Linear Prediction Error Method Research Articles

Adaptive feedback cancellation based on interaural level difference using lattice filter with correlation control for binaural hearing devices

This paper proposes a correlation control method for linear prediction filters based on howling detection using binaural information in hearing aids. In a closed-loop system, high autocorrelation of the input signal can cause estimation errors owing to biased solutions, resulting in an acoustic artifact known as entrainment. Adaptive feedback cancellation (AFC) based on the linear prediction error method (PEM) has been successfully used as a solution to this bias. However, if the convergence speed of the linear prediction filter is faster than the estimated speed of the feedback path, removal of the feedback signal would be difficult. The proposed method uses a strategy to improve convergence by detecting howling based on the interaural level difference (ILD) and controlling the decorrelation performance of the correlation control algorithm of the adaptive lattice filter. Experimental results indicate that the adaptive filter of the proposed method significantly improves the estimation accuracy and tracking performance. The robustness of the proposed method is also confirmed via objective and subjective evaluation experiments on the generation of artifacts. Experimental results obtained using autocorrelated input signals show that the proposed method significantly improves robustness to entrainment compared to the conventional bilateral AFC method.

Identification of stochastic nonlinear models using optimal estimating functions

The first part of the paper examines the asymptotic properties of linear prediction error method estimators, which were recently suggested for the identification of nonlinear stochastic dynamical models. It is shown that their accuracy depends not only on how the model is parameterized, but also on the shape of the unknown distribution of the data. Therefore, it is not obvious in general which linear prediction error method should be preferred. In the second part, the estimating functions approach is introduced and used to construct estimators that are asymptotically optimal with respect to a specific class of estimators. These estimators rely on partial probabilistic parametric models, and therefore neither require the computations of the likelihood function nor any marginalization integrals. The convergence and consistency of the proposed estimators are established under standard regularity and identifiability assumptions akin to those of prediction error methods. The paper is concluded by several numerical simulation examples.

Adaptive Radar Sensitivity Time Control Based on Linear Prediction Sea Clutter

Sea clutter is interference background in radar target processing. It is difficult to detect long-range weak target in sea clutter. Sea clutter signal changes complex, with high intensity. For close signal, radar receiver will overload and reach saturation point. We are interested in the most dynamic range for displaying target echo and fast changing component of clutter. By analyzing spatial and temporal correlation of sea clutter, slow changing component can be minus from sea clutter. The innovation of this paper is to propose a linear prediction error method for sea clutter to increase dynamic range of radar receiver. It prevents overloading and reaching saturation point at close range.

Modeling and System Identification of the muFly Micro Helicopter

An accurate mathematical model is indispensable for simulation and control of a micro helicopter. The nonlinear model in this work is based on the rigid body motion where all external forces and moments as well as the dynamics of the different hardware elements are discussed and derived in detail. The important model parameters are estimated, measured or identified in an identification process. While most parameters are identified from test bench measurements, the remaining ones are identified on subsystems using the linear prediction error method on real flight data. The good results allow to use the systems for the attitude and altitude controller design.

FPEF-based blind adaptive channel equalization with arbitrary decision delay

Blind equalization of the communication channel is important because it does not need a training signal, nor does it require a priori channel information to increase bandwidth efficiency. The linear prediction error method is perhaps the most attractive in practice due to its sensitivity to blind channel equalizer length mismatch and simple adaptive implementation. Unfortunately, the previous one-step prediction error method has a limited arbitrary decision delay. In this paper, we propose a method for fractionally spaced blind equalizer with arbitrary decision delay using one-step forward prediction error filter from the second-order statistics of the received signals for SIMO channel. Our algorithm utilizes the forward prediction error as the training signal and computes the best decision delay from all the possible decision delay. Simulation results are presented to demonstrate the performance of our proposed algorithm.

Blind adaptive channel equalization using multichannel linear prediction-based cross-correlation vector estimation

Blind equalization of transmission channel is important in communication areas and signal processing applications because it does not need training sequences, nor does it require a priori channel information. In this paper, an adaptive blind MMSE channel equalization technique based on second-order statistics is investigated. We present an adaptive blind MMSE channel equalization using multichannel linear prediction error method for estimating cross-correlation vector. They can be implemented as RLS or LMS algorithms to recursively update the cross-correlation vector. Once cross-correlation vector is available, it can be used for MMSE channel equalization. Unlike many known subspace methods, our proposed algorithms do not require channel order estimation. Therefore, our algorithms are robust to channel order mismatch. Performance of our algorithms and comparison with existing algorithms are shown for real measured digital microwave channel.

Linear prediction error method for blind identification of periodically time-varying channels

Blind channel estimation for single-input multiple-output (SIMO) periodically time-varying channels is considered using only the second-order statistics of the data. The time-varying channel is assumed to be described by a complex exponential basis expansion model (CE-BEM). The linear prediction error method for blind identification of time-invariant channels is extended to time-varying channels represented by a CE-BEM. Sufficient conditions for identifiability are investigated. The cyclostationary nature of the received signal is exploited to consistently estimate the time-varying correlation function of the data from a single observation record. The proposed method requires the knowledge of the active basis functions but not the channel length (an upper bound suffices). Several existing methods require precise knowledge of the channel length. Equalization of the time-varying channel, given the estimated channel, is investigated. Computer simulation examples are presented to illustrate the approach and to compare it with two existing approaches.

Prediction error method for second-order blind identification

Blind channel identification methods based on the oversampled channel output are a problem of current theoretical and practical interest. In this paper, we introduce a second-order blind identification technique based on a linear prediction approach. In contrast to eigenstructure-based methods, it will be shown that the linear prediction error method is robust to order overdetermination. An asymptotic performance analysis of the proposed estimation method is carried out, consistency and asymptotic normality of the estimates is established. A closed-form expression for the asymptotic covariance of the estimates is given. Numerical simulations and investigations are finally presented to demonstrate the potential and the robustness of the proposed method.