Colored Noise Interference Research Articles

A novel recursive multivariate nonlinear time-series modeling method by using the coupling identification concept

This paper focuses on the modeling problem of complex time-series based on a type of multivariate nonlinear model with colored noise, i.e., the M-RBF-ARMA model. For the purpose of achieving highly accuracy modeling performance under colored noise interference, a novel recursive parameter identification algorithm for the M-RBF-ARMA model is investigated. In the framework of constructing several univariate system and noise sub-identification models using the mixed parameter characteristic of the model, three recursive sub-algorithms are presented by applying the coupling identification concept. Based on the singular value decomposition, a recursive update approach with numerical stability for covariance matrices is given. Then a three-stage coupled average extended recursive algorithm is proposed for the M-RBF-ARMA model by using the interactive strategy, which can implement the separable and interactive identification of different type of parameters. The effectiveness of the proposed algorithm is verified by an illustrative and a real multivariate nonlinear time-series.

A Fast and Efficient Parameter Estimation Algorithm for Generalized Output Error Models

One kind of the colored noise interference systems is generalized output error model (OEARMA). This paper presents a two-stage recursive least squares algorithm for OEARMA. Aiming at the OEARMA, this paper puts forward a two-stage recursive least squares algorithm. The basic idea of the algorithm is to combinie the auxiliary model identification idea and the decomposition technique to decompose a system into two subsystems. Each subsystem contains a parameter vector. With auxiliary model-based recursive extended least squares theory, an unknown intermediate variable output instead of the auxiliary model identification model vector, instead of unmeasurable noise terms in the information vector with the estimated residuals, which can use recursive identification idea to estimated all the parameters of the system, the algorithm has a high computational efficiency. The example of simulation states the effectiveness of the proposed algorithm.

A New Learning Algorithm Based on MCC for Colored Noise Interference Cancellation

In view of the defect of adaptive filtering algorithm based on the LMS in colored noise interference cancellation, we put forward a kind of new adaptive iterative learning algorithm using correntropy and Maximum Correntropy Criterion (MCC). The MCC and the gradient descent algorithm are used to update the weights of filter. The proposed algorithm can obtain an optimal factor of step length μ(k) from the square error of angle to undertake an analysis, and in order to illustrate the feasibility of the algorithm, the convergence of the algorithm from the sequence of the convergence in norm were analyzed. At the same time, a step-size control parameters was introduced in the algorithm, according to the parameters set constant values or variable values, the algorithm is defined as NC-MCC (Normalized Constant-MCC) and NV-MCC (Normalized Variable-MCC). The simulation results show that, in the signal adding the colored noise, compared with NLMS algorithm, the proposed algorithm is better and has good stability.

Principal components, covariance matrix tapers, and the subspace leakage problem

A new class of robust space-time adaptive beamforming techniques is introduced to address a broad range of subspace leakage phenomena that arise in many sensor array applications. When present, these leakage phenomena can significantly increase the effective rank of the dominant colored noise interference spectrum, thereby reducing the appeal of techniques that exploit low-rank dominant interference (such as principal components (PC) or diagonal loading) to reduce sample support (training) requirements. By combining the covariance matrix taper (CMT) approach with either PC or diagonal loading, the minimal sample support properties of these techniques can be preserved.