Sparse Active Rectangular Array with Few Closely Spaced Elements

Sparse sensor arrays offer a cost effective alternative to uniform arrays. By utilizing the co-array, a sparse array can match the performance of a filled array, despite having significantly fewer sensors. However, even sparse arrays can have many closely spaced elements, which may deteriorate the array performance in the presence of mutual coupling. This paper proposes a novel sparse planar array configuration with few unit inter-element spacings. This Concentric Rectangular Array (CRA) is designed for active sensing tasks, such as microwave or ultra-sound imaging, in which the same elements are used for both transmission and reception. The properties of the CRA are compared to two well-known sparse geometries: the Boundary Array and the Minimum-Redundancy Array (MRA). Numerical searches reveal that the CRA is the MRA with the fewest unit element displacements for certain array dimensions.Comment: 4+1 pages, 5 figures, 1 tabl

The impact of sensor positioning on the array manifold

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Exploration vs. Data Refinement via Multiple Mobile Sensors

We examine the deployment of multiple mobile sensors to explore an unknown region to map regions containing concentration of a physical quantity such as heat, electron density, and so on. The exploration trades off between two desiderata: to continue taking data in a region known to contain the quantity of interest with the intent of refining the measurements vs. taking data in unobserved areas to attempt to discover new regions where the quantity may exist. Making reasonable and practical decisions to simultaneously fulfill both goals of exploration and data refinement seem to be hard and contradictory. For this purpose, we propose a general framework that makes value-laden decisions for the trajectory of mobile sensors. The framework employs a Gaussian process regression model to predict the distribution of the physical quantity of interest at unseen locations. Then, the decision-making on the trajectories of sensors is performed using an epistemic utility controller. An example is provided to illustrate the merit and applicability of the proposed framework

Nested Arrays: A Novel Approach to Array Processing With Enhanced Degrees of Freedom

A new array geometry, which is capable of significantly increasing the degrees of freedom of linear arrays, is proposed. This structure is obtained by systematically nesting two or more uniform linear arrays and can provide O(N^2) degrees of freedom using only physical sensors when the second-order statistics of the received data is used. The concept of nesting is shown to be easily extensible to multiple stages and the structure of the optimally nested array is found analytically. It is possible to provide closed form expressions for the sensor locations and the exact degrees of freedom obtainable from the proposed array as a function of the total number of sensors. This cannot be done for existing classes of arrays like minimum redundancy arrays which have been used earlier for detecting more sources than the number of physical sensors. In minimum-input–minimum-output (MIMO) radar, the degrees of freedom are increased by constructing a longer virtual array through active sensing. The method proposed here, however, does not require active sensing and is capable of providing increased degrees of freedom in a completely passive setting. To utilize the degrees of freedom of the nested co-array, a novel spatial smoothing based approach to DOA estimation is also proposed, which does not require the inherent assumptions of the traditional techniques based on fourth-order cumulants or quasi stationary signals. As another potential application of the nested array, a new approach to beamforming based on a nonlinear preprocessing is also introduced, which can effectively utilize the degrees of freedom offered by the nested arrays. The usefulness of all the proposed methods is verified through extensive computer simulations

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

Compressive Sensing Based Estimation of Direction of Arrival in Antenna Arrays

This thesis is concerned with the development of new compressive sensing (CS) techniques both in element space and beamspace for estimating the direction of arrival of various types of sources, including moving sources as well as fluctuating sources, using one-dimensional antenna arrays. The problem of estimating the angle of arrival of a plane electromagnetic wave is referred to as the direction of arrival (DOA) estimation problem. Such algorithms for estimating DOA in antenna arrays are often used in wireless communication network to increase their capacity and throughput. DOA techniques can be used to design and adapt the directivity of the array antennas. For example, an antenna array can be designed to detect a number of incoming signals and accept signals from certain directions only, while rejecting signals that are declared as interference. This spatio-temporal estimation and filtering capability can be exploited for multiplexing co-channel users and rejecting harmful co-channel interference that may occur because of jamming or multipath effects. In this study, three CS-based DOA estimation methods are proposed, one in the element space (ES), and the other two in the beamspace (BS). The proposed techniques do not require a priori knowledge of the number of sources to be estimated. Further, all these techniques are capable of handling both non-fluctuating and fluctuating source signals as well as moving signals. The virtual array concept is utilized in order to be able to identify more number of sources than the number of the sensors used. In element space, an extended version of the least absolute shrinkage and selection operator (LASSO) algorithm, the adaptable LASSO (A-LASSO), is presented. A-LASSO is utilized to solve the DOA problem in compressive sensing framework. It is shown through extensive simulations that the proposed algorithm outperforms the classical DOA estimation techniques as well as LASSO using a small number of snapshots. Furthermore, it is able to estimate coherent as well as spatially-close sources. This technique is then extended to the case of DOA estimation of the sources in unknown noise fields. In beamspace, two compressive sensing techniques are proposed for DOA estimation, one in full beamspace and the other in multiple beam beamspace. Both these techniques are able to estimate correlated source signals as well as spatially-close sources using a small number of snapshots. Furthermore, it is shown that the computational complexity of the two beamspace-based techniques is much less than that of the element-space based technique. It is shown through simulations that the performance of the DOA estimation techniques in multiple beam beamspace is superior to that of the other two techniques proposed in this thesis, in addition to having the lowest computational complexity. Finally, the feasibility for real-time implementation of the proposed CS-based DOA estimation techniques, both in the element-space and the beamspace, is examined. It is shown that the execution time of the proposed algorithms on Raspberry Pi board are compatible for real-time implementation

Applications of Continuous Spatial Models in Multiple Antenna Signal Processing

This thesis covers the investigation and application of continuous spatial models for multiple antenna signal processing. The use of antenna arrays for advanced sensing and communications systems has been facilitated by the rapid increase in the capabilities of digital signal processing systems. The wireless communications channel will vary across space as different signal paths from the same source combine and interfere. This creates a level of spatial diversity that can be exploited to improve the robustness and overall capacity of the wireless channel. Conventional approaches to using spatial diversity have centered on smart, adaptive antennas and spatial beam forming. Recently, the more general theory of multiple input, multiple output (MIMO) systems has been developed to utilise the independent spatial communication modes offered in a scattering environment. ¶ ..