Sensor array processing: localisation of wireless sources

In this thesis, various subspace array processing techniques for wireless source localisation are presented and investigated in the following three aspects. First, in the environment of indoor optical wireless communications, the paths of different sources and/or from different reflectors may impinge on the receiver from closely spaced directions with a high probability. In this case, the ranges of the paths, together with their directions, are important especially for isolating the desired source from the interferers. A blind multi-source localisation approach, which can be used as a channel estimator in the receiver of a communication system, is proposed for direction, range, and path gain estimation. Utilising the above channel parameter estimates, two subspace multibeam beamformers are also presented to achieve complete interference cancellation. Second, in applications such as wireless sensor networks and ubiquitous computing, both the location and orientation of an array are important parameters of interest to be estimated. Hence, array localisation and orientation estimation approaches are proposed for two cases. In the first case, a number of sources of known locations are employed to estimate these parameters of a receiver array. In the second case, a receiver array is utilised to estimate these parameters of multiple sources with each one being a transmitter array. Last, when sources operate in the near field of an array, the spherical wave propagation model needs to be considered. A problem associated with such a scenario is source localisation under the wideband assumption, where the wavefront of a baseband signal varies when traversing through the sensors of the array. Two novel approaches with the employment of the subcovariance of the received signal and the rotation of the array reference point are proposed to localise multiple sources under the wideband assumption. Throughout the thesis, computer simulation studies are presented for evaluating the performance of the proposed approaches.Open Acces

Space Time MUSIC: Consistent Signal Subspace Estimation for Wide-band Sensor Arrays

Wide-band Direction of Arrival (DOA) estimation with sensor arrays is an essential task in sonar, radar, acoustics, biomedical and multimedia applications. Many state of the art wide-band DOA estimators coherently process frequency binned array outputs by approximate Maximum Likelihood, Weighted Subspace Fitting or focusing techniques. This paper shows that bin signals obtained by filter-bank approaches do not obey the finite rank narrow-band array model, because spectral leakage and the change of the array response with frequency within the bin create \emph{ghost sources} dependent on the particular realization of the source process. Therefore, existing DOA estimators based on binning cannot claim consistency even with the perfect knowledge of the array response. In this work, a more realistic array model with a finite length of the sensor impulse responses is assumed, which still has finite rank under a space-time formulation. It is shown that signal subspaces at arbitrary frequencies can be consistently recovered under mild conditions by applying MUSIC-type (ST-MUSIC) estimators to the dominant eigenvectors of the wide-band space-time sensor cross-correlation matrix. A novel Maximum Likelihood based ST-MUSIC subspace estimate is developed in order to recover consistency. The number of sources active at each frequency are estimated by Information Theoretic Criteria. The sample ST-MUSIC subspaces can be fed to any subspace fitting DOA estimator at single or multiple frequencies. Simulations confirm that the new technique clearly outperforms binning approaches at sufficiently high signal to noise ratio, when model mismatches exceed the noise floor.Comment: 15 pages, 10 figures. Accepted in a revised form by the IEEE Trans. on Signal Processing on 12 February 1918. @IEEE201

Bags of Affine Subspaces for Robust Object Tracking

We propose an adaptive tracking algorithm where the object is modelled as a continuously updated bag of affine subspaces, with each subspace constructed from the object's appearance over several consecutive frames. In contrast to linear subspaces, affine subspaces explicitly model the origin of subspaces. Furthermore, instead of using a brittle point-to-subspace distance during the search for the object in a new frame, we propose to use a subspace-to-subspace distance by representing candidate image areas also as affine subspaces. Distances between subspaces are then obtained by exploiting the non-Euclidean geometry of Grassmann manifolds. Experiments on challenging videos (containing object occlusions, deformations, as well as variations in pose and illumination) indicate that the proposed method achieves higher tracking accuracy than several recent discriminative trackers.Comment: in International Conference on Digital Image Computing: Techniques and Applications, 201

A Method for Neuronal Source Identification

Multi-sensor microelectrodes for extracellular action potential recording have significantly improved the quality of in vivo recorded neuronal signals. These microelectrodes have also been instrumental in the localization of neuronal signal sources. However, existing neuron localization methods have been mostly utilized in vivo, where the true neuron location remains unknown. Therefore, these methods could not be experimentally validated. This article presents experimental validation of a method capable of estimating both the location and intensity of an electrical signal source. A four-sensor microelectrode (tetrode) immersed in a saline solution was used to record stimulus patterns at multiple intensity levels generated by a stimulating electrode. The location of the tetrode was varied with respect to the stimulator. The location and intensity of the stimulator were estimated using the Multiple Signal Classification (MUSIC) algorithm, and the results were quantified by comparison to the true values. The localization results, with an accuracy and precision of ~ 10 microns, and ~ 11 microns respectively, imply that MUSIC can resolve individual neuronal sources. Similarly, source intensity estimations indicate that this approach can track changes in signal amplitude over time. Together, these results suggest that MUSIC can be used to characterize neuronal signal sources in vivo.Comment: 14 pages, 5 figure