Pub Date : 2021-11-01DOI: 10.23919/fusion49465.2021.9627027
Anton Kullberg, I. Skog, Gustaf Hendeby
A new approach to anomaly detection in maritime traffic based on Automatic Identification System (AIS) data is proposed. The method recursively learns a model of the nominal vessel routes from AIS data and simultaneously estimates the current state of the vessels. A distinction between anomalies and measurement outliers is made and a method to detect and distinguish between the two is proposed. The anomaly and outlier detection is based on statistical testing relative to the current motion model. The proposed method is evaluated on historical AIS data from a coastal area in Sweden and is shown to detect previously unseen motions.
{"title":"Learning Motion Patterns in AIS Data and Detecting Anomalous Vessel Behavior","authors":"Anton Kullberg, I. Skog, Gustaf Hendeby","doi":"10.23919/fusion49465.2021.9627027","DOIUrl":"https://doi.org/10.23919/fusion49465.2021.9627027","url":null,"abstract":"A new approach to anomaly detection in maritime traffic based on Automatic Identification System (AIS) data is proposed. The method recursively learns a model of the nominal vessel routes from AIS data and simultaneously estimates the current state of the vessels. A distinction between anomalies and measurement outliers is made and a method to detect and distinguish between the two is proposed. The anomaly and outlier detection is based on statistical testing relative to the current motion model. The proposed method is evaluated on historical AIS data from a coastal area in Sweden and is shown to detect previously unseen motions.","PeriodicalId":226850,"journal":{"name":"2021 IEEE 24th International Conference on Information Fusion (FUSION)","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127118816","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/fusion49465.2021.9627033
Joshua Gehlen, F. Govaers, W. Koch
Closely spaced multi target tracking remains a challenging problem in state estimation and data fusion. A recent formulation of the problem using antisymmetric square roots of density functions, which may be interpreted as multi target wave functions, has proposed a separation of densities by means of the resulting "Pauli-Notch". In this paper, this formulation is extended for non-Gaussian posterior densities, which are given in discretized and Candecomp-/Parafac decomposed form. Such densities can be predicted by a numerical solution of the Fokker-Planck-Equation. A modified operator for the respective wave function is presented together with the Bayes recursion in order to solve state estimation based on antisymmetric wave functions.
{"title":"On Tracking Closely-Spaced Targets in a PARAFAC-Representation of the Fermionic Wave Function Formulation","authors":"Joshua Gehlen, F. Govaers, W. Koch","doi":"10.23919/fusion49465.2021.9627033","DOIUrl":"https://doi.org/10.23919/fusion49465.2021.9627033","url":null,"abstract":"Closely spaced multi target tracking remains a challenging problem in state estimation and data fusion. A recent formulation of the problem using antisymmetric square roots of density functions, which may be interpreted as multi target wave functions, has proposed a separation of densities by means of the resulting \"Pauli-Notch\". In this paper, this formulation is extended for non-Gaussian posterior densities, which are given in discretized and Candecomp-/Parafac decomposed form. Such densities can be predicted by a numerical solution of the Fokker-Planck-Equation. A modified operator for the respective wave function is presented together with the Bayes recursion in order to solve state estimation based on antisymmetric wave functions.","PeriodicalId":226850,"journal":{"name":"2021 IEEE 24th International Conference on Information Fusion (FUSION)","volume":"91 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127323211","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/fusion49465.2021.9626853
Qing Deng, Wei Tian, Yuyao Huang, Lu Xiong, Xin Bi
Pedestrian detection in low-light environment is an essential part for autonomous driving in all-day and all-weather situations. A current trend is utilizing multispectral information such as RGB and infrared images to detect pedestrians. Despite its efficacy, such an approach suffers from underperformance in dealing with varied object scales due to its limited feature fusion on semantic levels. To address the above problem, we propose a novel multi-layer fusion network called as MLF-FRCNN. In this network, multi-scale feature maps are created from RGB and infrared channels from each backbone block. A feature pyramid network module is further introduced to facilitate predictions on multi-layer feature maps. The experimental results on the KAIST Dataset reveal that our method achieves a runtime performance of 0.14s per frame and an average precision of 91.2% which outperforms state-of-the-art multispectral fusion methods. The effectiveness of our approach in dealing with scaled objects in low-light environment is further proven by ablation studies.
