Yu Hen Hu

Detection, classification, and tracking of targets

Networks of small, densely distributed wireless sensor nodes are being envisioned and developed for a variety of applications involving monitoring and the physical world in a tetherless fashion. Typically, each individual node can sense in multiple modalities but has limited communication and computation capabilities. Many challenges must be overcome before the concept of sensor networks In particular, there are two critical problems underlying successful operation of sensor networks: (1) efficient methods for exchanging information between the nodes and (2) collaborative signal processing (CSP) between the nodes to gather useful information about the physical world. This article describes the key ideas behind the CSP algorithms for distributed sensor networks being developed at the University of Wisconsin (UW). We also describe the basic ideas on how the CSP algorithms interface with the networking/routing algorithms being developed at Wisconsin (UW-API). We motivate the framework via the problem of detecting and tracking a single maneuvering target. This example illustrates the essential ideas behind the integration between UW-API and UW-CSP algorithms and also highlights the key aspects of detection and localization algorithms. We then build on these ideas to present our approach to tracking multiple targets that necessarily requires classification techniques becomes a reality.

Maximum likelihood multiple-source localization using acoustic energy measurements with wireless sensor networks

A maximum likelihood (ML) acoustic source location estimation method is presented for the application in a wireless ad hoc sensor network. This method uses acoustic signal energy measurements taken at individual sensors of an ad hoc wireless sensor network to estimate the locations of multiple acoustic sources. Compared to the existing acoustic energy based source localization methods, this proposed ML method delivers more accurate results and offers the enhanced capability of multiple source localization. A multiresolution search algorithm and an expectation-maximization (EM) like iterative algorithm are proposed to expedite the computation of source locations. The Crame/spl acute/r-Rao Bound (CRB) of the ML source location estimate has been derived. The CRB is used to analyze the impacts of sensor placement to the accuracy of location estimates for single target scenario. Extensive simulations have been conducted. It is observed that the proposed ML method consistently outperforms existing acoustic energy based source localization methods. An example applying this method to track military vehicles using real world experiment data also demonstrates the performance advantage of this proposed method over a previously proposed acoustic energy source localization method.

CORDIC-based VLSI architectures for digital signal processing

The evolution of CORDIC, an iterative arithmetic computing algorithm capable of evaluating various elementary functions using a unified shift-and-add approach, and of CORDIC processors is reviewed. A method to utilize a CORDIC processor array to implement digital signal processing algorithms is presented. The approach is to reformulate existing DSP algorithms so that they are suitable for implementation with an array performing circular or hyperbolic rotation operations. Three categories of algorithm are surveyed: linear transformations, digital filters, and matrix-based DSP algorithms.<<ETX>>

Vehicle classification in distributed sensor networks

The task of classifying the types of moving vehicles in a distributed, wireless sensor network is investigated. Specifically, based on an extensive real world experiment, we have compiled a data set that consists of 820 MByte raw time series data, 70 MByte of preprocessed, extracted spectral feature vectors, and baseline classification results using the maximum likelihood classifier. The purpose of this paper is to detail the data collection procedure, the feature extraction and pre-processing steps, and baseline classifier development. The database is available for download at http://www.ece.wisc.edu/~sensit starting on July 2003.

Neural-network-based adaptive matched filtering for QRS detection

The authors have developed an adaptive matched filtering algorithm based upon an artificial neural network (ANN) for QRS detection. They use an ANN adaptive whitening filter to model the lower frequencies of the electrocardiogram (ECG) which are inherently nonlinear and nonstationary. The residual signal which contains mostly higher frequency QRS complex energy is then passed through a linear matched filter to detect the location of the QRS complex. The authors developed an algorithm to adaptively update the matched filter template from the detected QRS complex in the ECG signal itself so that the template can be customized to an individual subject. This ANN whitening filter is very effective at removing the time-varying, nonlinear noise characteristic of ECG signals. The detection rate for a very noisy patient record in the MIT/BIH arrhythmia database is 99.5% with this approach, which compares favorably to the 97.5% obtained using a linear adaptive whitening filter and the 96.5% achieved with a bandpass filtering method.<<ETX>>

