KEYWORDS: Sensors, Signal to noise ratio, Data modeling, Interference (communication), Target detection, Data processing, Matrices, Acoustics, Signal detection, Fourier transforms
We have been interested in the analytical and experimental study of real-life bird song sources for several years. Bird sources are characterized by either a single or multiple bird vocalizations independent of each other or in response to others. The sources may be physically-stationary or exhibit movements and the signals are wide-band in frequency and often intermittent with pauses and possibly restarting with repeating previously used songs or with new songs. Thus, the detection, classification, and 2D or 3D localization of these birds pose challenging signal and array problems. Due to the fact that some birds can mimic other birds, time-domain waveform characterization may not be sufficient for determining the number of birds. Similarly, due to the intermittent nature of the vocalizations, data collected over a long period cannot be used naively. Thus, it is necessary to use short-time Fourier transform (STFT) to fully exploit the intricate natures of the time and frequency properties of these sources and displayed on a spectrogram. Various dominant spectral data over the relevant frames are used to form sample covariance matrices. Eigenvectors associated with the decompositions of these matrices for these spectral indices can be used to provide 2D/3D DOA estimations of the sources over different frames for intermittent sources. Proper cluttering of these data can be used to perform enhanced detection, classification, and localization of multiple bird sources. Two sets of collected bird data will be used to demonstrate these claims.
KEYWORDS: Sensors, Acoustics, Picosecond phenomena, Particles, Computer simulations, Monte Carlo methods, Data fusion, Statistical analysis, Particle filters, Systems modeling
Multisource passive acoustic tracking is useful in animal bio-behavioral study by replacing or enhancing human
involvement during and after field data collection. Multiple simultaneous vocalizations are a common occurrence
in a forest or a jungle, where many species are encountered. Given a set of nodes that are capable of producing
multiple direction-of-arrivals (DOAs), such data needs to be combined into meaningful estimates. Random
Finite Set provides the mathematical probabilistic model, which is suitable for analysis and optimal estimation
algorithm synthesis. Then the proposed algorithm has been verified using a simulation and a controlled test
experiment.
Variation in the number of targets and sensors needs to be addressed in any realistic sensor system. Targets may
come in or out of a region or may suddenly stop emitting detectable signal. Sensors can be subject to failure for
many reasons. We derive a tracking algorithm with a model that includes these variations using Random Finite
Set Theory (RFST). RFST is a generalization of standard probability theory into the finite set theory domain.
This generalization does come with additional mathematical complexity. However, many of the manipulations
in RSFT are similar in behavior and intuition to those of standard probability theory.
Joint estimation and detection for multi-sensor and multi-target algorithms are often hybrids of both analytical
and ad-hoc approaches at various levels. The intricacies of the resulting solution formulation often obscures
design intuition leaving many design choices to a largely trial and error based approach. Random Finite Set
Theory (RFST)1,2 is a formal generalization of classical probability theory to the random set domain. By treating
multi-target and multi-sensor jointly, RFST is able to provide a systematic theoretical framework for rigorous
mathematical analysis. Because of its set theory domain, RFST is able to model the randomness of missed
detection, sensor failure, target appearance and disappearance, clutter, jammer, ambiguous measurements, and
other practical artifacts within its probability framework. Furthermore, a rigorous statistical framework, the
Finite Set Statistics, has been developed for RFST that includes statistical operations such as: Maximum
Likelihood, Bayesian prediction-correction filter, sensor fusion, and even the Cramer-Rao Lower Bound (CRB).
In this paper we will apply RFST to jointly detect and locate a target in a power-aware wireless sensor network
setting. We will further derive the CRB using both classical and RFST approaches as verification. Then we will
use analytical results in conjunction with simulations to develop insights for choosing the design parameters.
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.
In this paper, we consider the use of a seismic sensor array for the localization and tracking of a wideband
moving source. The proposed solution consists of two steps: source Direction-Of-Arrival (DOA) estimation and
localization via DOA estimates. Three DOA estimation methods are considered. The Covariance Matrix Analysis
and the Surface Wave Analysis are previously published DOA estimation algorithms shown to be effective in the
localization of a stationary wideband source. This paper investigates their performance on moving wideband
sources. A novel DOA estimation algorithm, the Modified Kirlin's Method was also developed for the localization
of a moving source. The DOAs estimated by these algorithms are combined using a least-squares optimization
for source localization. The application of these algorithms to real-life data show the effectiveness of both the
Surface Wave Analysis and the Modified Kirlin's Method in locating and tracking a wideband moving source.
