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Takeshi Hattori - One of the best experts on this subject based on the ideXlab platform.

  • a proposal of location estimation with Maximum Likelihood Function using joint pdf of received signals and prior measured signals
    Vehicular Technology Conference, 2006
    Co-Authors: K. Azuma, K. Matsumoto, Takeshi Hattori
    Abstract:

    A novel location estimation with Maximum Log-Likelihood Function using joint probability Function (PDF) of Received Signals and prior Measured Signals is proposed in order to improve the accuracy of the location estimation of Mobile Station (MS). We assume a spatial correlation between the received signals and the prior measured signals in the vicinity of the measured points. Using Bayesian theorem, a Log-Likelihood Function is derived based on a new conditional PDF associated with a correlation. Computer simulation is carried out to clarify the improvement of the accuracy of the location estimation. It is shown that the estimation accuracy is improved by 25.4% with 16 prior measured points in 200 square meters as compared with that in case of conventional method.

  • a new location estimation method based on Maximum Likelihood Function in cellular systems
    Vehicular Technology Conference, 2001
    Co-Authors: M Aso, M Kawabata, Takeshi Hattori
    Abstract:

    This paper presents a proposal of a new mobile station (MS) location estimation method based on the Maximum Likelihood Function in cellular systems. This method is applied to signal strength, time of arrival (TOA), or time difference of arrival (TDOA) as measurements for location estimation. We evaluate the performance of this new method through simulations under various conditions in case signal strength is measured, including the comparison of the conventional methods.

  • VTC Spring - A Proposal of Location Estimation with Maximum Likelihood Function using Joint PDF of Received Signals and prior Measured Signals
    2006 IEEE 63rd Vehicular Technology Conference, 1
    Co-Authors: K. Azuma, K. Matsumoto, Takeshi Hattori
    Abstract:

    A novel location estimation with Maximum Log-Likelihood Function using joint probability Function (PDF) of Received Signals and prior Measured Signals is proposed in order to improve the accuracy of the location estimation of Mobile Station (MS). We assume a spatial correlation between the received signals and the prior measured signals in the vicinity of the measured points. Using Bayesian theorem, a Log-Likelihood Function is derived based on a new conditional PDF associated with a correlation. Computer simulation is carried out to clarify the improvement of the accuracy of the location estimation. It is shown that the estimation accuracy is improved by 25.4% with 16 prior measured points in 200 square meters as compared with that in case of conventional method.

  • VTC Fall - A new location estimation method based on Maximum Likelihood Function in cellular systems
    IEEE 54th Vehicular Technology Conference. VTC Fall 2001. Proceedings (Cat. No.01CH37211), 1
    Co-Authors: M Aso, M Kawabata, Takeshi Hattori
    Abstract:

    This paper presents a proposal of a new mobile station (MS) location estimation method based on the Maximum Likelihood Function in cellular systems. This method is applied to signal strength, time of arrival (TOA), or time difference of arrival (TDOA) as measurements for location estimation. We evaluate the performance of this new method through simulations under various conditions in case signal strength is measured, including the comparison of the conventional methods.

  • Mobile station location estimation using the Maximum Likelihood method in sector cell systems
    Proceedings IEEE 56th Vehicular Technology Conference, 1
    Co-Authors: M Aso, T. Saikawa, Takeshi Hattori
    Abstract:

    This paper presents mobile station (MS) location estimation using the Maximum Likelihood method (see Aso, M. et al., IEEE Vehicular Technology Conf. - Fall, 2001) in sector cell systems. We propose a propagation model for signal strength in sector cell systems and show the results of the performance evaluation. We apply the Maximum Likelihood method to signal strength. First, the location estimation method based on the Maximum Likelihood Function is explained. Next, the proposed propagation model is introduced. We compare the performance of sector cell with omni-cell systems under various conditions.

Daisuke Anzai - One of the best experts on this subject based on the ideXlab platform.

  • use of a simplified Maximum Likelihood Function in a wlan based location estimation
    Wireless Communications and Networking Conference, 2009
    Co-Authors: Shinsuke Hara, Daisuke Anzai
    Abstract:

    In a location estimation with the received signal strength indication (RSSI) of a wireless signal in an area, the Maximum Likelihood (ML) Function should match the real statistical property of the RSSI in the area. For a wireless local area network (WLAN)-based RSSI location estimation with a wideband signal in an office environment, the wideband signal experiences frequency selectively Rayleigh fading, so a complicated ML Function containing several channel parameters needs to be derived in the environment. This paper shows that a simplified ML Function containing only two channel parameters, which is optimum for a narrowband signal, is also applicable to an IEEE 802.11g WLAN-based location estimation with a wideband signal. Computer simulation and experimental results show that the use of the simplified ML Function introduces almost no degradation in the location estimation performance in typical office environments, as compared with the use of an exact ML Function.

