Pub Date : 2020-09-14DOI: 10.1109/MFI49285.2020.9235228
D. Lindgren, Andreas Nordzell
Doppler shift measurements on opportunistic radio sources can be an alternative to GNSS in disturbed environments. Mobile measurements on a GSM base station indicate that the uncertainty is sufficiently low for vehicle positioning, provided that at least two sources are within range and that measurements are fused with an odometer and a rate gyro. A key idea is to fuse the relatively uncertain Doppler measurements with accurate measurements of the vehicle speed. The positioning performance is analyzed by Monte Carlo simulations. A position RMSE in the interval 15 – 44 m can be expected in a suburban environment with limited occlusion.
{"title":"Robust Positioning Based on Opportunistic Radio Sources and Doppler","authors":"D. Lindgren, Andreas Nordzell","doi":"10.1109/MFI49285.2020.9235228","DOIUrl":"https://doi.org/10.1109/MFI49285.2020.9235228","url":null,"abstract":"Doppler shift measurements on opportunistic radio sources can be an alternative to GNSS in disturbed environments. Mobile measurements on a GSM base station indicate that the uncertainty is sufficiently low for vehicle positioning, provided that at least two sources are within range and that measurements are fused with an odometer and a rate gyro. A key idea is to fuse the relatively uncertain Doppler measurements with accurate measurements of the vehicle speed. The positioning performance is analyzed by Monte Carlo simulations. A position RMSE in the interval 15 – 44 m can be expected in a suburban environment with limited occlusion.","PeriodicalId":446154,"journal":{"name":"2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116967542","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 : 2020-09-14DOI: 10.1109/MFI49285.2020.9235252
S. Sen, N. Rao, C. Wu, R. Brooks, Christopher Temples
We consider a problem of detecting a low-level radiation source using a network of Gamma spectral sensors placed on the periphery of a monitored region. We propose a computationally light-weight, correlation-based method which is primarily intended for systems with limited computing capacity. Sensor measurements are combined at the fusion by first generating decisions at each time step and then taking their majority vote within a time widow. At each time step, decisions are generated using two strategies: (i) SUM method based on a threshold decision on a correlation statistic derived from measurements from all sensors, and (ii) OR method based on logical-OR of threshold decisions based on correlations statistics of individual sensor measurements. We derive analytical performance bounds for false alarm rates of SUM and OR methods, and show that their performance is enhanced by the temporal smoothing of majority vote within a time window. Using measurements from a test campaign, we generate a border monitoring scenario with twelve 2" ×2" NaI Gamma sensors deployed on the periphery of 42 × 42 m2 outdoor region. A Cs-137 source is moved in a straight-line across this region, starting several meters outside and finally moving away from it. We illustrate the performance of both correlation-based detection methods, and compare their performances with each other and with a particle filter method. Overall, under small false-alarm conditions, the OR fusion is found to produce better detection performance.
{"title":"Detecting Low-level Radiation Sources Using Border Monitoring Gamma Sensors","authors":"S. Sen, N. Rao, C. Wu, R. Brooks, Christopher Temples","doi":"10.1109/MFI49285.2020.9235252","DOIUrl":"https://doi.org/10.1109/MFI49285.2020.9235252","url":null,"abstract":"We consider a problem of detecting a low-level radiation source using a network of Gamma spectral sensors placed on the periphery of a monitored region. We propose a computationally light-weight, correlation-based method which is primarily intended for systems with limited computing capacity. Sensor measurements are combined at the fusion by first generating decisions at each time step and then taking their majority vote within a time widow. At each time step, decisions are generated using two strategies: (i) SUM method based on a threshold decision on a correlation statistic derived from measurements from all sensors, and (ii) OR method based on logical-OR of threshold decisions based on correlations statistics of individual sensor measurements. We derive analytical performance bounds for false alarm rates of SUM and OR methods, and show that their performance is enhanced by the temporal smoothing of majority vote within a time window. Using