A False-alarm-controllable Modified AdaBoost Wake Detection Method Using SAR Images

A false-alarm-controllable modified AdaBoost-based method is proposed for detecting ship wake from sea clutter in synthetic aperture radar (SAR) images. It reformulates the wake detection problem as a binary classification task in the multifeature space. The update strategy of the sample weights in the original AdaBoost is modified for wake detection. First, a detection result confidence factor is designed to deal with class imbalance between sea clutter and ship wake; then, the AdaBoost is further modified as a false alarm rate (FAR) controllable detector by introducing penalty parameters to adjust weights update strategies for the sea clutter. Meanwhile, the multifeature space is spanned by a novel frequency peak height ratio (FPHA) feature and four salient features. FPHA is proposed to enhance the separation between the wake and sea clutter, which is computed from the amplitude spectrum peak of the image after the Fourier transform. Experimental results show that the proposed detector can tackle the imbalanced data problem and flexibly control FAR by adjusting penalty parameters. Moreover, improved detection probability is also achieved compared with existing methods

DragonflEYE: a passive approach to aerial collision sensing

"This dissertation describes the design, development and test of a passive wide-field optical aircraft collision sensing instrument titled 'DragonflEYE'. Such a ""sense-and-avoid"" instrument is desired for autonomous unmanned aerial systems operating in civilian airspace. The instrument was configured as a network of smart camera nodes and implemented using commercial, off-the-shelf components. An end-to-end imaging train model was developed and important figures of merit were derived. Transfer functions arising from intermediate mediums were discussed and their impact assessed. Multiple prototypes were developed. The expected performance of the instrument was iteratively evaluated on the prototypes, beginning with modeling activities followed by laboratory tests, ground tests and flight tests. A prototype was mounted on a Bell 205 helicopter for flight tests, with a Bell 206 helicopter acting as the target. Raw imagery was recorded alongside ancillary aircraft data, and stored for the offline assessment of performance. The ""range at first detection"" (R0), is presented as a robust measure of sensor performance, based on a suitably defined signal-to-noise ratio. The analysis treats target radiance fluctuations, ground clutter, atmospheric effects, platform motion and random noise elements. Under the measurement conditions, R0 exceeded flight crew acquisition ranges. Secondary figures of merit are also discussed, including time to impact, target size and growth, and the impact of resolution on detection range. The hardware was structured to facilitate a real-time hierarchical image-processing pipeline, with selected image processing techniques introduced. In particular, the height of an observed event above the horizon compensates for angular motion of the helicopter platform.

Polarization techniques for mitigation of low grazing angle sea clutter

Maritime surveillance radars are critical in commerce, transportation, navigation, and defense. However, the sea environment is perhaps the most challenging of natural radar backdrops because maritime radars must contend with electromagnetic backscatter from the sea surface, or sea clutter. Sea clutter poses unique challenges in very low grazing angle geometries, where typical statistical assumptions regarding sea clutter backscatter do not hold. As a result, traditional constant false alarm rate (CFAR) detection schemes may yield a large number of false alarms while objects of interest may be challenging to detect. Solutions posed in the literature to date have been either computationally impractical or lacked robustness. This dissertation explores whether fully polarimetric radar offers a means of enhancing detection performance in low grazing angle sea clutter. To this end, MIT Lincoln Laboratory funded an experimental data collection using a fully polarimetric X-band radar assembled largely from commercial off-the-shelf components. The Point de Chene Dataset, collected on the Atlantic coast of Massachusetts’ Cape Ann in October 2015, comprises multiple sea states, bandwidths, and various objects of opportunity. The dataset also comprises three different polarimetric transmit schemes. In addition to discussing the radar, the dataset, and associated post-processing, this dissertation presents a derivation showing that an established multiple input, multiple output radar technique provides a novel means of simultaneous polarimetric scattering matrix measurement. A novel scheme for polarimetric radar calibration using a single active calibration target is also presented. Subsequent research leveraged this dataset to develop Polarimetric Co-location Layering (PCL), a practical algorithm for mitigation of low grazing angle sea clutter, which is the most significant contribution of this dissertation. PCL routinely achieves a significant reduction in the standard CFAR false alarm rate while maintaining detections on objects of interest. Moreover, PCL is elegant: It exploits fundamental characteristics of both sea clutter and object returns to determine which CFAR detections are due to sea clutter. We demonstrate that PCL is robust across a range of bandwidths, pulse repetition frequencies, and object types. Finally, we show that PCL integrates in parallel into the standard radar signal processing chain without incurring a computational time penalty

Quad polarimetric synthetic aperture radar analysis of icebergs in Greenland and Svalbard

