The Experts below are selected from a list of 252 Experts worldwide ranked by ideXlab platform
Ronald A. Iltis - One of the best experts on this subject based on the ideXlab platform.
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Density function approximation using reduced sufficient statistics for joint estimation of linear and nonlinear parameters
IEEE Transactions on Signal Processing, 1999Co-Authors: Ronald A. IltisAbstract:A new algorithm is presented for the joint estimation of linear and nonlinear parameters of a deterministic signal embedded in additive Gaussian noise. The algorithm is an approximation to the reduced sufficient statistics (RSS) method introduced by Kulhavy (1990) which estimates the posterior parameter density via minimization of the cross-entropy (Kullback-Leibler distance). In the modified RSS algorithm presented, the components of the posterior density representing the nonlinear parameter are modeled using Haar basis scale functions, and the components corresponding to the linear parameters are represented by Gaussian densities. In the additive Gaussian noise measurement model, the RSS algorithm employs a Parallel Bank of modified least-squares estimators for the linear parameters, coupled with a nonlinear estimator for the nonlinear parameters. Simulation results are presented for the problem of estimating parameters of a chirp signal embedded in multipath, and the averaged squared error (ASE) of the parameter estimates is compared with the Cramer-Rao bound. Finally, an application of the algorithm is presented in which the delay, multipath coefficients, and Doppler shift of a digitally modulated waveform received over a fading channel are jointly estimated.
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Adaptive parameter estimation using Parallel Kalman filtering for spread spectrum code and Doppler tracking
IEEE Transactions on Communications, 1994Co-Authors: Alfred W Fuxjaeger, Ronald A. IltisAbstract:We present a new digital direct-sequence (DS) receiver with joint estimation of code delay, multipath gains and Doppler shift. A parameter estimator consisting of a Parallel Bank of extended Kalman filters (EKF’s) extracts estimates of the timing, T and the multipath coefficients, fl distorting the received signal. A "detected" estimate of the Doppler shift, Vr distorting the received signal is also provided by the estimator. We compute the bit error rate that results when a RAKE matched filter uses the estimated parameters to detect the DPSK encoded binary data in the received signal. The bit-error rate (BER) is evaluated, and successful performance of the proposed receiver in the presence of Doppler shift distortion is observed in many cases. We demonstrate that the receiver can operate when the multipath coefficients vary in time (Doppler spread).
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Joint estimation of linear and nonlinear parameters using reduced sufficient statistics
Conference Record of The Thirtieth Asilomar Conference on Signals Systems and Computers, 1Co-Authors: Ronald A. IltisAbstract:A new algorithm is presented for the joint estimation of linear and nonlinear parameters of a deterministic signal embedded in additive Gaussian noise. The algorithm is a modification of the reduced sufficient statistics (RSS) method introduced by Kulhavy (1990), which estimates the posterior parameter density via minimization of the cross-entropy. In the additive Gaussian noise measurement model, the modified RSS algorithm employs a Parallel Bank of least-squares type estimators for the linear parameters, coupled with an approximate minimum variance estimate for the nonlinear parameter. Simulation results are presented for the problem of estimating parameters of a chirp signal embedded in multipath, and the averaged squared error (ASE) of the parameter estimates is compared with the Cramer-Rao bound.
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Recursive Bayesian algorithms for blind equalization
[1991] Conference Record of the Twenty-Fifth Asilomar Conference on Signals Systems & Computers, 1Co-Authors: Ronald A. Iltis, John J. Shynk, Krishnamurthy GiridharAbstract:A novel blind equalization algorithm based on a suboptimum Bayesian symbol sequence estimator is presented. It is shown that a Parallel Bank of Kalman filters can be used to update a suboptimum Bayesian formula for the sequence possibilities. Two methods are used to reduce the computational complexity of the algorithm. First, it is shown that the Kalman filters can be replaced by simpler least-mean-square (LMS) adaptive filters. Second, the technique of reduced-state sequence estimation is adopted to reduce the number of symbol subsequences considered in the Bayesian updating, and hence the number of Parallel filters required. The performance properties of the resulting algorithms are evaluated through bit error simulations, and these are compared to the bounds of optimum maximum-likelihood sequence estimation. It is shown that the Kalman filter and LMS-based algorithms achieve blind start-up and rapid convergence (within 200 iterations) for both binary phase-shift keying (BPSK) and quadrature phase-shift keying (QPSK) modulation formats. >
R.a. Iltis - One of the best experts on this subject based on the ideXlab platform.