measurements from a test campaign, we generate a border monitoring scenario with twelve 2\" ×2\" NaI Gamma sensors deployed on the periphery of 42 × 42 m2 outdoor region. A Cs-137 source is moved in a straight-line across this region, starting several meters outside and finally moving away from it. We illustrate the performance of both correlation-based detection methods, and compare their performances with each other and with a particle filter method. Overall, under small false-alarm conditions, the OR fusion is found to produce better detection performance.","PeriodicalId":446154,"journal":{"name":"2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128103109","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 : 2020-09-14DOI: 10.1109/MFI49285.2020.9235266
O. Dagan, N. Ahmed
This paper studies a family of heterogeneous Bayesian decentralized data fusion problems. Heterogeneous fusion considers the set of problems in which either the communicated or the estimated distributions describe different, but overlapping, states of interest which are subsets of a larger full global joint state. On the other hand, in homogeneous decentralized fusion, each agent is required to process and communicate the full global joint distribution. This might lead to high computation and communication costs irrespective of relevancy to an agent's particular mission, for example, in autonomous multi-platform multi-target tracking problems, since the number of states scales with the number of targets and agent platforms, not with each agent’s specific local mission. In this paper, we exploit the conditional independence structure of such problems and provide a rigorous derivation for a family of exact and approximate, heterogeneous, conditionally factorized channel filter methods. Numerical examples show more than 95% potential communication reduction for heterogeneous channel filter fusion, and a multi-target tracking simulation shows that these methods provide consistent estimates.
{"title":"Heterogeneous Decentralized Fusion Using Conditionally Factorized Channel Filters","authors":"O. Dagan, N. Ahmed","doi":"10.1109/MFI49285.2020.9235266","DOIUrl":"https://doi.org/10.1109/MFI49285.2020.9235266","url":null,"abstract":"This paper studies a family of heterogeneous Bayesian decentralized data fusion problems. Heterogeneous fusion considers the set of problems in which either the communicated or the estimated distributions describe different, but overlapping, states of interest which are subsets of a larger full global joint state. On the other hand, in homogeneous decentralized fusion, each agent is required to process and communicate the full global joint distribution. This might lead to high computation and communication costs irrespective of relevancy to an agent's particular mission, for example, in autonomous multi-platform multi-target tracking problems, since the number of states scales with the number of targets and agent platforms, not with each agent’s specific local mission. In this paper, we exploit the conditional independence structure of such problems and provide a rigorous derivation for a family of exact and approximate, heterogeneous, conditionally factorized channel filter methods. Numerical examples show more than 95% potential communication reduction for heterogeneous channel filter fusion, and a multi-target tracking simulation shows that these methods provide consistent estimates.","PeriodicalId":446154,"journal":{"name":"2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130905263","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 : 2020-09-14DOI: 10.1109/MFI49285.2020.9235267
J. Wetzel, Astrid Laubenheimer, M. Heizmann
Wide-area indoor people detection in a network of depth sensors is the basis for many applications, e.g. people counting or customer behavior analysis. Existing probabilistic methods use approximative stochastic inference to estimate the marginal probability distribution of people present in the scene for a single time step. In this work we investigate how the temporal context, given by a time series of multi-view depth observations, can be exploited to regularize a mean-field variational inference optimization process. We present a probabilistic grid based dynamic model and deduce the corresponding mean-field update regulations to effectively approximate the joint probability distribution of people present in the scene across space and time. Our experiments show that the proposed temporal regularization leads to a more robust estimation of the desired probability distribution and increases the detection performance.
