The Experts below are selected from a list of 36837 Experts worldwide ranked by ideXlab platform

Thomas Kailath - One of the best experts on this subject based on the ideXlab platform.

  • ICASSP - Eigenstructure approach to Direction-of-Arrival estimation in IR detector arrays
    ICASSP '86. IEEE International Conference on Acoustics Speech and Signal Processing, 1
    Co-Authors: Daniel M. Spielman, Arogyaswami Paulraj, Thomas Kailath
    Abstract:

    Eigenstructure methods for Direction of Arrival estimation have become well established for radar, sonar and radio array applications for which coherent (i.e. amplitude and phase) measurements are available. In many IR and optical applications incoherent detectors are used that measure the power rather than signal amplitude/phase. In this paper, we derive a subspace algorithm for high resolution Direction of Arrival estimation for such measurement models. Results of computer simulation that verify the performance of our proposed algorithm are also presented.

  • Passive Direction-of-Arrival and range estimation for near-field sources
    Fourth Annual ASSP Workshop on Spectrum Estimation and Modeling, 1
    Co-Authors: A. L. Swindlehurst, Thomas Kailath
    Abstract:

    An algorithm for simultaneously estimating the range and bearing of multiple near-field sources is presented. The method is based on the application of signal subspace ideas to the spatial Wigner-Ville distribution approach originally presented by B.R. Breed and T.E. Posch (Proc. ICASSP'84, p.41B.9.1, 1984). The principal advantages of using signal-subspace methods are that the range/Direction-of-Arrival estimates are obtained with improved precision and resolution, and without computation or search of a complicated spectral surface. Additionally, these methods allow a simple and more effective extension of the spatial Wigner-Ville approach to cases in which noise and/or multiple signals are present. Simulations have shown that the algorithm performs well for a wide variety of test cases, and comparisons with the Cramer-Rao bound indicate near-optimal Direction-of-Arrival estimates. >

Bill Correll - One of the best experts on this subject based on the ideXlab platform.

  • Prior mismatch in Bayesian Direction of Arrival estimation for sparse arrays
    2015 IEEE Radar Conference (RadarCon), 2015
    Co-Authors: Joshua M. Kantor, Christ D. Richmond, Bill Correll, Daniel W. Bliss
    Abstract:

    We study the mean-squared-error (MSE) performance of Bayesian Direction-of-Arrival (DOA) estimation for sparse linear arrays in which prior belief about the target location is incorporated into the estimation process. We utilize a recent extension of the method of interval errors (MIE) to the case of maximum a posteriori (MAP) Direction-of-Arrival estimation to more accurately predict low-medium MSE values in the presence of prior mismatch. We also develop a misspecified Cramer-Rao bound on MAP estimation that can improve the performance of MIE. We specialize to log-periodic arrays to conduct a notional trade study in which we consider the trade in improved estimation performance potentially possible with larger sparser arrays vs the increased sensitivity to incorrectly specified priors.

  • Mean-Squared-Error Prediction for Bayesian Direction-of-Arrival Estimation
    IEEE Transactions on Signal Processing, 2013
    Co-Authors: Joshua M. Kantor, Christ D. Richmond, Daniel W. Bliss, Bill Correll
    Abstract:

    In this article, we study the mean-squared-error performance of Bayesian Direction-of-Arrival (DOA) estimation in which prior belief about the target location is incorporated into the estimation process. Our primary result is an extension of the method of interval errors (MIE) to the case of maximum a posteriori (MAP) Direction-of-Arrival estimation. We work in a general framework in which the prior information used in the MAP estimation may not match the actual target distribution. In particular, when the prior is incorrect, the MAP estimator degrades relative to the performance of a MAP estimator with the correct prior. Our methods are able to accurately predict the performance of a MAP estimator in this more general situation. We apply our methods to investigate the sensitivity of MAP Direction-of-Arrival estimation to mismatches between the chosen prior and the actual angular distribution of the target.

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

  • Prior mismatch in Bayesian Direction of Arrival estimation for sparse arrays
    2015 IEEE Radar Conference (RadarCon), 2015
    Co-Authors: Joshua M. Kantor, Christ D. Richmond, Bill Correll, Daniel W. Bliss
    Abstract:

    We study the mean-squared-error (MSE) performance of Bayesian Direction-of-Arrival (DOA) estimation for sparse linear arrays in which prior belief about the target location is incorporated into the estimation process. We utilize a recent extension of the method of interval errors (MIE) to the case of maximum a posteriori (MAP) Direction-of-Arrival estimation to more accurately predict low-medium MSE values in the presence of prior mismatch. We also develop a misspecified Cramer-Rao bound on MAP estimation that can improve the performance of MIE. We specialize to log-periodic arrays to conduct a notional trade study in which we consider the trade in improved estimation performance potentially possible with larger sparser arrays vs the increased sensitivity to incorrectly specified priors.