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GLOBECOM - WLC42-1: Multiple-Model Rao-Blackwellized Gauss-Hermite Filter for Joint Frequency Offset and Channel Estimator for the Semi-Blind MIMO-OFDM Receiver
IEEE Globecom 2006, 2006Co-Authors: Kyeong Jin Kim, T. Reid, R.a. IltisAbstract:In this paper, we propose a new statistical joint frequency offset and channel estimation for the blind MIMO-OFDM system, where a state and parameters of dynamic systems are unknown. To reduce a channel mismatch problem, we use the fixed structure interacting multiple model (FS-IMM) and the Rao-Blackwellized Gauss-Hermite filter to estimate a model-dependent channel and offset parameter, which enters an observation function in a nonlinear manner. The resulting structure consists of M Parallel Bank of Kalman filters driven by sampled nonlinear state variable sequences.
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adaptive parameter estimation using Parallel kalman filtering for spread spectrum code and doppler tracking
IEEE Transactions on Communications, 1994Co-Authors: Alfred W Fuxjaeger, R.a. IltisAbstract:We present a new digital direct-sequence (DS) receiver with joint estimation of code delay, multipath gains and Doppler shift. A parameter estimator consisting of a Parallel Bank of extended Kalman filters (EKF's) extracts estimates of the timing, τ and the multipath coefficients, f l distorting the received signal. A «detected» estimate of the Doppler shift, υ r distorting the received signal is also provided by the estimator. We compute the bit error rate that results when a RAKE matched filter uses the estimated parameters to detect the DPSK encoded binary data in the received signal. The bit-error rate (BER) is evaluated, and successful performance of the proposed receiver in the presence of Doppler shift distortion is observed in many cases
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bayesian algorithms for blind equalization using Parallel adaptive filtering
IEEE Transactions on Communications, 1994Co-Authors: R.a. Iltis, J J Shynk, Krishnamurthy GiridharAbstract:A new blind equalization algorithm based on a suboptimum Bayesian symbol-by-symbol detector is presented. It is first shown that the maximum a posteriori (MAP) sequence probabilities can be approximated using the innovations likelihoods generated by a Parallel Bank of Kalman filters. These filters generate a set of channel estimates conditioned on the possible symbol subsequences contributing to the intersymbol interference. The conditional estimates and MAP symbol metrics are then combined using a suboptimum Bayesian formula. Two methods are considered to reduce the computational complexity of the algorithm. First, the technique of reduced-state sequence estimation is adopted to reduce the number of symbol subsequences considered in the channel estimation process and hence the number of Parallel filters required. Second, it is shown that the Kalman filters can be replaced by simpler least-mean-square (LMS) adaptive filters. A computational complexity analysis of the LMS Bayesian equalizer demonstrates that its implementation in Parallel programmable digital signal processing devices is feasible at 16 kbps. The performance of the resulting algorithms is evaluated through bit-error-rate simulations, which are compared to the performance bounds of the maximum-likelihood sequence estimator. It is shown that the Kalman filter and LMS-based algorithms achieve blind start-up and rapid convergence (typically within 200 iterations) for both BPSK and QPSK modulation formats. >
Jeff B. Burl - One of the best experts on this subject based on the ideXlab platform.
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A Reduced Order Extended Kalman Filter for Sequential Images Containing a Moving Object
IEEE Transactions on Image Processing, 1993Co-Authors: Jeff B. BurlAbstract:The extended Kalman filter (EKF) is applied to the reduction of noise in sequential images containing a moving object and to the estimation of the object's velocity. A computationally tractable approximation of the EKF, called the Parallel extended Kalman filter (PEKF), is generated. The PEKF consists of a Parallel Bank of third-order EKFs, operating on the Fourier coefficients of the image, followed by a finite impulse response filter. The PEKF is shown to converge to the optimal (in the mean square sense) algorithm in the limit as the velocity estimation errors approach zero. The performance of the PEKF is demonstrated for very low signal-to-noise ratio (SNR) images. The PEKF also provides a natural setting for tracking slow changes in the object (real or apparent) and its velocity, since these variations are included in the model. The relation of the PEKF to another frequency domain algorithm for velocity estimation is discussed. The algorithm is illustrated by application to an example and its performance is demonstrated in the presence of velocity estimation errors
Alfred W Fuxjaeger - One of the best experts on this subject based on the ideXlab platform.
