Maritime Radar Target Detection Using Time Frequency Analysis

Small target detection in sea clutter remains a challenging problem for radar operators as the backscatter from the sea-surface is complex, involving both time and range varying Doppler spectra with strong breaking waves which can last for seconds and resemble targets. The goal of this thesis is to investigate two different time frequency wavelet transforms to filter the sea clutter and improve target detection performance. The first technique looks at an application of stationary wavelet transforms (SWT) to improve target detection. The SWT decomposes a signal into different components (or sub-bands) which contain different characteristics of the interference (clutter + noise) and target. A method of selecting the sub-band with the most information about the target is then presented using an ‘entropy’ based metric. To validate the SWT detection scheme, real radar data recorded from both an airborne and a ground based radar systems are analysed. A Monte-Carlo simulation using a cell averaging constant false alarm rate detector is implemented to demonstrate and quantify the improvement of the new scheme against unfiltered data. The second technique utilises a sparse signal separation method known as basis pursuit denoising (BPD). Two main factors contribute to the quality of the separation between the target and sea-clutter: choice of dictionary that promotes sparsity, and the regularisation (or penalty) parameter in the BPD formulation. In this implementation, a tuned Q-factor wavelet transform (TQWT) is used for the dictionary with parameters chosen to match the desired target velocity. An adaptive method is then developed to improve the separation of targets from sea-clutter based on a smoothed estimate of the sea clutter standard deviation across range. A new detection scheme is then developed and the detection improvement is demonstrated using a Monte-Carlo simulation.Thesis (Ph.D.) -- University of Adelaide, School of Electrical & Electronic Engineering, 201

Scattering Models in Remote Sensing: Application to SAR Despeckling and Sea Target Detection from GNSS-R Imagery

Imaging sensors are an essential tool for the observation of the Earth’ surface and the study of other celestial bodies. The capability to produce radar images of the illuminated surface is strictly related with the complex phenomenology of the radiation-matter interaction. The electromagnetic scattering theory is a well-established and well-assessed topic in electromagnetics. However, its usage in the remote sensing field is not adequately investigated and studied. This Ph.D. Thesis addresses the exploitation of electromagnetic scattering models suitable for natural surfaces in two applications of remotely sensed data, namely despeckling of synthetic aperture radar (SAR) imagery, and the detection of sea targets in delay-Doppler Maps (DDM) acquired from spaceborne Global Navigation Satellite System-Reflectometry (GNSS-R). The first issue was addressed by conceiving, developing, implementing and validating two despeckling algorithms for SAR images. The developed algorithms introduce some a priori information about the electromagnetic behavior of the resolution cell in the despeckling chain and were conceived as a scattering-based version of pre-existing filters, namely the Probabilistic Patch-Based (PPB) and SAR-Block-Matching 3-D (SARBM3D) algorithms. The scattering behavior of the sensed surface is modeled assuming a fractal surface roughness and using the Small Perturbation Method (SPM) to describe the radar cross section (RCS) of the surface. Performances of the proposed algorithms have been assessed using both canonical test (simulated) and actual images acquired from the COSMO\SkyMed constellation. The robustness of the proposed filters against different error sources, such as the scattering behavior of the surface, surface parameters, Digital Elevation Model (DEM) resolution and the SAR image-DEM coregistration step, has been evaluated via an experimental sensitivity analysis. The problem of detecting sea targets from GNSS-R data in near real-time has been investigated by analyzing the revisit time achieved by constellations of GNSS-R instruments. A statistical analysis of the global revisit time has been performed by means of mission simulation, in which three realistic scenario have been defined. Time requirements for near real-time ship detection purposes are shown to be fulfilled in multi-GNSS constellation scenarios. A four-step sea target has been developed. The detector is a Constant False Alarm Rate (CFAR) algorithm and is based on the suppression of the sea clutter contribution, modeled via the Geometrical Optics (GO) approach. Performance assessment is performed by deriving the Receiver Operating Curves (ROC) of the detector. Finally, the proposed sea target detection algorithm has been tested using actual UK TechDemoSat-1 data