KEYWORDS: Sensors, Signal to noise ratio, 3D metrology, Acoustics, Algorithms, Signal processing, Spherical lenses, Data modeling, Biological research, Data processing
The focus of most direction-of-arrival (DOA) estimation problems has been based mainly on a two-dimensional (2D)
scenario where we only need to estimate the azimuth angle. But in various practical situations we have to deal with a
three-dimensional scenario. The importance of being able to estimate both azimuth and elevation angles with high
accuracy and low complexity is of interest. We present the theoretical and the practical issues of DOA estimation using
the Approximate-Maximum-Likelihood (AML) algorithm in a 3D scenario. We show that the performance of the
proposed 3D AML algorithm converges to the Cramer-Rao Bound. We use the concept of an isotropic array to reduce
the complexity of the proposed algorithm by advocating a decoupled 3D version. We also explore a modified version of
the decoupled 3D AML algorithm which can be used for DOA estimation with non-isotropic arrays. Various numerical
results are presented. We use two acoustic arrays each consisting of 8 microphones to do some field measurements. The
processing of the measured data from the acoustic arrays for different azimuth and elevation angles confirms the
effectiveness of the proposed methods.
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.
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.
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.
KEYWORDS: Orthogonal frequency division multiplexing, Modulation, Antennas, Lutetium, Signal to noise ratio, Signal processing, Demodulation, Computer simulations
Concatenation of space-time (ST) coding with orthogonal frequency-division multiplexing (OFDM) has gained much interest recently. In this work, we derive the exact pairwise error probability (PEP) of space-frequency (SF) codes for MIMO OFDM Systems. Based on the exact PEP, we derive the tighter upper and lower bounds for the PEP. For asymptotically high SNRs, the design criteria for SF codes differ significantly from those for ST codes over flat fading channels. In this paper, by drawing an analogy between SF and ST codes, we show that when the number of receive antennas is large, the minimum Euclidean distance among code words dominates the performance of SF codes. Therefore, SF codes can be optimized by using the Euclidean-distance criterion valid for AWGN channels. Simulation results are given to show that the results valid for a number of receive antennas tending to infinity still provide correct indications when the number of antennas is small.
Recently some efficient parallel architectures for turbo decoder have been proposed. In these architectures the bottleneck for the parallelization of the decoder is the interleaver. On the other hand, turbo codes achieve a very impressive near Shannon-capacity performance in which the interleaver plays a crucial role. Therefore, it is of great interest to design interleavers that are good from both performance and parallelization point of views. In this paper we have proposed an interleaver structure that is suitable for parallelization of turbo decoders. It is shown that such an interleaver can be designed to have good BER performance as well. By this structure not only fast decoders with very low latency can be built, but also the regularity of the decoder and the simplicity of the interleaver structure make them promising for VLSI implementation. We also present a fast algorithm to design such an interleaver, which can be used to design S-random interleavers as well. Such interleavers have been designed and the performances are compared to that of S-random interleavers by simulations.
Multiple input multiple output (MIMO) communications have been a hot
research area in recent years. Most literature makes the assumption that the channel information is not known at the transmitter but known perfectly at the receiver. We focus on the situation where both the transmitter and the receiver know the channel information. We consider a transmit diversity scheme that maximizes the signal to noise ratio at the receiver. We analyze its performance in terms of capacity, duality and asymptotic behavior. By simulation, we compare this scheme with Alamouti's transmit diversity to show the advantage of utilizing the channel side information to improve the performance of the wireless systems.
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.
KEYWORDS: Orthogonal frequency division multiplexing, Signal to noise ratio, Phase shift keying, Antennas, Receivers, Frequency shift keying, Binary data, Telecommunications, Systems modeling, Transmitters
In this paper, we analytically evaluate the bit error rate (BER) performance of space-time coded orthogonal frequency division multiplex (OFDM) transmit diversity over correlated Nakagami-m fading channel. Coherent and incoherent detection of binary frequency shift-keying (FSK) and phase-shift keying (PSK) signals are considered. Numerical results of the BER corresponding to different fading parameters and correlation coefficients are demonstrated.
