Pub Date : 2021-11-01DOI: 10.23919/fusion49465.2021.9627057
D. Herrero, David Sánchez Pedroche, Jesús García, J. M. Molina
Geographic data is very valuable for decision making. There are many hand-adapted datasets of roads or buildings available. However, datasets of other objects are not available, and it is very difficult to generate them manually. Remote sensing can help us to generate datasets of specific objects. This work introduces the main components for an automatic dataset generation process using any kind of sensors. To validate this process, an implementation using an open-source dataset is developed, geolocating traffic barriers using 360-degrees images captured from a car. Its results are validated with the positions extracted from a 3D LiDAR, solving the same problem at a much lower cost, providing an acceptable error for some use cases.
{"title":"Automatic context learning based on 360 imageries triangulation and 3D LiDAR validation","authors":"D. Herrero, David Sánchez Pedroche, Jesús García, J. M. Molina","doi":"10.23919/fusion49465.2021.9627057","DOIUrl":"https://doi.org/10.23919/fusion49465.2021.9627057","url":null,"abstract":"Geographic data is very valuable for decision making. There are many hand-adapted datasets of roads or buildings available. However, datasets of other objects are not available, and it is very difficult to generate them manually. Remote sensing can help us to generate datasets of specific objects. This work introduces the main components for an automatic dataset generation process using any kind of sensors. To validate this process, an implementation using an open-source dataset is developed, geolocating traffic barriers using 360-degrees images captured from a car. Its results are validated with the positions extracted from a 3D LiDAR, solving the same problem at a much lower cost, providing an acceptable error for some use cases.","PeriodicalId":226850,"journal":{"name":"2021 IEEE 24th International Conference on Information Fusion (FUSION)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130964728","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 : 2021-11-01DOI: 10.23919/fusion49465.2021.9626876
Daniel Cunnington, Mark Law, A. Russo, Jorge Lobo, L. Kaplan
This paper investigates neural-symbolic policy learning for information fusion in distributed human-agent operations. The architecture integrates a pre-trained neural network for feature extraction, with a state-of-the-art symbolic Inductive Logic Programming (ILP) system to learn policies, expressed as a set of logical rules. We firstly outline the challenge of policy learning within a military environment, by investigating the accuracy and confidence of neural network predictions given data outside the training distribution. Secondly, we introduce a neural-symbolic integration for policy learning and demonstrate that the symbolic ILP component, when considering the length of the learned policy rules, can generalise and learn a robust policy despite unstructured data observed at policy learning time originating from a different distribution than observed during training.
{"title":"Towards Neural-Symbolic Learning to support Human-Agent Operations","authors":"Daniel Cunnington, Mark Law, A. Russo, Jorge Lobo, L. Kaplan","doi":"10.23919/fusion49465.2021.9626876","DOIUrl":"https://doi.org/10.23919/fusion49465.2021.9626876","url":null,"abstract":"This paper investigates neural-symbolic policy learning for information fusion in distributed human-agent operations. The architecture integrates a pre-trained neural network for feature extraction, with a state-of-the-art symbolic Inductive Logic Programming (ILP) system to learn policies, expressed as a set of logical rules. We firstly outline the challenge of policy learning within a military environment, by investigating the accuracy and confidence of neural network predictions given data outside the training distribution. Secondly, we introduce a neural-symbolic integration for policy learning and demonstrate that the symbolic ILP component, when considering the length of the learned policy rules, can generalise and learn a robust policy despite unstructured data observed at policy learning time originating from a different distribution than observed during training.","PeriodicalId":226850,"journal":{"name":"2021 IEEE 24th International Conference on Information Fusion (FUSION)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130905680","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 : 2021-11-01DOI: 10.23919/fusion49465.2021.9627038
C. Laudy, N. Museux
This work presents Peacock, a framework that aims at generating benchmarks for High level information fusion. Peacock makes it possible to generate several structured information sets, that are representative, coherent, diversified and controlled. The principle of Peacock lies in the generation of several information sets from one scenario. The scenario contains on the one hand, a storyboard of perfectly described events and a chronology of perfectly structured information. On the other hand, it contains communicating entities, organized in a network. These entities will alter the scenario events as well as the information exchanged. The modifications on the information consist in the introduction of imperfections (over-precision, imprecision, incompleteness, uncertainty, irrelevance, incomprehension) according to the entities communication behaviors. In this paper, we present the principles under the Peacock framework. We detail its characteristics and describe its implementation.
