K. Yao

Source localization and spatial filtering using wideband MUSIC and maximum power beamforming for multimedia applications

We propose a 2-D beamforming system that is designed for multiple source localization, signal enhancement, interference suppression, and noise reduction. The 2-D locations of the sources can be estimated by the wideband MUSIC algorithm. After estimating the locations of the sources, the maximum power beamforming algorithm is applied to enhance the desired signal and attenuate undesired spatially distributed interferences and background noises. Performance gains from simulations and experiments are shown to be promising for multimedia applications.

Acoustic sensor networks for woodpecker localization

Sensor network technology can revolutionize the study of animal ecology by providing a means of non-intrusive, simultaneous monitoring of interaction among multiple animals. In this paper, we investigate design, analysis, and testing of acoustic arrays for localizing acorn woodpeckers using their vocalizations. Each acoustic array consists of four microphones arranged in a square. All four audio channels within the same acoustic array are finely synchronized within a few micro seconds. We apply the approximate maximum likelihood (AML) method to synchronized audio channels of each acoustic array for estimating the direction-of-arrival (DOA) of woodpecker vocalizations. The woodpecker location is estimated by applying least square (LS) methods to DOA bearing crossings of multiple acoustic arrays. We have revealed the critical relation between microphone spacing of acoustic arrays and robustness of beamforming of woodpecker vocalizations. Woodpecker localization experiments using robust array element spacing in different types of environments are conducted and compared. Practical issues about calibration of acoustic array orientation are also discussed.

Microphone array for hearing aid and speech enhancement applications

Microphone array technology has been proposed for various audio, teleconference, hearing aid and voice recognition applications. By forming a focused beam toward the desired speech source, attenuating background noises and rejecting discrete spatial interferers, a microphone array can enhance the signal-to-noise-ratio (SNR) in a noisy environment with notable improvement in speech intelligibility. At the threshold of intelligibility, a one dB improvement in SNR can increase 10-15% speech intelligibility. It is also known that increasing SNR can result in significant improvement in the recognition rate of various automatic voice recognition systems. We propose a novel electronically steerable microphone array based on the maximum energy (ME) concentration criterion, which results in high SNR in rooms with reverberations and competing interferences. We present a prototype PC-based microphone array system designed for hearing aid applications but also applicable to other tasks.

Array signal processing for a wireless MEM sensor network

We first review the high-level signal processing architecture of a wireless MEM sensor system for source detection, signal enhancement, localization, and identification. A blind beamformer using only the measured data of randomly distributed sensors to form a sample correlation matrix is proposed. The maximum power collection criterion is used to obtain array weights from the dominant eigenvector of the sample correlation matrix. An effective blind beamforming estimation of the time delays of the dominant source is demonstrated. Source localization based on a novel least-squares method for time delay estimation is also given. Array system performance based on analysis, simulation, and measured acoustical/seismic sensor data is presented. Applications of such a system to multimedia, intrusion detection, and surveillance are briefly discussed.

Beamforming performance of a randomly distributed sensor array system

We consider a digital signal processing sensor array system based on randomly distributed sensor nodes for intrusion and surveillance applications. The nodes having acoustical, seismic, and other sensors are self organized into a synchronized network using low powered spread spectrum transceivers. Beamforming array techniques for enhanced detection and estimation and performances under various ideal and practical conditions are presented.

Architecture for adaptive eigenstructure decomposition based on systolic QRD

Eigenstructure decomposition of correlation matrices is an important pre-processing stage in many modern signal processing applications. In an unknown and possibly changing environment, adaptive algorithms that are efficient and numerically stable as well as readily implementable in hardware for eigendecomposition are highly desirable. Most modern real- time signal processing applications involve processing large amounts of input data and require high throughput rates in order to fulfill the needs of tracking and updating. In this paper, we consider the use of a novel systolic array architecture for the high throughput on-line implementation of the adaptive simultaneous iteration method (SIM) algorithm for the estimation of the p largest eigenvalues and associated eigenvectors of quasi-stationary or slowly varying correlation matrices.