KEYWORDS: Algorithm development, Signal to noise ratio, Projection systems, Composites, Acoustics, Interference (communication), Matrices, Data communications, Data modeling, Receivers
This paper presents a new approach for blind reverberation cancellation by adaptively estimating the channels. The key idea of this approach is to exploit the connection between the noise projection matrix and the cost matrix in the well-known subspace approach. A special weighted spectral decomposition is suggested to approximate the noise projection matrix directly from the inverse of the data autocorrelation matrix. We develop an off-line batch algorithm without eigendecomposition first. Combined with RLS-type matrix updating, an on-line adaptive algorithm is derived next to track time-varying channels. Simulations show our methods are robust for speech distorted by FIR reverberation.
In this paper we present a new adaptive blind equalization algorithm for multicarrier CDMA systems with single or multiple receive antennas. We analyze the cost function in the well-known subspace method and interpret it in terms of the noise projection matrix. The projection matrix is shown to be a special weighted spectral decomposition of the data autocorrelation matrix, which can be effectively approximated by inverting the data autocorrelation matrix. By adding a user specific correction term to the common cost function, we show that all user channel impulse responses can be estimated in parallel. In this way we develop a block algorithm with low complexity first. We then derive a recursive algorithm using RLS-type matrix updating. Simulations show our recursive algorithm has fast convergence and is near-far resistant. The bit error rate performance is also shown to improve as receiver diversity increases.
KEYWORDS: Sensors, Source localization, Error analysis, Near field, Signal to noise ratio, Data modeling, Algorithm development, Calibration, Acoustics, Computer simulations
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.
The problem of source localization from arrival time delay estimates requires a computationally costly iterative solution of a set of nonlinear equations. Most known methods assume that the propagation speed is known. In this paper, we provide several effective source localization and propagation velocity estimation methods which only use measurements of the relative arrival time delays between sensors. The formulae for source localization and propagation speed estimation are derived based on least squares, total least squares, bounded data uncertainty, and constrained least squares methods. Statistical performance of these methods are compared via computer simulation. In addition, in order to avoid time delay ambiguity problems and obtain smoother time delays, two time delay smoothing methods based on the forward backward algorithm and the Viterbi algorithm are also proposed. Field experiment results based on these techniques are also presented.
The problems of blind decorrelation and blind deconvolution have attracted considerable interest recently. These two problems traditionally have been studied as two different subjects, and a variety of algorithms have been proposed to solve them. In this paper, we consider these two problems jointly in the application of a multi-sensor network and propose a new algorithm for them. In our model, the system is a MIMO system (multiple-input multiple-output) which consists of linearly independent FIR channels. The unknown inputs are assumed to be uncorrelated and persistently excited. Furthermore, inputs can be colored sources and their distributions can be unknown. The new algorithm is capable of separating multiple input sources passing through some dispersive channels. Our algorithm is a generalization of Moulines' algorithm from single input to multiple inputs. The new algorithm is based on second order statistics which require shorter data length than the higher order statistics algorithms for the same estimation accuracy.
KEYWORDS: Deconvolution, Computer simulations, Sensors, Telecommunications, Monte Carlo methods, Quadrature amplitude modulation, Signal processing, Signal to noise ratio, Modulation, Data communications
For single-input multiple-output (SIMO) systems blind deconvolution based on second-order statistics has been shown promising given that the sources and channels meet certain assumptions. In our previous paper we extend the work to multiple-input multiple-output (MIMO) systems by introducing a blind deconvolution algorithm to remove all channel dispersion followed by a blind decorrelation algorithm to separate different sources from their instantaneous mixture. In this paper we first explore more details embedded in our algorithm. Then we present simulation results to show that our algorithm is applicable to MIMO systems excited by a broad class of signals such as speech, music and digitally modulated symbols.
The analysis of vehicle signals with methods derived from the theory of nonlinear dynamics is a potential tool to classify different vehicles. The nonlinear dynamical methodologies provide alternate system information that the linear analysis tools have ignored. In order to observe the nonlinear dynamic phenomena more clearly, and estimate system invariants more robustly, we exploit the maximum power blind beamforming algorithm as a signal enhancement and noise reduction method when locations of a source and sensors are unknown. The dynamical behavior of an acoustic vehicle signal is studied with the use of correlation dimension D2 and Lyapunov exponents. To characterize the nonlinear dynamic behavior of the acoustic vehicle signal, Taken's embedded theory is used to form an attractor in phase space based on a single observed time series. The time series is obtained from the coherently enhanced output of a blind beamforming array. Then the Grassberger- Procaccia algorithm and Sano-Sawada method are exploited to compute the correlation dimension and Lyapunov exponents. In this paper, we also propose some efficient computational methods for evaluating these system invariants. Experimental classification results show that the maximum power blind beamforming processing improves the estimation of the invariants of the nonlinear dynamic system. Preliminary results show that the nonlinear dynamics is useful for classification applications.