{"title":"Temporal Smoothing for Joint Probabilistic People Detection in a Depth Sensor Network","authors":"J. Wetzel, Astrid Laubenheimer, M. Heizmann","doi":"10.1109/MFI49285.2020.9235267","DOIUrl":"https://doi.org/10.1109/MFI49285.2020.9235267","url":null,"abstract":"Wide-area indoor people detection in a network of depth sensors is the basis for many applications, e.g. people counting or customer behavior analysis. Existing probabilistic methods use approximative stochastic inference to estimate the marginal probability distribution of people present in the scene for a single time step. In this work we investigate how the temporal context, given by a time series of multi-view depth observations, can be exploited to regularize a mean-field variational inference optimization process. We present a probabilistic grid based dynamic model and deduce the corresponding mean-field update regulations to effectively approximate the joint probability distribution of people present in the scene across space and time. Our experiments show that the proposed temporal regularization leads to a more robust estimation of the desired probability distribution and increases the detection performance.","PeriodicalId":446154,"journal":{"name":"2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","volume":"253 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130620700","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 : 2020-09-14DOI: 10.1109/MFI49285.2020.9235251
M. Herrmann, Aldi Piroli, Jan Strohbeck, Johannes Müller, M. Buchholz
Autonomous vehicles need precise knowledge on dynamic objects in their surroundings. Especially in urban areas with many objects and possible occlusions, an infrastructure system based on a multi-sensor setup can provide the required environment model for the vehicles. Previously, we have published a concept of object reference points (e.g. the corners of an object), which allows for generic sensor "plug and play" interfaces and relatively cheap sensors. This paper describes a novel method to additionally incorporate multiple hypotheses for fusing the measurements of the object reference points using an extension to the previously presented Labeled Multi-Bernoulli (LMB) filter. In contrast to the previous work, this approach improves the tracking quality in the cases where the correct association of the measurement and the object reference point is unknown. Furthermore, this paper identifies options based on physical models to sort out inconsistent and unfeasible associations at an early stage in order to keep the method computationally tractable for real-time applications. The method is evaluated on simulations as well as on real scenarios. In comparison to comparable methods, the proposed approach shows a considerable performance increase, especially the number of non-continuous tracks is decreased significantly.
{"title":"LMB Filter Based Tracking Allowing for Multiple Hypotheses in Object Reference Point Association","authors":"M. Herrmann, Aldi Piroli, Jan Strohbeck, Johannes Müller, M. Buchholz","doi":"10.1109/MFI49285.2020.9235251","DOIUrl":"https://doi.org/10.1109/MFI49285.2020.9235251","url":null,"abstract":"Autonomous vehicles need precise knowledge on dynamic objects in their surroundings. Especially in urban areas with many objects and possible occlusions, an infrastructure system based on a multi-sensor setup can provide the required environment model for the vehicles. Previously, we have published a concept of object reference points (e.g. the corners of an object), which allows for generic sensor \"plug and play\" interfaces and relatively cheap sensors. This paper describes a novel method to additionally incorporate multiple hypotheses for fusing the measurements of the object reference points using an extension to the previously presented Labeled Multi-Bernoulli (LMB) filter. In contrast to the previous work, this approach improves the tracking quality in the cases where the correct association of the measurement and the object reference point is unknown. Furthermore, this paper identifies options based on physical models to sort out inconsistent and unfeasible associations at an early stage in order to keep the method computationally tractable for real-time applications. The method is evaluated on simulations as well as on real scenarios. In comparison to comparable methods, the proposed approach shows a considerable performance increase, especially the number of non-continuous tracks is decreased significantly.","PeriodicalId":446154,"journal":{"name":"2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123183556","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 : 2020-09-14DOI: 10.1109/MFI49285.2020.9235241
R. Rezaie, X. Rong Li
A trajectory of a destination-directed moving object (e.g. an aircraft from an origin airport to a destination airport) has three main components: an origin, a destination, and motion in between. We call such a trajectory that end up at the destination destination-directed trajectory (DDT). A class of conditionally Markov (CM) sequences (called CML) has the following main components: a joint density of two endpoints and a Markov-like evolution law. A CML dynamic model can describe the evolution of a DDT but not of a guided object chasing a moving guide. The trajectory of a guided object is called a guided trajectory (GT). Inspired by a CML model, this paper proposes a model for a GT with a moving guide. The proposed model reduces to a CML model if the guide is not moving. We also study filtering and trajectory prediction based on the proposed model. Simulation results are presented.
