A fully synchronized modular multichannel software-defined radio (SDR) testbed has been developed for the rapid prototyping and evaluation of array processing algorithms. Based on multiple universal software radio peripherals, this testbed is low cost, wideband, and highly reconfigurable. The testbed can be used to develop new techniques and algorithms in a variety of areas including, but not limited to, direction finding, source triangulation, and wireless sensor networks. A combination of hardware and software techniques is presented, which is shown to successfully remove the inherent phase and frequency uncertainties that exist between the individual SDR peripherals. The adequacy of the developed techniques is demonstrated through the application of the testbed to super-resolution direction finding algorithms, which rely on accurate phase synchronization.

Communications and Signal ProcessingBeamforming, pp. 189-219 (2015) No AccessChapter 6: Towed Arrays: Channel Estimation, Tracking and BeamformingVidhya Sridhar, Marc Willerton, and Athanassios ManikasVidhya SridharCommunications and Array Processing, Department of Electrical and Electronic Engineering, Imperial College London, UK, Marc WillertonCommunications and Array Processing, Department of Electrical and Electronic Engineering, Imperial College London, UK, and Athanassios ManikasCommunications and Array Processing, Department of Electrical and Electronic Engineering, Imperial College London, UKhttps://doi.org/10.1142/9781783262755_0006Cited by:2 (Source: Crossref) PreviousNext AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsRecommend to Library ShareShare onFacebookTwitterLinked InRedditEmail Abstract: Ocean towed arrays find applications in a variety of areas such as defence, oil and gas exploration and geological and marine life studies. The elements/ sensors of the towed arrays are usually hydrophones that receive acoustic energy which characterise the feature or environment being monitored. This acoustic energy may be generated by the feature under study itself (such as a marine animal) or be a reflected signal emitted by an acoustic source (such as a sparker or boomer). The major challenge in towed array processing is the receiver positional uncertainties resulting from the array's flexible structure in combination with the ship's turning maneuvers or water currents. These uncertainties can cause a significant reduction in the performance of the receiver array of hydrophones. Initially, in this chapter, we briefly explore and classify prevalent towed array signal processing techniques developed to address these uncertainties and efficiently perform tracking, channel estimation and reception/beamforming. Then, two specific techniques, namely subspace pilot calibration and H∞-based robustification, are presented, evaluated and compared using both "synthetic" and real data. FiguresReferencesRelatedDetailsCited By 2Cited by lists all citing articles based on Crossref citation.A New Approach to Construct Virtual Array With Increased Degrees of Freedom for Moving Sparse ArraysShuang Li and Xiao-Ping Zhang1 Jan 2020 | IEEE Signal Processing Letters, Vol. 27Target Tracking with a Flexible UAV Cluster ArrayVidhya Sridhar and Athanassios Manikas1 Dec 2016 Recommended BeamformingMetrics History PDF download

The emergence of software defined radio (SDR) aims to increase flexibility as well as reduce cost, size, weight and power (SWAP) inherent in traditional hardware radios. This paper is concerned with addressing issues associated with the formation of an array system from multiple SDR boards where each has an independent local oscillators (LO) in a multi-antenna system using representative examples such as localization and array shape estimation. In particular, practical experimental results are initially presented for estimating the unknown location of a single source using an SDR array of known array geometry. Furthermore, in the case that the SDR array geometry is unknown, a novel array shape estimation algorithm is proposed. The proposed algorithm estimates the antenna locations without requiring any external sources. This is achieved by allowing the array elements operate as transceivers.

In this paper, the performance of different localization algorithms are compared in the context of the sequential Wireless Sensor Network (WSN) discovery problem. Here, all sensor nodes are at unknown locations except for a very small number of so called anchor nodes at known locations. The locations of nodes are sequentially estimated such that when the location of a given node is found, it may be used to localize others. The underlying performance of such an approach is largely dependent upon the localization technique employed. In this paper, several well-known localization techniques are presented using a united notation. These methods are time of arrival (TOA), time difference of arrival (TDOA), received signal strength (RSS), direction of arrival (DOA) and large aperture array (LAA) localization. The performance of a sequential network discovery process is then compared when using each of these localization algorithms. These algorithms are implemented in the Java-DSP software package as part of a localization toolbox.

In this paper, a novel source/target localization approach is proposed using a number of sensors (surrounding or not surrounding one or more sources) to form a sparse large aperture array of known geometry. Under a large array aperture, the array response (manifold vector) obeys a spherical wave rather than a plane wave propagation model. By rotating the array reference point to be at each of the array sensors, a number of covariance matrices are constructed. It is shown that the eigenvalues of these covariance matrices are related to the source location with respect to the array reference point. The proposed approach is robust to channel fading and considers both wideband and narrowband assumptions. The performance of the proposed approach is evaluated via simulations as a function of array geometry, number of snapshots ( L ) and signal to noise ratio (SNR) and is shown to exceed existing techniques.

In this paper, a novel wireless sensor network discovery algorithm is presented which estimates the locations of a large number of low powered, randomly distributed sensor nodes. Initially, all nodes are at unknown locations except for a small number which are termed the "anchor" nodes. The remaining nodes are to be located as part of the discovery procedure. As the locations of sensor nodes are estimated, they can be used in the localization of other nodes. The locations of transmitting nodes are estimated in a decentralized manner by using a set of receiving sensor nodes at known or estimated locations within its coverage area to form an array. Initially a coarse localization of all nodes is performed to identify their approximate positions. A fine grained localization procedure then follows for enhancement. This paper will focus on the coarse localization approach. Simulations demonstrate the effectiveness of the proposed method.

In this paper, a novel single pilot array shape calibration algorithm is proposed for an arbitrary planar array. The method requires a single multi-carrier pilot operating at a known location with respect to the arbitrary array reference point. Typically two or more sources are required to calibrate the shape of a planar array. However, by exploiting the difference in the array response model when the source operates in the near-far and far field of the array, it is shown how this can be reduced to just one. Simulation results exhibiting the performance of the proposed method are also presented. (6 pages)

In this paper, a novel transformation connecting an arbitrary Planar Array to a virtual Uniform Linear Array (ULA) with a larger number of sensors is proposed. The array orientation, number of sensors and phase characteristics of the virtual array are a function of the array shape of the planar array. The two parameter manifold (azimuth,elevation) of a planar array of N sensors is a conoid surface embedded into an N -dimensional complex space which can be described by two families of curves (θ-curves and φ-curves). While the family of φ-curves have hyperhelical shape, the family of θcurves do not. The proposed transformation will allow the θcurves to be transformed to the manifold of a virtual ULA that has hyperhelical shape. This will allow a planar array system to be analysed or designed by analysing or designing simple hyperhelical curves associated with the virtual linear array.