Polarimetric synthetic aperture radar (PolSAR) has been widely used in ocean and cryospheric applications. This is because, PolSAR can be used in all-day operations and in areas of cloud cover, and therefore can provide valuable large-scale monitoring in polar regions, which is very helpful to shipping and offshore maritime operations. In the last decades, attention has turned to the potential of PolSAR to detect icebergs in the Arctic since they are a major hazard to vessels. However, there is a substantial lack of literature exploring the potentialities of PolSAR and the understanding of iceberg scattering mechanisms. Additionally, it is not known if high resolution PolSAR can be used to detect icebergs smaller than 120 metres. This thesis aims to improve the knowledge of the use of PolSAR scattering mechanisms of icebergs, and detection of small icebergs. First, an introduction to PolSAR is outlined in chapter two, and monitoring of icebergs is presented in chapter three. The first data chapter (Chapter 4) is focused on developing a multi-scale analysis of icebergs using parameters from the Cloude-Pottier and the Yamaguchi decompositions, the polarimetric span and the Pauli scattering vector. This method is carried out using ALOS-2 PALSAR quad polarimetric L-band SAR on icebergs in Greenland. This approach outlines the good potential for using PolSAR for future iceberg classification. One of the main important outcomes is that icebergs are composed by a combination of single targets, which therefore may require a more complex way of processing SAR data to properly extract physical information. In chapter five, the problem of detecting icebergs is addressed by introducing six state-of-the-art detectors previously applied to vessel monitoring. These detectors are the Dual Intensity Polarisation Ratio Anomaly Detector (iDPolRAD), Polarimetric Notch Filter (PNF), Polarimetric Matched Filter (PMF), reflection symmetry (sym), Optimal Polarimetric Detector (OPD) and the Polarimetric Whitening Filter (PWF). Cloude-Pottier entropy, and first and third eigenvalues (eig1 and eig3) of the coherency matrix are also utilised as parameters for comparison. This approach uses the same ALOS-2 dataset, but also evaluates detection performance in two scenarios: icebergs in open ocean, and in sea ice. Polarimetric modes (quad-pol, dual-pol, and single intensities) are also considered for comparison. Currently it is very difficult to detect icebergs less than 120 metres in length using this approach, due to the scattering mechanisms of icebergs and sea ice being very similar. However, it was possible to obtain detection performances of the OPD and PWF, which both showed a Probability of Detection (PF) of 0.99 when the Probability of False Alarms (PF) was set to 10-5 in open ocean. Similarly, in dual pol images, the PWF gave the best performance with a PD of 0.90. Results in sea ice found eig3 to be the best detector with a PD of 0.90 while in dual-pol mode, iDPolRAD gave a PD of 0.978. Single intensity detector performance found the HV channel gave the best detection with a PD of 0.99 in open ocean and 0.87 in sea ice. In the previous two approaches, only satellite data is used. However, in chapter six, data from a ground-based Ku-band Gamma Portable Radio Interferometer (GPRI) instrument is introduced, providing images that are synchronised with the satellite acquisitions. In this approach, the same six detectors are applied to three multitemporal RADARSAT-2 quad pol C-band SAR images on icebergs in Kongsfjorden, Svalbard to evaluate the detection performance within a changing fjord environment. As before, we also make use of Cloude-Pottier entropy, eig1 and eig3. Finally, we evaluate the target-to-clutter ratio (TCR) of the icebergs and check for correlation between the backscattering coefficients and the iceberg dimension. The results obtained from this thesis present original additions to the literature that contributes to the understanding of PolSAR in cryospheric applications. Although these methods are applied to PolSAR and ground-based radar on vessels, they have been applied for the first time on icebergs in this thesis. To summarise, the main findings are that icebergs cannot be represented as single or partial targets, but they do exhibit a collection of single targets clustered together. This result leads to the fact that entropy is not sufficient as a parameter to detect icebergs. Detection results show that the OPD and PWF detectors perform best in an open ocean setting and using quad-pol mode. These results are degraded in dual-pol mode, while single intensity detection is best in the HV cross polarisation channel. When these detectors are applied to the RADARSAT-2 in Svalbard, the OPD and PWF detectors also perform best with PD values ranging between 0.5-0.75 for a PF of 0.01-0.05. However, the sea ice present in the fjord degrades performance across all detectors. Correlation plots with iceberg size show that a regression is not straightforward and Computer Vision methodologies may work best for this

Generalized likelihood ratio test for optical subpixel objects’ detection with hypothesis-dependent background covariance matrix

Much interest has arisen in the problem of detecting weak optical subpixel objects in a sequence of images immersed in a heavy homogeneous Gaussian clutter background. In optical systems, the presence of the objects changes the background plus the channel noise covariance matri

Target detection and localization using thermal camera, mmWave radar and deep learning.

