Localization and tracking of electronic devices with their unintended emissions

The precise localization and tracking of electronic devices via their unintended emissions has a broad range of commercial and security applications. Active stimulation of the receivers of such devices with a known signal generates very low power unintended emissions. This dissertation presents localization and tracking of multiple devices using both simulation and experimental data in the form of five papers. First the localization of multiple emitting devices through active stimulation under multipath fading with a Smooth MUSIC based scheme in the near field region is presented. Spatial smoothing helps to separate the correlated sources and the multipath fading and results confirm improved accuracy. A cost effective near-field localization method is proposed next to locate multiple correlated unintended emitting devices under colored noise conditions using two well separated antenna arrays since colored noise in the environment degrades the subspace-based localization techniques. Subsequently, in order to track moving sources, a near-field scheme by using array output is introduced to monitor direction of arrival (DOA) and the distance between the antenna array and the moving source. The array output, which is a nonlinear function of DOA and distance information, is employed in the Extended Kalman Filter (EKF). In order to show the near- and far-field effect on estimation accuracy, computer simulation results are included for localization and tracking techniques. Finally, an L-shaped array is constructed and a suite of schemes are introduced for localization and tracking of such devices in the three-dimensional environment. Experimental results for localization and tracking of unintended emissions from single and multiple devices in the near-field environment of an antenna array are demonstrated --Abstract, page iv

Multiarray Signal Processing: Tensor decomposition meets compressed sensing

We discuss how recently discovered techniques and tools from compressed sensing can be used in tensor decompositions, with a view towards modeling signals from multiple arrays of multiple sensors. We show that with appropriate bounds on a measure of separation between radiating sources called coherence, one could always guarantee the existence and uniqueness of a best rank-r approximation of the tensor representing the signal. We also deduce a computationally feasible variant of Kruskal's uniqueness condition, where the coherence appears as a proxy for k-rank. Problems of sparsest recovery with an infinite continuous dictionary, lowest-rank tensor representation, and blind source separation are treated in a uniform fashion. The decomposition of the measurement tensor leads to simultaneous localization and extraction of radiating sources, in an entirely deterministic manner.Comment: 10 pages, 1 figur

Blind Multilinear Identification

We discuss a technique that allows blind recovery of signals or blind identification of mixtures in instances where such recovery or identification were previously thought to be impossible: (i) closely located or highly correlated sources in antenna array processing, (ii) highly correlated spreading codes in CDMA radio communication, (iii) nearly dependent spectra in fluorescent spectroscopy. This has important implications --- in the case of antenna array processing, it allows for joint localization and extraction of multiple sources from the measurement of a noisy mixture recorded on multiple sensors in an entirely deterministic manner. In the case of CDMA, it allows the possibility of having a number of users larger than the spreading gain. In the case of fluorescent spectroscopy, it allows for detection of nearly identical chemical constituents. The proposed technique involves the solution of a bounded coherence low-rank multilinear approximation problem. We show that bounded coherence allows us to establish existence and uniqueness of the recovered solution. We will provide some statistical motivation for the approximation problem and discuss greedy approximation bounds. To provide the theoretical underpinnings for this technique, we develop a corresponding theory of sparse separable decompositions of functions, including notions of rank and nuclear norm that specialize to the usual ones for matrices and operators but apply to also hypermatrices and tensors.Comment: 20 pages, to appear in IEEE Transactions on Information Theor

A Novel Method Based on Oblique Projection Technology for Mixed Sources Estimation

Reducing the computational complexity of the near-field sources and far-field sources localization algorithms has been considered as a serious problem in the field of array signal processing. A novel algorithm caring for mixed sources location estimation based on oblique projection is proposed in this paper. The sources are estimated at two different stages and the sensor noise power is estimated and eliminated from the covariance which improve the accuracy of the estimation of mixed sources. Using the idea of compress, the range information of near-field sources is obtained by searching the partial area instead of the whole Fresnel area which can reduce the processing time. Compared with the traditional algorithms, the proposed algorithm has the lower computation complexity and has the ability to solve the two closed-spaced sources with high resolution and accuracy. The duplication of range estimation is also avoided. Finally, simulation results are provided to demonstrate the performance of the proposed method

Sensor array signal processing : two decades later

Caption title.Includes bibliographical references (p. 55-65).Supported by Army Research Office. DAAL03-92-G-115 Supported by the Air Force Office of Scientific Research. F49620-92-J-2002 Supported by the National Science Foundation. MIP-9015281 Supported by the ONR. N00014-91-J-1967 Supported by the AFOSR. F49620-93-1-0102Hamid Krim, Mats Viberg

Cross-Field Channel Estimation for Ultra Massive-MIMO THz Systems

The large bandwidth combined with ultra-massive multiple-input multiple-output (UM-MIMO) arrays enables terahertz (THz) systems to achieve terabits-per-second throughput. The THz systems are expected to operate in the near, intermediate, as well as the far-field. As such, channel estimation strategies suitable for the near, intermediate, or far-field have been introduced in the literature. In this work, we propose a cross-field, i.e., able to operate in near, intermediate, and far-field, compressive channel estimation strategy. For an array-of-subarrays (AoSA) architecture, the proposed method compares the received signals across the arrays to determine whether a near, intermediate, or far-field channel estimation approach will be appropriate. Subsequently, compressed estimation is performed in which the proximity of multiple subarrays (SAs) at the transmitter and receiver is exploited to reduce computational complexity and increase estimation accuracy. Numerical results show that the proposed method can enhance channel estimation accuracy and complexity at all distances of interest.Comment: 30 pages, 7 pages, journa

MICROPHONE ARRAY OPTIMIZATION IN IMMERSIVE ENVIRONMENTS

The complex relationship between array gain patterns and microphone distributions limits the application of traditional optimization algorithms on irregular arrays, which show enhanced beamforming performance for human speech capture in immersive environments. This work analyzes the relationship between irregular microphone geometries and spatial filtering performance with statistical methods. Novel geometry descriptors are developed to capture the properties of irregular microphone distributions showing their impact on array performance. General guidelines and optimization methods for regular and irregular array design are proposed in immersive (near-field) environments to obtain superior beamforming ability for speech applications. Optimization times are greatly reduced through the objective function rules using performance-based geometric descriptions of microphone distributions that circumvent direct array gain computations over the space of interest. In addition, probabilistic descriptions of acoustic scenes are introduced to incorporate various levels of prior knowledge for the source distribution. To verify the effectiveness of the proposed optimization methods, simulated gain patterns and real SNR results of the optimized arrays are compared to corresponding traditional regular arrays and arrays obtained from direct exhaustive searching methods. Results show large SNR enhancements for the optimized arrays over arbitrary randomly generated arrays and regular arrays, especially at low microphone densities. The rapid convergence and acceptable processing times observed during the experiments establish the feasibility of proposed optimization methods for array geometry design in immersive environments where rapid deployment is required with limited knowledge of the acoustic scene, such as in mobile platforms and audio surveillance applications