Joe C. Chen

Source localization and beamforming

Distributed sensor networks have been proposed for a wide range of applications. The main purpose of a sensor network is to monitor an area, including detecting, identifying, localizing, and tracking one or more objects of interest. These networks may be used by the military in surveillance, reconnaissance, and combat scenarios or around the perimeter of a manufacturing plant for intrusion detection. In other applications such as hearing aids and multimedia, microphone networks are capable of enhancing audio signals under noisy conditions for improved intelligibility, recognition, and cuing for camera aiming. Previous developments in integrated circuit technology have allowed the construction of low-cost miniature sensor nodes with signal processing and wireless communication capabilities. These technological advances not only open up many possibilities but also introduce challenging issues for the collaborative processing of wideband acoustic and seismic signals for source localization and beamforming in an energy-constrained distributed sensor network. The purpose of this article is to provide an overview of these issues.

Maximum-likelihood source localization and unknown sensor location estimation for wideband signals in the near-field

In this paper, we derive the maximum-likelihood (ML) location estimator for wideband sources in the near field of the sensor array. The ML estimator is optimized in a single step, as opposed to other estimators that are optimized separately in relative time-delay and source location estimations. For the multisource case, we propose and demonstrate an efficient alternating projection procedure based on sequential iterative search on single-source parameters. The proposed algorithm is shown to yield superior performance over other suboptimal techniques, including the wideband MUSIC and the two-step least-squares methods, and is efficient with respect to the derived Cramer-Rao bound (CRB). From the CRB analysis, we find that better source location estimates can be obtained for high-frequency signals than low-frequency signals. In addition, large range estimation error results when the source signal is unknown, but such unknown parameter does not have much impact on angle estimation. In some applications, the locations of some sensors may be unknown and must be estimated. The proposed method is extended to estimate the range from a source to an unknown sensor location. After a number of source-location frames, the location of the uncalibrated sensor can be determined based on a least-squares unknown sensor location estimator.

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.

A maximum-likelihood parametric approach to source localizations

Source localization using passive sensor arrays has been an active research problem for many years. Most near-field source localization algorithms involve two separate estimations, namely, relative time-delay estimations and source location estimation. A one-step maximum-likelihood parametric source localization algorithm is proposed based on the maximum correlation between time shifted sensor data at the true source location. The performance of the algorithm is evaluated and shown to approach the Cramer-Rao bound asymptotically in simulations.

Source Localization and Tracking of a Wideband Source Using a Randomly Distributed Beamforming Sensor Array

We consider the array signal processing aspect of a power efficient self-organized and synchronized wireless sensor network for source detection, signal enhancement, localization, and identification. The beamforming and source localization are the most computationally intensive operations in the network. In this paper, we introduce a class of computationally efficient source localization algorithms. The novel source localization estimators also include the speed of propagation estimation since it is often unknown. For a more robust solution, a parametric source tracking algorithm is developed based on a linear track assumption. We also derive the Cramér-Rao bound for source localization and speed of propagation estimations using a randomly distributed sensor array. A blind beamforming approach, which enhances the strongest signal while attenuating other interferences, using only the measured data is presented. The maximum power collection criterion is used to obtain array weights from the dominant eigenvector of the sample correlation matrix. In many practical scenarios, the locations of some of the sensors may be unknown or non-stationary. From three or more source locations and source distances, we formulate a least-squares estimator for the unknown sensor location. Systematic evaluations via simulations show the proposed algorithms are effective and efficient with respect to the Cramér-Rao bound. The proposed algorithms are also shown to be effective in the examples using measured data.

Joint maximum-likelihood source localization and unknown sensor location estimation for near-field wideband signals

In this paper, we derive the maximum-likelihood (ML) location estimator for wideband sources in the near-field of a passive array. The parameters of interest are expanded to include the source range in addition to the angles in the far-field case. The ML estimator is optimized in a single step as opposed to many that are optimized separately in relative time-delay and source location estimations. The ML method is capable of estimating multiple source locations, while such case is rather difficult for the time-delay methods. To avoid a multi-dimensional search in the ML metric, we propose an efficient alternating projection procedure that is based on sequential iterative search on single source parameters. In the single source case, the ML estimator is shown to be equivalent to maximizing the sum of the weighted cross-correlations between time shifted sensor data. Furthermore, the ML formulation can expand the parameters to include the distance of a source to a sensor with unknown location. This provides inputs to our online unknown sensor location estimator, which is based on a least-squares fit to observations from multiple sources. The proposed algorithm has been shown to yield superior performance over other suboptimal techniques, and is efficient with respect to the derived Cramer-Rao bound. From the Cramer-Rao bound analyses, we find that better source location estimates can be obtained for high frequency signals than low frequency signals. In addition, large range estimation error results when the source signal is unknown, but such unknown parameter does not have much impact on angle estimation.

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.

Maximum-likelihood acoustic source localization: Experimental results

In this paper, we show several experimental acoustic source localization results using the maximum-likelihood parametric method. A direct localization is obtained when the sound source is placed inside the convex hull of the array. In the far-field case, we demonstrate the localization via the cross bearing from several widely separated arrays. In the case of two sources, an alternating projection procedure is applied to the ML method to estimate the two DOAs from the observed data. The ML algorithm is shown to be effective in locating sound sources of various types, e.g., vehicle, music, and even white noise.

Cramer-Rao bound analysis of wideband source localization and DOA estimation

In this paper, we derive the Cramér-Rao Bound (CRB) for wideband source localization and DOA estimation. The resulting CRB formula can be decomposed into two terms: one that depends on the signal characteristic and one that depends on the array geometry. For a uniformly spaced circular array (UCA), a concise analytical form of the CRB can be given by using some algebraic approximation. We further define a DOA beamwidth based on the resulting CRB formula. The DOA beamwidth can be used to design the sampling angular spacing for the Maximum-likelihood (ML) algorithm. For a randomly distributed array, we use an elliptical model to determine the largest and smallest effective beamwidth. The effective beamwidth and the CRB analysis of source localization allow us to design an efficient algorithm for the ML estimator. Finally, our simulation results of the Approximated Maximum Likelihood (AML) algorithm are demonstrated to match well to the CRB analysis at high SNR.

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.