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Advances in sensor systems have resulted in the availability of high resolution sensors, capable of generating massive amounts of data. For complex systems to run online, the primary focus is on computationally efficient filters for the estimation of latent states related to the data. In this paper a novel method for efficient state estimation with the unscented Kalman Filter is proposed. The focus is on applications consisting of a massive amount of data. From a modelling perspective, this amounts to a measurement vector with dimensionality significantly greater than the dimensionality of the state vector. The efficiency of the filter is derived from a parallel filter structure which is enabled by the expectation propagation algorithm. A novel parallel measurement processing expectation propagation unscented Kalman filter is developed. The primary advantage of the novel algorithm is in the ability to achieve computational improvements with negligible loses in filter accuracy. An example of robot localization with a high resolution laser rangefinder sensor is presented. A 47.53% decrease in computational time was exhibited for a scenario with a processing platform consisting of 4 processors, with a negligible loss in accuracy., Funding Agencies|EC Seventh Framework Programme TRAcking in compleX sensor systems (TRAX) [607400]; UK Royal Society fund [E150823]

The commercial interest in proximity services is increasing. Application examples include location-based information and advertisements, logistics, social networking, file sharing, etc. In this paper, we consider network-based positioning based on times series of proximity reports from a mobile device, either only a proximity indicator, or a vector of RSS from observed nodes. Such positioning corresponds to a latent and nonlinear observation model. To address these problems, we combine two powerful tools, namely particle filtering and Gaussian process regression (GPR) for radio signal propagation modeling. The latter also provides some insights into the spatial correlation of the radio propagation in the considered area. Radio propagation modeling and positioning performance are evaluated in a typical office area with Bluetooth-Low-Energy (BLE) beacons deployed for proximity detection and reports. Results show that the positioning accuracy can be improved by using GPR.

The commercial interest in proximity services is increasing. Application examples include location-based information and advertisements, logistics, social networking, file sharing, etc. In this paper, we consider network-based positioning based on times series of proximity reports from a mobile device, either only a proximity indicator, or a vector of RSS from observed nodes. Such positioning corresponds to a latent and nonlinear observation model. To address these problems, we combine two powerful tools, namely particle filtering and Gaussian process regression (GPR) for radio signal propagation modeling. The latter also provides some insights into the spatial correlation of the radio propagation in the considered area. Radio propagation modeling and positioning performance are evaluated in a typical office area with Bluetooth-Low-Energy (BLE) beacons deployed for proximity detection and reports. Results show that the positioning accuracy can be improved by using GPR.

The commercial interest in proximity services is increasing. Application examples include location-based information and advertisements, logistics, social networking, file sharing, etc. In this paper, we consider network-based positioning based on times series of proximity reports from a mobile device, either only a proximity indicator, or a vector of RSS from observed nodes. Such positioning corresponds to a latent and nonlinear observation model. To address these problems, we combine two powerful tools, namely particle filtering and Gaussian process regression (GPR) for radio signal propagation modeling. The latter also provides some insights into the spatial correlation of the radio propagation in the considered area. Radio propagation modeling and positioning performance are evaluated in a typical office area with Bluetooth-Low-Energy (BLE) beacons deployed for proximity detection and reports. Results show that the positioning accuracy can be improved by using GPR.

The commercial interest in proximity services is increasing. Application examples include location-based information and advertisements, logistics, social networking, file sharing, etc. In this paper, we consider network-based positioning based on times series of proximity reports from a mobile device, either only a proximity indicator, or a vector of RSS from observed nodes. Such positioning corresponds to a latent and nonlinear observation model. To address these problems, we combine two powerful tools, namely particle filtering and Gaussian process regression (GPR) for radio signal propagation modeling. The latter also provides some insights into the spatial correlation of the radio propagation in the considered area. Radio propagation modeling and positioning performance are evaluated in a typical office area with Bluetooth-Low-Energy (BLE) beacons deployed for proximity detection and reports. Results show that the positioning accuracy can be improved by using GPR.

The commercial interest in proximity services is increasing. Application examples include location-based information and advertisements, logistics, social networking, file sharing, etc. In this paper, we consider network-based positioning based on times series of proximity reports from a mobile device, either only a proximity indicator, or a vector of RSS from observed nodes. Such positioning corresponds to a latent and nonlinear observation model. To address these problems, we combine two powerful tools, namely particle filtering and Gaussian process regression (GPR) for radio signal propagation modeling. The latter also provides some insights into the spatial correlation of the radio propagation in the considered area. Radio propagation modeling and positioning performance are evaluated in a typical office area with Bluetooth-Low-Energy (BLE) beacons deployed for proximity detection and reports. Results show that the positioning accuracy can be improved by using GPR.

