Development of a Resource Manager Framework for Adaptive Beamformer Selection

Adaptive digital beamforming (DBF) algorithms are designed to mitigate the effects of interference and noise in the electromagnetic (EM) environment encountered by modern electronic support (ES) receivers. Traditionally, an ES receiver employs a single adaptive DBF algorithm that is part of the design of the receiver system. While the traditional form of receiver implementation is effective in many scenarios it has inherent limitations. This dissertation proposes a new ES receiver framework capable of overcoming the limitations of traditional ES receivers. The proposed receiver framework is capable of forming multiple, independent, simultaneous adaptive digital beams toward multiple signals of interest in an electromagnetic environment. The main contribution of the research is the development, validation, and verification of a resource manager (RM) algorithm. The RM estimates a set of parameters that characterizes the electromagnetic environment and selects an adaptive digital beam forming DBF algorithm for implementation toward all each signal of interest (SOI) in the environment. Adaptive DBF algorithms are chosen by the RM based upon their signal to interference plus noise ratio (SINR) improvement ratio and their computational complexity. The proposed receiver framework is demonstrated to correctly estimate the desired electromagnetic parameters and select an adaptive DBF from the LUT

Wideband data-independent beamforming for subarrays

The desire to operate large antenna arrays for e.g. RADAR applications over a wider frequency range is currently limited by the hardware, which due to weight, cost and size only permits complex multipliers behind each element. In contrast, wideband processing would have to rely on tap delay lines enabling digital filters for every element.As an intermediate step, in this thesis we consider a design where elements are grouped into subarrays, within which elements are still individually controlled by narrowband complex weights, but where each subarray output is given a tap delay line or finite impulse response digital filter for further wideband processing. Firstly, this thesis explores how a tap delay line attached to every subarray can be designed as a delay-and-sum beamformer. This filter is set to realised a fractional delay design based on a windowed sinc function. At the element level, we show that designing a narrowband beam w.r.t. a centre frequency of wideband operation is suboptimal,and suggest an optimisation technique that can yield sufficiently accurate gain over a frequency band of interest for an arbitrary look direction, which however comes at the cost of reduced aperture efficiency, as well as significantly increased sidelobes. We also suggest an adaptive method to enhance the frequency characteristic of a partial wideband array design, by utilising subarrays pointing in different directions in different frequency bands - resolved by means of a filter bank - to adaptively suppress undesired components in the beam patterns of the subarrays. Finally, the thesis proposes a novel array design approach obtained by rotational tiling of subarrays such that the overall array aperture is densely constructed from the same geometric subarray by rotation and translation only. Since the grating lobes of differently oriented subarrays do not necessarily align, an effective grating lobe attenuation w.r.t. the main beam is achieved. Based on a review of findings from geometry,a number of designs are highlight and transformed into numerical examples, and the theoretically expected grating lobe suppression is compared to uniformly spaced arrays.Supported by a number of models and simulations, the thesis thus suggests various numerical and hardware design techniques, mainly the addition of tap-delay-line per subarray and some added processing overhead, that can help to construct a large partial wideband array close in wideband performance to currently existing hardware.The desire to operate large antenna arrays for e.g. RADAR applications over a wider frequency range is currently limited by the hardware, which due to weight, cost and size only permits complex multipliers behind each element. In contrast, wideband processing would have to rely on tap delay lines enabling digital filters for every element.As an intermediate step, in this thesis we consider a design where elements are grouped into subarrays, within which elements are still individually controlled by narrowband complex weights, but where each subarray output is given a tap delay line or finite impulse response digital filter for further wideband processing. Firstly, this thesis explores how a tap delay line attached to every subarray can be designed as a delay-and-sum beamformer. This filter is set to realised a fractional delay design based on a windowed sinc function. At the element level, we show that designing a narrowband beam w.r.t. a centre frequency of wideband operation is suboptimal,and suggest an optimisation technique that can yield sufficiently accurate gain over a frequency band of interest for an arbitrary look direction, which however comes at the cost of reduced aperture efficiency, as well as significantly increased sidelobes. We also suggest an adaptive method to enhance the frequency characteristic of a partial wideband array design, by utilising subarrays pointing in different directions in different frequency bands - resolved by means of a filter bank - to adaptively suppress undesired components in the beam patterns of the subarrays. Finally, the thesis proposes a novel array design approach obtained by rotational tiling of subarrays such that the overall array aperture is densely constructed from the same geometric subarray by rotation and translation only. Since the grating lobes of differently oriented subarrays do not necessarily align, an effective grating lobe attenuation w.r.t. the main beam is achieved. Based on a review of findings from geometry,a number of designs are highlight and transformed into numerical examples, and the theoretically expected grating lobe suppression is compared to uniformly spaced arrays.Supported by a number of models and simulations, the thesis thus suggests various numerical and hardware design techniques, mainly the addition of tap-delay-line per subarray and some added processing overhead, that can help to construct a large partial wideband array close in wideband performance to currently existing hardware