Sonar image interpretation for sub-sea operations

Mine Counter-Measure (MCM) missions are conducted to neutralise underwater explosives. Automatic Target Recognition (ATR) assists operators by increasing the speed and accuracy of data review. ATR embedded on vehicles enables adaptive missions which increase the speed of data acquisition. This thesis addresses three challenges; the speed of data processing, robustness of ATR to environmental conditions and the large quantities of data required to train an algorithm. The main contribution of this thesis is a novel ATR algorithm. The algorithm uses features derived from the projection of 3D boxes to produce a set of 2D templates. The template responses are independent of grazing angle, range and target orientation. Integer skewed integral images, are derived to accelerate the calculation of the template responses. The algorithm is compared to the Haar cascade algorithm. For a single model of sonar and cylindrical targets the algorithm reduces the Probability of False Alarm (PFA) by 80% at a Probability of Detection (PD) of 85%. The algorithm is trained on target data from another model of sonar. The PD is only 6% lower even though no representative target data was used for training. The second major contribution is an adaptive ATR algorithm that uses local sea-floor characteristics to address the problem of ATR robustness with respect to the local environment. A dual-tree wavelet decomposition of the sea-floor and an Markov Random Field (MRF) based graph-cut algorithm is used to segment the terrain. A Neural Network (NN) is then trained to filter ATR results based on the local sea-floor context. It is shown, for the Haar Cascade algorithm, that the PFA can be reduced by 70% at a PD of 85%. Speed of data processing is addressed using novel pre-processing techniques. The standard three class MRF, for sonar image segmentation, is formulated using graph-cuts. Consequently, a 1.2 million pixel image is segmented in 1.2 seconds. Additionally, local estimation of class models is introduced to remove range dependent segmentation quality. Finally, an A* graph search is developed to remove the surface return, a line of saturated pixels often detected as false alarms by ATR. The A* search identifies the surface return in 199 of 220 images tested with a runtime of 2.1 seconds. The algorithm is robust to the presence of ripples and rocks

Advanced Geoscience Remote Sensing

Nowadays, advanced remote sensing technology plays tremendous roles to build a quantitative and comprehensive understanding of how the Earth system operates. The advanced remote sensing technology is also used widely to monitor and survey the natural disasters and man-made pollution. Besides, telecommunication is considered as precise advanced remote sensing technology tool. Indeed precise usages of remote sensing and telecommunication without a comprehensive understanding of mathematics and physics. This book has three parts (i) microwave remote sensing applications, (ii) nuclear, geophysics and telecommunication; and (iii) environment remote sensing investigations

Sonar systems for object recognition

The deep sea exploration and exploitation is one of the biggest challenges of the next century. Military, oil & gas, o shore wind farming, underwater mining, oceanography are some of the actors interested in this eld. The engineering and technical challenges to perform any tasks underwater are great but the most crucial element in any underwater systems has to be the sensors. In air numerous sensor systems have been developed: optic cameras, laser scanner or radar systems. Unfortunately electro magnetic waves propagate poorly in water, therefore acoustic sensors are a much preferred tool then optical ones. This thesis is dedicated to the study of the present and the future of acoustic sensors for detection, identi cation or survey. We will explore several sonar con gurations and designs and their corresponding models for target scattering. We will show that object echoes can contain essential information concerning its structure and/or composition

Study of the speckle noise effects over the eigen decomposition of polarimetric SAR data: a review

This paper is focused on considering the effects of speckle noise on the eigen decomposition of the co- herency matrix. Based on a perturbation analysis of the matrix, it is possible to obtain an analytical expression for the mean value of the eigenvalues and the eigenvectors, as well as for the Entropy, the Anisotroopy and the dif- ferent a angles. The analytical expressions are compared against simulated polarimetric SAR data, demonstrating the correctness of the different expressions.Peer ReviewedPostprint (published version

Survey on deep learning based computer vision for sonar imagery

Research on the automatic analysis of sonar images has focused on classical, i.e. non deep learning based, approaches for a long time. Over the past 15 years, however, the application of deep learning in this research field has constantly grown. This paper gives a broad overview of past and current research involving deep learning for feature extraction, classification, detection and segmentation of sidescan and synthetic aperture sonar imagery. Most research in this field has been directed towards the investigation of convolutional neural networks (CNN) for feature extraction and classification tasks, with the result that even small CNNs with up to four layers outperform conventional methods. The purpose of this work is twofold. On one hand, due to the quick development of deep learning it serves as an introduction for researchers, either just starting their work in this specific field or working on classical methods for the past years, and helps them to learn about the recent achievements. On the other hand, our main goal is to guide further research in this field by identifying main research gaps to bridge. We propose to leverage the research in this field by combining available data into an open source dataset as well as carrying out comparative studies on developed deep learning methods.Article number 10515711

