Compact Real-Time Control System for Autonomous Vehicle

Due to global warming, there is an increase in the number of natural disasters occurring around the world. With more disasters happening, post disaster search and rescue personnel are putting their life on the line more often in scouting out the post disaster site and sending in first aid supplies while waiting for rescue vehicles like fire trucks and ambulance to arrive. In Malaysia alone, the use of compact autonomous landed vehicle (ALV) for this purpose is limited. There are many compact ALV that are being developed with microcontrollers and microprocessors such as Arduino and Raspberry Pi as the central control system, but they are fragile and can be damaged easily when used in harsh environments. In addition, multiple microcontrollers and microprocessors are needed for the ALV as parallel processing and limited gates are some of the common problems with microprocessors and microcontrollers. In this paper, a central control system using an FPGA is proposed together with the design of a prototype for the ALV. The ALV consists of three systems: Propulsion System, Sensor System, and Remote-Control System. These systems are integrated together with the Altera DE-115 board as the central control unit of the ALV. Verilog Hardware Description language (Verilog HDL) is used for designing the control system for the ALV. The proposed system is stable, low cost, allows parallel processing and the compact size of the ALV allows smooth manoeuvring through small areas of post disaster sites to scout out the area and send in first aid supplies to the victims

POINT CLOUD GENERATION FROM GAUSSIAN DECOMPOSITION OF THE WAVEFORM LASER SIGNAL WITH GENETIC ALGORITHMS

Recent developments in LIDAR technology lead to the availability of the waveform systems, which capture and digitize the whole return of the emitted LASER pulse. As many objects may cause multiple returns in the same echo, one task is to detect and separate different echoes within the same digitized measurement. In this paper the results of a study aimed at LASER signal waveform decomposition using genetic algorithms are introduced. The proposed method is based on the Gaussian decomposition approach and analyzes each digitized return to compute one or more points. Initially, the number of peaks contained in the waveform is determined by a simple peak detection method, with a local maximum point algorithm. When more than one peak is detected, genetic algorithms are applied to estimate the amplitude, time and standard deviation of each peak within the digitized signal. With this methodology it was possible to increase the number of points by approximately 17 % compared to the point cloud obtained using commercial software. The best results were obtained in areas with high vegetation, and thus the methodology can be applied to the generation of denser points cloud in forest area

Free Space Ranging Utilizing Chaotic Light

We report our recent works on free space ranging with chaotic light. Using a laser diode with optical feedback as chaotic source, a prototype of chaotic lidar has been developed and it can achieve a range-independent resolution of 18 cm and measurable distance of 130 m at least. And its antijamming performance is presented experimentally and numerically. Finally, we, respectively, employ the wavelet denoising method and the correlation average discrete-component elimination algorithm to detect the chaotic signal in noisy environment and suppress the side-lobe noise of the correlation trace

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

Robust automotive radar interference mitigation using multiplicative-adaptive filtering and Hilbert transform

Radar is one of the sensors that have significant attention to be implemented in an autonomous vehicle since its robustness under many possible environmental conditions such as fog, rain, and poor light. However, the implementation risks interference because of transmitting and/or receiving radar signals from/to other vehicles. This interference will increase the floor noise that can mask the target signal. This paper proposes multiplicative-adaptive filtering and Hilbert transform to mitigate the interference effect and maintain the target signal detectability. The method exploited the trade-off between the step-size and sidelobe effect on the least mean square-based adaptive filtering to improve the target detection accuracy, especially in the long-range case. The numerical analysis on the millimeter-wave frequency modulated continuous wave radar with multiple interferers concluded that the proposed method could maintain and enhance the target signal even if the target range is relatively far from the victim radar

Arrayed LiDAR signal analysis for automotive applications

Light detection and ranging (LiDAR) is one of the enabling technologies for advanced driver assistance and autonomy. Advances in solid-state photon detector arrays offer the potential of high-performance LiDAR systems but require novel signal processing approaches to fully exploit the dramatic increase in data volume an arrayed detector can provide. This thesis presents two approaches applicable to arrayed solid-state LiDAR. First, a novel block independent sparse depth reconstruction framework is developed, which utilises a random and very sparse illumination scheme to reduce illumination density while improving sampling times, which further remain constant for any array size. Compressive sensing (CS) principles are used to reconstruct depth information from small measurement subsets. The smaller problem size of blocks reduces the reconstruction complexity, improves compressive depth reconstruction performance and enables fast concurrent processing. A feasibility study of a system proposal for this approach demonstrates that the required logic could be practically implemented within detector size constraints. Second, a novel deep learning architecture called LiDARNet is presented to localise surface returns from LiDAR waveforms with high throughput. This single data driven processing approach can unify a wide range of scenarios, making use of a training-by-simulation methodology. This augments real datasets with challenging simulated conditions such as multiple returns and high noise variance, while enabling rapid prototyping of fast data driven processing approaches for arrayed LiDAR systems. Both approaches are fast and practical processing methodologies for arrayed LiDAR systems. These retrieve depth information with excellent depth resolution for wide operating ranges, and are demonstrated on real and simulated data. LiDARNet is a rapid approach to determine surface locations from LiDAR waveforms for efficient point cloud generation, while block sparse depth reconstruction is an efficient method to facilitate high-resolution depth maps at high frame rates with reduced power and memory requirements.Engineering and Physical Sciences Research Council (EPSRC

