In this thesis, a target detection technique using a rotational invariant wavelet-based scheme is presented. The technique is evaluated on Synthetic Aperture Rader (SAR) imaging and compared with a previously developed fractal-based technique, namely the extended fractal (EF) model. Both techniques attempt to exploit the textural characteristics of SAR imagery. Recently, a wavelet-based fractal feature set, similar to the proposed one, was compared with the EF feature for a general texture classification problem. The wavelet-based technique yielded a lower classification error than EF, which motivated the comparison between the two techniques presented in this paper. Experimental results show that the proposed techniques feature map provides a lower false alarm rate than the previously developed method.

This thesis describes the work carried out in the development and testing of parametric sonar systems for application in the fields of seabed sediment characterisation and classification, and the detection of seabed embedded objects. Parametric sonar systems offer a number of advantages over conventional sonar systems. This is especially true of the conflicting requirements of both seabed delineation and penetration required for a practical sub-seabed profiling system. Echoes from sub-bottom layers vary in strength dependent on both the boundary acoustic reflectivity and the absorption characteristics of the layer above. Absorption effects are usually frequency dependent, allowing better penetration to lower frequency signals.

Previous research has shown that when observers hold a tool, they experience action-oriented visual biases in the area around this tool that are similar to visual biases that exist around the hands. Some researchers have theorized this effect is due to the tool being incorporated into the body schema following active tool use, while others argue that this effect may simply be due to the tool’s visual salience. The goal of the present study was to test these competing explanations of near-tool visual biases. In the first experiment, participants completed a target detection task under one of three conditions: 1) while holding a small rake next to one side of a monitor, preceded by an active object retrieval task; 2) while holding a rake next to a monitor, preceded by a passive looking task; or 3) with the rake placed next to a monitor by a researcher, preceded by a passive looking task. Participants detected targets near the rake faster than targets far from the rake in the first two conditions, but no target detection facilitation was seen in the third condition. Participants in Experiment 2 held a small rake next to a monitor after an active object retrieval task, but a paper shield blocked the tool from view, eliminating its visual salience. While the pattern of near-tool target detection facilitation did not significantly differ between shielded and unshielded conditions, the shield did reduce the magnitude of the near-tool effect. Taken together, these results suggest that near-tool effects cannot be driven by the visual presence of a tool alone, but they also indicate that a period of active use may not be necessary to introduce visual biases near tools.

This dissertation develops new approaches for improving the performance of hyperspectral target detection. Different aspects of hyperspectral target detection are reviewed and studied to effectively distinguish target features from background interference. The contributions of this dissertation are detailed as follows. 1) Propose an adaptive background characterization method that integrates region segmentation with target detection. In the experiments, not only unstructured matched filter based detectors are considered, but also two hybrid detectors combining fully constrained least squared abundance estimation with statistic test (i.e., adaptive matched subspace detector and adaptive cosine/coherent detector) are investigated. The experimental results demonstrate that using local adaptive background characterization, background clutters can be better suppressed than the original algorithms with global characterization. 2) Propose a new approach to estimate abundance fractions based on the linear spectral mixture model for hybrid structured and unstructured detectors. The new approach utilizes the sparseness constraint to estimate abundance fractions, and achieves better performance than the popular non-negative and fully constrained methods in the situations when background endmember spectra are not accurately acquired or estimated, which is very common in practical applications. To improve the dictionary incoherence, the use of band selection is proposed to improve the sparseness constrained linear unmixing. 3) Propose random projection based dimensionality reduction and decision fusion approach for detection improvement. Such a data independent dimensionality reduction process has very low computational cost, and it is capable of preserving the original data structure. Target detection can be robustly improved by decision fusion of multiple runs of random projection. A graphics processing unit (GPU) parallel implementation scheme is developed to expedite the overall process. 4) Propose nonlinear dimensionality reduction approaches for target detection. Auto-associative neural network-based Nonlinear Principal Component Analysis (NLPCA) and Kernel Principal Component Analysis (KPCA) are applied to the original data to extract principal components as features for target detection. The results show that NLPCA and KPCA can efficiently suppress trivial spectral variations, and perform better than the traditional linear version of PCA in target detection. Their performance may be even better than the directly kernelized detectors.