Ultra wideband antenna array processing under spatial aliasing

Given a certain transmission frequency, Shannon spatial sampling limit de¯nes an upper bound for the antenna element spacing. Beyond this bound, the exceeded ambiguity avoids correct estimation of the signal parameters (i.e., array manifold crossing). This spacing limit is inversely proportional to the frequency of transmis- sion. Therefore, to meet a wider spectral support, the element spacing should be decreased. However, practical implementations of closely spaced elements result in a detrimental increase in electromagnetic mutual couplings among the sensors. Further- more, decreasing the spacing reduces the array angle resolution. In this dissertation, the problem of Direction of Arrival (DOA) estimation of broadband sources is ad- dressed when the element spacing of a Uniform Array Antenna (ULA) is inordinate. It is illustrated that one can resolve the aliasing ambiguity by utilizing the frequency diversity of the broadband sources. An algorithm, based on Maximum Likelihood Estimator (MLE), is proposed to estimate the transmitted data signal and the DOA of each source. In the sequel, a subspace-based algorithm is developed and the prob- lem of order estimation is discussed. The adopted signaling framework assumes a subband hopping transmission in order to resolve the problem of source associations and system identi¯cation. The proposed algorithms relax the stringent maximum element-spacing constraint of the arrays pertinent to the upper-bound of frequency transmission and suggest that, under some mild constraints, the element spacing can be conveniently increased. An approximate expression for the estimation error has also been developed to gauge the behavior of the proposed algorithms. Through con- ¯rmatory simulation, it is shown that the performance gain of the proposed setup is potentially signi¯cant, speci¯cally when the transmitters are closely spaced and under low Signal to Noise Ratio (SNR), which makes it applicable to license-free communication

The measurement of underwater acoustic noise radiated by a vessel using the vessel's own towed array

