Pub Date : 2019-10-01DOI: 10.1109/SDF.2019.8916659
F. Particke, Jiaren Zhou, M. Hiller, Christian Hofmann, J. Thielecke
Autonomous driving is one of the key challenges in recent time. As pedestrians are the most vulnerable traffic participants, collisions with pedestrians have to be avoided under all circumstances. Hence, prediction of pedestrian trajectories is of high interest for automated vehicles. For this purpose, a plethora of algorithms has been proposed to model the pedestrian in the last decades, reaching from simple kinematic models to advanced microscopic models. In addition, the machine learning community started to learn the behavior of pedestrians and showed major improvements in complex scenarios or unexpected situations. However, as most of the machine learning algorithms are treated as black boxes, the safeguarding of the software is one key challenge which has to be solved. This contribution proposes to combine classic modeling of pedestrians with machine learning algorithms by learning the model errors between a simple physical model and real data. In particular, it is proposed to combine a physical model based on potential fields with a neural network to predict the future behavior of pedestrians. It is shown that the combined approach outperforms the physical model in learnable areas, whereas the physical model without the neural network is more robust in areas where almost no training data is available. In addition, different structures of neural networks are analyzed.
{"title":"Neural Network Aided Potential Field Approach For Pedestrian Prediction","authors":"F. Particke, Jiaren Zhou, M. Hiller, Christian Hofmann, J. Thielecke","doi":"10.1109/SDF.2019.8916659","DOIUrl":"https://doi.org/10.1109/SDF.2019.8916659","url":null,"abstract":"Autonomous driving is one of the key challenges in recent time. As pedestrians are the most vulnerable traffic participants, collisions with pedestrians have to be avoided under all circumstances. Hence, prediction of pedestrian trajectories is of high interest for automated vehicles. For this purpose, a plethora of algorithms has been proposed to model the pedestrian in the last decades, reaching from simple kinematic models to advanced microscopic models. In addition, the machine learning community started to learn the behavior of pedestrians and showed major improvements in complex scenarios or unexpected situations. However, as most of the machine learning algorithms are treated as black boxes, the safeguarding of the software is one key challenge which has to be solved. This contribution proposes to combine classic modeling of pedestrians with machine learning algorithms by learning the model errors between a simple physical model and real data. In particular, it is proposed to combine a physical model based on potential fields with a neural network to predict the future behavior of pedestrians. It is shown that the combined approach outperforms the physical model in learnable areas, whereas the physical model without the neural network is more robust in areas where almost no training data is available. In addition, different structures of neural networks are analyzed.","PeriodicalId":186196,"journal":{"name":"2019 Sensor Data Fusion: Trends, Solutions, Applications (SDF)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116463091","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 : 2019-10-01DOI: 10.1109/SDF.2019.8916631
Josef Steinbaeck, H. Fuereder, C. Steger, E. Brenner, C. Schwarzl, N. Druml, T. Herndl, Stefan Loigge, Nadja Marko, Markus Postl, Georg Kail, Reinhard Hladik, Gerhard Hechenberger
Precise localization is crucial for autonomous navigation, especially for autonomous driving. GNSS localization is prone to a number of errors and is not sufficient to provide reliable positional data in all situations. Most existing approaches for fine-grained positioning are not working reliably in difficult weather conditions. In this paper we present a method to tackle that problem by performing precise localization by exploiting the angle-of-arrival of V2X communications. During a 30-months project, we built an unmanned vehicle capable of determining its precise location via V2X communication. In order to safely navigate in the environment and detect obstacles in its path, the robot is also equipped with environmental perception sensors (time-of-flight and radar). We evaluated the proposed localization method during a test-drive on a precisely mapped parking lot. The resulting localization precision was improved by over 60 percent compared to the standard GPS localization.