In this paper, a semi-parametric model for RSS measurements is introduced that can be used to predict coverage in cellular radio networks. The model is composed of an empirical log-distance model and a deterministic antenna gain model that accounts for possible non-uniform base station antenna radiation. A least-squares estimator is proposed to jointly estimate the path loss and antenna gain model parameters. Simulation as well as experimental results verify the efficacy of this approach. The method can provide improved accuracy compared to conventional path loss based estimation methods., ©2016 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

In this paper, a semi-parametric model for RSS measurements is introduced that can be used to predict coverage in cellular radio networks. The model is composed of an empirical log-distance model and a deterministic antenna gain model that accounts for possible non-uniform base station antenna radiation. A least-squares estimator is proposed to jointly estimate the path loss and antenna gain model parameters. Simulation as well as experimental results verify the efficacy of this approach. The method can provide improved accuracy compared to conventional path loss based estimation methods., ©2016 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

In this paper, a semi-parametric model for RSS measurements is introduced that can be used to predict coverage in cellular radio networks. The model is composed of an empirical log-distance model and a deterministic antenna gain model that accounts for possible non-uniform base station antenna radiation. A least-squares estimator is proposed to jointly estimate the path loss and antenna gain model parameters. Simulation as well as experimental results verify the efficacy of this approach. The method can provide improved accuracy compared to conventional path loss based estimation methods., ©2016 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

In this paper, a semi-parametric model for RSS measurements is introduced that can be used to predict coverage in cellular radio networks. The model is composed of an empirical log-distance model and a deterministic antenna gain model that accounts for possible non-uniform base station antenna radiation. A least-squares estimator is proposed to jointly estimate the path loss and antenna gain model parameters. Simulation as well as experimental results verify the efficacy of this approach. The method can provide improved accuracy compared to conventional path loss based estimation methods., ©2016 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

In this paper, a semi-parametric model for RSS measurements is introduced that can be used to predict coverage in cellular radio networks. The model is composed of an empirical log-distance model and a deterministic antenna gain model that accounts for possible non-uniform base station antenna radiation. A least-squares estimator is proposed to jointly estimate the path loss and antenna gain model parameters. Simulation as well as experimental results verify the efficacy of this approach. The method can provide improved accuracy compared to conventional path loss based estimation methods., ©2016 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

Classification of motion mode (walking, running, standing still) and device mode (hand-held, in pocket, in back-pack) is an enabler in personal navigation systems for the purpose of saving energy and design parameter settings and also for its own sake. Our main contribution is to publish one of the most extensive datasets for this problem, including inertial data from eight users, each one performing three pre-defined trajectories carrying four smartphones and seventeen inertial measurement units on the body. All kind of metadata is available such as the ground truth of all modes and position. A second contribution is the first study on a joint classifier of motion and device mode, respectively, where preliminary but promising results are presented., Funding Agencies|European Union FP7 Marie Curie training program on Tracking in Complex Sensor Systems (TRAX) [607400]

Classification of motion mode (walking, running, standing still) and device mode (hand-held, in pocket, in back-pack) is an enabler in personal navigation systems for the purpose of saving energy and design parameter settings and also for its own sake. Our main contribution is to publish one of the most extensive datasets for this problem, including inertial data from eight users, each one performing three pre-defined trajectories carrying four smartphones and seventeen inertial measurement units on the body. All kind of metadata is available such as the ground truth of all modes and position. A second contribution is the first study on a joint classifier of motion and device mode, respectively, where preliminary but promising results are presented., Funding Agencies|European Union FP7 Marie Curie training program on Tracking in Complex Sensor Systems (TRAX) [607400]

Classification of motion mode (walking, running, standing still) and device mode (hand-held, in pocket, in back-pack) is an enabler in personal navigation systems for the purpose of saving energy and design parameter settings and also for its own sake. Our main contribution is to publish one of the most extensive datasets for this problem, including inertial data from eight users, each one performing three pre-defined trajectories carrying four smartphones and seventeen inertial measurement units on the body. All kind of metadata is available such as the ground truth of all modes and position. A second contribution is the first study on a joint classifier of motion and device mode, respectively, where preliminary but promising results are presented., Funding Agencies|European Union FP7 Marie Curie training program on Tracking in Complex Sensor Systems (TRAX) [607400]

Classification of motion mode (walking, running, standing still) and device mode (hand-held, in pocket, in back-pack) is an enabler in personal navigation systems for the purpose of saving energy and design parameter settings and also for its own sake. Our main contribution is to publish one of the most extensive datasets for this problem, including inertial data from eight users, each one performing three pre-defined trajectories carrying four smartphones and seventeen inertial measurement units on the body. All kind of metadata is available such as the ground truth of all modes and position. A second contribution is the first study on a joint classifier of motion and device mode, respectively, where preliminary but promising results are presented., Funding Agencies|European Union FP7 Marie Curie training program on Tracking in Complex Sensor Systems (TRAX) [607400]