Statistical Analysis of Coherent Monostatic and Bistatic Radar Sea Clutter

Radar sea clutter analysis has been an important area of radar research for many years. Very limited research has been carried out on coherent monostatic sea clutter analysis and even less on bistatic sea clutter. This has left a significant gap in the global scientific knowledge within this area. This thesis describes research carried out to analyse, quantify and model coherent sea clutter statistics from multiple radar sources. The ultimate goal of the research is to improve maritime radars' ability to compensate for clutter and achieve effective detection of targets on or over the sea surface. The first analyses used monostatic data gathered during the fight trials of the Thales Searchwater 2000 AEW radar. A further sea clutter trials database from CSIR was then used to investigate the variation of clutter statistics with look angle and grazing angle. Finally simultaneous monostatic and bistatic sea clutter data recorded in South Africa using the S-band UCL radar system NetRAD were analysed. No simultaneous monostatic and bistatic coherent analysis has ever been reported before in the open literature. The datasets recorded included multiple bistatic angles at both horizontal and vertical polarisations. Throughout the analysis real data have been compared to accepted theoretic models of sea clutter. An additional metric of comparison was investigated relating to the area of information theoretic techniques. Information theory is a significant subject area, and some concepts from it have been applied in this research. In summary this research has produced quantifiable and novel results on the characteristics of sea clutter statistics as a function of Doppler. Analysis has been carried out on a wide range of monostatic and bistatic data. The results of this research will be extremely valuable in developing sea clutter suppression algorithms and thus improving detection performance in future maritime radar designs

Antenna Design and Signal Processing for Mechanical FMCW Coastal Surveillance Radar Systems

The demand for highly sophisticated radar systems to be implemented along the coastal waters of the Strait of Malacca for the surveillance and tracking of vessels that travels through this narrow Strait has risen rapidly over the last few decades. Along with the technological advancements in radar systems, the increased demand is in response to the success that radars have introduced in significantly reducing some of the biggest problems contributed from security and weather condition perspectives. The existing radars implemented by Indonesian authorities to fulfil the surveillance requirements of the Strait mainly comprises of electronic scanning systems. Nonetheless, several allocated radar sites along the Strait inevitably lacks the basic infrastructure and accessibility required to install more complex systems (i.e. electronic scanning radars) that are prone to higher maintenance. This have favoured authorities to opt for the use of mechanical scanning radars, which, unlike phased array systems, are simpler, less complex, and significantly more affordable systems. The resolution performance alongside the accuracy of the positional information of a detected target provided by the radar is highly dependent on the angular resolution of the antenna. For mechanically scanning radars, a highly directional beam is commonly produced by employing conventional parabolic reflector antennas. Reflector antennas are a popular choice for surveillance and tracking applications as it is known for producing beams with very high gain and narrow beamwidths in both planes. However, this is usually achieved by employing an undesirably large reflector, which tends to significantly increase the cost of the radar system, and most importantly its size and weight, which is critical especially for applications that highly values compact and mobile systems. To overcome this issue faced in many similar situations, several angle measurement techniques have been introduced to improve the detection performance of radars without increasing the physical size of the antenna, with the most notable and highly successful one being monopulse technique. This research project proposes a model of a reflector antenna design for a mechanical scanning radar that is suitable to provide coastal surveillance and monitoring of vessels and low flying objects. The objective of the antenna design is to significantly improve the resolution and accuracy for target detection without utilizing a larger dish, but instead through the implementation of amplitude monopulse and a novel post-detection processing technique that allows for the design of a more compact and cost-effective antenna system. The proposed reflector antenna system that achieves these objectives comprises of a dual-horn feed and a vertically truncated reflector of an optimal aperture shape and dimension to create a pair of simultaneous overlapping fan-shaped beams that is narrow in azimuth and several times wider in elevation. The design of the monopulse feed is modelled and simulated on a CEM tool (CST) to evaluate the monopulse patterns produced in the horizontal plane of the radar, which are optimized towards the requirements of the application. In addition, this thesis introduces a novel post-detection signal processing technique that uses priori information of the antenna response pattern to offer a substantial enhancement on the resolution and clutter resilience of any new or existing radar antenna system at a very low cost, which is especially likely to make a significant contribution to the safety at sea. In addition to limiting the size of the antenna as much as possible while fulfilling the requirements of the application in hand, employing the proposed antenna system along the coastal shores of the Strait of Malacca would largely prioritize on keeping the effective weight and cost of the antenna to a minimum. This is achieved by manufacturing the reflector through 3D printing technology and coating its surface with a copper compound spray to achieve the properties of a metal. A prototype of the reflector antenna is manufactured accordingly using the proposed method of fabrication in order to provide an assessment of the practical antenna performance. The radiation properties of the antenna pattern are measured in an outdoor range test facility, and the measurement results allows for an accurate validation of the electromagnetic (EM) simulations of the corresponding antenna design obtained on CST. Finally, in order to assess the enhancing effect of the proposed signal processing technique on the resolution performance of an existing antenna, MATLAB based codes have been developed to demonstrate the technique on several simulated far-field patterns of antennas with common line source distributions before it is applied on the sum pattern output of the monopulse antenna designed in this project