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This research is to study the fundamental theory of Synthetic Aperture Sonar (SAS) through numerical simulation and experimental analysis. The basic principle of SAS is to enhance the capability of spatial resolution by moving the transducer element to increase aperture so that it achieves a better resolution. The factors affecting the capability of resolution include the actual size of the transducers, frequency and its bandwidth, pulse length, and moving speeds. The effects of various factors on the resolution were examined through numerical simulation. The results have shown that the smaller the true size of the transducer, the better the resolution. Moreover, when the bandwidth is increased, the resolution also increases. The SAS is sensitive to the speed of movement due to the fact that data acquisition may be limited, therefore the speed can not be too high, e.g., less than 1.5 m/s. The experiment was carried out in a water tank of size 4 m x 3.5 m x 2 m. The transducers of AST MK VI 192 kHz were employed to transmit and receive signals. Copper spheres of various sizes (3 cm, 6 cm, 8 cm diameter) were used as targets. The data were obtained and analyzed, and the results have shown that the resolution may be achieved by SAS analysis, establishing the fundamental principle and offering opportunity for future study.

The exploration of oceans and sea beds is being made increasingly possible through the development of Autonomous Underwater Vehicles (AUVs). This is an activity that concerns the marine community and it must confront the existence of notable challenges. These include, for example, mining minerals, inspecting pipeline and mapping oceans, sampling in contaminated water. Also, there has been another growing interest for security forces in precluding submarines or intruders from a beach or harbour entrance as well as hunting shallow water mines. However, an automatic detecting and tracking system is the first and foremost element for an AUV or an aqueous surveillance network. Since accurate surrounding information is essential in order to manoeuvre the AUV efficiently and economically, while corrupt information can jeopardize an entire mission. By extracting the space information form sensors, an AUV can achieve the localisation and mapping which are currently two primary concerns in the robotics research. Meanwhile, such information will provide a fundament of protection for surface vessels or troops, harbour infrastructure and oil plant against the enemy and terrorism. Acoustic sensors are commonly used to detect and position underwater obstacles, suspicious objects or to map the surroundings because sound waves can propagate more appreciable distances than electromagnetic and optical energy in the water. The measurements from these sensors, however, are always bound up with noises and errors. Various underwater activities may further pollute sound signals and then threaten the AUV navigation process. To simplify the detection procedure, some researchers make use of acoustic beacons or apparent obstructions (such as rocks, concrete walls) because they have distinctive characteristics. Point or line features are extracted from the acoustic signals or images for localization and mapping purposes. The long propagation range of sound waves can present new problems when acoustic sensors operate in confined environments, such as water tanks, rivers and harbours. The multiple reflections will be recorded by the sensor and result in false alarms. Furthermore, with advances in manufacturing techniques, the downsizing in marine explosive ordnances is progressing significantly, making it more difficult to discriminate between surface reflections and explosive ordnances. Finally, under the consideration of cost effectiveness, a mechanically scanned sonar has been introduced for the AUV in this research. However, the sensor beam cannot cover a large region simultaneously and a moving object may be distorted in the acoustic image because of the relatively low scanning speed. Due to such distortions in the data flows, objects may be indistinguishable from random noise or reverberation in acoustic images. The research presented here addresses the afore-mentioned problems relating to the theme of automatic detection from acoustic images. It is concerned with the detection and tracking of small underwater objects in order to protect autonomous underwater vehicles using sonar (SOund Navigation and Range). In the present study, these vehicles operated in laboratory water tanks or natural river environments. This research made use of self provided analytical studies that differentiated between reverberation and real object echoes. Detections were achieved automatically by using signal and image processing techniques. This research consists of three important and linked strategies. Firstly, a simple and fast reverberation suppression filter was provided, based on the understanding of the mechanism of the sonar sensor. Secondly, a robust detection system was developed to perceive small suspended obstacles in the water. Thirdly and finally, arc features were successfully extracted from the acoustic images and mathematical maps were generated from those features. The majority of experiments were derived from the elliptical water tank and the River Torrens, Adelaide, South Australia. For this project, a sequence of sonar images was taken from the same sonar location in the elliptical water tank. Further, a sequence of sonar images was taken from a sequence of sonar locations in the natural river. They provided different data sets for the assessment and evaluation of self developed algorithms. Results shown in this thesis confirm the favourable outcomes of the investigation and applied methodology. / http://proxy.library.adelaide.edu.au/login?url= http://library.adelaide.edu.au/cgi-bin/Pwebrecon.cgi?BBID=1454839 / Thesis (M.Eng.Sc.) -- University of Adelaide, School of Mechanical Engineering, 2010