Classification of motion mode (walking, running, standing still) and device mode (hand-held, in pocket, in back-pack) is an enabler in personal navigation systems for the purpose of saving energy and design parameter settings and also for its own sake. Our main contribution is to publish one of the most extensive datasets for this problem, including inertial data from eight users, each one performing three pre-defined trajectories carrying four smartphones and seventeen inertial measurement units on the body. All kind of metadata is available such as the ground truth of all modes and position. A second contribution is the first study on a joint classifier of motion and device mode, respectively, where preliminary but promising results are presented., Funding Agencies|European Union FP7 Marie Curie training program on Tracking in Complex Sensor Systems (TRAX) [607400]

Positioning in radio networks is a well establishedresearch area. The dominating approach has been that positioningalgorithms are implemented in the higher levels of the communicationsystem based on position related information derivedin the lowest (physical) layer. Examples of measurement includereceived signal strength (RSS), time of arrival (TOA), angleof arrival (AOA), and fusion and filtering is a straightforwardtask. The technical driver for positioning has been E911 andfor commercially driver comes from location based services andlogistics management. These demands are fundamental in thedevelopment of positioning in future radio networks standards.There is today a trend for accuracy demand that goes beyondwhat can be achieved with todays measurements. Another trendis that measurements and positioning algorithms are approachingeach other, so some parts of the positioning are performed on thechip-sets (lowest layer) and low-level measurements are availableto the operating system (highest level). The purpose of thissurvey is to describe this trend in more detail, with examples ofdevelopments in cellular networks as well as WiFi and Bluetooth., TRAX

Positioning in radio networks is a well establishedresearch area. The dominating approach has been that positioningalgorithms are implemented in the higher levels of the communicationsystem based on position related information derivedin the lowest (physical) layer. Examples of measurement includereceived signal strength (RSS), time of arrival (TOA), angleof arrival (AOA), and fusion and filtering is a straightforwardtask. The technical driver for positioning has been E911 andfor commercially driver comes from location based services andlogistics management. These demands are fundamental in thedevelopment of positioning in future radio networks standards.There is today a trend for accuracy demand that goes beyondwhat can be achieved with todays measurements. Another trendis that measurements and positioning algorithms are approachingeach other, so some parts of the positioning are performed on thechip-sets (lowest layer) and low-level measurements are availableto the operating system (highest level). The purpose of thissurvey is to describe this trend in more detail, with examples ofdevelopments in cellular networks as well as WiFi and Bluetooth., TRAX

Positioning in radio networks is a well establishedresearch area. The dominating approach has been that positioningalgorithms are implemented in the higher levels of the communicationsystem based on position related information derivedin the lowest (physical) layer. Examples of measurement includereceived signal strength (RSS), time of arrival (TOA), angleof arrival (AOA), and fusion and filtering is a straightforwardtask. The technical driver for positioning has been E911 andfor commercially driver comes from location based services andlogistics management. These demands are fundamental in thedevelopment of positioning in future radio networks standards.There is today a trend for accuracy demand that goes beyondwhat can be achieved with todays measurements. Another trendis that measurements and positioning algorithms are approachingeach other, so some parts of the positioning are performed on thechip-sets (lowest layer) and low-level measurements are availableto the operating system (highest level). The purpose of thissurvey is to describe this trend in more detail, with examples ofdevelopments in cellular networks as well as WiFi and Bluetooth., TRAX

Positioning in radio networks is a well establishedresearch area. The dominating approach has been that positioningalgorithms are implemented in the higher levels of the communicationsystem based on position related information derivedin the lowest (physical) layer. Examples of measurement includereceived signal strength (RSS), time of arrival (TOA), angleof arrival (AOA), and fusion and filtering is a straightforwardtask. The technical driver for positioning has been E911 andfor commercially driver comes from location based services andlogistics management. These demands are fundamental in thedevelopment of positioning in future radio networks standards.There is today a trend for accuracy demand that goes beyondwhat can be achieved with todays measurements. Another trendis that measurements and positioning algorithms are approachingeach other, so some parts of the positioning are performed on thechip-sets (lowest layer) and low-level measurements are availableto the operating system (highest level). The purpose of thissurvey is to describe this trend in more detail, with examples ofdevelopments in cellular networks as well as WiFi and Bluetooth., TRAX

Positioning in radio networks is a well establishedresearch area. The dominating approach has been that positioningalgorithms are implemented in the higher levels of the communicationsystem based on position related information derivedin the lowest (physical) layer. Examples of measurement includereceived signal strength (RSS), time of arrival (TOA), angleof arrival (AOA), and fusion and filtering is a straightforwardtask. The technical driver for positioning has been E911 andfor commercially driver comes from location based services andlogistics management. These demands are fundamental in thedevelopment of positioning in future radio networks standards.There is today a trend for accuracy demand that goes beyondwhat can be achieved with todays measurements. Another trendis that measurements and positioning algorithms are approachingeach other, so some parts of the positioning are performed on thechip-sets (lowest layer) and low-level measurements are availableto the operating system (highest level). The purpose of thissurvey is to describe this trend in more detail, with examples ofdevelopments in cellular networks as well as WiFi and Bluetooth., TRAX