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adaptive parameter estimation using Parallel kalman filtering for spread spectrum code and doppler tracking
IEEE Transactions on Communications, 1994Co-Authors: Alfred W Fuxjaeger, R.a. IltisAbstract:We present a new digital direct-sequence (DS) receiver with joint estimation of code delay, multipath gains and Doppler shift. A parameter estimator consisting of a Parallel Bank of extended Kalman filters (EKF's) extracts estimates of the timing, τ and the multipath coefficients, f l distorting the received signal. A «detected» estimate of the Doppler shift, υ r distorting the received signal is also provided by the estimator. We compute the bit error rate that results when a RAKE matched filter uses the estimated parameters to detect the DPSK encoded binary data in the received signal. The bit-error rate (BER) is evaluated, and successful performance of the proposed receiver in the presence of Doppler shift distortion is observed in many cases
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Adaptive parameter estimation using Parallel Kalman filtering for spread spectrum code and Doppler tracking
IEEE Transactions on Communications, 1994Co-Authors: Alfred W Fuxjaeger, Ronald A. IltisAbstract:We present a new digital direct-sequence (DS) receiver with joint estimation of code delay, multipath gains and Doppler shift. A parameter estimator consisting of a Parallel Bank of extended Kalman filters (EKF’s) extracts estimates of the timing, T and the multipath coefficients, fl distorting the received signal. A "detected" estimate of the Doppler shift, Vr distorting the received signal is also provided by the estimator. We compute the bit error rate that results when a RAKE matched filter uses the estimated parameters to detect the DPSK encoded binary data in the received signal. The bit-error rate (BER) is evaluated, and successful performance of the proposed receiver in the presence of Doppler shift distortion is observed in many cases. We demonstrate that the receiver can operate when the multipath coefficients vary in time (Doppler spread).
Michael D. Duncan - One of the best experts on this subject based on the ideXlab platform.
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Mine detection with a multichannel stepped-frequency ground-penetrating radar
Detection and Remediation Technologies for Mines and Minelike Targets IV, 1999Co-Authors: Marshall R. Bradley, Thomas R. Witten, Robert Mccummins, Michael P. Crowe, Scott Stewart, Michael D. DuncanAbstract:In order to separate buried land mines from clutter a multi- channel stepped-frequency ground penetrating radar has been developed. The system operates over the frequency band 800 MHz to 2.0 GHz. The radar incorporates advanced digital signal processing and radio frequency integrated circuit components. It uses an all-digital modulator coupled with a coherent digital quadrature receiver for making precise magnitude and phase measurements. The control interface to the radar consists of an Ethernet TCP/IP link. A Parallel Bank of transmit-receive antennas is used to achieve cross track sampling. System motion is used to achieve along track data sampling. Synthetic aperture near field beamforming techniques are used to image buried objects. The system is designed to detect shallowly buried metallic and non- metallic mines. A system overview is presented and result from data collection exercises are included. Images and analysis of data from a mine lane is presented.
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Mine detection with a multi-channel stepped-frequency ground penetrating radar
1999Co-Authors: Marshall R. Bradley, Thomas R. Witten, Robert Mccummins, Michael P. Crowe, Scott Stewart, Michael D. DuncanAbstract:In order to separate buried land mines from clutter a multi-channel stepped-frequency ground penetrating radar has been developed. The system operates over the frequency band 800 MHz to 2.0 GHz. The radar incorporates advanced digital signal processing and radio frequency integrated circuit components. It uses an all-digital modulator coupled with a coherent digital quadrature receiver for making precise magnitude and phase measurements. The control interface to the radar consists of an Ethernet TCP/IP link. A Parallel Bank of transmit-receive antennas is used to achieve cross track sampling. System motion is used to achieve along track data sampling. Synthetic aperture nearfield beamforming techniques are used to image buried objects. The system is designed to detect shallowly buried metallic and non-metallic mines. A system overview is presented and results from data collection exercises are included. Images and analysis of data from a mine lane is presented.