{"title":"Peacock: a Benchmarks Generation Framework for High-Level Information Fusion Evaluation","authors":"C. Laudy, N. Museux","doi":"10.23919/fusion49465.2021.9627038","DOIUrl":"https://doi.org/10.23919/fusion49465.2021.9627038","url":null,"abstract":"This work presents Peacock, a framework that aims at generating benchmarks for High level information fusion. Peacock makes it possible to generate several structured information sets, that are representative, coherent, diversified and controlled. The principle of Peacock lies in the generation of several information sets from one scenario. The scenario contains on the one hand, a storyboard of perfectly described events and a chronology of perfectly structured information. On the other hand, it contains communicating entities, organized in a network. These entities will alter the scenario events as well as the information exchanged. The modifications on the information consist in the introduction of imperfections (over-precision, imprecision, incompleteness, uncertainty, irrelevance, incomprehension) according to the entities communication behaviors. In this paper, we present the principles under the Peacock framework. We detail its characteristics and describe its implementation.","PeriodicalId":226850,"journal":{"name":"2021 IEEE 24th International Conference on Information Fusion (FUSION)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131389442","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 : 2021-11-01DOI: 10.23919/fusion49465.2021.9626944
Z. Baird, M. McDonald, S. Rajan, Simon J. Lee
A convolutional neural network (CNN) detector with fixed probability of false alarm (PFA) for application to non-coherent wide area surveillance (WAS) maritime radars is proposed. This detector is trained using a novel cost function-based on Neyman-Pearson (NP) criterion. The use of machine learning allows the detector to learn a complex non-linear model of sea clutter and obviates the need for specifying complex, likely intractable, target plus clutter statistical models. The NP-CNN is shown to perform better than a simple cell-averaging constant false alarm rate (CA-CFAR) statistical detector and a CNN trained using the cross-entropy cost function.
{"title":"A Neyman-Pearson Criterion-Based Neural Network Detector for Maritime Radar","authors":"Z. Baird, M. McDonald, S. Rajan, Simon J. Lee","doi":"10.23919/fusion49465.2021.9626944","DOIUrl":"https://doi.org/10.23919/fusion49465.2021.9626944","url":null,"abstract":"A convolutional neural network (CNN) detector with fixed probability of false alarm (PFA) for application to non-coherent wide area surveillance (WAS) maritime radars is proposed. This detector is trained using a novel cost function-based on Neyman-Pearson (NP) criterion. The use of machine learning allows the detector to learn a complex non-linear model of sea clutter and obviates the need for specifying complex, likely intractable, target plus clutter statistical models. The NP-CNN is shown to perform better than a simple cell-averaging constant false alarm rate (CA-CFAR) statistical detector and a CNN trained using the cross-entropy cost function.","PeriodicalId":226850,"journal":{"name":"2021 IEEE 24th International Conference on Information Fusion (FUSION)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125433351","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 : 2021-11-01DOI: 10.23919/fusion49465.2021.9626956
David Sánchez Pedroche, D. Herrero, J. G. Herrero, J. M. M. López
This paper presents an analysis on Automatic Identification System (AIS) real world ship data to build a system with the capability to extract useful information for an anomaly detection problem. The study focuses on the adjustment of a clustering technique to trajectory data, specifically using a DBSCAN algorithm that is adapted by means of two approaches. On the one hand, the DTW trajectory similarity metric is used to obtain a distance between two trajectories. On the other hand, an extraction of features of interest from each trajectory allowing a summary of the trajectory in a single multidimensional instance. The results show that both approaches are feasible, although not very scalable to larger problems due to the computational complexity of the used algorithms. In addition, the study analyses possible uses of these approaches to existing data mining problems.