The work described in this thesis tested the feasibility of using a towed array of hydrophones to: 1. localise sources of underwater acoustic noise radiated by the towvessel, 2. determine the absolute amplitudes of these sources, and 3. determine the resulting far-field acoustic signature of the tow-vessel. The concept was for the towvessel to carry out a U-turn manoeuvre so as to bring the acoustic section of the array into a location suitable for beamforming along the length of the tow-vessel. All three of the above were shown to be feasible using both simulated and field data, although no independent field measurements were available to fully evaluate the accuracy of the far-field acoustic signature determinations. A computer program was written to simulate the acoustic signals received by moving hydrophones. This program had the ability to model a variety of acoustic sources and to deal with realistic acoustic propagation conditions, including shallow water propagation with significant bottom interactions. The latter was accomplished using both ray and wave methods and it was found that, for simple fluid half-space seabeds, a modified ray method gave results that were virtually identical to those obtained with a full wave method, even at very low frequencies, and with a substantial saving in execution time. A field experiment was carried out during which a tug towing a 60-hydrophone array carried out a series of U-turn manoeuvres. The signals received by the array included noise radiated by the tow-vessel, signals from acoustic tracking beacons mounted on the tow-vessel, and transient signals generated by imploding sources deployed from a second vessel.Algorithms were developed to obtain snapshots of the vertical plane and horizontal plane shapes of the array from the transient data and to use range data derived from the tracking beacon signals to track the hydrophones in the horizontal plane. The latter was complicated by a high proportion of dropouts and outliers in the range data caused by the directionality of the hydrophones at the high frequencies emitted by the beacons. Despite this, excellent tracking performance was obtained. Matched field inversion was used to determine the vertical plane array shapes at times when no transient signals were available, and to provide information about the geoacoustic properties of the seabed. There was very good agreement between the inversion results and array shapes determined using transient signals. During trial manoeuvres the array was moving rapidly relative to the vessel and changing shape. A number of different array-processing algorithms were developed to provide source localisation and amplitude estimates in this situation: a timedomain beamformer; two frequency-domain, data independent beamformers; an adaptive frequency-domain beamformer; and an array processor based on a regularised least-squares inversion. The relative performance of each of these algorithms was assessed using simulated and field data. Data from three different manoeuvres were processed and in each case a calibrated source was localised to within 1 m of its known position at the source's fundamental frequency of 112 Hz.Localisation was also successful in most instances at 336 Hz, 560 Hz and 784 Hz, although with somewhat reduced accuracy due to lower signal to noise ratios. Localisation results for vessel noise sources were also consistent with the positions of the corresponding items of machinery. The estimated levels of the calibrated source obtained during the three manoeuvres were all within 4.1 dB of the calibrated value, and varied by only 1.3 dB between manoeuvres. Results at the higher frequencies had larger errors, with a maximum variation of 3.8 dB between serials, and a maximum deviation from the calibrated value of 6.8 dB. An algorithm was also developed to predict the far-field signature of the tow-vessel from the measured data and results were produced. This algorithm performed well with simulated data but no independent measurements were available to compare with the field results

Mixture of beamformers for speech separation and extraction

In many audio applications, the signal of interest is corrupted by acoustic background noise, interference, and reverberation. The presence of these contaminations can significantly degrade the quality and intelligibility of the audio signal. This makes it important to develop signal processing methods that can separate the competing sources and extract a source of interest. The estimated signals may then be either directly listened to, transmitted, or further processed, giving rise to a wide range of applications such as hearing aids, noise-cancelling headphones, human-computer interaction, surveillance, and hands-free telephony. Many of the existing approaches to speech separation/extraction relied on beamforming techniques. These techniques approach the problem from a spatial point of view; a microphone array is used to form a spatial filter which can extract a signal from a specific direction and reduce the contamination of signals from other directions. However, when there are fewer microphones than sources (the underdetermined case), perfect attenuation of all interferers becomes impossible and only partial interference attenuation is possible. In this thesis, we present a framework which extends the use of beamforming techniques to underdetermined speech mixtures. We describe frequency domain non-linear mixture of beamformers that can extract a speech source from a known direction. Our approach models the data in each frequency bin via Gaussian mixture distributions, which can be learned using the expectation maximization algorithm. The model learning is performed using the observed mixture signals only, and no prior training is required. The signal estimator comprises of a set of minimum mean square error (MMSE), minimum variance distortionless response (MVDR), or minimum power distortionless response (MPDR) beamformers. In order to estimate the signal, all beamformers are concurrently applied to the observed signal, and the weighted sum of the beamformers’ outputs is used as the signal estimator, where the weights are the estimated posterior probabilities of the Gaussian mixture states. These weights are specific to each timefrequency point. The resulting non-linear beamformers do not need to know or estimate the number of sources, and can be applied to microphone arrays with two or more microphones with arbitrary array configuration. We test and evaluate the described methods on underdetermined speech mixtures. Experimental results for the non-linear beamformers in underdetermined mixtures with room reverberation confirm their capability to successfully extract speech sources

Adaptive antenna array beamforming using a concatenation of recursive least square and least mean square algorithms

