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

Target detection in bistatic radar sea clutter using stationary wavelet transforms

Radar backscatter from the sea-surface is typically non-stationary and may contain sea-spikes which resemble targets and can cause false detections in a radar system. To improve detection performance, this paper investigates a technique based on stationary wavelet transforms (SWT) and demonstrates its performance on both monostatic and bistatic sea-clutter data. By exploiting the different components (sub-bands) of the SWT decomposition, this technique can better distinguish between the different spectral components of the returned signal. The detection performance is measured by Monte Carlo simulation with a cell averaging constant false alarm rate (CA-CFAR) detector. The most appropriate choice of sub-band is selected using an a entropy based metric

Detection of Small Targets in Sea Clutter Based on RepVGG and Continuous Wavelet Transform

Constructing a high-performance target detector under the background of sea clutter is always necessary and important. In this work, we propose a RepVGGA0-CWT detector, where RepVGG is a residual network that gains a high detection accuracy. Different from traditional residual networks, RepVGG keeps an acceptable calculation speed. Giving consideration to both accuracy and speed, the RepVGGA0 is selected among all the variants of RepVGG. Also, continuous wavelet transform (CWT) is employed to extract the radar echoes' time-frequency feature effectively. In the tests, other networks (ResNet50, ResNet18 and AlexNet) and feature extraction methods (short-time Fourier transform (STFT), CWT) are combined to build detectors for comparison. The result of different datasets shows that the RepVGGA0-CWT detector performs better than those detectors in terms of low controllable false alarm rate, high training speed, high inference speed and low memory usage. This RepVGGA0-CWT detector is hardware-friendly and can be applied in real-time scenes for its high inference speed in detection

Constant False Alarm Rate Target Detection in Synthetic Aperture Radar Imagery

Target detection plays a significant role in many synthetic aperture radar (SAR) applications, ranging from surveillance of military tanks and enemy territories to crop monitoring in agricultural uses. Detection of targets faces two major problems namely, first, how to remotely acquire high resolution images of targets, second, how to efficiently extract information regarding features of clutter-embedded targets. The first problem is addressed by the use of high penetration radar like synthetic aperture radar. The second problem is tackled by efficient algorithms for accurate and fast detection. So far, there are many methods of target detection for SAR imagery available such as CFAR, generalized likelihood ratio test (GLRT) method, multiscale autoregressive method, wavelet transform based method etc. The CFAR method has been extensively used because of its attractive features like simple computation and fast detection of targets. The CFAR algorithm incorporates precise statistical description of background clutter which determines how accurately target detection is achieved. The primary goal of this project is to investigate the statistical distribution of SAR background clutter from homogeneous and heterogeneous ground areas and analyze suitability of statistical distributions mathematically modelled for SAR clutter. The threshold has to be accurately computed based on statistical distribution so as to efficiently distinguish target from SAR clutter. Several distributions such as lognormal, Weibull, K, KK, G0, generalized Gamma (GGD) distributions are considered for clutter amplitude modeling in SAR images. The CFAR detection algorithm based on appropriate background clutter distribution is applied to moving and stationary target acquisition and recognition (MSTAR) images. The experimental results show that, CFAR detector based on GGD outmatches CFAR detectors based on lognormal, Weibull, K, KK, G0 distributions in terms of accuracy and computation time.

Wavelet Operators and Multiplicative Observation Models - Application to Change-Enhanced Regularization of SAR Image Time Series

This paper first provides statistical properties of wavelet operators when the observation model can be seen as the product of a deterministic piecewise regular function (signal) and a stationary random field (noise). This multiplicative observation model is analyzed in two standard frameworks by considering either (1) a direct wavelet transform of the model or (2) a log-transform of the model prior to wavelet decomposition. The paper shows that, in Framework (1), wavelet coefficients of the time series are affected by intricate correlation structures which affect the signal singularities. Framework (2) is shown to be associated with a multiplicative (or geometric) wavelet transform and the multiplicative interactions between wavelets and the model highlight both sparsity of signal changes near singularities (dominant coefficients) and decorrelation of speckle wavelet coefficients. The paper then derives that, for time series of synthetic aperture radar data, geometric wavelets represent a more intuitive and relevant framework for the analysis of smooth earth fields observed in the presence of speckle. From this analysis, the paper proposes a fast-and-concise geometric wavelet based method for joint change detection and regularization of synthetic aperture radar image time series. In this method, geometric wavelet details are first computed with respect to the temporal axis in order to derive generalized-ratio change-images from the time series. The changes are then enhanced and speckle is attenuated by using spatial bloc sigmoid shrinkage. Finally, a regularized time series is reconstructed from the sigmoid shrunken change-images. An application of this method highlights the relevancy of the method for change detection and regularization of SENTINEL-1A dual-polarimetric image time series over Chamonix-Mont-Blanc test site

Wavelet Operators and Multiplicative Observation Models -Application to SAR Image Time Series Analysis

International audienceThis paper first provides statistical properties of wavelet operators when the observation model can be seen as the product of a deterministic piece-wise regular function (signal) and a stationary random field (noise). This multiplicative observation model is analyzed in two standard frameworks by considering either (1) a direct wavelet transform of the model or (2) a log-transform of the model prior to wavelet decomposition. The paper shows that, in Framework (1), wavelet coefficients of the time series are affected by intricate correlation structures which blur signal singularities. Framework (2) is shown to be associated with a multiplicative (or geometric) wavelet transform and the multiplicative interactions between wavelets and the model highlight both sparsity of signal changes near singularities (dominant coefficients) and decorre-lation of speckle wavelet coefficients. The paper then derives that, for time series of synthetic aperture radar data, geometric wavelets represent a more intuitive and relevant framework for the analysis of smooth earth fields observed in the presence of speckle. From this analysis, the paper proposes a fast-and-concise geometric wavelet based method for joint change detection and regularization of synthetic aperture radar image time series. In this method, geometric wavelet details are first computed with respect to the temporal axis in order to derive generalized-ratio change-images from the time series. The changes are then enhanced and speckle is attenuated by using spatial block sigmoid shrinkage. Finally, a regularized time series is reconstructed from the sigmoid shrunken change-images. Some applications highlight relevancy of the method for the analysis of SENTINEL-1A and TerraSAR-X image time series over Chamonix-Mont-Blanc

Review of radar classification and RCS characterisation techniques for small UAVs or drones

This review explores radar-based techniques currently utilised in the literature to monitor small unmanned aerial vehicle (UAV) or drones; several challenges have arisen due to their rapid emergence and commercialisation within the mass market. The potential security threats posed by these systems are collectively presented and the legal issues surrounding their successful integration are briefly outlined. Key difficulties involved in the identification and hence tracking of these `radar elusive' systems are discussed, along with how research efforts relating to drone detection, classification and radar cross section (RCS) characterisation are being directed in order to address this emerging challenge. Such methods are thoroughly analysed and critiqued; finally, an overall picture of the field in its current state is painted, alongside scope for future work over a broad spectrum