{"title":"Clustering of maritime trajectories with AIS features for context learning","authors":"David Sánchez Pedroche, D. Herrero, J. G. Herrero, J. M. M. López","doi":"10.23919/fusion49465.2021.9626956","DOIUrl":"https://doi.org/10.23919/fusion49465.2021.9626956","url":null,"abstract":"This paper presents an analysis on Automatic Identification System (AIS) real world ship data to build a system with the capability to extract useful information for an anomaly detection problem. The study focuses on the adjustment of a clustering technique to trajectory data, specifically using a DBSCAN algorithm that is adapted by means of two approaches. On the one hand, the DTW trajectory similarity metric is used to obtain a distance between two trajectories. On the other hand, an extraction of features of interest from each trajectory allowing a summary of the trajectory in a single multidimensional instance. The results show that both approaches are feasible, although not very scalable to larger problems due to the computational complexity of the used algorithms. In addition, the study analyses possible uses of these approaches to existing data mining problems.","PeriodicalId":226850,"journal":{"name":"2021 IEEE 24th International Conference on Information Fusion (FUSION)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123188477","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 : 2021-11-01DOI: 10.23919/fusion49465.2021.9626982
Noor M. Al-Shakarji, Ke Gao, F. Bunyak, H. Aliakbarpour, Erik Blasch, Priya Narayaran, G. Seetharaman, K. Palaniappan
Advances in sensor technologies and embedded low-power processing provide new opportunities for using Wide Area Motion Imagery (WAMI) across a spectrum of mapping and monitoring applications covering large geospatial areas for extended time periods. While significant developments have been made in video analytics for ground or low-altitude aerial videos, methods for WAMI have been limited due to lack of benchmarking datasets, data format complexities, lack of labeled training videos, and high data processing requirements. This paper aims to help advance the broader use of WAMI by evaluating the georegistration accuracy and its impact on downstream video analytics using two benchmark datasets (CLIF 2007, ABQ 2013). In addition to the current intensified interest in using deep learning for aerial object recognition and tracking, this paper motivates the need for further development of more robust and fast georegistration algorithms for multi-camera WAMI systems.
{"title":"Impact of Georegistration Accuracy on Wide Area Motion Imagery Object Detection and Tracking","authors":"Noor M. Al-Shakarji, Ke Gao, F. Bunyak, H. Aliakbarpour, Erik Blasch, Priya Narayaran, G. Seetharaman, K. Palaniappan","doi":"10.23919/fusion49465.2021.9626982","DOIUrl":"https://doi.org/10.23919/fusion49465.2021.9626982","url":null,"abstract":"Advances in sensor technologies and embedded low-power processing provide new opportunities for using Wide Area Motion Imagery (WAMI) across a spectrum of mapping and monitoring applications covering large geospatial areas for extended time periods. While significant developments have been made in video analytics for ground or low-altitude aerial videos, methods for WAMI have been limited due to lack of benchmarking datasets, data format complexities, lack of labeled training videos, and high data processing requirements. This paper aims to help advance the broader use of WAMI by evaluating the georegistration accuracy and its impact on downstream video analytics using two benchmark datasets (CLIF 2007, ABQ 2013). In addition to the current intensified interest in using deep learning for aerial object recognition and tracking, this paper motivates the need for further development of more robust and fast georegistration algorithms for multi-camera WAMI systems.","PeriodicalId":226850,"journal":{"name":"2021 IEEE 24th International Conference on Information Fusion (FUSION)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113983988","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 : 2021-11-01DOI: 10.48550/arXiv.2203.01151
Frank Bieder, Maximilian Link, Simon Romanski, Haohao Hu, C. Stiller
In this paper we introduce a novel way to predict semantic information from sparse, single-shot LiDAR measurements in the context of autonomous driving. In particular, we fuse learned features from complementary representations. The approach is aimed specifically at improving the semantic segmentation of top-view grid maps. Towards this goal the 3D LiDAR point cloud is projected onto two orthogonal 2D representations. For each representation a tailored deep learning architecture is developed to effectively extract semantic information which are fused by a superordinate deep neural network. The contribution of this work is threefold: (1) We examine different stages within the segmentation network for fusion. (2) We quantify the impact of embedding different features. (3) We use the findings of this survey to design a tailored deep neural network architecture leveraging respective advantages of different representations. Our method is evaluated using the SemanticKITTI dataset which provides a point-wise semantic annotation of more than 23.000 LiDAR measurements.