Handbook of Neural Network Signal Processing

From the Publisher: The use of neural networks is permeating every area of signal processing. They can provide powerful means for solving many problems, especially in nonlinear, real-time, adaptive, and blind signal processing. The Handbook of Neural Network Signal Processing brings together applications that were previously scattered among various publications to provide an up-to-date, detailed treatment of the subject from an engineering point of view.The authors cover basic principles, modeling, algorithms, architectures, implementation procedures, and well-designed simulation examples of audio, video, speech, communication, geophysical, sonar, radar, medical, and many other signals. The subject of neural networks and their application to signal processing is constantly improving. You need a handy reference that will inform you of current applications in this new area. The Handbook of Neural Network Signal Processing provides this much needed service for all engineers and scientists in the field.

One-lead ECG for identity verification

This research investigates the feasibility of using the electrocardiogram (ECG) as a new biometric for human identity verification. It is well known that the shapes of the ECG waveforms of different persons are different but it is unclear whether such differences can be used to identify different individuals. In this research, we demonstrated successfully that it is possible to identify a specific person from a group of candidates using a one-lead ECG. A one-lead ECG, unlike two-dimensional biometrics, such as the fingerprint, is a one-dimensional, low-frequency signal that can be recorded from electrodes on the hands. This research applied two techniques, template matching and a decision-based neural network (DBNN), to implement the identity verification. Using each of the two methods separately on a predetermined group of 20 subjects, the experimental results showed that the rate of correct identity verification was 95% for template matching and 80% for the DBNN. Combining the two methods produced a 100% correct rate. Our results show that ECG analysis is a potentially applicable method for human identity verification.

Energy-based collaborative source localization using acoustic microsensor array

A novel sensor network source localization method based on acoustic energy measurements is presented. This method makes use of the characteristics that the acoustic energy decays inversely with respect to the square of distance from the source. By comparing energy readings measured at surrounding acoustic sensors, the source location during that time interval can be accurately estimated as the intersection of multiple hyperspheres. Theoretical bounds on the number of sensors required to yield unique solution are derived. Extensive simulations have been conducted to characterize the performance of this method under various parameter perturbations and noise conditions. Potential advantages of this approach include low intersensor communication requirement, robustness with respect to parameter perturbations and measurement noise, and low-complexity implementation.

Distributed particle filter with GMM approximation for multiple targets localization and tracking in wireless sensor network

Two novel distributed particle filters with Gaussian mixer approximation are proposed to localize and track multiple moving targets in a wireless sensor network. The distributed particle filters run on a set of uncorrelated sensor cliques that are dynamically organized based on moving target trajectories. These two algorithms differ in how the distributive computing is performed. In the first algorithm, partial results are updated at each sensor clique sequentially based on partial results forwarded from a neighboring clique and local observations. In the second algorithm, all individual cliques compute partial estimates based only on local observations in parallel, and forward their estimates to a fusion center to obtain final output. In order to conserve bandwidth and power, the local sufficient statistics (belief) is approximated by a low dimensional Gaussian mixture model (GMM) before propagating among sensor cliques. We further prove that the posterior distribution estimated by distributed particle filter convergence almost surely to the posterior distribution estimated from a centralized Bayesian formula. Moreover, a data-adaptive application layer communication protocol is proposed to facilitate sensor self-organization and collaboration. Simulation results show that the proposed DPF with GMM approximation algorithms provide robust localization and tracking performance at much reduced communication overhead.

The quantization effects of the CORDIC algorithm

A detailed analysis of the quantization error encountered in the CORDIC (coordinate rotation digital computer) algorithm is presented. Two types of quantization error are examined: an approximation error due to the quantized representation of rotation angles, and a rounding error due to the finite precision representation in both fixed-point and floating-point arithmetic. Tight error bounds for these two types of error are derived. The rounding error due to a scaling (normalization) operation in the CORDIC algorithm is also discussed. An expression for overall quantization error is derived, and several simulation examples are presented. >