{"title":"Mathematical Modeling and Optimal Inference of Guided Markov-Like Trajectory","authors":"R. Rezaie, X. Rong Li","doi":"10.1109/MFI49285.2020.9235241","DOIUrl":"https://doi.org/10.1109/MFI49285.2020.9235241","url":null,"abstract":"A trajectory of a destination-directed moving object (e.g. an aircraft from an origin airport to a destination airport) has three main components: an origin, a destination, and motion in between. We call such a trajectory that end up at the destination destination-directed trajectory (DDT). A class of conditionally Markov (CM) sequences (called CML) has the following main components: a joint density of two endpoints and a Markov-like evolution law. A CML dynamic model can describe the evolution of a DDT but not of a guided object chasing a moving guide. The trajectory of a guided object is called a guided trajectory (GT). Inspired by a CML model, this paper proposes a model for a GT with a moving guide. The proposed model reduces to a CML model if the guide is not moving. We also study filtering and trajectory prediction based on the proposed model. Simulation results are presented.","PeriodicalId":446154,"journal":{"name":"2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","volume":"379 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124732390","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 : 2020-09-14DOI: 10.1109/MFI49285.2020.9235245
Dibyendu Ghosh, V. Honkote, Karthik Narayanan
The estimation of robot pose in an indoor and unknown environment is a challenging problem. Traditional methods using wheel odometry and inertial measurement unit (IMU) are inaccurate due to wheel slippage and drift related issues. Ultra-wide-band (UWB) technology fused with extended Kalman filter (EKF) approach provides relatively accurate ranging and localization in a line-of-sight (LOS) scenario. However, the presence of physical obstacles {such as, walls, doors etc. called as non-line-of-sight (NLOS)} in an indoor environment pose additional challenges which are difficult to address using UWB alone. Identification of LOS/NLOS information can greatly benefit many location-related applications. To this end, an algorithm based on variance measurement technique of distance estimates along with power envelope of the received signal is proposed for NLOS identification. Further, adaptive adjustment of sensor noise covariance approach is devised to mitigate the NLOS effect. The proposed method-ology is computationally light and is thoroughly tested. The results demonstrate that the proposed method achieves 2X improvement in accuracy compared to existing approach.∼
{"title":"Dynamic Adaption of Noise Covariance for Accurate Indoor Localization of Mobile Robots in Non-Line-of-Sight Environments","authors":"Dibyendu Ghosh, V. Honkote, Karthik Narayanan","doi":"10.1109/MFI49285.2020.9235245","DOIUrl":"https://doi.org/10.1109/MFI49285.2020.9235245","url":null,"abstract":"The estimation of robot pose in an indoor and unknown environment is a challenging problem. Traditional methods using wheel odometry and inertial measurement unit (IMU) are inaccurate due to wheel slippage and drift related issues. Ultra-wide-band (UWB) technology fused with extended Kalman filter (EKF) approach provides relatively accurate ranging and localization in a line-of-sight (LOS) scenario. However, the presence of physical obstacles {such as, walls, doors etc. called as non-line-of-sight (NLOS)} in an indoor environment pose additional challenges which are difficult to address using UWB alone. Identification of LOS/NLOS information can greatly benefit many location-related applications. To this end, an algorithm based on variance measurement technique of distance estimates along with power envelope of the received signal is proposed for NLOS identification. Further, adaptive adjustment of sensor noise covariance approach is devised to mitigate the NLOS effect. The proposed method-ology is computationally light and is thoroughly tested. The results demonstrate that the proposed method achieves 2X improvement in accuracy compared to existing approach.∼","PeriodicalId":446154,"journal":{"name":"2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124791683","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 : 2020-09-14DOI: 10.1109/MFI49285.2020.9235250
Süleyman Fatih Kara, Burak Basaran
With their serious impact on the safe and economic operation of railway domains, train positioning systems play a crucial part in railway signalling. In this paper, we present a solution for such a train positioning system by making use of a tachometer, a Doppler radar and a magnetic positioning sensor (a.k.a tag). An IMM (Interacting Multiple Model) filter based sensor fusion algorithm has been used to calculate the velocity and position of the train using the above sensors. The algorithm has been developed with SCADE (Safety Critical Application Development Environment) which is a tool frequently used for development in safety-critical systems because it drastically simplifies and accelerates the certification process required of EN 50128.
