The Experts below are selected from a list of 75 Experts worldwide ranked by ideXlab platform

Chenren Xu - One of the best experts on this subject based on the ideXlab platform.

  • the case for efficient and robust rf based device free localization
    IEEE Transactions on Mobile Computing, 2016
    Co-Authors: Chenren Xu, Bernhard Firner, Yanyong Zhang, R E Howard
    Abstract:

    Radio frequency based device-free localization has been proposed as an alternative localization technique. Unlike its active localization counterpart, it does not require subjects to wear any radio device, but tries to determine the subject’s location by observing how much the subject disturbs the radio propagation patterns. This problem is very challenging due to the well known multipath effect, especially in a complex indoor environment where it is impractical to accurately model the effects of a subject on the surrounding radio links. In this article, we formulate the device-free localization problem using probabilistic classification approaches that are based on discriminant analysis.To boost the localization accuracies, we adopt methods to mitigate errors caused by the multipath effect, as well as methods to automatically recalibrate training data so that accuracy can be maintained as the environment evolves. We validate our method in a one-bedroom apartment that consists of 32 Cells, using eight fixed transmitters and eight fixed receivers. When the space has a single occupant, our method can correctly estimate the Occupied Cell with a likelihood as high as 97.2 percent. Further, we show that we can maintain a high localization accuracy, while substantially reducing the deployment overhead, which is an important concern for device-free localization methods. To achieve this goal, we have improved our training and testing procedures to reduce the overhead, studied the radio device placement to optimize the device cost, devised algorithms to extend the lifetime of the training data, and designed a set of auxiliary sensors and incorporate them into the system to achieve automatic re-calibration.

  • improving rf based device free passive localization in cluttered indoor environments through probabilistic classification methods
    Information Processing in Sensor Networks, 2012
    Co-Authors: Chenren Xu, Bernhard Firner, Yanyong Zhang, R E Howard, Jun Li, Xiaodong Lin
    Abstract:

    ABSTRACT Radio frequency based device-free passive localization has been proposed as an alternative to indoor localization because it does not require subjects to wear a radio device. This technique observes how people disturb the pattern of radio waves in an indoor space and derives their positions accordingly. The well-known multipath effect makes this problem very challenging, because in a complex environment it is impractical to have enough knowledge to be able to accurately model the effects of a subject on the surrounding radio links. In addition, even minor changes in the environment over time change radio propagation sufficiently to invalidate the datasets needed by simple fingerprint-based methods. In this paper, we develop a fingerprinting-based method using probabilistic classification approaches based on discriminant analysis. We also devise ways to mitigate the error caused by multipath effect in data collection, further boosting the classification likelihood. We validate our method in a one-bedroom apartment that has 8 transmitters, 8 receivers, and a total of 32 Cells that can be Occupied. We show that our method can correctly estimate the Occupied Cell with a likelihood of 97.2%. Further, we show that the accuracy remains high, even when we significantly reduce the training overhead, consider fewer radio devices, or conduct a test one month later after the training. We also show that our method can be used to track a person in motion and to localize multiple people with high accuracies. Finally, we deploy our method in a completely different commercial environment with two times the area achieving a Cell estimation accuracy of 93.8% as an evidence of applicability to multiple environments.

  • IPSN - Improving RF-based device-free passive localization in cluttered indoor environments through probabilistic classification methods
    Proceedings of the 11th international conference on Information Processing in Sensor Networks - IPSN '12, 2012
    Co-Authors: Chenren Xu, Bernhard Firner, Yanyong Zhang, Richard Howard, Jun Li
    Abstract:

    ABSTRACT Radio frequency based device-free passive localization has been proposed as an alternative to indoor localization because it does not require subjects to wear a radio device. This technique observes how people disturb the pattern of radio waves in an indoor space and derives their positions accordingly. The well-known multipath effect makes this problem very challenging, because in a complex environment it is impractical to have enough knowledge to be able to accurately model the effects of a subject on the surrounding radio links. In addition, even minor changes in the environment over time change radio propagation sufficiently to invalidate the datasets needed by simple fingerprint-based methods. In this paper, we develop a fingerprinting-based method using probabilistic classification approaches based on discriminant analysis. We also devise ways to mitigate the error caused by multipath effect in data collection, further boosting the classification likelihood. We validate our method in a one-bedroom apartment that has 8 transmitters, 8 receivers, and a total of 32 Cells that can be Occupied. We show that our method can correctly estimate the Occupied Cell with a likelihood of 97.2%. Further, we show that the accuracy remains high, even when we significantly reduce the training overhead, consider fewer radio devices, or conduct a test one month later after the training. We also show that our method can be used to track a person in motion and to localize multiple people with high accuracies. Finally, we deploy our method in a completely different commercial environment with two times the area achieving a Cell estimation accuracy of 93.8% as an evidence of applicability to multiple environments.

R E Howard - One of the best experts on this subject based on the ideXlab platform.

  • the case for efficient and robust rf based device free localization
    IEEE Transactions on Mobile Computing, 2016
    Co-Authors: Chenren Xu, Bernhard Firner, Yanyong Zhang, R E Howard
    Abstract:

    Radio frequency based device-free localization has been proposed as an alternative localization technique. Unlike its active localization counterpart, it does not require subjects to wear any radio device, but tries to determine the subject’s location by observing how much the subject disturbs the radio propagation patterns. This problem is very challenging due to the well known multipath effect, especially in a complex indoor environment where it is impractical to accurately model the effects of a subject on the surrounding radio links. In this article, we formulate the device-free localization problem using probabilistic classification approaches that are based on discriminant analysis.To boost the localization accuracies, we adopt methods to mitigate errors caused by the multipath effect, as well as methods to automatically recalibrate training data so that accuracy can be maintained as the environment evolves. We validate our method in a one-bedroom apartment that consists of 32 Cells, using eight fixed transmitters and eight fixed receivers. When the space has a single occupant, our method can correctly estimate the Occupied Cell with a likelihood as high as 97.2 percent. Further, we show that we can maintain a high localization accuracy, while substantially reducing the deployment overhead, which is an important concern for device-free localization methods. To achieve this goal, we have improved our training and testing procedures to reduce the overhead, studied the radio device placement to optimize the device cost, devised algorithms to extend the lifetime of the training data, and designed a set of auxiliary sensors and incorporate them into the system to achieve automatic re-calibration.

  • improving rf based device free passive localization in cluttered indoor environments through probabilistic classification methods
    Information Processing in Sensor Networks, 2012
    Co-Authors: Chenren Xu, Bernhard Firner, Yanyong Zhang, R E Howard, Jun Li, Xiaodong Lin
    Abstract:

    ABSTRACT Radio frequency based device-free passive localization has been proposed as an alternative to indoor localization because it does not require subjects to wear a radio device. This technique observes how people disturb the pattern of radio waves in an indoor space and derives their positions accordingly. The well-known multipath effect makes this problem very challenging, because in a complex environment it is impractical to have enough knowledge to be able to accurately model the effects of a subject on the surrounding radio links. In addition, even minor changes in the environment over time change radio propagation sufficiently to invalidate the datasets needed by simple fingerprint-based methods. In this paper, we develop a fingerprinting-based method using probabilistic classification approaches based on discriminant analysis. We also devise ways to mitigate the error caused by multipath effect in data collection, further boosting the classification likelihood. We validate our method in a one-bedroom apartment that has 8 transmitters, 8 receivers, and a total of 32 Cells that can be Occupied. We show that our method can correctly estimate the Occupied Cell with a likelihood of 97.2%. Further, we show that the accuracy remains high, even when we significantly reduce the training overhead, consider fewer radio devices, or conduct a test one month later after the training. We also show that our method can be used to track a person in motion and to localize multiple people with high accuracies. Finally, we deploy our method in a completely different commercial environment with two times the area achieving a Cell estimation accuracy of 93.8% as an evidence of applicability to multiple environments.

