The Experts below are selected from a list of 4077 Experts worldwide ranked by ideXlab platform

Jinhong Yuan - One of the best experts on this subject based on the ideXlab platform.

  • Channel Estimation in OFDM Systems with Unknown Power Delay Profile using Trans-Dimensional MCMC via Stochastic Approximation
    VTC Spring 2009 - IEEE 69th Vehicular Technology Conference, 2009
    Co-Authors: Ido Nevat, Gareth W. Peters, Jinhong Yuan
    Abstract:

    This paper considers the problem of channel estimation for OFDM systems, where the number of channel taps and their Power Delay Profile (PDP) are unknown. Using a Bayesian approach, we construct a model in which we estimate jointly the coefficients of the channel taps, the channel order and decay rate of the PDP. In order to sample from the resulting posterior distribution we develop a novel Trans-dimensional Markov chain Monte Carlo (TDMCMC) algorithm. This is done using a Stochastic Approximation (SA) approach to develop an adaptively learning algorithm to improve mixing rates of the basic Birth-Death (B-D) Markov chain for the between model subspaces. Using simulations we assess its performance in terms of channel order estimation and bit error rate (BER). It is shown that the proposed algorithm can achieve results very close to the case where both the channel length and the PDP are known.

  • Channel Estimation in OFDM Systems With Unknown Power Delay Profile Using Transdimensional MCMC
    IEEE Transactions on Signal Processing, 2009
    Co-Authors: Gareth W. Peters, Ido Nevat, Jinhong Yuan
    Abstract:

    This paper considers the problem of channel estimation for orthogonal-frequency-division multiplexing (OFDM) systems, where the number of channel taps and their Power Delay Profile are unknown. Using a Bayesian approach, we construct a model in which we estimate jointly the coefficients of the channel taps, the channel order and decay rate of the Power Delay Profile (PDP). In order to sample from the resulting posterior distribution we develop three novel Trans-dimensional Markov chain Monte Carlo (TDMCMC) algorithms and compare their performance. The first is the basic birth and death TDMCMC algorithm. The second utilizes Stochastic Approximation to develop an adaptively learning algorithm to improve mixing rates of the Markov chain between model subspaces. The third approximates the optimal TDMCMC proposal distribution for between-model moves using conditional path sampling proposals. We assess several aspects of the model in terms of sensitivities to different prior choices. Next we perform a detailed analysis of the performance of each of the TDMCMC algorithms. This allows us to contrast the resulting computational effort required under each approach versus the estimation performance. Finally, using the TDMCMC algorithm which produces the best performance in terms of exploration of the model subspaces, we assess its performance in terms of channel estimation mean-square error (MSE) and bit error rate (BER). It is shown that the proposed algorithm can achieve results very close to the case where both the channel length and the PDP decay rate are known.

  • VTC Spring - Channel Estimation in OFDM Systems with Unknown Power Delay Profile using Trans-Dimensional MCMC via Stochastic Approximation
    VTC Spring 2009 - IEEE 69th Vehicular Technology Conference, 2009
    Co-Authors: Ido Nevat, Gareth W. Peters, Jinhong Yuan
    Abstract:

    This paper considers the problem of channel estimation for OFDM systems, where the number of channel taps and their Power Delay Profile (PDP) are unknown. Using a Bayesian approach, we construct a model in which we estimate jointly the coefficients of the channel taps, the channel order and decay rate of the PDP. In order to sample from the resulting posterior distribution we develop a novel Trans-dimensional Markov chain Monte Carlo (TDMCMC) algorithm. This is done using a Stochastic Approximation (SA) approach to develop an adaptively learning algorithm to improve mixing rates of the basic Birth-Death (B-D) Markov chain for the between model subspaces. Using simulations we assess its performance in terms of channel order estimation and bit error rate (BER). It is shown that the proposed algorithm can achieve results very close to the case where both the channel length and the PDP are known.

Dirk Slock - One of the best experts on this subject based on the ideXlab platform.

  • Diversity aspects of Power Delay Profile based location fingerprinting
    2012 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2012
    Co-Authors: Dirk Slock
    Abstract:

    Although most of the conventional localization algorithms rely on Line of Sight (LOS) conditions, fingerprinting allows positioning in multipath and even in Non-LOS (NLOS) environments. In contrast to the traditional Received Signal Strength (RSS), the Power Delay Profile (PDP) fingerprint may allow positioning on the basis of a single link if the multipath is rich enough. Fingerprinting is a pattern matching technique for which a performance analysis may be difficult in general. In this paper we focus on a global performance indicator, in the form of Pairwise Error Probability (PEP). Similarly to PEP analysis in communication over fading channels, we find that the PEP for PDP fingerprinting exhibits a certain diversity order, linked to the number of paths. We investigate and show the results for Gaussian Maximum Likelihood (GML) based approaches for the Rayleigh fading path amplitude case.

