Len Yip

Coherent acoustic array processing and localization on wireless sensor networks

Advances in microelectronics, array processing, and wireless networking have motivated the analysis and design of low-cost integrated sensing, computing, and communicating nodes capable of performing various demanding collaborative space–time processing tasks. In this paper, we consider the problem of coherent acoustic sensor array processing and localization on distributed wireless sensor networks. We first introduce some basic concepts of beamforming and localization for wide-band acoustic sources. A review of various known localization algorithms based on time-delay followed by least-squares estimations as well as the maximum–likelihood method is given. Issues related to practical implementation of coherent array processing, including the need for fine-grain time synchronization, are discussed. Then we describe the implementation of a Linux-based wireless networked acoustic sensor array testbed, utilizing commercially available iPAQs with built-in microphones, codecs, and microprocessors, plus wireless Ethernet cards, to perform acoustic source localization. Various field-measured results using two localization algorithms show the effectiveness of the proposed testbed. An extensive list of references related to this work is also included.

Collaborative sensor networking towards real-time acoustical beamforming in free-space and limited reverberance

Wireless sensor networks have been attracting increasing research interest given the recent advances in microelectronics, array processing, and wireless networking. Consisting of a large collection of small, wireless, low-cost, integrated sensing, computing and communicating nodes capable of performing various demanding collaborative space-time processing tasks, wireless sensor network technology poses various unique design challenges, particularly for real-time operation. We review the approximate maximum-likelihood (AML) method for source localization and direction-of-arrival (DOA) estimation. Then, we consider the use of least-squares method (LS) method applied to DOA bearing crossings to perform source localization. A novel virtual array model applicable to the AML-DOA estimation method is proposed for reverberant scenarios. Details on the wireless acoustical testbed are given. We consider the use of Compaq iPAQ 3760s, which are handheld, battery-powered device normally meant to be used as personal organizers (PDAs), as sensor nodes. The iPAQ provide a reasonable balance of cost, availability, and functionality. It has a build in StrongARM processor, microphone, codec for acoustic acquisition and processing, and a PCMCIA bus for external IEEE 802.11b wireless cards for radio communication. The iPAQs form a distributed sensor network to perform real-time acoustical beamforming. Computational times and associated real-time processing tasks are described. Field measured results for linear, triangular, and square subarrays in free-space and reverberant scenarios are presented. These results show the effective and robust operation of the proposed algorithms and their implementations on a real-time acoustical wireless testbed.Wireless sensor networks have been attracting increasing research interest given the recent advances in microelectronics, array processing, and wireless networking. Consisting of a large collection of small, wireless, low-cost, integrated sensing, computing and communicating nodes capable of performing various demanding collaborative space-time processing tasks, wireless sensor network technology poses various unique design challenges, particularly for real-time operation. We review the approximate maximum-likelihood (AML) method for source localization and direction-of-arrival (DOA) estimation. Then, we consider the use of least-squares method (LS) method applied to DOA bearing crossings to perform source localization. A novel virtual array model applicable to the AML-DOA estimation method is proposed for reverberant scenarios. Details on the wireless acoustical testbed are given. We consider the use of Compaq iPAQ 3760s, which are handheld, battery-powered device normally meant to be used as personal organizers (PDAs), as sensor nodes. The iPAQ provide a reasonable balance of cost, availability, and functionality. It has a build in StrongARM processor, microphone, codec for acoustic acquisition and processing, and a PCMCIA bus for external IEEE 802.11b wireless cards for radio communication. The iPAQs form a distributed sensor network to perform real-time acoustical beamforming. Computational times and associated real-time processing tasks are described. Field measured results for linear, triangular, and square subarrays in free-space and reverberant scenarios are presented. These results show the effective and robust operation of the proposed algorithms and their implementations on a real-time acoustical wireless testbed.

Lower bounds of localization uncertainty in sensor networks

Localization is a key application for sensor networks. We propose a Bayesian method to analyze the lower bound of localization uncertainty in sensor networks. Given the location and sensing uncertainty of individual sensors, the method computes the minimum-entropy target location distribution estimated by the network of sensors. We define the Bayesian bound (BB) as the covariance of such distribution, which is compared with the Cramer-Rao bound (CRB) through simulations. When the observation uncertainty is Gaussian, the BB equals the CRB. The BB is much simpler to derive than the CRB when sensing models are complex. We also characterize the localization uncertainty attributable to the sensor network topology and the sensor observation type through the analysis of the minimum entropy and the CRB. Given the sensor network topology and the sensor observation type, such characteristics can be used to approximately predict where the target can be relatively accurately located.

Array processing for target DOA, localization, and classification based on AML and SVM algorithms in sensor networks

We propose to use the Approximate Maximum-Likelihood (AML) method to estimate the direction-of-arrival (DOA) of multiple targets from various spatially distributed sub-arrays, with each subarray having multiple acoustical/seismic sensors. Localization of the targets can with possibly some ambiguity be obtained from the cross bearings of the sub-arrays. Spectra from the AML-DOA estimation of the target can be used for classification as well as possibly to resolve the ambiguity in the localization process. We use the Support Vector Machine (SVM) supervised learning method to perform the target classification based on the estimated target spectra. The SVM method extends in a robust manner to the nonseparable data case. In the learning phase, classifier hyperplanes are generated off-line via a primal-dual interior point method using the training data of each target spectra obtained from a single acoustical/seismic sensor. In the application phase, the classification process can be performed in real-time involving only a simple inner product of the classifier hyperplane with the AML-DOA estimated target spectra vector. Analysis based on Cramer-Rao bound (CRB) and simulated and measured data is used to illustrate the effectiveness of AML and SVM algorithms for wideband acoustical/seismic target DOA, localization, and classification.

Numerical implemention of the AML algorithm for wideband DOA estimation

In this work, three algorithms are proposed to reduce the computational complexity of the Approximated Maximum Likelihood (AML) for wideband Direction of Arrival (DOA) estimation. The first two methods, conjugate gradient and Gauss-Newton, are iterative methods that are based on gradient information of the log-likelihood function. The third method, Alienor method, is based on function approximation theory which transform a multi-variable function into a one-variable function. Therefore, a multi-dimension search is reduced to a one-dimension search. Complexity as well as computational time of these methods are compared by simulations. Effectiveness of the AML algorithm is also demonstrated by experimental data.

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).

DSP implementation of a distributed acoustical beamformer on a wireless sensor platform

In this paper, we consider the use of a Compaq iPAQ 3760s, equipped with a built-in microphone and an external wireless card, for acoustic acquisition and processing to perform a distributed acoustical beamforming. Time synchronization among the microphones is achieved by the reference-broadcast synchronization method. Two beamforming algorithms, based on the time difference of arrivals (TDOA) among the microphones followed by a least-squares estimation, and the maximum-likelihood (ML) parameter estimation method, are used to perform source detection, enhancement, localization, delay-steered beamforming, and direction-of-arrival estimation. Experimental beamforming results using the iPAQs and the wireless network are reported.