In recent years, adaptive or smart antennas have become a key component for various wireless applications, such as radar, sonar and cellular mobile communications including worldwide interoperability for microwave access (WiMAX). They lead to an increase in the detection range of radar and sonar systems, and the capacity of mobile radio communication systems. These antennas are used as spatial filters for receiving the desired signals coming from a specific direction or directions, while minimizing the reception of unwanted signals emanating from other directions.Because of its simplicity and robustness, the LMS algorithm has become one of the most popular adaptive signal processing techniques adopted in many applications, including antenna array beamforming. Over the last three decades, several improvements have been proposed to speed up the convergence of the LMS algorithm. These include the normalized-LMS (NLMS), variable-length LMS algorithm, transform domain algorithms, and more recently the constrained-stability LMS (CSLMS) algorithm and modified robust variable step size LMS (MRVSS) algorithm. Yet another approach for attempting to speed up the convergence of the LMS algorithm without having to sacrifice too much of its error floor performance, is through the use of a variable step size LMS (VSSLMS) algorithm. All the published VSSLMS algorithms make use of an initial large adaptation step size to speed up the convergence. Upon approaching the steady state, smaller step sizes are then introduced to decrease the level of adjustment, hence maintaining a lower error floor. This convergence improvement of the LMS algorithm increases its complexity from 2N in the case of LMS algorithm to 9N in the case of the MRVSS algorithm, where N is the number of array elements.An alternative to the LMS algorithm is the RLS algorithm. Although higher complexity is required for the RLS algorithm compared to the LMS algorithm, it can achieve faster convergence, thus, better performance compared to the LMS algorithm. There are also improvements that have been made to the RLS algorithm families to enhance tracking ability as well as stability. Examples are, the adaptive forgetting factor RLS algorithm (AFF-RLS), variable forgetting factor RLS (VFFRLS) and the extended recursive least squares (EX-KRLS) algorithm. The multiplication complexity of VFFRLS, AFF-RLS and EX-KRLS algorithms are 2.5N2 + 3N + 20 , 9N2 + 7N , and 15N3 + 7N2 + 2N + 4 respectively, while the RLS algorithm requires 2.5N2 + 3N .All the above well known algorithms require an accurate reference signal for their proper operation. In some cases, several additional operating parameters should be specified. For example, MRVSS needs twelve predefined parameters. As a result, its performance highly depends on the input signal.In this study, two adaptive beamforming algorithms have been proposed. They are called recursive least square - least mean square (RLMS) algorithm, and least mean square - least mean square (LLMS) algorithm. These algorithms have been proposed for meeting future beamforming requirements, such as very high convergence rate, robust to noise and flexible modes of operation. The RLMS algorithm makes use of two individual algorithm stages, based on the RLS and LMS algorithms, connected in tandem via an array image vector. On the other hand, the LLMS algorithm is a simpler version of the RLMS algorithm. It makes use of two LMS algorithm stages instead of the RLS – LMS combination as used in the RLMS algorithm.Unlike other adaptive beamforming algorithms, for both of these algorithms, the error signal of the second algorithm stage is fed back and combined with the error signal of the first algorithm stage to form an overall error signal for use update the tap weights of the first algorithm stage.Upon convergence, usually after few iterations, the proposed algorithms can be switched to the self-referencing mode. In this mode, the entire algorithm outputs are swapped, replacing their reference signals. In moving target applications, the array image vector, F, should also be updated to the new position. This scenario is also studied for both proposed algorithms. A simple and effective method for calculate the required array image vector is also proposed. Moreover, since the RLMS and the LLMS algorithms employ the array image vector in their operation, they can be used to generate fixed beams by pre-setting the values of the array image vector to the specified direction.The convergence of RLMS and LLMS algorithms is analyzed for two different operation modes; namely with external reference or self-referencing. Array image vector calculations, ranges of step sizes values for stable operation, fixed beam generation, and fixed-point arithmetic have also been studied in this thesis. All of these analyses have been confirmed by computer simulations for different signal conditions. Computer simulation results show that both proposed algorithms are superior in convergence performances to the algorithms, such as the CSLMS, MRVSS, LMS, VFFRLS and RLS algorithms, and are quite insensitive to variations in input SNR and the actual step size values used. Furthermore, RLMS and LLMS algorithms remain stable even when their reference signals are corrupted by additive white Gaussian noise (AWGN). In addition, they are robust when operating in the presence of Rayleigh fading. Finally, the fidelity of the signal at the output of the proposed algorithms beamformers is demonstrated by means of the resultant values of error vector magnitude (EVM), and scatter plots. It is also shown that, the implementation of an eight element uniform linear array using the proposed algorithms with a wordlength of nine bits is sufficient to achieve performance close to that provided by full precision