  • ICASSP - Diversity aspects of Power Delay Profile based location fingerprinting
    2012 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2012
    Co-Authors: Dirk Slock
    Abstract:

    Although most of the conventional localization algorithms rely on Line of Sight (LOS) conditions, fingerprinting allows positioning in multipath and even in Non-LOS (NLOS) environments. In contrast to the traditional Received Signal Strength (RSS), the Power Delay Profile (PDP) fingerprint may allow positioning on the basis of a single link if the multipath is rich enough. Fingerprinting is a pattern matching technique for which a performance analysis may be difficult in general. In this paper we focus on a global performance indicator, in the form of Pairwise Error Probability (PEP). Similarly to PEP analysis in communication over fading channels, we find that the PEP for PDP fingerprinting exhibits a certain diversity order, linked to the number of paths. We investigate and show the results for Gaussian Maximum Likelihood (GML) based approaches for the Rayleigh fading path amplitude case.

  • cramer rao bounds for Power Delay Profile fingerprinting based positioning
    International Conference on Acoustics Speech and Signal Processing, 2011
    Co-Authors: Turgut Oktem, Dirk Slock
    Abstract:

    Power Delay Profile-Fingerprinting (PDP-F) allows to do positioning in multipath and even in NLOS environments. Although many algorithms for position fingerprinting have been developed, analytical investigation in this area is still not matured. In this paper, we derive Cramer-Rao bounds (CRBs) for location dependent parameters (LDPs) when they are finite and perform local identifiability analysis under different path amplitude assumptions. We show that local identifiability of the position vector can be accomplished if a condition for the pulse shape is satisfied even with one path under the assumption that path amplitude is a genuine function of position (anisotropic path attenuation). On the other hand at least two paths are required to achieve local identifiability for a distance dependent attenuation model (isotropic path attenuation) for path amplitudes. In order to simplify the analysis we assume that pulses from different paths are non-overlapping. Fisher Information Matrix (FIM) for LDPs and the position vector is derived to prove the statements.

  • pairwise error probability analysis for Power Delay Profile fingerprinting based localization
    Vehicular Technology Conference, 2011
    Co-Authors: Turgut Oktem, Dirk Slock
    Abstract:

    Although most of the conventional localization algorithms rely on LOS conditions, it is possible to do positioning with Power Delay Profile-Fingerprinting (PDP-F) in multipath and even in NLOS environments. Many algorithms for position fingerprinting have been developed, but analytical investigation in this area is still not matured yet. In this paper we aim to find the pairwise error probability (PEP) for PDP-F based localization systems. The objective is to see the performance of PDP-F algorithms under different cost functions and also under different path amplitude assumptions. By PEP, what is meant is the same as in the PEP analysis in digital communication channels. Hence the approach is similar for PDP-F. However its analysis is not as straightforward as it is for the digital communication channel case. We investigate and show the results for least squares (LS) based algorithm under deterministic path amplitude modeling and Gaussian Maximum Likelihood (GML) based algorithm for the Rayleigh fading modeling of the path amplitudes.

  • pairwise error probability analysis for Power Delay Profile fingerprinting based localization
    Vehicular Technology Conference, 2011
    Co-Authors: Turgut Oktem, Dirk Slock
    Abstract:

    Although most of the conventional localization algorithms rely on LOS conditions, it is possible to do positioning with Power Delay Profile-Fingerprinting (PDP-F) in multipath and even in NLOS environments. Many algorithms for position fingerprinting have been developed, but analytical investigation in this area is still not matured yet. In this paper we aim to find the pairwise error probability (PEP) for PDP-F based localization systems. The objective is to see the performance of PDP-F algorithms under different cost functions and also under different path amplitude assumptions. By PEP, what is meant is the same as in the PEP analysis in digital communication channels. Hence the approach is similar for PDP-F. However its analysis is not as straightforward as it is for the digital communication channel case. We investigate and show the results for least squares (LS) based algorithm under deterministic path amplitude modeling and Gaussian Maximum Likelihood (GML) based algorithm for the Rayleigh fading modeling of the path amplitudes.

Ido Nevat - One of the best experts on this subject based on the ideXlab platform.