DOA estimation method for wideband color signals based on least-squares Joint Approximate Diagonalization

Direction of arrival (DOA) estimation is one of the major task in many acoustic array signal processing applications. In this work, we show that a modified joint approximate diagonlization (MJAD) method utilizing temporal structure of the wideband color signals can outperform the conventional wideband DOA estimation method such as approximate maximum likelihood (AML) and coherent signal-subspace processing(CSSP).

Decision fusion in sensor networks for spectrum sensing based on likelihood ratio tests

Sensor networks have been shown to be useful in diverse applications. One of the important applications is the collaborative detection based on multiple sensors to increase the detection performance. To exploit the spectrum vacancies in cognitive radios, we consider the collaborative spectrum sensing by sensor networks in the likelihood ratio test (LRT) frameworks. In the LRT, the sensors make individual decisions. These individual decisions are then transmitted to the fusion center to make the final decision, which provides better detection accuracy than the individual sensor decisions. We provide the lowered-bounded probability of detection (LBPD) criterion as an alternative criterion to the conventional Neyman-Pearson (NP) criterion. In the LBPD criterion, the detector pursues the minimization of the probability of false alarm while maintaining the probability of detection above the pre-defined value. In cognitive radios, the LBPD criterion limits the probabilities of channel conflicts to the primary users. Under the NP and LBPD criteria, we provide explicit algorithms to solve the LRT fusion rules, the probability of false alarm, and the probability of detection for the fusion center. The fusion rules generated by the algorithms are optimal under the specified criteria. In the spectrum sensing, the fading channels influence the detection accuracies. We investigate the single-sensor detection and collaborative detections of multiple sensors under various fading channels, and derive testing statistics of the LRT with known fading statistics.

Empirical Connectivity for Mobile Ad Hoc Networks under Square and Rectangular Covering Scenarios

In the mobile ad hoc networks (MANET), one basic problem is that some nodes are unable to communicate with certain nodes because they are disconnected at the graph level even when a strong routing protocol has been imposed. Thus, connectivity is an important factor affecting the performance of MANETs. The connectivity is related to many factors of the MANET scenario. In this paper, we investigate how the connectivity is related to the region of interest, the number of nodes, and the transmission radius of the nodes. Firstly, we propose the modeling of the MANET connectivity problem for rectangular and square covering scenarios. From these models and associated empirical rules, we present schemes to normalize these two scenarios. We also propose to use second-order polynomial equations for modeling different connectivity levels. The coefficients of these equations are explicitly estimated by the least-square method. Applications of these equations to solve typical connectivity-related problems are discussed. Finally, the precisions of these equations for estimating connectivity are evaluated, which show that the proposed empirical rules are practical and useful. These empirical rules are proposed as simple guidelines for deploying MANETs and sensor networks

Collection and processing of acoustic and seismic array data for source localization

Distributed sensor networks have been proposed for a wide range of applications. In this paper, our goal is to locate a wideband source, generating both acoustic and seismic signals, using both seismic and acoustic sensors. For a far-field acoustic source, only the direction-of-arrival (DOA) in the coordinate system of the sensors is observable. We use the approximate Maximum-Likelihood (AML) method for DOA estimations from severalacoustic arrays. For a seismic source, we use data collected at a single tri-axial accelerometer to perform DOA estimation. Two seismic DOA estimation methods, the eigen-decomposition of the sample covariance matrix method and the surface wave method are used. Field measurements of acoustic and seismic signals generated by vertically striking a heavy metal plate placed on the ground in an open field are collected. Each acoustic array uses four low-cost microphones placed in a square configuration and separated by one meter. The microphone outputs of each array are collected by a synchronized A/D recording system and processed locally based on the AML algorithm for DOA estimation. An array of six tri-axial accelerometers arranged in two rows whose outputs are fed into an ultra low power and high resolution network-aware seismic recording system. Field measured data from the acoustic and seismic arrays show the estimated DOAs and consequent localizations of the source are quite accurate and useful.