{"title":"An Application of IMM Based Sensor Fusion Algorithm in Train Positioning System","authors":"Süleyman Fatih Kara, Burak Basaran","doi":"10.1109/MFI49285.2020.9235250","DOIUrl":"https://doi.org/10.1109/MFI49285.2020.9235250","url":null,"abstract":"With their serious impact on the safe and economic operation of railway domains, train positioning systems play a crucial part in railway signalling. In this paper, we present a solution for such a train positioning system by making use of a tachometer, a Doppler radar and a magnetic positioning sensor (a.k.a tag). An IMM (Interacting Multiple Model) filter based sensor fusion algorithm has been used to calculate the velocity and position of the train using the above sensors. The algorithm has been developed with SCADE (Safety Critical Application Development Environment) which is a tool frequently used for development in safety-critical systems because it drastically simplifies and accelerates the certification process required of EN 50128.","PeriodicalId":446154,"journal":{"name":"2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125521510","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 : 2020-09-14DOI: 10.1109/MFI49285.2020.9235229
Bruno Maric, Marsela Polic, Tomislav Tabak, M. Orsag
In this work we are proposing an intuitive tool based on motion capture system for programming by demonstration tasks in robot manipulation. For a robot manipulator set in a working environment equipped with any external measurement sys-tem, we propose an online calibration method based on unsupervised learning and simplex optimization. Without loos of generality the Nelder-Mead simplex method is used to calibrate the rigid transforms of the robot tools and environment based on motion capture system recordings. Fast optimization procedure is enabled through dataset subsampling using iterative clustering and outlier detection procedure. The online calibration enables customization and execution of programming by demonstration tasks in real time.
{"title":"Unsupervised optimization approach to in situ calibration of collaborative human-robot interaction tools","authors":"Bruno Maric, Marsela Polic, Tomislav Tabak, M. Orsag","doi":"10.1109/MFI49285.2020.9235229","DOIUrl":"https://doi.org/10.1109/MFI49285.2020.9235229","url":null,"abstract":"In this work we are proposing an intuitive tool based on motion capture system for programming by demonstration tasks in robot manipulation. For a robot manipulator set in a working environment equipped with any external measurement sys-tem, we propose an online calibration method based on unsupervised learning and simplex optimization. Without loos of generality the Nelder-Mead simplex method is used to calibrate the rigid transforms of the robot tools and environment based on motion capture system recordings. Fast optimization procedure is enabled through dataset subsampling using iterative clustering and outlier detection procedure. The online calibration enables customization and execution of programming by demonstration tasks in real time.","PeriodicalId":446154,"journal":{"name":"2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130369020","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 : 2020-09-14DOI: 10.1109/MFI49285.2020.9235247
Dennis Bruggner, Daniel Clarke, Dhiraj Gulati
Estimating the number of targets and their states is an important aspect of sensor fusion. In some applications, like autonomous driving, multiple measurements stem from extended targets because of multiple reflections from the target’s shape when using high resolution sensors like LiDAR or Radar. Multi-target tracking techniques using point based target assumptions are generally not suitable for these types of sensor measurements. In the last years, a number of techniques have been introduced which use a known shape or estimate the shape to retrieve the position of the object. In this paper we will introduce a novel approach without knowing/estimating the shape but using all the available information by fusing the measurements from one object with a conservative fusion technique based on the Weighted Exponential Product rule. The results show that we obtain similar performance to state-of-the-art approaches in our simulations.
{"title":"Extended Object Framework based on Weighted Exponential Products","authors":"Dennis Bruggner, Daniel Clarke, Dhiraj Gulati","doi":"10.1109/MFI49285.2020.9235247","DOIUrl":"https://doi.org/10.1109/MFI49285.2020.9235247","url":null,"abstract":"Estimating the number of targets and their states is an important aspect of sensor fusion. In some applications, like autonomous driving, multiple measurements stem from extended targets because of multiple reflections from the target’s shape when using high resolution sensors like LiDAR or Radar. Multi-target tracking techniques using point based target assumptions are generally not suitable for these types of sensor measurements. In the last years, a number of techniques have been introduced which use a known shape or estimate the shape to retrieve the position of the object. In this paper we will introduce a novel approach without knowing/estimating the shape but using all the available information by fusing the measurements from one object with a conservative fusion technique based on the Weighted Exponential Product rule. The results show that we obtain similar performance to state-of-the-art approaches in our simulations.","PeriodicalId":446154,"journal":{"name":"2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126469559","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：