abstract: Constant false alarm rate is one of the essential algorithms in a RADAR detection system. It allows the RADAR system to dynamically set thresholds based on the data power level to distinguish targets with interfering noise and clutters. To have a better acknowledgment of constant false alarm rate approaches performance, three clutter models, Gamma, Weibull, and Log-normal, have been introduced to evaluate the detection's capability of each constant false alarm rate algorithm. The order statistical constant false alarm rate approach outperforms other conventional constant false alarm rate methods, especially in clutter evolved environments. However, this method requires high power consumption due to repeat sorting. In the automotive RADAR system, the computational complexity of algorithms is essential because this system is in real-time. Therefore, the algorithms must be fast and efficient to ensure low power consumption and processing time. The reduced computational complexity implementations of cell-averaging and order statistic constant false alarm rate were explored. Their big O and processing time has been reduced. / Dissertation/Thesis / Masters Thesis Electrical Engineering 2020

Le titre de cette thèse de doctorat est formé de trois mots clés: parcimonie, image hyperspectrale, et détection de cibles. La parcimonie signifie généralement « petit en nombre ou quantité, souvent répartie sur une grande zone ». Une image hyperspectrale est constituée d'une série d'images de la même scène spatiale, mais prises dans plusieurs dizaines de longueurs d'onde contiguës et très étroites, qui correspondent à autant de "couleurs". Lorsque la dimension spectrale est très grande, la détection de cibles devient délicate et caractérise une des applications les plus importantes pour les images hyperspectrales. Le but principal de cette thèse de doctorat est de répondre à la question « Comment et Pourquoi la parcimonie peut-elle être exploitée pour détecter de cibles dans les images hyperspectrales ? ». La réponse à cette question nous a permis de développer des méthodes de détection de cibles prenant en compte l'hétérogénéité de l'environnement, le fait que les objets d'intérêt sont situés dans des parties relativement réduites de l'image observée et enfin que l'estimation de la matrice de covariance d'un pixel d'une image hyperspectrale peut être compliquée car cette matrice appartient à un espace de grande dimension. Les méthodes proposées sont évaluées sur des données synthétiques ainsi que réelles, dont les résultats démontrent leur efficacité pour la détection de cibles dans les images hyperspectrales. / The title of this PhD thesis is formed by three keywords: sparsity, hyperspectral image, and target detection. Sparsity is a word that is used everywhere and in everyday life. It generally means « small in number or amount, often spread over a large area ». A hyperspectral image is a three dimensional data cube consisting of a series of images of the same spatial scene in a contiguous and multiple narrow spectral wavelength (color) bands. According to the high spectral dimensionality, target detection is not surprisingly one of the most important applications in hyperspectral imagery. The main objective of this PhD thesis is to answer the question « How and Why can sparsity be exploited for hyperspectral target detection? ». Answering this question has allowed us to develop different target detection methods that mainly take into consideration the heterogeneity of the environment, the fact that the total image area of all the targets is very small relative to the whole image, and the estimation challenge of the covariance matrix (surrounding the test pixel) in large dimensions. The proposed mehods are evaluated on both synthetic and real experiments, the results of which demonstrate their effectiveness for hyperspectral target detection.

In recent years, ultra-wideband (UWB) radars are gaining popularity in the radar field mainly inindustrial and commercial areas. The UWB radar has the potential of dramatically improving thecontrol and surveillance of industrial processes in confined areas.The report provides an introduction to radar systems and detail working principle of M-sequenceUWB radar and methodology of how detection of targets is carried out. First two chapters of thereport describes the working of radar systems and M-sequence radar whereas in the later part ofthe report, different detection algorithms are discussed which has been implemented in thepresent radar simulations. In conventional radar the main detection algorithm is matched filteringwhere the transmitted signal is correlated with the received signal. Whereas UWB signal is nonsinusoidalthat is vulnerable to change in its shape during entire radar operation. This is thereason, the traditional signal processing methods like matched filtering or correlation process arenot advisable for UWB signals. Therefore, a different detection scheme known as Inter-periodcorrelation process (IPCP) has been studied.IPCP technique had been implemented and a comparison was made with the conventional targetdetection algorithm. On the basis of comparison made in this project, it has been observed thatthe conventional target detection methods are not effective in case of M-sequence UWB radar.The simulation results shows that by implementing IPCP method, performance close to 8-bitADC can be achievable with 1-bit comparator, also with IPCP implementation system resolutioncan be enhance effectively.Main focus was to analyze how close the system can detect two targets, therefore in all themeasurements i.e. practical and simulated measurements, only two targets were used.