Bernhard Firner - One of the best experts on this subject based on the ideXlab platform.

  • the case for efficient and robust rf based device free localization
    IEEE Transactions on Mobile Computing, 2016
    Co-Authors: Chenren Xu, Bernhard Firner, Yanyong Zhang, R E Howard
    Abstract:

    Radio frequency based device-free localization has been proposed as an alternative localization technique. Unlike its active localization counterpart, it does not require subjects to wear any radio device, but tries to determine the subject’s location by observing how much the subject disturbs the radio propagation patterns. This problem is very challenging due to the well known multipath effect, especially in a complex indoor environment where it is impractical to accurately model the effects of a subject on the surrounding radio links. In this article, we formulate the device-free localization problem using probabilistic classification approaches that are based on discriminant analysis.To boost the localization accuracies, we adopt methods to mitigate errors caused by the multipath effect, as well as methods to automatically recalibrate training data so that accuracy can be maintained as the environment evolves. We validate our method in a one-bedroom apartment that consists of 32 Cells, using eight fixed transmitters and eight fixed receivers. When the space has a single occupant, our method can correctly estimate the Occupied Cell with a likelihood as high as 97.2 percent. Further, we show that we can maintain a high localization accuracy, while substantially reducing the deployment overhead, which is an important concern for device-free localization methods. To achieve this goal, we have improved our training and testing procedures to reduce the overhead, studied the radio device placement to optimize the device cost, devised algorithms to extend the lifetime of the training data, and designed a set of auxiliary sensors and incorporate them into the system to achieve automatic re-calibration.

  • improving rf based device free passive localization in cluttered indoor environments through probabilistic classification methods
    Information Processing in Sensor Networks, 2012
    Co-Authors: Chenren Xu, Bernhard Firner, Yanyong Zhang, R E Howard, Jun Li, Xiaodong Lin
    Abstract:

    ABSTRACT Radio frequency based device-free passive localization has been proposed as an alternative to indoor localization because it does not require subjects to wear a radio device. This technique observes how people disturb the pattern of radio waves in an indoor space and derives their positions accordingly. The well-known multipath effect makes this problem very challenging, because in a complex environment it is impractical to have enough knowledge to be able to accurately model the effects of a subject on the surrounding radio links. In addition, even minor changes in the environment over time change radio propagation sufficiently to invalidate the datasets needed by simple fingerprint-based methods. In this paper, we develop a fingerprinting-based method using probabilistic classification approaches based on discriminant analysis. We also devise ways to mitigate the error caused by multipath effect in data collection, further boosting the classification likelihood. We validate our method in a one-bedroom apartment that has 8 transmitters, 8 receivers, and a total of 32 Cells that can be Occupied. We show that our method can correctly estimate the Occupied Cell with a likelihood of 97.2%. Further, we show that the accuracy remains high, even when we significantly reduce the training overhead, consider fewer radio devices, or conduct a test one month later after the training. We also show that our method can be used to track a person in motion and to localize multiple people with high accuracies. Finally, we deploy our method in a completely different commercial environment with two times the area achieving a Cell estimation accuracy of 93.8% as an evidence of applicability to multiple environments.

  • IPSN - Improving RF-based device-free passive localization in cluttered indoor environments through probabilistic classification methods
    Proceedings of the 11th international conference on Information Processing in Sensor Networks - IPSN '12, 2012
    Co-Authors: Chenren Xu, Bernhard Firner, Yanyong Zhang, Richard Howard, Jun Li
    Abstract:

    ABSTRACT Radio frequency based device-free passive localization has been proposed as an alternative to indoor localization because it does not require subjects to wear a radio device. This technique observes how people disturb the pattern of radio waves in an indoor space and derives their positions accordingly. The well-known multipath effect makes this problem very challenging, because in a complex environment it is impractical to have enough knowledge to be able to accurately model the effects of a subject on the surrounding radio links. In addition, even minor changes in the environment over time change radio propagation sufficiently to invalidate the datasets needed by simple fingerprint-based methods. In this paper, we develop a fingerprinting-based method using probabilistic classification approaches based on discriminant analysis. We also devise ways to mitigate the error caused by multipath effect in data collection, further boosting the classification likelihood. We validate our method in a one-bedroom apartment that has 8 transmitters, 8 receivers, and a total of 32 Cells that can be Occupied. We show that our method can correctly estimate the Occupied Cell with a likelihood of 97.2%. Further, we show that the accuracy remains high, even when we significantly reduce the training overhead, consider fewer radio devices, or conduct a test one month later after the training. We also show that our method can be used to track a person in motion and to localize multiple people with high accuracies. Finally, we deploy our method in a completely different commercial environment with two times the area achieving a Cell estimation accuracy of 93.8% as an evidence of applicability to multiple environments.

Yanyong Zhang - One of the best experts on this subject based on the ideXlab platform.

  • the case for efficient and robust rf based device free localization
    IEEE Transactions on Mobile Computing, 2016
    Co-Authors: Chenren Xu, Bernhard Firner, Yanyong Zhang, R E Howard
    Abstract:

    Radio frequency based device-free localization has been proposed as an alternative localization technique. Unlike its active localization counterpart, it does not require subjects to wear any radio device, but tries to determine the subject’s location by observing how much the subject disturbs the radio propagation patterns. This problem is very challenging due to the well known multipath effect, especially in a complex indoor environment where it is impractical to accurately model the effects of a subject on the surrounding radio links. In this article, we formulate the device-free localization problem using probabilistic classification approaches that are based on discriminant analysis.To boost the localization accuracies, we adopt methods to mitigate errors caused by the multipath effect, as well as methods to automatically recalibrate training data so that accuracy can be maintained as the environment evolves. We validate our method in a one-bedroom apartment that consists of 32 Cells, using eight fixed transmitters and eight fixed receivers. When the space has a single occupant, our method can correctly estimate the Occupied Cell with a likelihood as high as 97.2 percent. Further, we show that we can maintain a high localization accuracy, while substantially reducing the deployment overhead, which is an important concern for device-free localization methods. To achieve this goal, we have improved our training and testing procedures to reduce the overhead, studied the radio device placement to optimize the device cost, devised algorithms to extend the lifetime of the training data, and designed a set of auxiliary sensors and incorporate them into the system to achieve automatic re-calibration.

  • improving rf based device free passive localization in cluttered indoor environments through probabilistic classification methods
    Information Processing in Sensor Networks, 2012
    Co-Authors: Chenren Xu, Bernhard Firner, Yanyong Zhang, R E Howard, Jun Li, Xiaodong Lin
    Abstract:

    ABSTRACT Radio frequency based device-free passive localization has been proposed as an alternative to indoor localization because it does not require subjects to wear a radio device. This technique observes how people disturb the pattern of radio waves in an indoor space and derives their positions accordingly. The well-known multipath effect makes this problem very challenging, because in a complex environment it is impractical to have enough knowledge to be able to accurately model the effects of a subject on the surrounding radio links. In addition, even minor changes in the environment over time change radio propagation sufficiently to invalidate the datasets needed by simple fingerprint-based methods. In this paper, we develop a fingerprinting-based method using probabilistic classification approaches based on discriminant analysis. We also devise ways to mitigate the error caused by multipath effect in data collection, further boosting the classification likelihood. We validate our method in a one-bedroom apartment that has 8 transmitters, 8 receivers, and a total of 32 Cells that can be Occupied. We show that our method can correctly estimate the Occupied Cell with a likelihood of 97.2%. Further, we show that the accuracy remains high, even when we significantly reduce the training overhead, consider fewer radio devices, or conduct a test one month later after the training. We also show that our method can be used to track a person in motion and to localize multiple people with high accuracies. Finally, we deploy our method in a completely different commercial environment with two times the area achieving a Cell estimation accuracy of 93.8% as an evidence of applicability to multiple environments.