{"title":"Pedestrian Detection by Fusion of RGB and Infrared Images in Low-Light Environment","authors":"Qing Deng, Wei Tian, Yuyao Huang, Lu Xiong, Xin Bi","doi":"10.23919/fusion49465.2021.9626853","DOIUrl":"https://doi.org/10.23919/fusion49465.2021.9626853","url":null,"abstract":"Pedestrian detection in low-light environment is an essential part for autonomous driving in all-day and all-weather situations. A current trend is utilizing multispectral information such as RGB and infrared images to detect pedestrians. Despite its efficacy, such an approach suffers from underperformance in dealing with varied object scales due to its limited feature fusion on semantic levels. To address the above problem, we propose a novel multi-layer fusion network called as MLF-FRCNN. In this network, multi-scale feature maps are created from RGB and infrared channels from each backbone block. A feature pyramid network module is further introduced to facilitate predictions on multi-layer feature maps. The experimental results on the KAIST Dataset reveal that our method achieves a runtime performance of 0.14s per frame and an average precision of 91.2% which outperforms state-of-the-art multispectral fusion methods. The effectiveness of our approach in dealing with scaled objects in low-light environment is further proven by ablation studies.","PeriodicalId":226850,"journal":{"name":"2021 IEEE 24th International Conference on Information Fusion (FUSION)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126998906","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/fusion49465.2021.9626933
J. Nordlöf, Gustaf Hendeby, Daniel Axehill
A belief-space planning problem for GNSS-denied areas is studied where the location and number of landmarks available are unknown when performing the planning. To be able to plan an informative path in this situation, an algorithm using virtual landmarks to position the platform during the planning phase is studied. The virtual landmarks are selected to capture the expected information available in different regions of the map, based on the beforehand known landmark density. The main contribution of this work is a better approximation of the obtained information from the virtual landmarks and a theoretical study of the properties of the approximation. Furthermore, the proposed approximation, in itself and its use in a path planner, is investigated with successful results.
{"title":"Improved Virtual Landmark Approximation for Belief-Space Planning","authors":"J. Nordlöf, Gustaf Hendeby, Daniel Axehill","doi":"10.23919/fusion49465.2021.9626933","DOIUrl":"https://doi.org/10.23919/fusion49465.2021.9626933","url":null,"abstract":"A belief-space planning problem for GNSS-denied areas is studied where the location and number of landmarks available are unknown when performing the planning. To be able to plan an informative path in this situation, an algorithm using virtual landmarks to position the platform during the planning phase is studied. The virtual landmarks are selected to capture the expected information available in different regions of the map, based on the beforehand known landmark density. The main contribution of this work is a better approximation of the obtained information from the virtual landmarks and a theoretical study of the properties of the approximation. Furthermore, the proposed approximation, in itself and its use in a path planner, is investigated with successful results.","PeriodicalId":226850,"journal":{"name":"2021 IEEE 24th International Conference on Information Fusion (FUSION)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129946480","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/fusion49465.2021.9626857
S. Lotz, A. D. Waal, C. Roux
Solar activity, through geomagnetic storms, has the ability to cause a number of negative effects on critical technologies such as power grids and various communication systems. Geomagnetic storms are intervals of disturbed geomagnetic field lasting ∼ 10 hours. The most intense storms are caused by energetic plasma from coronal mass ejections impacting the geomagnetic field after propagating the 1.5 × 108km (= 1AU) via the solar wind to Earth. The relationship between the shocked solar wind and the geomagnetic field can be viewed as a highly non-linear, non-stationary transfer function. Fully understanding the coupling between the solar wind and the magnetosphere is an important task for space physicists striving to provide accurate predictions of geomagnetic storms. With this in mind we investigate the use of copulas as a way to quantify the coupling efficiency between the solar wind and magnetosphere for the three known phases of storms: onset, main and recovery. Seven intense storms are identified and the dynamic and static copulas between two solar wind parameters (BZ and Vsw) and a geomagnetic disturbance index (SYM-H) are calculated. We find that copula functions can be used to reliably identify storm phase changes, and to quantify the changes in coupling efficiency for different storm phases.