Reliable detection, and localization of tiny unmanned aerial vehicles (UAVs), birds, and other aerial vehicles with small cross-sections is an ongoing challenge. The detection task becomes even more challenging in harsh weather conditions such as snow, fog, and dust. RGB camera-based sensing is widely used for some tasks, especially navigation. However, the RGB camera's performance degrades in poor lighting conditions. On the other hand, mmWave radars perform very well in harsh weather conditions also. Additionally, thermal cameras perform reliably in low lighting conditions too. The combination of these two sensors makes an excellent choice for many of these applications. In this work, a model to detect and localize UAVs is made using an integrated system of a thermal camera and mmWave radar. Data collected with the integrated sensors are used to train a model for object detection using the yolov5 algorithm. The model detects and classifies objects such as humans, cars and UAVs. The images from the thermal camera are used in combination with the trained model to localize UAVs in the cameras Field of View(FOV)

Target detection and localization using thermal camera, mmWave radar and deep learning

Reliable detection, and localization of tiny unmanned aerial vehicles (UAVs), birds, and other aerial vehicles with small cross-sections is an ongoing challenge. The detection task becomes even more challenging in harsh weather conditions such as snow, fog, and dust. RGB camera-based sensing is widely used for some tasks, especially navigation. However, the RGB camera's performance degrades in poor lighting conditions. On the other hand, mmWave radars perform very well in harsh weather conditions also. Additionally, thermal cameras perform reliably in low lighting conditions too. The combination of these two sensors makes an excellent choice for many of these applications. In this work, a model to detect and localize UAVs is made using an integrated system of a thermal camera and mmWave radar. Data collected with the integrated sensors are used to train a model for object detection using the yolov5 algorithm. The model detects and classifies objects such as humans, cars and UAVs. The images from the thermal camera are used in combination with the trained model to localize UAVs in the cameras Field of View(FOV)

The application of digital techniques to an automatic radar track extraction system

'Modern' radar systems have come in for much criticism in recent years, particularly in the aftermath of the Falklands campaign. There have also been notable failures in commercial designs, including the well-publicised 'Nimrod' project which was abandoned due to persistent inability to meet signal processing requirements. There is clearly a need for improvement in radar signal processing techniques as many designs rely on technology dating from the late 1970's, much of which is obsolete by today’s standards. The Durham Radar Automatic Track Extraction System (RATES) is a practical implementation of current microprocessor technology, applied to plot extraction of surveillance radar data. In addition to suggestions for the design of such a system, results are quoted for the predicted performance when compared with a similar product using 1970's design methodology. Suggestions are given for the use of other VLSI techniques in plot extraction, including logic arrays and digital signal processors. In conclusion, there is an illustrated discussion concerning the use of systolic arrays in RATES and a prediction that this will represent the optimum architecture for future high-speed radar signal processors

Environmentally adaptive noise estimation for active sonar

Noise is frequently encountered when processing data from the natural environment, and is of particular concern for remote-sensing applications where the accuracy of data gathered is limited by the noise present. Rather than merely accepting that sonar noise results in unavoidable error in active sonar systems, this research explores various methodologies to reduce the detrimental effect of noise. Our approach is to analyse the statistics of sonar noise in trial data, collected by a long-range active sonar system in a shallow water environment, and apply this knowledge to target detection. Our detectors are evaluated against imulated targets in simulated noise, simulated targets embedded in noise-only trial data, and trial data containing real targets. First, we demonstrate that the Weibull and K-distributions offer good models of sonar noise in a cluttered environment, and that the K-distribution achieves the greatest accuracy in the tail of the distribution. We demonstrate the limitations of the Kolmogorov-Smirnov goodness-of-fit test in the context of detection by thresholding, and investigate the upper-tail Anderson-Darling test for goodness-of-fit analysis. The upper-tail Anderson-Darling test is shown to be more suitable in the context of detection by thresholding, as it is sensitive to the far-right tail of the distribution, which is of particular interest for detection at low false alarm rates. We have also produced tables of critical values for K-distributed data evaluated by the upper-tail Anderson-Darling test. Having established suitable models for sonar noise, we develop a number of detection statistics. These are based on the box-car detector, and the generalized likelihood ratio test with a Rician target model. Our performance analysis shows that both types of detector benefit from the use of the noise model provided by the K-distribution. We also demonstrate that for weak signals, our GLRT detectors are able to achieve greater probability of detection than the box-car detectors. The GLRT detectors are also easily extended to use more than one sample in a single test, an approach that we show to increase probability of detection when processing simulated targets. A fundamental difficulty in estimating model parameters is the small sample size. Many of the pings in our trial data overlap, covering the same region of the sea. It is therefore possible to make use of samples from multiple pings of a region, increasing the sample size. For static targets, the GLRT detector is easily extended to multi-ping processing, but this is not as easy for moving targets. We derive a new method of combining noise estimates over multiple pings. This calculation can be applied to either static or moving targets, and is also shown to be useful for generating clutter maps. We then perform a brief performance analysis on trial data containing real targets, where we show that in order to perform well, the GLRT detector requires a more accurate model of the target than the Rician distribution is able to provide. Despite this, we show that both GLRT and box-car detectors, when using the K-distribution as a noise model, can achieve a small improvement in the probability of detection by combining estimates of the noise parameters over multiple pings.EThOS - Electronic Theses Online ServiceGBUnited Kingdo