  • IPSN - Improving RF-based device-free passive localization in cluttered indoor environments through probabilistic classification methods
    Proceedings of the 11th international conference on Information Processing in Sensor Networks - IPSN '12, 2012
    Co-Authors: Chenren Xu, Bernhard Firner, Yanyong Zhang, Richard Howard, Jun Li
    Abstract:

    ABSTRACT Radio frequency based device-free passive localization has been proposed as an alternative to indoor localization because it does not require subjects to wear a radio device. This technique observes how people disturb the pattern of radio waves in an indoor space and derives their positions accordingly. The well-known multipath effect makes this problem very challenging, because in a complex environment it is impractical to have enough knowledge to be able to accurately model the effects of a subject on the surrounding radio links. In addition, even minor changes in the environment over time change radio propagation sufficiently to invalidate the datasets needed by simple fingerprint-based methods. In this paper, we develop a fingerprinting-based method using probabilistic classification approaches based on discriminant analysis. We also devise ways to mitigate the error caused by multipath effect in data collection, further boosting the classification likelihood. We validate our method in a one-bedroom apartment that has 8 transmitters, 8 receivers, and a total of 32 Cells that can be Occupied. We show that our method can correctly estimate the Occupied Cell with a likelihood of 97.2%. Further, we show that the accuracy remains high, even when we significantly reduce the training overhead, consider fewer radio devices, or conduct a test one month later after the training. We also show that our method can be used to track a person in motion and to localize multiple people with high accuracies. Finally, we deploy our method in a completely different commercial environment with two times the area achieving a Cell estimation accuracy of 93.8% as an evidence of applicability to multiple environments.

Norihiro Hagita - One of the best experts on this subject based on the ideXlab platform.

  • ICRA - Probabilistic approach for building auditory maps with a mobile microphone array
    2013 IEEE International Conference on Robotics and Automation, 2013
    Co-Authors: Nagasrikanth Kallakuri, Carlos Ishi, Jani Even, Yoichi Morales, Norihiro Hagita
    Abstract:

    This paper presents a multi-modal sensor approach for mapping sound sources using an omni-directional microphone array on an autonomous mobile robot. A fusion of audio data (from the microphone array), odometry information and the laser range scan data (from the robot) was used to precisely localize and map the audio sources in an environment. An audio map is created while the robot is autonomously navigating through the environment by continuously generating audio scans with a steered response power (SRP) algorithm. Using the poses of the robot, rays are cast in the map in all directions given by the SRP. Then each Occupied Cell in the geometric map hit by a ray is assigned a likelihood of containing a sound source. This likelihood is derived from the SRP at that particular instant. Since the localization of the robot is probabilistic, the uncertainty in the pose of the robot in the geometric map is propagated to the Occupied Cells hit during the ray casting. This process is repeated while the robot is in motion and the map is updated after every audio scan. The generated sound maps were reused and the changes in the audio environment were updated by the robot as it identifies these changes.

  • Probabilistic approach for building auditory maps with a mobile microphone array
    Proceedings - IEEE International Conference on Robotics and Automation, 2013
    Co-Authors: Nagasrikanth Kallakuri, Carlos Ishi, Jani Even, Yoichi Morales, Norihiro Hagita
    Abstract:

    This paper presents a multi-modal sensor approach for mapping sound sources using an omni-directional microphone array on an autonomous mobile robot. A fusion of audio data (from the microphone array), odometry information and the laser range scan data (from the robot) was used to precisely localize and map the audio sources in an environment. An audio map is created while the robot is autonomously navigating through the environment by continuously generating audio scans with a steered response power (SRP) algorithm. Using the poses of the robot, rays are cast in the map in all directions given by the SRP. Then each Occupied Cell in the geometric map hit by a ray is assigned a likelihood of containing a sound source. This likelihood is derived from the SRP at that particular instant. Since the localization of the robot is probabilistic, the uncertainty in the pose of the robot in the geometric map is propagated to the Occupied Cells hit during the ray casting. This process is repeated while the robot is in motion and the map is updated after every audio scan. The generated sound maps were reused and the changes in the audio environment were updated by the robot as it identifies these changes.