Linear Noise Model Research Articles

Sparse Bayesian Learning Based Direction-of-Arrival Estimation under Spatially Colored Noise Using Acoustic Hydrophone Arrays

Direction-of-arrival (DOA) estimation in a spatially isotropic white noise background has been widely researched for decades. However, in practice, such as underwater acoustic ambient noise in shallow water, the ambient noise can be spatially colored, which may severely degrade the performance of DOA estimation. To solve this problem, this paper proposes a DOA estimation method based on sparse Bayesian learning with the modified noise model using acoustic vector hydrophone arrays. Firstly, an applicable linear noise model is established by using the prolate spheroidal wave functions (PSWFs) to characterize spatially colored noise and exploiting the excellent performance of the PSWFs in extrapolating band-limited signals to the space domain. Then, using the proposed noise model, an iterative method for sparse spectrum reconstruction is developed under a sparse Bayesian learning (SBL) framework to fit the actual noise field received by the acoustic vector hydrophone array. Finally, a DOA estimation algorithm under the modified noise model is also presented, which has a superior performance under spatially colored noise. Numerical results validate the effectiveness of the proposed method.

Sparse spatial spectral estimation in directional noise environment.

White Gaussian noise assumption is widely used in the field of spatial spectral estimation. But, the practical ambient noise usually does not satisfy this assumption, which may seriously affect the performance of direction of arrival estimation. This letter presents a linear noise model that can provide a more complete description of the practical ambient noise field, and then proposes a directional noise field sparse spectrum fitting (DN-SpSF) algorithm to estimate the spatial spectral in a directional noise field. Simulations and experimental results demonstrate the good performance of the DN-SpSF algorithm in a spatially directional noise environment.

Quantization Noise Analysis of Time-to-Digital-Converter-Based All-Digital Phase-Locked Loop and Frequency Discriminators

All-digital phase-locked loops (ADPLLs) based on the time-to-digital converter (TDC) and the frequency discriminator (FD) are modeled and analyzed in terms of quantization effects. Using linear models with quantization noise sources, theoretical analysis and simulation are carried out to obtain the output phase noise of each building block of the TDC-based ADPLL. It is newly derived that the TDC noise component caused by the delta-sigma modulator (DSM) and the finite resolution of the digitally controlled oscillator is not white. Namely, the in-band phase noise caused by the DSM-induced TDC is not white, which is due to the integrate-and-dump and subsampling operations of the TDC. This can give some insight into the design of low-noise ADPLLs. Some structures of delta-sigma FDs, which can serve as an alternative to the TDC, are also newly analyzed in terms of quantization noise, using the derived linear noise model.

Order and delay selection for neural network modelling by identification of linearized models

An approach to selecting the order and delay for neural network modelling of nonlinear dynamic systems is proposed by identifying local linear models at points spanning the system operating range. The method is based on relationships between linear and nonlinear models and is developed for three popular nonlinear model structures; nonlinear autoregressive with exogenous inputs (NARX), NARX with a linear noise model and nonlinear autoregressive moving-average with exogenous inputs (NARMAX). Simulation results illustrate the application of the method, and the suitability of the orders and delays selected are demonstrated by nonlinear system identification using radial basis function neural networks. The method is also shown to indicate the suitability of a particular nonlinear model structure for representing a system.

A harmonic noise model for direction finding in colored ambient noise

High-resolution algorithms for direction finding of narrowband sources require a good estimate of the noise correlation matrix. We derive a physically motivated linear noise model. It is based on a Fourier expansion of the ambient noise field and is applicable to arbitrary antenna arrays. We also show that for contrived cases, a linear model may not be uniquely identifiable. >

Analysis of bilinear noise models in circuits and devices

There are a number of applications in which linear noise models are inappropriate. In this paper, the use of bilinear noise models in circuits and devices is considered. Several physical problems are studied in this framework. These include circuits involving varying parameters (such as variable resistance circuits constructed using field-effect transistors), the effect of switching jitter on sampled data system performance and communication systems involving voltage-controlled oscillators and phase-lock loops. In addition, several types of analytical techniques for stochastic bilinear systems are considered. Specifically, the moment equations of Brockett for bilinear systems driven by white noise are discussed, and closed-form expressions for certain bilinear systems (those that evolve on abelian or solvable Lie groups) driven by white or colored noise are derived. In addition, an approximate statistical technique involving the use of harmonic expansions is described.