{"title":"ACTIVE - Autonomous Car to Infrastructure Communication Mastering Adverse Environments","authors":"Josef Steinbaeck, H. Fuereder, C. Steger, E. Brenner, C. Schwarzl, N. Druml, T. Herndl, Stefan Loigge, Nadja Marko, Markus Postl, Georg Kail, Reinhard Hladik, Gerhard Hechenberger","doi":"10.1109/SDF.2019.8916631","DOIUrl":"https://doi.org/10.1109/SDF.2019.8916631","url":null,"abstract":"Precise localization is crucial for autonomous navigation, especially for autonomous driving. GNSS localization is prone to a number of errors and is not sufficient to provide reliable positional data in all situations. Most existing approaches for fine-grained positioning are not working reliably in difficult weather conditions. In this paper we present a method to tackle that problem by performing precise localization by exploiting the angle-of-arrival of V2X communications. During a 30-months project, we built an unmanned vehicle capable of determining its precise location via V2X communication. In order to safely navigate in the environment and detect obstacles in its path, the robot is also equipped with environmental perception sensors (time-of-flight and radar). We evaluated the proposed localization method during a test-drive on a precisely mapped parking lot. The resulting localization precision was improved by over 60 percent compared to the standard GPS localization.","PeriodicalId":186196,"journal":{"name":"2019 Sensor Data Fusion: Trends, Solutions, Applications (SDF)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114830096","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 : 2019-10-01DOI: 10.1109/SDF.2019.8916656
Julian Böhler, Tim Baur, S. Wirtensohn, J. Reuter
This paper presents a new likelihood-based partitioning method of the measurement set for the extended object probability hypothesis density (PHD) filter framework. Recent work has mostly relied on heuristic partitioning methods that cluster the measurement data based on a distance measure between the single measurements. This can lead to poor filter performance if the tracked extended objects are closely spaced. The proposed method called Stochastic Partitioning (StP) is based on sampling methods and was inspired by a former work of Granström et. al. In this work, the StP method is applied to a Gaussian inverse Wishart (GIW) PHD filter and compared to a second filter implementation that uses the heuristic Distance Partitioning (DP) method. The performance is evaluated in Monte Carlo simulations in a scenario where two objects approach each other. It is shown that the sampling based StP method leads to an improved filter performance compared to DP.
{"title":"Stochastic Partitioning for Extended Object Probability Hypothesis Density Filters","authors":"Julian Böhler, Tim Baur, S. Wirtensohn, J. Reuter","doi":"10.1109/SDF.2019.8916656","DOIUrl":"https://doi.org/10.1109/SDF.2019.8916656","url":null,"abstract":"This paper presents a new likelihood-based partitioning method of the measurement set for the extended object probability hypothesis density (PHD) filter framework. Recent work has mostly relied on heuristic partitioning methods that cluster the measurement data based on a distance measure between the single measurements. This can lead to poor filter performance if the tracked extended objects are closely spaced. The proposed method called Stochastic Partitioning (StP) is based on sampling methods and was inspired by a former work of Granström et. al. In this work, the StP method is applied to a Gaussian inverse Wishart (GIW) PHD filter and compared to a second filter implementation that uses the heuristic Distance Partitioning (DP) method. The performance is evaluated in Monte Carlo simulations in a scenario where two objects approach each other. It is shown that the sampling based StP method leads to an improved filter performance compared to DP.","PeriodicalId":186196,"journal":{"name":"2019 Sensor Data Fusion: Trends, Solutions, Applications (SDF)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125213701","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 : 2019-10-01DOI: 10.1109/SDF.2019.8916636
Karsten Schwalbe, Alexander Groh, Frank Hertwig, U. Scheunert
Automatic object recognition plays a major role in many industrial applications. This task is mostly performed by using optical sensors and image processing methods. Degeneration processes, such as surface wear, however, can pose quite some challenges when it comes to high-quality optical recognition. In this article we present our solution to optical character recognition of strongly degenerated numbers, characterized by a varying embossing depth and texture intensity, imprinted on metal surfaces. Under these conditions Machine Learning (ML) based recognition models seem to perform better than conventional ones. Typically, ML models have a black box character in the sense that the algorithm steps have no direct interpretable meaning and are kind of arbitrary. Consequently, the results of such models are difficult to interpret with respect to their trustworthiness. In order to receive more reliable recognition results, we have developed a rule-based fusion strategy that combines the output of several different AI models. This approach not only leads to a higher rate of correctly recognized objects, it also indicates when the recognition result is uncertain. As a result, our method increases the process safety and makes object recognition in industrial applications more flexible and robust.
