covariances are inferred based on the variational Bayesian (VB) inference. Moreover, a closed-form implementation of the proposed RLMB filter is given for linear Gaussian system and the predictive likelihood function is calculated by minimizing the Kullback-Leibler (KL) divergence by the VB lower bound. Simulation results illustrate that the proposed RLMB filter outperforms the existing LMB filter in the tracking performance.
{"title":"Robust Labeled Multi-Bernoulli Filter with Inaccurate Noise Covariances","authors":"Yiru Lian, Feng Lian, Liming Hou","doi":"10.23919/fusion49751.2022.9841248","DOIUrl":"https://doi.org/10.23919/fusion49751.2022.9841248","url":null,"abstract":"In this paper, a robust labeled multi-Bernoulli (RLMB) filter for the multi-target tracking (MTT) scenarios with inaccurate and time-varying process and measurement noise covariances is proposed. The process noise covariance and measurement noise covariance are modeled as inverse Wishart (IW) distributions, respectively. The state together with the predicted error and measurement noise covariances are inferred based on the variational Bayesian (VB) inference. Moreover, a closed-form implementation of the proposed RLMB filter is given for linear Gaussian system and the predictive likelihood function is calculated by minimizing the Kullback-Leibler (KL) divergence by the VB lower bound. Simulation results illustrate that the proposed RLMB filter outperforms the existing LMB filter in the tracking performance.","PeriodicalId":176447,"journal":{"name":"2022 25th International Conference on Information Fusion (FUSION)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115402770","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-04DOI: 10.23919/fusion49751.2022.9841276
Bryan Pogorelsky, Kristen Michaelson, Renato Zanetti
Nonlinear estimation can be performed in many ways, with the particle filter being one of the most common. It is well known that ignoring the latest measurement value in the choice of importance density can result in poor particle filter performance for certain classes of challenging problems. In this paper a novel particle filter with importance density based on the linear minimum mean square error (LMMSE) estimator is presented. Performance is evaluated using Monte Carlo simulations of a highly nonlinear growth model. The proposed algorithm is compared with existing particle filter formulations using metrics for accuracy, consistency, and particle diversity.
{"title":"Particle Filter with LMMSE Importance Sampling","authors":"Bryan Pogorelsky, Kristen Michaelson, Renato Zanetti","doi":"10.23919/fusion49751.2022.9841276","DOIUrl":"https://doi.org/10.23919/fusion49751.2022.9841276","url":null,"abstract":"Nonlinear estimation can be performed in many ways, with the particle filter being one of the most common. It is well known that ignoring the latest measurement value in the choice of importance density can result in poor particle filter performance for certain classes of challenging problems. In this paper a novel particle filter with importance density based on the linear minimum mean square error (LMMSE) estimator is presented. Performance is evaluated using Monte Carlo simulations of a highly nonlinear growth model. The proposed algorithm is compared with existing particle filter formulations using metrics for accuracy, consistency, and particle diversity.","PeriodicalId":176447,"journal":{"name":"2022 25th International Conference on Information Fusion (FUSION)","volume":"341 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123417510","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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