{"title":"Estimation of copulas between solar wind parameters and a geomagnetic index during intense geomagnetic storms","authors":"S. Lotz, A. D. Waal, C. Roux","doi":"10.23919/fusion49465.2021.9626857","DOIUrl":"https://doi.org/10.23919/fusion49465.2021.9626857","url":null,"abstract":"Solar activity, through geomagnetic storms, has the ability to cause a number of negative effects on critical technologies such as power grids and various communication systems. Geomagnetic storms are intervals of disturbed geomagnetic field lasting ∼ 10 hours. The most intense storms are caused by energetic plasma from coronal mass ejections impacting the geomagnetic field after propagating the 1.5 × 108km (= 1AU) via the solar wind to Earth. The relationship between the shocked solar wind and the geomagnetic field can be viewed as a highly non-linear, non-stationary transfer function. Fully understanding the coupling between the solar wind and the magnetosphere is an important task for space physicists striving to provide accurate predictions of geomagnetic storms. With this in mind we investigate the use of copulas as a way to quantify the coupling efficiency between the solar wind and magnetosphere for the three known phases of storms: onset, main and recovery. Seven intense storms are identified and the dynamic and static copulas between two solar wind parameters (BZ and Vsw) and a geomagnetic disturbance index (SYM-H) are calculated. We find that copula functions can be used to reliably identify storm phase changes, and to quantify the changes in coupling efficiency for different storm phases.","PeriodicalId":226850,"journal":{"name":"2021 IEEE 24th International Conference on Information Fusion (FUSION)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128290453","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/fusion49465.2021.9626992
Qichao Tang, Z. Duan
In multi-sensor distributed estimation fusion, local estimation errors are generally correlated among local estimates. Usually, correlation is known to exist but unavailable or unclear to be how large it is, and needed to be considered. For this situation, a sensible way is to set up an optimality criterion and optimize it over all possible such correlations. Based on the framework of minimizing the statistical distance sum between the fused density and local posterior densities, a new method is proposed by utilizing Bhattacharyya distance, which is commonly used in measuring the closeness or similarity between two densities. First, the objective function is introduced. Then, we investigate the convexity form of the objective function, and separate the solving procedure into two steps during settling the original optimization problem, which benefit us to acquire the solution of that problem. At last, the acquired solution (fused estimate) is given in an implicit form, however, it can be obtained through iterative algorithm. And, it is pessimistic definite in mean square error (MSE). Numerical examples illustrate this and show the effectiveness of the proposed distributed method by comparing with several other fusion methods under the same framework but using other kinds of statistical distance.
{"title":"Multi-sensor Distributed Estimation Fusion Based on Minimizing the Bhattacharyya Distance Sum","authors":"Qichao Tang, Z. Duan","doi":"10.23919/fusion49465.2021.9626992","DOIUrl":"https://doi.org/10.23919/fusion49465.2021.9626992","url":null,"abstract":"In multi-sensor distributed estimation fusion, local estimation errors are generally correlated among local estimates. Usually, correlation is known to exist but unavailable or unclear to be how large it is, and needed to be considered. For this situation, a sensible way is to set up an optimality criterion and optimize it over all possible such correlations. Based on the framework of minimizing the statistical distance sum between the fused density and local posterior densities, a new method is proposed by utilizing Bhattacharyya distance, which is commonly used in measuring the closeness or similarity between two densities. First, the objective function is introduced. Then, we investigate the convexity form of the objective function, and separate the solving procedure into two steps during settling the original optimization problem, which benefit us to acquire the solution of that problem. At last, the acquired solution (fused estimate) is given in an implicit form, however, it can be obtained through iterative algorithm. And, it is pessimistic definite in mean square error (MSE). Numerical examples illustrate this and show the effectiveness of the proposed distributed method by comparing with several other fusion methods under the same framework but using other kinds of statistical distance.","PeriodicalId":226850,"journal":{"name":"2021 IEEE 24th International Conference on Information Fusion (FUSION)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128922823","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/fusion49465.2021.9626918
N. Rao
Machine learning (ML) methods are increasingly being applied to solve complex, data-driven problems in diverse areas, by exploiting the physical laws derived from first principles such as thermal hydraulics and the abstract laws developed recently for data and computing infrastructures. These physical and abstract laws encapsulate, typically in compact algebraic forms, the critical knowledge that complements data-driven ML models. We present a unified perspective of these laws and ML methods using an abstract algebra $(mathcal{A}; oplus , otimes )$, wherein the performance estimation and classification tasks are characterized by the additive ⊕ operations, and the diagnosis, reconstruction, and optimization tasks are characterized by the difference ⊗ operations. This abstraction provides ML codes and their performance characterizations that are transferable across different areas. We describe practical applications of these abstract operations using examples of throughput profile estimation tasks in data transport infrastructures, and power-level and sensor error estimation tasks in nuclear reactor systems.