  • Channel Estimation in OFDM Systems with Unknown Power Delay Profile using Trans-Dimensional MCMC via Stochastic Approximation
    VTC Spring 2009 - IEEE 69th Vehicular Technology Conference, 2009
    Co-Authors: Ido Nevat, Gareth W. Peters, Jinhong Yuan
    Abstract:

    This paper considers the problem of channel estimation for OFDM systems, where the number of channel taps and their Power Delay Profile (PDP) are unknown. Using a Bayesian approach, we construct a model in which we estimate jointly the coefficients of the channel taps, the channel order and decay rate of the PDP. In order to sample from the resulting posterior distribution we develop a novel Trans-dimensional Markov chain Monte Carlo (TDMCMC) algorithm. This is done using a Stochastic Approximation (SA) approach to develop an adaptively learning algorithm to improve mixing rates of the basic Birth-Death (B-D) Markov chain for the between model subspaces. Using simulations we assess its performance in terms of channel order estimation and bit error rate (BER). It is shown that the proposed algorithm can achieve results very close to the case where both the channel length and the PDP are known.

  • Channel Estimation in OFDM Systems With Unknown Power Delay Profile Using Transdimensional MCMC
    IEEE Transactions on Signal Processing, 2009
    Co-Authors: Gareth W. Peters, Ido Nevat, Jinhong Yuan
    Abstract:

    This paper considers the problem of channel estimation for orthogonal-frequency-division multiplexing (OFDM) systems, where the number of channel taps and their Power Delay Profile are unknown. Using a Bayesian approach, we construct a model in which we estimate jointly the coefficients of the channel taps, the channel order and decay rate of the Power Delay Profile (PDP). In order to sample from the resulting posterior distribution we develop three novel Trans-dimensional Markov chain Monte Carlo (TDMCMC) algorithms and compare their performance. The first is the basic birth and death TDMCMC algorithm. The second utilizes Stochastic Approximation to develop an adaptively learning algorithm to improve mixing rates of the Markov chain between model subspaces. The third approximates the optimal TDMCMC proposal distribution for between-model moves using conditional path sampling proposals. We assess several aspects of the model in terms of sensitivities to different prior choices. Next we perform a detailed analysis of the performance of each of the TDMCMC algorithms. This allows us to contrast the resulting computational effort required under each approach versus the estimation performance. Finally, using the TDMCMC algorithm which produces the best performance in terms of exploration of the model subspaces, we assess its performance in terms of channel estimation mean-square error (MSE) and bit error rate (BER). It is shown that the proposed algorithm can achieve results very close to the case where both the channel length and the PDP decay rate are known.

  • VTC Spring - Channel Estimation in OFDM Systems with Unknown Power Delay Profile using Trans-Dimensional MCMC via Stochastic Approximation
    VTC Spring 2009 - IEEE 69th Vehicular Technology Conference, 2009
    Co-Authors: Ido Nevat, Gareth W. Peters, Jinhong Yuan
    Abstract:

    This paper considers the problem of channel estimation for OFDM systems, where the number of channel taps and their Power Delay Profile (PDP) are unknown. Using a Bayesian approach, we construct a model in which we estimate jointly the coefficients of the channel taps, the channel order and decay rate of the PDP. In order to sample from the resulting posterior distribution we develop a novel Trans-dimensional Markov chain Monte Carlo (TDMCMC) algorithm. This is done using a Stochastic Approximation (SA) approach to develop an adaptively learning algorithm to improve mixing rates of the basic Birth-Death (B-D) Markov chain for the between model subspaces. Using simulations we assess its performance in terms of channel order estimation and bit error rate (BER). It is shown that the proposed algorithm can achieve results very close to the case where both the channel length and the PDP are known.

Akihiro Kajiwara - One of the best experts on this subject based on the ideXlab platform.

  • VTC Fall - Power Delay Profile Matching for Vehicle Target Recognition
    2009 IEEE 70th Vehicular Technology Conference Fall, 2009
    Co-Authors: Isamu Matsunami, Youichiro Nakahata, Akihiro Kajiwara
    Abstract:

    Radar echo contains unwanted echoes called as clutter, which make it difficult to detect vehicle or obstacle. Especially short-range/wide-angle vehicle radar at 24GHz suffers from heavy clutter unlike long-range radar at 77GHz. It is therefore expect to improve the detection performance by suppressing the clutter using a pulse integration and CFAR (Constant False Alarm Rate). However the radar echo should include multiple vehicle targets as well as the clutter, thereby it is not easy to improve the performance. In this paper the target discrimination technique, let say, Power Delay Profile matching scheme is proposed and the usefulness is investigated by conducting the measurement at 24GHz where the clutter suppression scheme is also considered. As a result, a vehicle target is found to be recognized from multiple vehicles. We have also investigated the effect of bandwidth on the target discrimination capability.

