Multiple Source Tracking Research Articles

Bayesian tracking of multiple sources in an uncertain ocean environment.

This paper describes a Bayesian approach to the problem of simultaneous tracking of multiple acoustic sources in a shallow-water environment in which water-column and seabed properties are not well known. The Bayesian formulation is based on treating the environmental parameters, noise statistics, and locations and complex strengths (amplitudes and phases) of multiple sources as unknown random variables constrained by acoustic data and prior information. Markov-chain Monte Carlo methods are applied to numerically sample the posterior probability density to integrate over unknown environmental parameters in a principal-component space. Closed-form maximum-likelihood expressions for source strengths and noise variance at each frequency allow these parameters to be sampled implicitly, substantially reducing the dimensionality of the inversion. The result is a set of time-ordered joint marginal probability distributions for the range and depth of each source, from which optimal track estimates and uncertainties are obtained.

Library-dependent and library-independent microbial source tracking to identify spatial variation in faecal contamination sources along a Lake Ontario beach (Ontario, Canada)

Multiple microbial source tracking methods were applied to investigate spatial variation in faecal pollution sources impacting a 1.7 km freshwater beach on Lake Ontario (Canada). The highest E. coli concentrations measured in the study area were from interstitial sand pore water at Sunnyside Beach, reaching 2.6 x 10(6) CFU/100 ml. These E. coli concentrations exceeded those in the nearby Humber River and Black Creek, which are impacted by combined sewer overflows containing municipal wastewater and by stormwater conveying washoff from the urban area. Library-independent Bacteroidales HF183 analyses identified the more frequent occurrence of municipal wastewater contamination in the Humber River and at a Sunnyside Beach location closest to the mouth of the river. Library-dependent E. coli antibiotic resistance and rep-PCR DNA fingerprinting analyses identified the more frequent occurrence of bird faecal contamination at Sunnyside Beach locations away from the river mouth. These microbial source tracking results raise caution about managing beaches with multiple sources of contamination as a single entity without considering spatial variability in faecal pollution sources and the need for more localized beach management practices.

Beam Steering in Global Positioning System Receivers using Synthetic Phased Arrays

This correspondence introduces a concept of synthetically generated phased arrays to the area of Global Positioning System (GPS). GPS antennas generated at different spatial locations are combined into a synthetic phased array to improve the beam sharpening performance without increasing the antenna's physical size. A fast Fourier transforms (FFT)-based method is applied for simultaneous steering of the synthetic array's beams in multiple directions. The synthetic beam-steering approach discussed can be applied for improving GPS robustness to RF interference and for simultaneous tracking of multiple signal sources (i.e., direct signal and multipath reflections). Simulation results and experimental test results are presented to demonstrate the efficacy of synthetic beam steering techniques for GPS antennas.

Neural network method for direction of arrival estimation with uniform cylindrical microstrip patch array

In this study, a new neural network algorithm is proposed for real-time multiple source tracking problem with cylindrical patch antenna array based on a previously reported Modified Neural Multiple Source Tracking (MN-MUST) algorithm. The proposed algorithm, namely cylindrical microstrip patch array modified neural multiple source tracking (CMN-MUST) algorithm implements MN-MUST algorithm on a cylindrical microstrip patch array structure. CMN-MUST algorithm uses the advantage of directive pattern of microstrip patch elements by considering only a part of array elements for a chosen sector. This reduces neural network sizes and also improves the spatial filtering performance. The proposed algorithm improves MN-MUST algorithm in the sense of accuracy and speed while covering the full azimuth range.

Relative-pitch tracking of multiple arbitrary sounds

Perceived-pitch tracking of potentially aperiodic sounds, as well as pitch tracking of multiple simultaneous sources, is shown to be feasible using a probabilistic methodology. The use of a shift-invariant representation in the constant-Q domain allows the modeling of perceived pitch changes as vertical shifts of spectra. This enables the tracking of these changes in sounds with an arbitrary spectral profile, even those where pitch would be an ill-defined quantity. It is further possible to extend this approach to a mixture model, which allows simultaneous tracking of varying mixed sounds. Demonstrations on real recordings highlight the robustness of such a model under various adverse conditions, and also show some of its unique conceptual differences when compared to traditional pitch tracking approaches.

Information Theoretic Enumeration and Tracking of Multiple Sources

The problem of multiple target tracking using a passive direction-finding system is addressed when the number of targets is not known a priori. A new method is proposed that is suitable for systems operating in low signal-to-noise ratio and high clutter. Such conditions cause unpredictable variations of stochastic characteristics of noise and signals (especially for wideband frequency ones) and create ambiguity in the output of direction-finding algorithms. In this paper, we use the predictive description length (PDL) technique, which is an information theoretic approach, and by suitable modeling, we minimize the predictive codelength for statistical data description of position measurements. The PDL-dynamic programming (DP) method is also presented, which employs the DP algorithm to reduce the computational load of the PDL technique. The concept of tracking time-varying number of targets makes PDL-DP a suitable technique for target tracking in practical systems

Detection and remediation of human-origin pollution at two public beaches in Virginia using multiple source tracking methods

Two public beaches (Anderson and Hilton) in Newport News, Virginia, were frequently closed to swimming in 2004 due to high Enterococcus spp. counts that exceeded the regulatory standard. The microbial source tracking (MST) methods of antibiotic resistance analysis (ARA) and fluorometry (to detect optical brighteners) were used in the summer of 2004 to determine the origins of fecal pollution at the two beaches. Both MST methods detected substantial human-origin pollution at the two beaches, in locations producing consistently high levels of Enterococcus spp. Investigations by municipal officials led to the fluorometric detection and subsequent repair of sewage infrastructure problems at both beaches. The success of the mitigation efforts was confirmed during the summer of 2005 using ARA and fluorometry, with the results cross-validated by pulsed-field gel electrophoresis (PFGE).