{"title":"An Algebra of Machine Learners with Applications","authors":"N. Rao","doi":"10.23919/fusion49465.2021.9626918","DOIUrl":"https://doi.org/10.23919/fusion49465.2021.9626918","url":null,"abstract":"Machine learning (ML) methods are increasingly being applied to solve complex, data-driven problems in diverse areas, by exploiting the physical laws derived from first principles such as thermal hydraulics and the abstract laws developed recently for data and computing infrastructures. These physical and abstract laws encapsulate, typically in compact algebraic forms, the critical knowledge that complements data-driven ML models. We present a unified perspective of these laws and ML methods using an abstract algebra $(mathcal{A}; oplus , otimes )$, wherein the performance estimation and classification tasks are characterized by the additive ⊕ operations, and the diagnosis, reconstruction, and optimization tasks are characterized by the difference ⊗ operations. This abstraction provides ML codes and their performance characterizations that are transferable across different areas. We describe practical applications of these abstract operations using examples of throughput profile estimation tasks in data transport infrastructures, and power-level and sensor error estimation tasks in nuclear reactor systems.","PeriodicalId":226850,"journal":{"name":"2021 IEEE 24th International Conference on Information Fusion (FUSION)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125417475","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/fusion49465.2021.9627049
Enrica d’Afflisio, P. Braca, L. Chisci, G. Battistelli, P. Willett
Based on a proper stochastic formulation of the vessel dynamic, exploiting piecewise Ornstein-Uhlenbeck (OU) mean-reverting processes, we propose an effective anomaly detection procedure to jointly reveal Automatic Identification System (AIS) data spoofing and/or surreptitious deviations from the planned route. Supported by reliable information from monitoring systems (coastal radars and spaceborne satellite sensors), an expanded five-hypothesis testing problem is posed involving two anomaly detection strategies based on the Generalized Likelihood Ratio Test (GLRT) and the Model Order Selection (MOS) methodologies.
{"title":"Maritime Anomaly Detection of Malicious Data Spoofing and Stealth Deviations from Nominal Route Exploiting Heterogeneous Sources of Information","authors":"Enrica d’Afflisio, P. Braca, L. Chisci, G. Battistelli, P. Willett","doi":"10.23919/fusion49465.2021.9627049","DOIUrl":"https://doi.org/10.23919/fusion49465.2021.9627049","url":null,"abstract":"Based on a proper stochastic formulation of the vessel dynamic, exploiting piecewise Ornstein-Uhlenbeck (OU) mean-reverting processes, we propose an effective anomaly detection procedure to jointly reveal Automatic Identification System (AIS) data spoofing and/or surreptitious deviations from the planned route. Supported by reliable information from monitoring systems (coastal radars and spaceborne satellite sensors), an expanded five-hypothesis testing problem is posed involving two anomaly detection strategies based on the Generalized Likelihood Ratio Test (GLRT) and the Model Order Selection (MOS) methodologies.","PeriodicalId":226850,"journal":{"name":"2021 IEEE 24th International Conference on Information Fusion (FUSION)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123599594","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/fusion49465.2021.9627020
Ashton Harvey, Kathryn B. Laskey, Kuo-Chu Chang
Space is becoming a more crowded and contested domain, but the techniques used to task the sensors monitoring this environment have not significantly changed since the implementation of James Miller’s marginal analysis technique used in the Special Perturbations (SP) Tasker in 2007. Centralized tasker / scheduler approaches have used a Markov Decision Process (MDP) formulation, but myopic solutions fail to account for future states and non-myopic solutions tend to be computationally infeasible at scale. Linares and Furfaro proposed solving an MDP formulation of the Sensor Allocation Problem (SAP) using Deep Reinforcement Learning (DRL). DRL has been instrumental in solving many high-dimensional control problems previously considered too complex to solve at an expert level, including Go, Atari 2600, Dota 2, Starcraft 2 and autonomous driving. Linares and Furfaro showed DRL could converge on effective policies for sets of up to 300 objects in the same orbital plane. Jones expanded on that work to a full three-dimensional case with objects in diverse orbits. DRL methods can require significant training time to learn from an a priori state. This paper builds on past work by applying imitation learning to bootstrap DRL methods with existing heuristic solutions. We show that a Demonstration Guided DRL (DG-DRL) approach can effectively replicate a near-optimal tasker’s performance using trajectories from a sub-optimal heuristic. Further, we show that our approach avoids the poor initial performance typical of online DRL approaches. Code is available as an open source library at: https://github.com/AshHarvey/ssa-gym
