Hanbiao Wang

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.

Target classification and localization in habitat monitoring

We are developing an acoustic habitat-monitoring sensor network that recognizes and locates specific animal calls in real time. We investigate the system requirements of such a real-time acoustic monitoring network. We propose a system architecture and a set of lightweight collaborative signal processing algorithms that achieve real-time behavior while minimizing inter-node communication to extend the system lifetime. In particular, the target classification is based on spectrogram pattern matching while the target localization is based on beamforming using time difference of arrival (TDOA). We describe our preliminary implementation on a commercial off the shelf (COTS) testbed and present its performance based on testbed measurements.

Preprocessing in a tiered sensor network for habitat monitoring

We investigate task decomposition and collaboration in a two-tiered sensor network for habitat monitoring. The system recognizes and localizes a specified type of birdcalls. The system has a few powerful macronodes in the first tier, and many less powerful micronodes in the second tier. Each macronode combines data collected by multiple micronodes for target classification and localization. We describe two types of lightweight preprocessing which significantly reduce data transmission from micronodes to macronodes. Micronodes classify events according to their cross-zero rates and discard irrelevant events. Data about events of interest is reduced and compressed before being transmitted to macronodes for target localization. Preliminary experiments illustrate the effectiveness of event filtering and data reduction at micronodes.

Information-theoretic approaches for sensor selection and placement in sensor networks for target localization and tracking

In this paper, we describes the information-theoretic approaches to sensor selection and sensor placement in sensor networks for target localization and tracking. We have developed a sensor selection heuristic to activate the most informative candidate sensor for collaborative target localization and tracking. The fusion of the observation by the selected sensor with the prior target location distribution yields nearly the greatest reduction of the entropy of the expected posterior target location distribution. Our sensor selection heuristic is computationally less complex and thus more suitable to sensor networks with moderate computing power than the mutual information sensor selection criteria. We have also developed a method to compute the posterior target location distribution with the minimum entropy that could be achieved by the fusion of observations of the sensor network with a given deployment geometry. We have found that the covariance matrix of the posterior target location distribution with the minimum entropy is consistent with the Cramer-Rao lower bound (CRB) of the target location estimate. Using the minimum entropy of the posterior target location distribution, we have characterized the effect of the sensor placement geometry on the localization accuracy.

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.

Poster abstract: entropy-based sensor selection for localization

We propose a novel entropy-based sensor selection heuristic for localization. Given 1) a prior probability distribution of the target location, and 2) the locations and the sensing characteristics of a set of additional sensors, we would like to select an optimal additional sensor such that fusion of its measurements with existing information would yield the greatest entropy reduction of the target location distribution. The heuristic can select a sub-optimal additional sensor without retrieving the measurements of candidate sensors. The heuristic is computationally much simpler than the mutual information based sensor selection approaches for localization and tracking [1, 2]. Just as those existing approaches do, the heuristic greedily selects one sensor in each step.

Particle Filtering Approach to Localization and Tracking of a Moving Acoustic Source in a Reverberant Room

We propose a novel algorithm employing particle filters for acoustic source tracking in a reverberant environment. By incorporating the likelihood function computed through approximate maximum-likelihood (AML) method, the proposed algorithm is applicable to wideband sources and can be implemented for multiple sources tracking. Both computer simulation and experimental results show the effectiveness of the proposed algorithm

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.

Entropy-based Sensor Selection Heuristic for Target Localization

We propose an entropy-based sensor selection heuristic for localization. Given 1) a prior probability distribution of the target location, and 2) the locations and the sensing models of a set of candidate sensors for selection, the heuristic selects an informative sensor such that the fusion of the selected sensor observation with the prior target location distribution would yield on average the greatest or nearly the greatest reduction in the entropy of the target location distribution. The heuristic greedily selects one sensor in each step without retrieving any actual sensor observations. The heuristic is also computationally much simpler than the mutual-information-based approaches. The effectiveness of the heuristic is evaluated using localization simulations in which Gaussian sensing models are assumed for simplicity. The heuristic is more effective when the optimal candidate sensor is more informative.