Tracking of multiple sound sources defined by interaural time differences: brain potential evidence in humans

Among other cues, interaural time differences (ITD) can be used to compute the location of a sound. To investigate whether ITD can be used for the preattentive detection of sounds coming from a different location than standard sounds ( P=0.7, no ITD), left and right ‘far’ (900 μs ITD, 80° excentricity) and ‘near ’ (300 μs ITD, 30°) deviant stimuli (each deviant P=0.075) were presented via headphones. While 15 young healthy subjects passively listened to these sequences, the mismatch negativity (MMN) of the event-related potential was obtained for the deviant sounds. MMN was reliable for all four deviants but larger for the far-lateralized sounds. Moreover, MMN was larger contralateral to the location of the deviant. This indicates a role of preattentive mechanisms in the spatial analysis of auditory scenes.

California Crisis Influences Further Reforms in Latin America

A new technique for analyzing partial discharge (PD) signals based on ultra-high frequency (UHF) measurements is presented, using results from site tests. Signals from simultaneously active discharge sources can be mapped according to their point of origin in a manner that will simplify the interpretation of phase-resolved discharge patterns. The scheme has the potential to allow automated location and tracking of multiple discharge sources in power transformers and other metal-clad high-voltage plants.

Separation and tracking of multiple broadband sources with one electromagnetic vector sensor

A structure for adaptively separating, enhancing and tracking uncorrelated sources with an electromagnetic vector sensor (EMVS) is presented. The structure consists of a set of parallel spatial processors, one for each individual source. Two stages of processing are involved in each spatial processor. The first preprocessing stage rejects all other sources except the one of interest, while the second stage is an adaptive one for maximizing the signal-to-noise ratio (SNR) and tracking the desired source. The preprocessings are designed using the latest source parameter estimates obtained from the source trackers, and a redesign is activated periodically or whenever any source has been detected by the source trackers to have made significant movement. Compared with conventional adaptive beamforming, the algorithm has the advantage that no a priori information on any desired signal location is needed, the sources are separated at maximum SNR, and their locations are available. The structure is also well suited for parallel implementation. Numerical examples are included to illustrate the capability and performance of the algorithm.

Applying UHF Partial Discharge Detection to Power Transformers

A new technique for analyzing partial discharge (PD) signals based on ultra-high frequency (UHF) measurements is presented, using results from site tests. Signals from simultaneously active discharge sources can be mapped according to their point of origin in a manner that will simplify the interpretation of phase-resolved discharge patterns. The scheme has the potential to allow automated location and tracking of multiple discharge sources in power transformers and other metal-clad high-voltage plants.

A neural network-based smart antenna for multiple source tracking

This paper considers the problem of multiple-source tracking with neural network-based smart antennas for wireless terrestrial and satellite mobile communications. The neural multiple-source tracking (N-MUST) algorithm is based on an architecture of a family of radial basis function neural networks (RBFNN) to perform both detection and direction of arrival (DOA) estimation. The field of view of the antenna array is divided into spatial angular sectors, which are in turn assigned to a different pair of RBFNNs. When a network detects one or more sources in the first stage, the corresponding second stage network(s) are activated to perform the DOA estimation. Simulation results are performed to investigate the performance of the algorithm for various angular separations, with sources of random relative signal-to-noise ratio and when the system suffers from Doppler spread.

Maximum Eigenvalue (Vector) Beamforming with an Adaptive Antenna Array

An adaptive array beamforming technique based on an eigenvector interpretation of the conventional (i.e., time delay and sum) single-frequency array directional response pattern is presented. In particular, the directional response pattern of a K-element antenna array can be expressed in terms of the spectral density matrix of the spatially received data. This directional response pattern can, in turn, be expressed as a sum of K terms, each involving a single eigenvector of the spatial spectral density matrix where each term in the sum is weighted by the corresponding eigenvalue. The array beamforming technique presented exploits the fact that positional information regarding the spatially coherent, source-like components of the array's environment concentrates in the larger spatial eigenvalues and related eigenvectors. This “clustering” effect allows the conventional directional response pattern to be approximated by a truncation of the eigenvalue-weighted sum. A single-term approximation to the conventional bearing response pattern is the subject of this paper. An adaptive array processing technique is proposed which recursively obtains the single-term truncation approximation to the bearing response pattern. The potential of this array processing technique for automatic multiple source location and tracking is examined.