{"title":"Data fusion strategy to improve the realiability of machine learning based classifications","authors":"Karsten Schwalbe, Alexander Groh, Frank Hertwig, U. Scheunert","doi":"10.1109/SDF.2019.8916636","DOIUrl":"https://doi.org/10.1109/SDF.2019.8916636","url":null,"abstract":"Automatic object recognition plays a major role in many industrial applications. This task is mostly performed by using optical sensors and image processing methods. Degeneration processes, such as surface wear, however, can pose quite some challenges when it comes to high-quality optical recognition. In this article we present our solution to optical character recognition of strongly degenerated numbers, characterized by a varying embossing depth and texture intensity, imprinted on metal surfaces. Under these conditions Machine Learning (ML) based recognition models seem to perform better than conventional ones. Typically, ML models have a black box character in the sense that the algorithm steps have no direct interpretable meaning and are kind of arbitrary. Consequently, the results of such models are difficult to interpret with respect to their trustworthiness. In order to receive more reliable recognition results, we have developed a rule-based fusion strategy that combines the output of several different AI models. This approach not only leads to a higher rate of correctly recognized objects, it also indicates when the recognition result is uncertain. As a result, our method increases the process safety and makes object recognition in industrial applications more flexible and robust.","PeriodicalId":186196,"journal":{"name":"2019 Sensor Data Fusion: Trends, Solutions, Applications (SDF)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125447574","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 : 2019-10-01DOI: 10.1109/SDF.2019.8916633
Yifan Zhou, James Wright, S. Maskell
This paper proposes a new generic method to detect anomalies (i.e., statistical outliers) which can be used with a generative topic model. In this paper, we specify this method in the context of the Mixture-of-unigrams model, which is widely used in text mining. It is possible to detect anomalies with a topic model by applying a threshold to the likelihood. However, it is challenging to choose the threshold since the choice needs to consider both the similarities of the topics and the length of documents. This paper describes a new intuitive method to detect anomalies which simply manipulates the output of the trained model. Such an approach is anticipated to have parameters that are more intuitive to define for a given problem. To assess the utility of the proposed approach, we also present a use case involving identifying ships misreporting their ship-type using geo-location data from the Automatic Identification System (AIS) messages. We show that, if we train a model using data for one type of ship, it is possible to identify ships of another type as anomalous.
{"title":"A Generic Anomaly Detection Approach Applied to Mixture-of-unigrams and Maritime Surveillance Data","authors":"Yifan Zhou, James Wright, S. Maskell","doi":"10.1109/SDF.2019.8916633","DOIUrl":"https://doi.org/10.1109/SDF.2019.8916633","url":null,"abstract":"This paper proposes a new generic method to detect anomalies (i.e., statistical outliers) which can be used with a generative topic model. In this paper, we specify this method in the context of the Mixture-of-unigrams model, which is widely used in text mining. It is possible to detect anomalies with a topic model by applying a threshold to the likelihood. However, it is challenging to choose the threshold since the choice needs to consider both the similarities of the topics and the length of documents. This paper describes a new intuitive method to detect anomalies which simply manipulates the output of the trained model. Such an approach is anticipated to have parameters that are more intuitive to define for a given problem. To assess the utility of the proposed approach, we also present a use case involving identifying ships misreporting their ship-type using geo-location data from the Automatic Identification System (AIS) messages. We show that, if we train a model using data for one type of ship, it is possible to identify ships of another type as anomalous.","PeriodicalId":186196,"journal":{"name":"2019 Sensor Data Fusion: Trends, Solutions, Applications (SDF)","volume":"121 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127194705","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 : 2019-10-01DOI: 10.1109/SDF.2019.8916630
S. Handke, Joshua Gehlen
A new randomized approach for highly maneuvering targets based on multi hypothesis tracking is presented. The acceleration range - a parameter in current evolution models is used to design various motion models. The approach randomises this parameter to cover a wider range of maneuver characteristics. Simulation shows that the performance of the new method results in a more reliable track continuity.
