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Airborne Radar Ground Clutter Suppression Using Multitaper Spectrum Estimation Comparison with Traditional Method

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Airborne Radar Ground Clutter Suppression Using Multitaper Spectrum Estimation Comparison with Traditional Method Airborne Radar Ground Clutter Suppression Using Multitaper Spectrum Estimation Comparison with Traditional Method Linus Ekvall Engineering Physics and Electrical Engineering, master's level 2018 Luleå University of Technology Department of Computer Science, Electrical and Space Engineering Airborne Radar Ground Clutter Suppression Using Multitaper Spectrum Estimation & Comparison with Traditional Method Linus C. Ekvall Lule˚aUniversity of Technology Dept. of Computer Science, Electrical and Space Engineering Div. Signals and Systems 20th September 2018 ABSTRACT During processing of data received by an airborne radar one of the issues is that the typical signal echo from the ground produces a large perturbation. Due to this perturbation it can be difficult to detect targets with low velocity or a low signal-to-noise ratio. Therefore, a filtering process is needed to separate the large perturbation from the target signal. The traditional method include a tapered Fourier transform that operates in parallel with a MTI filter to suppress the main spectral peak in order to produce a smoother spectral output. The difference between a typical signal echo produced from an object in the environment and the signal echo from the ground can be of a magnitude corresponding to more than a 60 dB difference. This thesis presents research of how the multitaper approach can be utilized in concurrence with the minimum variance estimation technique, to produce a spectral estimation that strives for a more effective clutter suppression. A simulation model of the ground clutter was constructed and also a number of simulations for the multitaper, minimum variance estimation technique was made. Compared to the traditional method defined in this thesis, there was a slight improve- ment of the improvement factor when using the multitaper approach. An analysis of how variations of the multitaper parameters influence the results with respect to minimum detectable velocity and improvement factor have been carried out. The analysis showed that a large number of time samples, a large number of tapers and a narrow bandwidth provided the best result. The analysis is based on a full factorial simulation that provides insight of how to choose the DPSS parameters if the method is to be implemented in a real radar system. Keywords: Ground clutter, tapering, multitaper, discrete prolate spheroidal sequences, minumum variance estimation, signal processing, radar, airborne radar. iii PREFACE This work concludes my MSc in Engineering Physics and Electrical Engineering at Lule˚a University of Technology between the years 2013-2018. The work has been conducted in Kalleb¨ack at Saab Surveillance who supplies solutions including security, surveillance, de- cision support and solutions for detecting and protecting against different types of threats. The work was done in collaboration with Carl-Henrik Hanquist who has been focusing on analysis of DPSS parameters using full factorial design [1]. The focus of this thesis has been to provide a simulation environment for the full factorial simulation and a comparison with a traditional method. I would like to thank my co-worker Carl-Henrik Hanquist for ideas and insightful dis- cussions. Also the team at Saab Surveillance, foremost my external supervisor Bj¨orn Hallberg. A sincere thanks goes to my supervisor, Professor Johan Carlson at Lule˚a University of Technology. I would also like to express my gratitude for my parents Hans Ekvall and Monica Ekvall for supporting me through the years. Linus C. Ekvall G¨oteborg, Sweden 2018 v ABBREVIATIONS CNR Clutter-to-noise ratio DFT Discrete Fourier transform DPSS Discrete prolate spheroidal sequences FFT Fast Fourier transform IF Improvement factor LST Linear subspace transform MDV Minimum detectable velocity MTI Moving target indicator PRF Pulse repetition frequency Radar Radio detection and ranging RCS Radar cross section SNR Signal-to-noise ratio ULA Uniform linear array vii CONTENTS Chapter 1 { Introduction 1 1.1 Background . .1 1.2 Problem statement . .2 1.3 Related work and literature review . .3 1.4 Goal . .3 1.5 Scientific, societal and ethical aspects . .3 Chapter 2 { Theory 5 2.1 Radar theory . .5 2.1.1 Wave propagation . .6 2.1.2 Radar cross section . .6 2.1.3 Radar equation . .7 2.1.4 The Doppler effect . .8 2.1.5 Radar equation and clutter . .9 2.2 Spectral analysis and estimation . 10 2.2.1 Fourier transform . 10 2.2.2 Autocorrelation matrix . 10 2.2.3 Tapering and window functions . 11 2.2.4 Moving target indicator & Minimum detectable velocity . 13 2.2.5 Parameters and ratios . 13 2.2.6 Discrete prolate spheroidal sequences . 14 2.2.7 Multitaper . 16 2.3 Other . 18 2.3.1 Linear subspace transform & Minimum variance estimation . 