Adaptive Signal Extraction Research Articles

Adaptive Signal Extraction Based on RLMD-SVD for Water Pipeline Leakage Localization

Adaptive Signal Extraction Based on RLMD-SVD for Water Pipeline Leakage Localization

Fault Diagnosis Method of Rotor Magnetic Field Local Demagnetization in Permanent Magnet Synchronous Motor Model Predictive Current Control System

In order to realize the reliable diagnosis of the rotor magnetic field local demagnetization fault of permanent magnet synchronous motor (PMSM) model predictive current control system, based on the establishment of the mathematical model of PMSM considering local demagnetization of rotor magnetic field and the simulation model of its model predictive current control system, a diagnosis method combining the adaptive signal extraction algorithm and Hilbert transform is proposed in this study. This method is first based on the adaptive signal extraction algorithm to extract the fault characteristic harmonics of rotor magnetic field local demagnetization fault in PMSM model predictive current control system under stationary and nonstationary operating conditions, and then, we use Hilbert transform to realize the time-frequency transformation of the extracted fault characteristic harmonics. Based on solving the problem that the weak fault characteristic signal near the fundamental wave component existing in the Hilbert–Huang transform is difficult to effectively decompose, the reliable diagnosis of the rotor magnetic field local demagnetization fault of the PMSM model predictive current control system is realized with the calculation amount less than Hilbert–Huang transform. The simulation and experimental results show that the proposed method is accurate and useful.

Forecasting Macroeconomic Time Series with Locally Adaptive Signal Extraction

We introduce a non-Gaussian dynamic mixture model for macroeconomic forecasting. The locally adaptive signal extraction and regression (LASER) model is designed to capture relatively persistent AR processes (signal) which are contaminated by high frequency noise. The distributions of the innovations in both noise and signal are modeled robustly using mixtures of normals. The mean of the process and the variances of the signal and noise are allowed to shift either suddenly or gradually at unknown locations and unknown numbers of times. The model is then capable of capturing movements in the mean and conditional variance of a series, as well as in the signal-to-noise ratio. Four versions of the model are estimated by Bayesian methods and used to forecast a total of nine quarterly macroeconomic series from the US, Sweden and Australia. We observe that allowing for infrequent and large parameter shifts while imposing normal and homoskedastic errors often leads to erratic forecasts, but that the model typically forecasts well if it is made more robust by allowing for non-normal errors and time varying variances. Our main finding is that, for the nine series we analyze, specifications with infrequent and large shifts in error variances outperform both fixed parameter specifications and smooth, continuous shifts when it comes to interval coverage.

Forecasting macroeconomic time series with locally adaptive signal extraction

We introduce a non-Gaussian dynamic mixture model for macroeconomic forecasting. The locally adaptive signal extraction and regression (LASER) model is designed to capture relatively persistent AR processes (signal) which are contaminated by high frequency noise. The distributions of the innovations in both noise and signal are modeled robustly using mixtures of normals. The mean of the process and the variances of the signal and noise are allowed to shift either suddenly or gradually at unknown locations and unknown numbers of times. The model is then capable of capturing movements in the mean and conditional variance of a series, as well as in the signal-to-noise ratio. Four versions of the model are estimated by Bayesian methods and used to forecast a total of nine quarterly macroeconomic series from the US, Sweden and Australia. We observe that allowing for infrequent and large parameter shifts while imposing normal and homoskedastic errors often leads to erratic forecasts, but that the model typically forecasts well if it is made more robust by allowing for non-normal errors and time varying variances. Our main finding is that, for the nine series we analyze, specifications with infrequent and large shifts in error variances outperform both fixed parameter specifications and smooth, continuous shifts when it comes to interval coverage.

Adaptive array beamforming for cyclostationary signals

This paper concerns the adaptive array beamforming using signal cyclostationarity. We present recursive algorithms for implementing the self-cohesive restoral (SCORE) approaches which were originally developed by Agee, Schell and Gardner (1990) to perform blind adaptive signal extraction using antenna arrays. Utilizing the theory of matrix factorization in conjunction with a power normalization scheme, we first reformulate the original problems considered by the SCORE approaches as simpler constrained optimization problems. By employing the modular Gram-Schmidt orthogonalization (GSO) structure, adaptive algorithms are then developed for finding the optimal weight vector. It is shown that although the GSO structure used requires a computation load O(N/sup 2/) for an adaptive-beamformer with N elements, the computing time required for producing an output data vector is reduced to O(N) due to the pipeline implementation suitable for the GSO process. The convergence property for each of the proposed algorithms is considered. A simple and efficient method is proposed for choosing appropriate initial guesses to initiate the adaptive algorithms. Simulation examples are given to show the effectiveness of the proposed algorithms.

Spectral self-coherence restoral: a new approach to blind adaptive signal extraction using antenna arrays

A new approach to blind adaptive signal extraction using narrowband antenna arrays is presented. The approach has the capability to extract communication signals from cochannel interference environments using only known spectral correlation properties of those signals, i.e. without using knowledge of the content or direction of arrival of the transmitted signal, or the array manifold or background noise covariance of the receiver, to train the antenna array. The class of spectral self-coherence restoral (SCORE) objective functions is introduced, and algorithms for adapting antenna arrays to optimize these objective functions are developed. Using the theory of spectral correlation, it is shown by analysis and simulation that these algorithms maximize the signal-to-interference-and-noise ratio at the output of the narrowband antenna array when a single communication signal with spectral self-coherence at a known value of frequency separation, along with an arbitrary number of interferers without spectral self-coherence at that frequency separation, are impinging on the array.

A theory of adaptive filtering

This paper considers the adaptive signal extraction problem for time-discrete data when only very general a priori assumptions regarding the distributions of signal and noise are possible. Specifically, it is assumed that the noise is white, additive, and signal independent with mean zero and unknown variance and that the signal is band-limited. No stationarity assumptions are required. After a procedure is found under these conditions, the mean-square-error is derived asymptotically under narrower conditions-stationary Gaussian data with mean zero. Finally, a method of estimating the error variance from the data (without knowing the signal directly) is found.