  • Power Delay Profile Matching for Vehicle Target Recognition
    2009 IEEE 70th Vehicular Technology Conference Fall, 2009
    Co-Authors: Isamu Matsunami, Youichiro Nakahata, Akihiro Kajiwara
    Abstract:

    Radar echo contains unwanted echoes called as clutter, which make it difficult to detect vehicle or obstacle. Especially short-range/wide-angle vehicle radar at 24 GHz suffers from heavy clutter unlike long-range radar at 77 GHz. It is therefore expect to improve the detection performance by suppressing the clutter using a pulse integration and CFAR (Constant False Alarm Rate). However the radar echo should include multiple vehicle targets as well as the clutter, thereby it is not easy to improve the performance. In this paper the target discrimination technique, let say, Power Delay Profile matching scheme is proposed and the usefulness is investigated by conducting the measurement at 24 GHz where the clutter suppression scheme is also considered. As a result, a vehicle target is found to be recognized from multiple vehicles. We have also investigated the effect of bandwidth on the target discrimination capability.

Gareth W. Peters - One of the best experts on this subject based on the ideXlab platform.

  • Channel Estimation in OFDM Systems with Unknown Power Delay Profile using Trans-Dimensional MCMC via Stochastic Approximation
    VTC Spring 2009 - IEEE 69th Vehicular Technology Conference, 2009
    Co-Authors: Ido Nevat, Gareth W. Peters, Jinhong Yuan
    Abstract:

    This paper considers the problem of channel estimation for OFDM systems, where the number of channel taps and their Power Delay Profile (PDP) are unknown. Using a Bayesian approach, we construct a model in which we estimate jointly the coefficients of the channel taps, the channel order and decay rate of the PDP. In order to sample from the resulting posterior distribution we develop a novel Trans-dimensional Markov chain Monte Carlo (TDMCMC) algorithm. This is done using a Stochastic Approximation (SA) approach to develop an adaptively learning algorithm to improve mixing rates of the basic Birth-Death (B-D) Markov chain for the between model subspaces. Using simulations we assess its performance in terms of channel order estimation and bit error rate (BER). It is shown that the proposed algorithm can achieve results very close to the case where both the channel length and the PDP are known.

  • Channel Estimation in OFDM Systems With Unknown Power Delay Profile Using Transdimensional MCMC
    IEEE Transactions on Signal Processing, 2009
    Co-Authors: Gareth W. Peters, Ido Nevat, Jinhong Yuan
    Abstract:

    This paper considers the problem of channel estimation for orthogonal-frequency-division multiplexing (OFDM) systems, where the number of channel taps and their Power Delay Profile are unknown. Using a Bayesian approach, we construct a model in which we estimate jointly the coefficients of the channel taps, the channel order and decay rate of the Power Delay Profile (PDP). In order to sample from the resulting posterior distribution we develop three novel Trans-dimensional Markov chain Monte Carlo (TDMCMC) algorithms and compare their performance. The first is the basic birth and death TDMCMC algorithm. The second utilizes Stochastic Approximation to develop an adaptively learning algorithm to improve mixing rates of the Markov chain between model subspaces. The third approximates the optimal TDMCMC proposal distribution for between-model moves using conditional path sampling proposals. We assess several aspects of the model in terms of sensitivities to different prior choices. Next we perform a detailed analysis of the performance of each of the TDMCMC algorithms. This allows us to contrast the resulting computational effort required under each approach versus the estimation performance. Finally, using the TDMCMC algorithm which produces the best performance in terms of exploration of the model subspaces, we assess its performance in terms of channel estimation mean-square error (MSE) and bit error rate (BER). It is shown that the proposed algorithm can achieve results very close to the case where both the channel length and the PDP decay rate are known.

  • VTC Spring - Channel Estimation in OFDM Systems with Unknown Power Delay Profile using Trans-Dimensional MCMC via Stochastic Approximation
    VTC Spring 2009 - IEEE 69th Vehicular Technology Conference, 2009
    Co-Authors: Ido Nevat, Gareth W. Peters, Jinhong Yuan
    Abstract:

    This paper considers the problem of channel estimation for OFDM systems, where the number of channel taps and their Power Delay Profile (PDP) are unknown. Using a Bayesian approach, we construct a model in which we estimate jointly the coefficients of the channel taps, the channel order and decay rate of the PDP. In order to sample from the resulting posterior distribution we develop a novel Trans-dimensional Markov chain Monte Carlo (TDMCMC) algorithm. This is done using a Stochastic Approximation (SA) approach to develop an adaptively learning algorithm to improve mixing rates of the basic Birth-Death (B-D) Markov chain for the between model subspaces. Using simulations we assess its performance in terms of channel order estimation and bit error rate (BER). It is shown that the proposed algorithm can achieve results very close to the case where both the channel length and the PDP are known.