  • WCNC - Use of a Simplified Maximum Likelihood Function in a WLAN-Based Location Estimation
    2009 IEEE Wireless Communications and Networking Conference, 2009
    Co-Authors: Shinsuke Hara, Daisuke Anzai
    Abstract:

    In a location estimation with the received signal strength indication (RSSI) of a wireless signal in an area, the Maximum Likelihood (ML) Function should match the real statistical property of the RSSI in the area. For a wireless local area network (WLAN)-based RSSI location estimation with a wideband signal in an office environment, the wideband signal experiences frequency selectively Rayleigh fading, so a complicated ML Function containing several channel parameters needs to be derived in the environment. This paper shows that a simplified ML Function containing only two channel parameters, which is optimum for a narrowband signal, is also applicable to an IEEE 802.11g WLAN-based location estimation with a wideband signal. Computer simulation and experimental results show that the use of the simplified ML Function introduces almost no degradation in the location estimation performance in typical office environments, as compared with the use of an exact ML Function.

Shinsuke Hara - One of the best experts on this subject based on the ideXlab platform.

  • use of a simplified Maximum Likelihood Function in a wlan based location estimation
    Wireless Communications and Networking Conference, 2009
    Co-Authors: Shinsuke Hara, Daisuke Anzai
    Abstract:

    In a location estimation with the received signal strength indication (RSSI) of a wireless signal in an area, the Maximum Likelihood (ML) Function should match the real statistical property of the RSSI in the area. For a wireless local area network (WLAN)-based RSSI location estimation with a wideband signal in an office environment, the wideband signal experiences frequency selectively Rayleigh fading, so a complicated ML Function containing several channel parameters needs to be derived in the environment. This paper shows that a simplified ML Function containing only two channel parameters, which is optimum for a narrowband signal, is also applicable to an IEEE 802.11g WLAN-based location estimation with a wideband signal. Computer simulation and experimental results show that the use of the simplified ML Function introduces almost no degradation in the location estimation performance in typical office environments, as compared with the use of an exact ML Function.

  • WCNC - Use of a Simplified Maximum Likelihood Function in a WLAN-Based Location Estimation
    2009 IEEE Wireless Communications and Networking Conference, 2009
    Co-Authors: Shinsuke Hara, Daisuke Anzai
    Abstract:

    In a location estimation with the received signal strength indication (RSSI) of a wireless signal in an area, the Maximum Likelihood (ML) Function should match the real statistical property of the RSSI in the area. For a wireless local area network (WLAN)-based RSSI location estimation with a wideband signal in an office environment, the wideband signal experiences frequency selectively Rayleigh fading, so a complicated ML Function containing several channel parameters needs to be derived in the environment. This paper shows that a simplified ML Function containing only two channel parameters, which is optimum for a narrowband signal, is also applicable to an IEEE 802.11g WLAN-based location estimation with a wideband signal. Computer simulation and experimental results show that the use of the simplified ML Function introduces almost no degradation in the location estimation performance in typical office environments, as compared with the use of an exact ML Function.

J.e. Piper - One of the best experts on this subject based on the ideXlab platform.

  • Constrained Likelihood Function for a uniform array
    IEEE Transactions on Signal Processing, 1994
    Co-Authors: J.e. Piper
    Abstract:

    The symmetry of a uniform linear array can be exploited to construct a simplified analytical representation for the Maximum Likelihood Function. This results in a significant decrease in the computational load and allows the algorithm to be used with large arrays.

  • The two source Maximum Likelihood Function
    IEEE Signal Processing Letters, 1994
    Co-Authors: J.e. Piper
    Abstract:

    The Maximum Likelihood method is designed to yield high resolution estimates in a multiple source environment. The article derive a simplified representation of the Maximum Likelihood Function for the two source case. This case is instructive to understand when and why the full power of the Maximum Likelihood method should be used. This approach extends the results of a previous paper (see J. Piper, IEEE Trans. Signal Processing, vol.42, no.2, p.412, 1994) that showed how the Maximum Likelihood Function calculation can be transformed from a matrix problem to a vector product. The Maximum Likelihood Function for spectral estimation is given.

M Aso - One of the best experts on this subject based on the ideXlab platform.