{"title":"Randomized Evolution Model for Multi Hypothesis Kalman Filter","authors":"S. Handke, Joshua Gehlen","doi":"10.1109/SDF.2019.8916630","DOIUrl":"https://doi.org/10.1109/SDF.2019.8916630","url":null,"abstract":"A new randomized approach for highly maneuvering targets based on multi hypothesis tracking is presented. The acceleration range - a parameter in current evolution models is used to design various motion models. The approach randomises this parameter to cover a wider range of maneuver characteristics. Simulation shows that the performance of the new method results in a more reliable track continuity.","PeriodicalId":186196,"journal":{"name":"2019 Sensor Data Fusion: Trends, Solutions, Applications (SDF)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131661623","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 : 2019-10-01DOI: 10.1109/SDF.2019.8916657
S. Coraluppi, C. Carthel
This paper first validates that the track-oriented multiple-hypothesis tracking recursion holds in the case of state-dependent detection probabilities, as is generally assumed. Next, we seek to extend the track-oriented multiple-hypothesis tracking recursion to allow for unresolved measurements. The formulation requires some simplifying assumptions, including an assumption that targets be resolved at birth and a restriction on the size of unresolved target clusters. The tracking recursion requires some approximation to admit track-oriented (factored) form, and leads to a nonlinear optimization problem. We discuss a multi-stage architecture that provides a simpler and more robust processing approach for practical settings.
{"title":"Track-Oriented MHT with Unresolved Measurements","authors":"S. Coraluppi, C. Carthel","doi":"10.1109/SDF.2019.8916657","DOIUrl":"https://doi.org/10.1109/SDF.2019.8916657","url":null,"abstract":"This paper first validates that the track-oriented multiple-hypothesis tracking recursion holds in the case of state-dependent detection probabilities, as is generally assumed. Next, we seek to extend the track-oriented multiple-hypothesis tracking recursion to allow for unresolved measurements. The formulation requires some simplifying assumptions, including an assumption that targets be resolved at birth and a restriction on the size of unresolved target clusters. The tracking recursion requires some approximation to admit track-oriented (factored) form, and leads to a nonlinear optimization problem. We discuss a multi-stage architecture that provides a simpler and more robust processing approach for practical settings.","PeriodicalId":186196,"journal":{"name":"2019 Sensor Data Fusion: Trends, Solutions, Applications (SDF)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121720654","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 : 2019-10-01DOI: 10.1109/SDF.2019.8916651
Luisa Still, M. Varela, W. Wirth, M. Oispuu
This paper addresses the problem of shooter localization using a single microphone array. Muzzle blast and shock wave are the two impulsive sounds generated by a projectile moving at supersonic speed. If a microphone array measures both sound waves, the shooter state in terms of shooter position and firing direction can be determined. Often, the projectile velocity is assumed to be constant. In this paper, the bullet speed is approximated by a linear model. For this model, an estimator of the shooter state is proposed and the corresponding Cramér-Rao bound is derived. Both cases with and without consideration of the deceleration are studied in Monte Carlo simulations and compared with the corresponding Cramér-Rao bound. The simulation results reveal that a superior state estimation accuracy can be achieved by using the considered projectile model.