{"title":"Bridging Heuristic and Deep Learning Approaches to Sensor Tasking","authors":"Ashton Harvey, Kathryn B. Laskey, Kuo-Chu Chang","doi":"10.23919/fusion49465.2021.9627020","DOIUrl":"https://doi.org/10.23919/fusion49465.2021.9627020","url":null,"abstract":"Space is becoming a more crowded and contested domain, but the techniques used to task the sensors monitoring this environment have not significantly changed since the implementation of James Miller’s marginal analysis technique used in the Special Perturbations (SP) Tasker in 2007. Centralized tasker / scheduler approaches have used a Markov Decision Process (MDP) formulation, but myopic solutions fail to account for future states and non-myopic solutions tend to be computationally infeasible at scale. Linares and Furfaro proposed solving an MDP formulation of the Sensor Allocation Problem (SAP) using Deep Reinforcement Learning (DRL). DRL has been instrumental in solving many high-dimensional control problems previously considered too complex to solve at an expert level, including Go, Atari 2600, Dota 2, Starcraft 2 and autonomous driving. Linares and Furfaro showed DRL could converge on effective policies for sets of up to 300 objects in the same orbital plane. Jones expanded on that work to a full three-dimensional case with objects in diverse orbits. DRL methods can require significant training time to learn from an a priori state. This paper builds on past work by applying imitation learning to bootstrap DRL methods with existing heuristic solutions. We show that a Demonstration Guided DRL (DG-DRL) approach can effectively replicate a near-optimal tasker’s performance using trajectories from a sub-optimal heuristic. Further, we show that our approach avoids the poor initial performance typical of online DRL approaches. Code is available as an open source library at: https://github.com/AshHarvey/ssa-gym","PeriodicalId":226850,"journal":{"name":"2021 IEEE 24th International Conference on Information Fusion (FUSION)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124465413","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/fusion49465.2021.9626841
Dimitri Hamidi, Elad Kevelevitch, P. Arora, Rick Gentile, Vincent Pellissier
Multi-object detection and tracking with spatially distributed sensor networks are used in many applications across the domains of autonomous and surveillance systems. The sensors typically used in these systems often provide incomplete observations such as bistatic and angle- or range-only measurements, thus posing a challenge to the task of retrieving the targets and estimating their state. In this paper, we first present a new variant of a multi-sensor tracking algorithm based on the Gaussian-mixture probability hypothesis density (GM-PHD) filter. Next, we show how it can be applied on fusing incomplete observations. For tracking asynchronous range- and angle-only measurements, we leverage the well-known concepts of angle and range parametrization, respectively, to describe the adaptive target birth density based on the parameters of received observations. In the case of multistatic tracking, we propose parametrizing the birth density from target hypotheses, generated by statically fusing bistatic range measurements, using the M-best S-D assignment algorithm. We investigate the performance using challenging simulation scenarios and evaluate it with established tracking metrics. Our preliminary results demonstrate the effectiveness of the proposed algorithms. Furthermore, for range- and angle-only fusion, the more common use case of unsynchronized sensor measurements is supported. While many algorithms in the literature are tailored for a specific problem, we show that the proposed GM-PHD tracker is generic and can be potentially leveraged in a wide range of sensor fusion and tracking applications.
{"title":"Angle-Only, Range-Only and Multistatic Tracking Based on GM-PHD Filter","authors":"Dimitri Hamidi, Elad Kevelevitch, P. Arora, Rick Gentile, Vincent Pellissier","doi":"10.23919/fusion49465.2021.9626841","DOIUrl":"https://doi.org/10.23919/fusion49465.2021.9626841","url":null,"abstract":"Multi-object detection and tracking with spatially distributed sensor networks are used in many applications across the domains of autonomous and surveillance systems. The sensors typically used in these systems often provide incomplete observations such as bistatic and angle- or range-only measurements, thus posing a challenge to the task of retrieving the targets and estimating their state. In this paper, we first present a new variant of a multi-sensor tracking algorithm based on the Gaussian-mixture probability hypothesis density (GM-PHD) filter. Next, we show how it can be applied on fusing incomplete observations. For tracking asynchronous range- and angle-only measurements, we leverage the well-known concepts of angle and range parametrization, respectively, to describe the adaptive target birth density based on the parameters of received observations. In the case of multistatic tracking, we propose parametrizing the birth density from target hypotheses, generated by statically fusing bistatic range measurements, using the M-best S-D assignment algorithm. We investigate the performance using challenging simulation scenarios and evaluate it with established tracking metrics. Our preliminary results demonstrate the effectiveness of the proposed algorithms. Furthermore, for range- and angle-only fusion, the more common use case of unsynchronized sensor measurements is supported. While many algorithms in the literature are tailored for a specific problem, we show that the proposed GM-PHD tracker is generic and can be potentially leveraged in a wide range of sensor fusion and tracking applications.","PeriodicalId":226850,"journal":{"name":"2021 IEEE 24th International Conference on Information Fusion (FUSION)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126408863","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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