18 2.3.2 Full factorial simulation theory . 18 Chapter 3 { Method 19 3.1 Antenna model . 19 3.1.1 Clutter model . 20 3.2 Minimum variance estimation & Multitaper . 22 3.3 Traditional method . 22 3.4 Simulation environment for full factorial simulation . 23 3.4.1 Overview . 23 3.4.2 Signal processing simulation environment . 24 Chapter 4 { Results 27 4.1 Clutter generation . 27 4.2 Simulated results . 28 4.3 Parameter analysis by full factorial simulation . 32 4.3.1 Full factorial simulation . 33 4.4 Comparison with traditional method . 40 4.4.1 Computation time . 43 Chapter 5 { Discussion 45 5.1 Discussion . 45 Chapter 6 { Conclusion 47 6.1 Conclusion . 47 6.2 Future work . 47 Appendix A { 173 factor simulation 49 ix CHAPTER 1 Introduction 1.1 Background The concept of radar has its origin in the 19th century where James Clerk Maxwell predicted the existence of radio waves in his theory of electromagnetism in 1864. The theory was verified by the experiments of the German physicist Heinrich Hertz in 1886-87 and provided insight in the reflective behaviour of electromagnetic waves. In the early 20th century these types of systems became widely available and started to be utilized for range measurements. The first system was implemented by the German inventor Christian H¨ulsmeyer who constructed a ship detection device with the intention to avoid collisions in fog, patented in 1904 [2]. Since then the technology has taken a vast leap and the research conducted in the area to- day is cloaked in advanced mathematical concepts which are constantly evolving. Radar engineers often refer to the "target" which can be an airplane, a ship, a vehicle etc. However, this can be generalized to any object in the surrounding environment that pro- duces a desired radar echo to show its position. Another common term is the "clutter" which is the radar echoes produced from unwanted objects in the propagation path such as birds, insects, rain, sea or the ground. In some cases the clutter can cause severe performance issues for the radar system and thus a reliable filtering process is often nec- essary to disregard these effects. The applications of today's radars have gone from range and angle measurements to applications including determining target velocity, amplitude measurement, recognition of targets based on characteristics and weather prediction to name a few. To make these applications both effective and efficient the data received by the radar is processed digitally to extract desired information from the received signal. Based on the desired outcome, the data is processed to unveil information that can be hard to extract without the digital signal processing tool [3], [4], [5]. As an electromagnetic wave propagates through space, gets reflected by an object and is received by the radar not only the distance to the object can be extracted but also its velocity. Since the propagating wave has a predetermined frequency upon transmission 1 2 Introduction it is possible to compare the frequency shift caused by the Doppler effect. To determine this frequency shift a common method is to use the Fourier transform that transforms the wave from time to frequency domain according to Z 1 F (!) = f(t)w(t)exp(−i!t) (1.1) −∞ in continuous time. This is a fundamental concept in the field of spectral analysis and is crucial in extracting information such as target velocity. With slight modifications it can be applied in the discrete time case in order to be processed digitally, which will be discussed in this thesis. The standard Fourier transform is weighted with a uniform window function w(t), as seen in (1.1). From a digital signal processing standpoint this corresponds to an equal gain for every sample in time. The uniform weighting can limit the information one wishes to extract from the spectrum such as targets with low signal-to-noise ratio (SNR). By applying different gain on each time sample, targets that may be misinterpreted as noise in the uniform weighted spectrum may appear. This method of gain calibration for the different time samples is called tapering and in digital signal processing this function is referred to as a window. Some common windows will be discussed such as Taylor or Gaussian. To apply the method in a broader sense it is possible to use several windows to extract information and combine them to get a desired outcome, which is referred to as multitapering [4], [6]. 1.2 Problem statement One of the challenges in airborne radar systems is that the typical signal echo received from the ground can be more than a million times larger than the signal echo from a target, such as another airborne unit or a moving vehicle on the ground. In order to suppress the magnitude of the unwanted ground clutter and increase the echo from the target there are different techniques associated to this pursuit, one which is digital signal processing, which will be studied in this thesis. The purpose of this thesis is to investigate the possibilities of using multitapering to suppress the ground clutter.
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