{"title":"Improving Lidar-Based Semantic Segmentation of Top-View Grid Maps by Learning Features in Complementary Representations","authors":"Frank Bieder, Maximilian Link, Simon Romanski, Haohao Hu, C. Stiller","doi":"10.48550/arXiv.2203.01151","DOIUrl":"https://doi.org/10.48550/arXiv.2203.01151","url":null,"abstract":"In this paper we introduce a novel way to predict semantic information from sparse, single-shot LiDAR measurements in the context of autonomous driving. In particular, we fuse learned features from complementary representations. The approach is aimed specifically at improving the semantic segmentation of top-view grid maps. Towards this goal the 3D LiDAR point cloud is projected onto two orthogonal 2D representations. For each representation a tailored deep learning architecture is developed to effectively extract semantic information which are fused by a superordinate deep neural network. The contribution of this work is threefold: (1) We examine different stages within the segmentation network for fusion. (2) We quantify the impact of embedding different features. (3) We use the findings of this survey to design a tailored deep neural network architecture leveraging respective advantages of different representations. Our method is evaluated using the SemanticKITTI dataset which provides a point-wise semantic annotation of more than 23.000 LiDAR measurements.","PeriodicalId":226850,"journal":{"name":"2021 IEEE 24th International Conference on Information Fusion (FUSION)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115880639","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 : 2021-11-01DOI: 10.23919/fusion49465.2021.9627058
Bas Van Der Werk, M. Schöpe, H. Driessen
Radar Resource Management in a multi-sensor multi-target scenario is considered. A dynamic resource balancing algorithm is proposed which optimizes target task parameters assuming an underlying partially observable Markov decision process (POMDP). By applying stochastic optimization methods, such as policy rollout, the POMDP is solved non-myopically. The approximately optimal approach is formulated assuming a central processor. Subsequently, a distributed implementation is introduced that converges to the same results as given by the centralized implementation and requires less computational resources. The performance of the proposed approach for both centralized and distributed implementation is demonstrated through dynamic tracking scenarios.
{"title":"Approximately Optimal Radar Resource Management for Multi-Sensor Multi-Target Tracking","authors":"Bas Van Der Werk, M. Schöpe, H. Driessen","doi":"10.23919/fusion49465.2021.9627058","DOIUrl":"https://doi.org/10.23919/fusion49465.2021.9627058","url":null,"abstract":"Radar Resource Management in a multi-sensor multi-target scenario is considered. A dynamic resource balancing algorithm is proposed which optimizes target task parameters assuming an underlying partially observable Markov decision process (POMDP). By applying stochastic optimization methods, such as policy rollout, the POMDP is solved non-myopically. The approximately optimal approach is formulated assuming a central processor. Subsequently, a distributed implementation is introduced that converges to the same results as given by the centralized implementation and requires less computational resources. The performance of the proposed approach for both centralized and distributed implementation is demonstrated through dynamic tracking scenarios.","PeriodicalId":226850,"journal":{"name":"2021 IEEE 24th International Conference on Information Fusion (FUSION)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116051908","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 : 2021-11-01DOI: 10.23919/fusion49465.2021.9626914
Shaoxiu Wei, Boxiang Zhang, Wei Yi
To account for joint tracking and classification (JTC) of multiple targets from observation sets in presence of detection uncertainty, noise and clutter, this paper develops a new trajectory probability hypothesis density (TPHD) filter, which is referred to as the JTC-TPHD filter. The JTC-TPHD filter classifies different targets based on their motion models and each target is assigned with multiple class hypotheses. By using this strategy, we can not only obtain the category information of the targets, but also a more accurate trajectory estimation than the traditional TPHD filter. The JTC-TPHD filter is derived by finding the best Poisson posterior approximation over trajectories on an augmented state space using the Kullback-Leibler divergence (KLD) minimization. The Gaussian mixture is adopted for the implementation, which is referred to as the GMJTC-TPHD filter. The L-scan approximation is also presented for the GM-JTC-TPHD filter, which possesses lower computational burden. Simulation results show that the GM-JTC-TPHD filter can classify targets correctly and obtain accurate trajectory estimation.