{"title":"Shooter Localization with a Microphone Array Based on a Linearly Modeled Bullet Speed","authors":"Luisa Still, M. Varela, W. Wirth, M. Oispuu","doi":"10.1109/SDF.2019.8916651","DOIUrl":"https://doi.org/10.1109/SDF.2019.8916651","url":null,"abstract":"This paper addresses the problem of shooter localization using a single microphone array. Muzzle blast and shock wave are the two impulsive sounds generated by a projectile moving at supersonic speed. If a microphone array measures both sound waves, the shooter state in terms of shooter position and firing direction can be determined. Often, the projectile velocity is assumed to be constant. In this paper, the bullet speed is approximated by a linear model. For this model, an estimator of the shooter state is proposed and the corresponding Cramér-Rao bound is derived. Both cases with and without consideration of the deceleration are studied in Monte Carlo simulations and compared with the corresponding Cramér-Rao bound. The simulation results reveal that a superior state estimation accuracy can be achieved by using the considered projectile model.","PeriodicalId":186196,"journal":{"name":"2019 Sensor Data Fusion: Trends, Solutions, Applications (SDF)","volume":"106 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127087702","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 : 2019-10-01DOI: 10.1109/SDF.2019.8916658
Stefan Haag, B. Duraisamy, F. Govaers, W. Koch, M. Fritzsche, J. Dickmann
Multiple Extended Object Tracking in autonomous driving scenarios must be applicable in highly varying environments such as highway scenarios as well as in urban and rural environments. In this paper, a flexible UKF-based Interacting Multiple Motion (IMM) model extension for the Random Matrix Model (RMM) framework is introduced for nonlinear models. In addition to that, an adaptive clustering method where the provided tracking prior information is invoked to obtain stable clustering and tracking in varying environments with different objects and varying object types is derived. The effectiveness of the filter and clustering method is demonstrated in a real-world scenario.
{"title":"Extended Object Tracking assisted Adaptive Clustering for Radar in Autonomous Driving Applications","authors":"Stefan Haag, B. Duraisamy, F. Govaers, W. Koch, M. Fritzsche, J. Dickmann","doi":"10.1109/SDF.2019.8916658","DOIUrl":"https://doi.org/10.1109/SDF.2019.8916658","url":null,"abstract":"Multiple Extended Object Tracking in autonomous driving scenarios must be applicable in highly varying environments such as highway scenarios as well as in urban and rural environments. In this paper, a flexible UKF-based Interacting Multiple Motion (IMM) model extension for the Random Matrix Model (RMM) framework is introduced for nonlinear models. In addition to that, an adaptive clustering method where the provided tracking prior information is invoked to obtain stable clustering and tracking in varying environments with different objects and varying object types is derived. The effectiveness of the filter and clustering method is demonstrated in a real-world scenario.","PeriodicalId":186196,"journal":{"name":"2019 Sensor Data Fusion: Trends, Solutions, Applications (SDF)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115285378","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 : 2019-10-01DOI: 10.1109/SDF.2019.8916653
M. Campbell, Daniel E. Clark
The calibration of an unknown sensor, such as a camera, is a key issue in the sensor fusion domain. This paper addresses this problem by expanding upon previously introduced work. This method uses a unified Bayesian framework with an alternative parameterisation known as disparity space to calibrate an unknown camera's spatial parameters in reference to a known camera. Here, the recently developedLinear-Complexity Cumulant (LCC) filter is used to improve the both the multitarget tracking and calibration facets of the framework. The new implementation is compared against a Probability Hypothesis Density (PHD) method upon simulated data.
{"title":"Joint stereo camera calibration and multi-target tracking using the linear-complexity factorial cumulant filter","authors":"M. Campbell, Daniel E. Clark","doi":"10.1109/SDF.2019.8916653","DOIUrl":"https://doi.org/10.1109/SDF.2019.8916653","url":null,"abstract":"The calibration of an unknown sensor, such as a camera, is a key issue in the sensor fusion domain. This paper addresses this problem by expanding upon previously introduced work. This method uses a unified Bayesian framework with an alternative parameterisation known as disparity space to calibrate an unknown camera's spatial parameters in reference to a known camera. Here, the recently developedLinear-Complexity Cumulant (LCC) filter is used to improve the both the multitarget tracking and calibration facets of the framework. The new implementation is compared against a Probability Hypothesis Density (PHD) method upon simulated data.","PeriodicalId":186196,"journal":{"name":"2019 Sensor Data Fusion: Trends, Solutions, Applications (SDF)","volume":"9 Volume 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127704019","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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