Noise correlation processing extracts coherent signals from seemingly random noise data. Although this technique has been successfully used in processing ocean ambient noise data it has severe limitations due to the changing ocean environment and the spatial and temporal variability of the ambient noise field. In this project we are: (1) investigating the physics of the noise processing procedu...

The idea of applying H1 estimation techniques to the \array uncertainties problem" is motivated by the fact that H1 estimation is robust to model uncertainties and lack of statistical information with respect to noise. In this paper, a new state space model for the received signal of a general array of sensors is developed which, in contrast to existing models, is capable of handling the simult...

In this paper, approach to incoherent signal detection in multi-element antenna array are researched and modeled. Two types of useful signals with unknown wavefront were considered: first one, deterministic (Barker code), and second one, random (Gaussian distribution). The derivation of the sufficient statistics took into account the linearity of the antenna array. The performance characteristi...

A noisy input signal is observed by means of a parallel array of one-bit threshold quantizers, in which all the quantizer outputs are added to produce the array output. This parsimonious signal representation is used to implement an optimal detection from the output of the array. Such conditions can be relevant for fast real-time processing in largescale sensor networks. We demonstrate that, ev...

Sensor arrays are able to enhance desired signal reception while simultaneously suppressing undesired components through the use of directionality. In many important applications, the return signal is best modeled as being nonstationary and may lose coherence between sensors, severely limiting the performance of traditional array processors based on matched-field beamforming. Quadratic array pr...

the record of human brain neural activities, namely electroencephalogram (eeg), is generally known as a non-stationary and nonlinear signal. in many applications, it is useful to divide the eegs into segments within which the signals can be considered stationary. combination of empirical mode decomposition (emd) and hilbert transform, called hilbert-huang transform (hht), is a new and powerful ...

The most commonly used spectrum sensing techniques in cognitive radio (CR) networks, such as the energy detector (ED), matched filter (MF), and others, suffer from the noise uncertainty and signal-to-noise ratio (SNR) wall phenomenon. These detectors cannot achieve the required signal detection performance regardless of the sensing time. In this paper, we explore a signal processing scheme, nam...

Mixed sounds can be separated from multiple sound sources by utilizing a microphone array and signal processing. We believe that promotion of interest in this technique can lead to significant developments in future science and technology. Consequently, in order to experience this technique, we designed a language game for children called “KIKIWAKE 3D” that uses a sound source separation system...

Vafa Ghazi-Moghadam M. Kaveh Department of Electrical Engineering, University of Minnesota, Minneapolis, MN 55455, USA ABSTRACT This paper addresses some practical issues in active array signal processing, such as signal design, calibration and algorithmically e cient processing for range and angle determination. First, the operation of an experimental ultrasound-in-air active sensing system is...

Detecting the presence of an active random wireless source with minimum latency utilizing array signal processing is considered. The problem is studied under the constraint that the analog-to-digital conversion at each radio sensor is restricted to the reading of the analog receive signal sign. We formulate the digital signal processing task as a sequential hypothesis test in simple form. To ci...