  • Mean-Squared-Error Prediction for Bayesian Direction-of-Arrival Estimation
    IEEE Transactions on Signal Processing, 2013
    Co-Authors: Joshua M. Kantor, Christ D. Richmond, Daniel W. Bliss, Bill Correll
    Abstract:

    In this article, we study the mean-squared-error performance of Bayesian Direction-of-Arrival (DOA) estimation in which prior belief about the target location is incorporated into the estimation process. Our primary result is an extension of the method of interval errors (MIE) to the case of maximum a posteriori (MAP) Direction-of-Arrival estimation. We work in a general framework in which the prior information used in the MAP estimation may not match the actual target distribution. In particular, when the prior is incorrect, the MAP estimator degrades relative to the performance of a MAP estimator with the correct prior. Our methods are able to accurately predict the performance of a MAP estimator in this more general situation. We apply our methods to investigate the sensitivity of MAP Direction-of-Arrival estimation to mismatches between the chosen prior and the actual angular distribution of the target.

Tong Yang - One of the best experts on this subject based on the ideXlab platform.

  • Direction of Arrival (DOA) Estimation Algorithm Based on the Radial Basis Function Neural Networks
    Advances in Intelligent and Soft Computing, 2011
    Co-Authors: Tong Yang
    Abstract:

    According to the problem of the large calculated quantity and unavailable of multiple sources tracking in real time in the traditional DOA estimation algorithm which is disabled when locating sources that are greater than the number of array elements number, a new Direction of Arrival estimation algorithm based on the radial basis function neural networks in smart antenna is proposed in this paper to solve the problem. The proposed neural multiple-source tracking (N-MUST) algorithm is based on architecture of a family of radial basis function neural networks (RBFNN) to perform both detection and Direction of Arrival estimation. The model of neural network in Direction of Arrival estimation is created and trained in this paper. Simulation results which compared the traditional algorithm and the new one are indicated that the Direction of Arrival (DOA) estimation algorithm based on the radial basis function neural networks implement multiple-source tracking exactly and fast.

Daniel W. Bliss - One of the best experts on this subject based on the ideXlab platform.

  • Prior mismatch in Bayesian Direction of Arrival estimation for sparse arrays
    2015 IEEE Radar Conference (RadarCon), 2015
    Co-Authors: Joshua M. Kantor, Christ D. Richmond, Bill Correll, Daniel W. Bliss
    Abstract:

    We study the mean-squared-error (MSE) performance of Bayesian Direction-of-Arrival (DOA) estimation for sparse linear arrays in which prior belief about the target location is incorporated into the estimation process. We utilize a recent extension of the method of interval errors (MIE) to the case of maximum a posteriori (MAP) Direction-of-Arrival estimation to more accurately predict low-medium MSE values in the presence of prior mismatch. We also develop a misspecified Cramer-Rao bound on MAP estimation that can improve the performance of MIE. We specialize to log-periodic arrays to conduct a notional trade study in which we consider the trade in improved estimation performance potentially possible with larger sparser arrays vs the increased sensitivity to incorrectly specified priors.

  • Mean-Squared-Error Prediction for Bayesian Direction-of-Arrival Estimation
    IEEE Transactions on Signal Processing, 2013
    Co-Authors: Joshua M. Kantor, Christ D. Richmond, Daniel W. Bliss, Bill Correll
    Abstract:

    In this article, we study the mean-squared-error performance of Bayesian Direction-of-Arrival (DOA) estimation in which prior belief about the target location is incorporated into the estimation process. Our primary result is an extension of the method of interval errors (MIE) to the case of maximum a posteriori (MAP) Direction-of-Arrival estimation. We work in a general framework in which the prior information used in the MAP estimation may not match the actual target distribution. In particular, when the prior is incorrect, the MAP estimator degrades relative to the performance of a MAP estimator with the correct prior. Our methods are able to accurately predict the performance of a MAP estimator in this more general situation. We apply our methods to investigate the sensitivity of MAP Direction-of-Arrival estimation to mismatches between the chosen prior and the actual angular distribution of the target.