{"title":"Multi-target Joint Tracking and Classification Using the Trajectory PHD Filter","authors":"Shaoxiu Wei, Boxiang Zhang, Wei Yi","doi":"10.23919/fusion49465.2021.9626914","DOIUrl":"https://doi.org/10.23919/fusion49465.2021.9626914","url":null,"abstract":"To account for joint tracking and classification (JTC) of multiple targets from observation sets in presence of detection uncertainty, noise and clutter, this paper develops a new trajectory probability hypothesis density (TPHD) filter, which is referred to as the JTC-TPHD filter. The JTC-TPHD filter classifies different targets based on their motion models and each target is assigned with multiple class hypotheses. By using this strategy, we can not only obtain the category information of the targets, but also a more accurate trajectory estimation than the traditional TPHD filter. The JTC-TPHD filter is derived by finding the best Poisson posterior approximation over trajectories on an augmented state space using the Kullback-Leibler divergence (KLD) minimization. The Gaussian mixture is adopted for the implementation, which is referred to as the GMJTC-TPHD filter. The L-scan approximation is also presented for the GM-JTC-TPHD filter, which possesses lower computational burden. Simulation results show that the GM-JTC-TPHD filter can classify targets correctly and obtain accurate trajectory estimation.","PeriodicalId":226850,"journal":{"name":"2021 IEEE 24th International Conference on Information Fusion (FUSION)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122847678","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 : 2021-11-01DOI: 10.23919/fusion49465.2021.9627044
Lino Antoni Giefer
State estimation plays an important role in various types of systems, such as moving object tracking in the field of robotics and autonomous driving. The correct and accurate representation of the state has a huge impact on the estimation results in terms of accuracy and reliability. An elegant way for the encapsulation of the Euclidean state vector is the use of Lie groups, which allows appropriate handling of the associated uncertainties. Although better results are obtained compared to working in the Euclidean space, the commonly used representations such as the special Euclidean group exclude one important part: uncertainty in time. In this paper, we investigate this aspect and look at the problem of state estimation of moving objects from a different perspective. We propose the Galilei group as an elegant way of state representation and analyze the effect of uncertainties of the separate parameters on an object’s state represented as an event in spacetime. To show the practical applicability, we derive the necessary equations for the integration of our novel representation into an extended Kalman filter to serve as the basis of an object tracking scenario.
{"title":"Uncertainties in Galilean Spacetime","authors":"Lino Antoni Giefer","doi":"10.23919/fusion49465.2021.9627044","DOIUrl":"https://doi.org/10.23919/fusion49465.2021.9627044","url":null,"abstract":"State estimation plays an important role in various types of systems, such as moving object tracking in the field of robotics and autonomous driving. The correct and accurate representation of the state has a huge impact on the estimation results in terms of accuracy and reliability. An elegant way for the encapsulation of the Euclidean state vector is the use of Lie groups, which allows appropriate handling of the associated uncertainties. Although better results are obtained compared to working in the Euclidean space, the commonly used representations such as the special Euclidean group exclude one important part: uncertainty in time. In this paper, we investigate this aspect and look at the problem of state estimation of moving objects from a different perspective. We propose the Galilei group as an elegant way of state representation and analyze the effect of uncertainties of the separate parameters on an object’s state represented as an event in spacetime. To show the practical applicability, we derive the necessary equations for the integration of our novel representation into an extended Kalman filter to serve as the basis of an object tracking scenario.","PeriodicalId":226850,"journal":{"name":"2021 IEEE 24th International Conference on Information Fusion (FUSION)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129813791","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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