We briefly review the 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 sensor 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.
Luk and Qiao introduced an algorithm for the generalized ULV decomposition (ULLVD). The proposed decomposition scheme performs the rank-revealing operation, but requires a lower computational cost in the updating of new data as compared to the generalized singular value decomposition (GSVD). In this paper, we extend their algorithm for handling downdating, and propose a systolic array structure for implementing both updating and downdating. A scheme for rank revealing is also implemented on the proposed systolic array.
KEYWORDS: Sensors, Signal to noise ratio, Array processing, Wavefronts, Sensor networks, Matrices, Microelectromechanical systems, Error analysis, Signal processing, Signal detection
This paper considers 'blind beamforming' operations on a wireless network of randomly distributed MEM sensors. Maximum power collection criterion is proposed and results in array weights obtained from the eigenvector corresponding to largest eigenvalue of a matrix eigenvalue problem. Theoretical justification of this approach to an extension of Szego's asymptotic distribution of eigenvalues is provided. Numerical results on propagation time delay estimation and loss of coherency due to propagation disturbances are presented.
KEYWORDS: Sensors, Calibration, Signal to noise ratio, Digital signal processing, Computer simulations, Near field, Acoustics, Computing systems, Signal processing, Speaker recognition
For various audio, teleconference, hearing aid, and voice recognition applications, a microphone array is known to be an effective method to enhance the SNR in noisy environments resulting in significant improvement of speech intelligibility or recognition. We propose an novel electronically steerable microphone array based on the Maximum Energy concentration criterion to form a focused beam toward the desired speech source, attenuating background noises and rejecting discrete spatial interferers. The design and implementation of a prototype DSP-based microphone array system are described. Some details on microphone measurement, calibration, and optimization needed to achieve a high performance microphone array are discussed. Computer simulated and measured array performances are presented to show the effectiveness of the array system.
KEYWORDS: Sensors, Signal detection, Interference (communication), Signal to noise ratio, Signal processing, Near field, Environmental sensing, Statistical analysis, Signal generators, Chemical elements
This paper considers a class of blind beamforming problems in which the amount of prior information is very limited. The array sensors are placed in unknown locations within a geographical region, and have unknown frequency/spatial response. The sensors communicate synchronously with a main processor unit. The array is illuminated by a source, which may be narrow- or broadband, in near or far field. Additional disturbances may be present, in the form of interferers, possibly correlated or coherent with the main signal, and additive noise. The goal is required to steer beam toward the desired signal. Four schemes are presented, each based on the type of prior available information.
This paper considers various wideband signal model optimization techniques and associated performance results for adaptive and steerable but fixed beam microphone array processing for hearing aid applications in freespace and reverberant conditions. We first review and compare various conventional broadband and narrowband array optimization techniques. These include the minimization of the output array power subject to desired signal distortion constraint; the maximization of the array gain subject to white noise gain and linear constraints; and maximum energy criterion subject to norm, linear, and quandratic distortion constraints. Then new results on maximum energy criterion broadband array optimization formulated for sub- band processing are presented. The uniformly spaced sub-band and the nonuniformly spaced sub-band using quandrature mirror filter approaches are treated. Finally, various simulation results under the maximum energy criterion for free-space and reverberant conditions are presented to demonstrate the usefulness of this class of microphone arrays.
KEYWORDS: Signal to noise ratio, Radon, Detection and tracking algorithms, Matrices, Interference (communication), Error analysis, Data processing, Algorithm development, Information operations, Solids
An effective updating algorithm for singular value decomposition, based on Jacobi rotations, has recently been proposed. This algorithm is composed of two basic steps: QR updating and rediagonalization. By proper interleaving these two operations, parallel implementations with very high updating rates are possible. In this paper, we are concerned with the behavior of this algorithm for nonstationary data, and the effect of the pipeline rate on tracking accuracy. In order to overcome the trade-off between accuracy and updating rate intrinsic in the original algorithm, we proposed two schemes which improve the overall performance when the rate of change of the data is high. In the `variable rotational rate' scheme, the number of Jacobi rotations per update is dynamically determined. The alternative approach is to make the forgetting factor variable and data-dependent. Behavior and performance of both schemes are discussed and compared.
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.
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