Experimental Validation of a Heterogeneous Radar Clutter Statistical Estimation Method

Radar clutter models are important for radar target detection when clutter is present. A new method for estimating single clutter type, homogeneous, radar clutter statistics through measurement of a multiple type, heterogeneous clutter was developed in 2015 by researchers at the Air Force Institute of Technology. The estimation method is greatly valued in the clutter research and modeling world for reducing clutter campaign measurement time and cost. This thesis looks at validation steps for the estimation method through the use of simulations and experimental radar clutter measurements

Technique-Based Exploitation Of Low Grazing Angle SAR Imagery Of Ship Wakes

The pursuit of the understanding of the effect a ship has on water is a field of study that is several hundreds of years old, accelerated during the years of the industrial revolution where the efficiency of a ship’s engine and hull determined the utility of the burgeoning globally important sea lines of communication. The dawn of radar sensing and electronic computation have expanding this field of study still further where new ground is still being broken. This thesis looks to address a niche area of synthetic aperture radar imagery of ship wakes, specifically the imaging geometry utilising a low grazing angle, where significant non-linear effects are often dominant in the environment. The nuances of the synthetic aperture radar processing techniques compounded with the low grazing angle geometry to produce unusual artefacts within the imagery. It is the understanding of these artefacts that is central to this thesis. A sub-aperture synthetic aperture radar technique is applied to real data alongside coarse modelling of a ship and its wake before finally developing a full hydrodynamic model for a ship’s wake from first principles. The model is validated through comparison with previously developed work. The analysis shows that the resultant artefacts are a culmination of individual synthetic aperture radar anomalies and the reaction of the radar energy to the ambient sea surface and spike events

Country-specific Ground-based Bistatic Radar Clutter Analysis of Rural Environments

This thesis presents a novel statistical analysis of bistatic radar rural ground clutter for different terrain types of German rural environments under low grazing angles. A country-specific clutter analysis for subgroups of rural environments rather than for the rural environment as a whole will be presented. Therefore, the rural environment is divided into four dominant subgroup terrain types, namely fields with low vegetation, fields with high vegetation, plantations of small trees and forest environments, representing a typical rural German or even Central European environment. The thesis will present the bistatic clutter characteristics for both the summer and the winter vegetation. Therefore, bistatic measurement campaigns have been carried out during the summer 2019 and the winter of 2019/20 in the aforementioned four different rural terrain types. The measurements were carried out according to a designed bistatic measurement methodology to obtain comparable results and to be used for different radar applications in the radar relevant X-band at a center frequency of 8.85 GHz and over a bandwidth of 100 MHz, according to available transmit permissions. The distinction of the rural terrain into different subgroups enables a more precise and accurate clutter analysis and modeling of the statistical properties as will be shown in the presented results. A clear separation of the different types of rural terrain and the influence of the seasons was worked out. Additionally, model functions for the relevant parameters, characterizing the the bistatic clutter, are presented for their analytical description. The statistical properties are derived from the clutter regions of processed range-Doppler domain data, using an improved range-Doppler processing approach, for each of the four terrain types and the corresponding seasons. The data basis for the clutter analysis are the processed range-Doppler maps from the bistatic radar measurements using a dual-channel measurement approach, with a separate reference and surveillance channel. According to the authors’ current knowledge, a similar investigation based on real bistatic radar measurement data with the division into terrain subgroups and additionally for different season has not yet been carried out and published for a German rural environment. The presented data and results therefore have a significant impact on the research field of bistatic ground clutter, in which there are currently only very few results in the frequency range discussed in this thesis

Cognitive Radar Detection in Nonstationary Environments and Target Tracking

Target detection and tracking are the most fundamental and important problems in a wide variety of defense and civilian radar systems. In recent years, to cope with complex environments and stealthy targets, the concept of cognitive radars has been proposed to integrate intelligent modules into conventional radar systems. To achieve better performance, cognitive radars are designed to sense, learn from, and adapt to environments. In this dissertation, we introduce cognitive radars for target detection in nonstationary environments and cognitive radar networks for target tracking.For target detection, many algorithms in the literature assume a stationary environment (clutter). However, in practical scenarios, changes in the nonstationary environment can perturb the parameters of the clutter distribution or even alter the clutter distribution family, which can greatly deteriorate the target detection capability. To avoid such potential performance degradation, cognitive radar systems are envisioned which can rapidly recognize the nonstationarity, accurately learn the new characteristics of the environment, and adaptively update the detector. To achieve this cognition, we propose a unifying framework that integrates three functions: (i) change-point detection of clutter distributions by using a data-driven cumulative sum (CUSUM) algorithm and its extended version, (ii) learning/identification of clutter distribution by using kernel density estimation (KDE) methods and similarity measures (iii) adaptive target detection by automatically modifying the likelihood-ratio test and the corresponding detection threshold. We also conduct extensive numerical experiments to show the merits of the proposed method compared to a nonadaptive case, an adaptive matched filter (AMF) method, and the clairvoyant case.For target tracking, with remarkable advances in sensor techniques and deployable platforms, a sensing system has freedom to select a subset of available radars, plan their trajectories, and transmit designed waveforms. Accordingly, we propose a general framework for single target tracking in cognitive networks of radars, including joint consideration of waveform design, path planning, and radar selection. We formulate the tracking procedure using the theories of dynamic graphical models (DGM) and recursive Bayesian state estimation (RBSE). This procedure includes two iterative steps: (i) solving a combinatorial optimization problem to select the optimal subset of radars, waveforms, and locations for the next tracking instant, and (ii) acquiring the recursive Bayesian state estimation to accurately track the target. Further, we use an illustrative example to introduce a specific scenario in 2-D space. Simulation results based on this scenario demonstrate that the proposed framework can accurately track the target under the management of a network of radars