Recent Advances in Signal Processing

The signal processing task is a very critical issue in the majority of new technological inventions and challenges in a variety of applications in both science and engineering fields. Classical signal processing techniques have largely worked with mathematical models that are linear, local, stationary, and Gaussian. They have always favored closed-form tractability over real-world accuracy. These constraints were imposed by the lack of powerful computing tools. During the last few decades, signal processing theories, developments, and applications have matured rapidly and now include tools from many areas of mathematics, computer science, physics, and engineering. This book is targeted primarily toward both students and researchers who want to be exposed to a wide variety of signal processing techniques and algorithms. It includes 27 chapters that can be categorized into five different areas depending on the application at hand. These five categories are ordered to address image processing, speech processing, communication systems, time-series analysis, and educational packages respectively. The book has the advantage of providing a collection of applications that are completely independent and self-contained; thus, the interested reader can choose any chapter and skip to another without losing continuity

Quaternion-Valued Adaptive Signal Processing and Its Applications to Adaptive Beamforming and Wind Profile Prediction

Quaternion-valued signal processing has received more and more attentions in the past ten years due to the increasing need to process three or four-dimensional signals, such as colour images, vector-sensor arrays, three-phase power systems, dual-polarisation based wireless communica- tion systems, and wind profile prediction. One key operation involved in the derivation of all kinds of adaptive signal processing algorithms is the gradient operator. Although there are some derivations of this operator in literature with different level of details in the quaternion domain, it is still not fully clear how this operator can be derived in the most general case and how it can be applied to various signal processing problems. In this study, we will give a detailed derivation of the quaternion-valued gradient operator with associated properties and then apply it to different areas. In particular, it will be employed to derive the quaternion-valued LMS (QLMS) algorithm and its sparse versions for adaptive beamforming for vector sensor arrays, and another one is its application to wind profile prediction in combination with the classic computational fluid dynamics (CFD) approach. For the adaptive beamforming problem for vector sensor arrays, we consider the crossed- dipole array and the problem of how to reduce the number of sensors involved in the adap- tive beamforming process, so that reduced system complexity and energy consumption can be achieved, whereas an acceptable performance can still be maintained, which is particularly use- ful for large array systems. The quaternion-valued steering vector model for crossed-dipole arrays will be employed, and a reweighted zero attracting (RZA) QLMS algorithm is then pro- posed by introducing a RZA term to the cost function of the original QLMS algorithm. The RZA term aims to have a closer approximation to the l0 norm so that the number of non-zero valued coefficients can be reduced more effectively in the adaptive beamforming process. For wind profile prediction, it can be considered as a signal processing problem and we can solve it using traditional linear and non-linear prediction techniques, such as the proposed QLMS algorithm and its enhanced frequency-domain multi-channel version. On the other hand,it using traditional linear and non-linear prediction techniques, such as the proposed QLMS algorithm and its enhanced frequency-domain multi-channel version. On the other hand,wind flow analysis is also a classical problem in the CFD field, which employs various simulation methods and models to calculate the speed of wind flow at different time. It is accurate but time-consuming with high computational cost. To tackle the problem, a combined approach based on synergies between the statistical signal processing approach and the CFD approach is proposed. There are different ways of combining the signal processing approach and the CFD approach to obtain a more effective and efficient method for wind profile prediction. In the combined method, the signal processing part employs the QLMS algorithm, while for the CFD part, large eddy simulation (LES) based on the Smagorinsky subgrid-scale (SGS) model will be employed so that more efficient wind profile prediction can be achieved