A Nonparametric Approach to Segmentation of Ladar Images

The advent of advanced laser radar (ladar) systems that record full-waveform signal data has inspired numerous inquisitions which aspire to extract additional, previously unavailable, information about the illuminated scene from the collected data. The quality of the information, however, is often related to the limitations of the ladar camera used to collect the data. This research project uses full-waveform analysis of ladar signals, and basic principles of optics, to propose a new formulation for an accepted signal model. A new waveform model taking into account backscatter reflectance is the key to overcoming specific deficiencies of the ladar camera at hand, namely the ability to discern pulse-spreading effects of elongated targets. A concert of non-parametric statistics and familiar image processing methods are used to calculate the orientation angle of the illuminated objects, and the deficiency of the hardware is circumvented. Segmentation of the various ladar images performed as part of the angle estimation, and this is shown to be a new and effective strategy for analyzing the output of the AFIT ladar camera

Improved time-frequency de-noising of acoustic signals for underwater detection system

The capability to communicate and perform target localization efficiently in underwater environment is important in many applications. Sound waves are more suitable for underwater communication and target localization because attenuation in water is high for electromagnetic waves. Sound waves are subjected to underwater acoustic noise (UWAN), which is either man-made or natural. Optimum signal detection in UWAN can be achieved with the knowledge of noise statistics. The assumption of Additive White Gaussian noise (AWGN) allows the use of linear correlation (LC) detector. However, the non-Gaussian nature of UWAN results in the poor performance of such detector. This research presents an empirical model of the characteristics of UWAN in shallow waters. Data was measured in Tanjung Balau, Johor, Malaysia on 5 November 2013 and the analysis results showed that the UWAN has a non-Gaussian distribution with characteristics similar to 1/f noise. A complete detection system based on the noise models consisting of a broadband hydrophone, time-frequency distribution, de-noising method, and detection is proposed. In this research, S-transform and wavelet transform were used to generate the time-frequency representation before soft thresholding with modified universal threshold estimation was applied. A Gaussian noise injection detector (GNID) was used to overcome the problem of non-Gaussianity of the UWAN, and its performance was compared with other nonlinear detectors, such as locally optimal (LO) detector, sign correlation (SC) detector, and more conventionally matched filter (MF) detector. This system was evaluated on two types of signals, namely fixed-frequency and linear frequency modulated signals. For de-noising purposes, the S-transform outperformed the wavelet transform in terms of signal-to-noise ratio and root-mean-square error at 4 dB and 3 dB, respectively. The performance of the detectors was evaluated based on the energy-to-noise ratio (ENR) to achieve detection probability of 90% and a false alarm probability of 0.01. Thus, the ENR of the GNID using S-transform denoising, LO detector, SC detector, and MF detector were 8.89 dB, 10.66 dB, 12.7dB, and 12.5 dB, respectively, for the time-varying signal. Among the four detectors, the proposed GNID achieved the best performance, whereas the LC detector showed the weakest performance in the presence of UWAN

Sub-canopy terrain modelling for archaeological prospecting in forested areas through multiple-echo discrete-pulse laser ranging: a case study from Chopwell Wood, Tyne & Wear

Airborne Light Detection and Ranging (LiDAR) technology is assessed for its effectiveness as a tool for measuring terrain under forest canopy. To evaluate the capability of multiple-return discrete-pulse airborne laser ranging for detecting and resolving sub-canopy archaeological features, LiDAR data were collected from a helicopter over a forest near Gateshead in July 2009. Coal mining and timber felling have characterised Chopwell Wood, a mixed coniferous and deciduous woodland of 360 hectares, since the Industrial Revolution. The state-of-the-art Optech ALTM 3100EA LiDAR system operated at 70,000 pulses per second and raw data were acquired over the study area at a point density of over 30 points per square metre. Reference terrain elevation data were acquired on-site to ‘train’ the progressive densification filtering algorithm of Axelsson (1999; 2000) to identify laser reflections from the terrain surface. A number of sites, offering a variety of tree species, variable terrain roughness & gradient and understorey vegetation cover of varying density, were identified in the wood to assess the accuracy of filtered LiDAR terrain data. Results showed that the laser scanner over-estimated the elevation of reference terrain data by 13±17 cm under deciduous canopy and 23±18 cm under coniferous canopy. Terrain point density was calculated as 4.1 and 2.4 points per square metre under deciduous and coniferous forest, respectively. Classified terrain points were modelled with the kriging interpolation technique and topographic archaeological features, such as coal tubways (transportation routes) and areas of subsidence over relic mine shafts, were identified in digital terrain models (DTMs) using advanced exaggeration and artificial illumination techniques. Airborne LiDAR is capable of recording high quality terrain data even under the most dense forest canopy, but the accuracy and density of terrain data are controlled by a combination of tree species, forest management practices and understorey vegetation