Random Actuation Pattern Optimization by Genetic Algorithm for Ultrasonic Structural Health Monitoring of Plates

The objective of this research is to investigate an optimized two-dimensional random pattern of uniformly excited points using the Genetic Algorithm (GA) technique for structural health monitoring. The point excitations generate ultrasonic waves in both isotropic and anisotropic materials that can be effective in diagnosing structural defects. The formed ultrasonic waves can constructively interfere and send out an intense wave beam to a predetermined target. The constructed wave beams can be steered to different directions with variable target distances. In the GA, the cost function is constructed to reduce main lobe beamwidth, eliminate grating lobes and suppress sidelobes’ levels. Mathematical modelling, finite element simulations, and optimizations are successively performed to achieve the objectives. Secondly Firstly, a mathematical beamforming model is developed to describe the excitation pattern of which each point is excited at the same time delay with a uniform weighting factor. The derived methodology accounts for enclosing all excitations within a certain aperture. The centroid of the emitting sources is also kept at the origin of the Cartesian coordinate within a slight tolerance range. For the near field, in isotropic materials, the excitation points lay on equally spaced circular arcs centered at the target point. In anisotropic materials, such as composites, the wave amplitude and phase velocity are highly dependent on fiber directions. Because of anisotropic nature, the excitation geometry becomes quite complicated. Secondly, finite element models for aluminum and composite plates are simulated to extract wave characteristics, such as displacement amplitudes, phase velocity profiles and slowness curves. These data are implemented later in the optimization algorithm. A quarter plate of radius 150mm and 1.125mm thickness is modelled as a three-dimensional solid part. A concentrated force with a 2.5 cycle-Hanning window sinusoidal signal is applied at the center of the plate and the boundaries are chosen to be symmetrical. Radial sensors at 5 degrees increments are positioned at 50mm from the excitation source to measure wave properties. The simulation results show that the amplitude and velocity are uniform for isotropic materials whereas the waves propagate rapidly with higher amplitudes along the fibers in anisotropic materials. Thirdly, after collecting all the required information, a GA optimization technique is applied to generate the excitation population of x- and y-coordinates. The pre-determined population is permutated, cross-overed and mutated so that additional possibilities are produced. The same process is repeated for many generations until the local optimum result is obtained. Finally, the near field beamforming is plotted in MATLAB at different actuation point numbers for the isotropic and anisotropic materials. The results are then compared to other linear, circular and planar patterns found in literature

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

Multi-objectives adaptive array synthesis using speedy-particle swarm method

A method of computing the optimum element distance position of multi-objectives adaptive linear antenna arrays (MLAA) is developed by taking several objectives (eg. adaptive capability, beamwidth and minimum sidelobe level (SLL)) into consideration. In this paper, the recently invented algorithm, known as Speedy-Particle Swarm Optimization (SpPSO) algorithm is adopted to optimize the distance between the MLAA elements. Different numerical examples of 8- and 12-element MLAA are presented to validate and illustrate the capability of SpPSO for pattern synthesis with a prescribed adaptive angle, controllable beamwidth and minimum SLL. It was found that by employing SpPSO method, the results provide considerable improvement over the conventional array. It is observed that the maximum normalized SLL of -12.27 dB has been achieved by using SpPSO for 8-element MLAA. The proposed SpPSO-based LAA also able to achieve a beampattern with sufficiently low sidelobes for 12-element MLAA by having maximum SLL of -16.46 dB, a desired wider FNBW of 50° and main beam that is pointing to 20°

Adaptive beamforming and switching in smart antenna systems

The ever increasing requirement for providing large bandwidth and seamless data access to commuters has prompted new challenges to wireless solution providers. The communication channel characteristics between mobile clients and base station change rapidly with the increasing traveling speed of vehicles. Smart antenna systems with adaptive beamforming and switching technology is the key component to tackle the challenges. As a spatial filter, beamformer has long been widely used in wireless communication, radar, acoustics, medical imaging systems to enhance the received signal from a particular looking direction while suppressing noise and interference from other directions. The adaptive beamforming algorithm provides the capability to track the varying nature of the communication channel characteristics. However, the conventional adaptive beamformer assumes that the Direction of Arrival (DOA) of the signal of interest changes slowly, although the interference direction could be changed dynamically. The proliferation of High Speed Rail (HSR) and seamless wireless communication between infrastructure ( roadside, trackside equipment) and the vehicles (train, car, boat etc.) brings a unique challenge for adaptive beamforming due to its rapid change of DOA. For a HSR train with 250km/h, the DOA change speed can be up to 4⁰ per millisecond. To address these unique challenges, faster algorithms to calculate the beamforming weight based on the rapid-changing DOA are needed. In this dissertation, two strategies are adopted to address the challenges. The first one is to improve the weight calculation speed. The second strategy is to improve the speed of DOA estimation for the impinging signal by leveraging on the predefined constrained route for the transportation market. Based on these concepts, various algorithms in beampattern generation and adaptive weight control are evaluated and investigated in this thesis. The well known Generalized Sidelobe Cancellation (GSC) architecture is adopted in this dissertation. But it faces serious signal cancellation problem when the estimated DOA deviates from the actual DOA which is severe in high mobility scenarios as in the transportation market. Algorithms to improve various parts of the GSC are proposed in this dissertation. Firstly, a Cyclic Variable Step Size (CVSS) algorithm for adjusting the Least Mean Square (LMS) step size with simplicity for implementation is proposed and evaluated. Secondly, a Kalman filter based solution to fuse different sensor information for a faster estimation and tracking of the DOA is investigated and proposed. Thirdly, to address the DOA mismatch issue caused by the rapid DOA change, a fast blocking matrix generation algorithm named Simplifized Zero Placement Algorithm (SZPA) is proposed to mitigate the signal cancellation in GSC. Fourthly, to make the beam pattern robust against DOA mismatch, a fast algorithm for the generation of at beam pattern named Zero Placement Flat Top (ZPFT) for the fixed beamforming path in GSC is proposed. Finally, to evaluate the effectiveness and performance of the beamforming algorithms, wireless channel simulation is needed. One of the challenging aspects for wireless simulation is the coupling between Probability Density Function (PDF) and Power Spectral Density (PSD) for a random variable. In this regard, a simplified solution to simulate Non Gaussian wireless channel is proposed, proved and evaluated for the effectiveness of the algorithm. With the above optimizations, the controlled simulation shows that the at top beampattern can be generated 380 times faster than iterative optimization method and blocking matrix can be generated 9 times faster than normal SVD method while the same overall optimum state performance can be achieved