Novel waveform design algorithms for pulse compression radars

Radar systems have been used widely for the detection of remote targets since World War II, and since then, they have become ubiquitous in remote sensing applications. At its core, the radar probing waveform has received considerable attention in the past decades with the advances in digital hardware and signal processing techniques. Indeed, waveforms and their synthesis methods can find diverse applications in areas, not only in active remote sensing but also in communications and medical imaging. In recent years, computational methods have been devised to synthesize arbitrary waveforms under various practical constraints, including the Low Peak-to-Average-Power-Ratio (PAPR), unimodularity, correlation constraints and spectrum allocation restrictions with the aim of improving the underlying system performance. With the new trends in radar techniques, such as noise radar and cognitive radar, the transmit waveforms and pertinent terms such as waveform diversity and waveform adaptivity have attracted more attention due to their practical benefits. In this thesis, we give an introduction to the new trends and techniques, i.e. noise radar technology and cognitive radar, and further propose two novel waveform design algorithms, particularly for noise radar technology and spectrum-aware sensing systems such as cognitive radar. The first algorithm named 'Combined Spectral Shaping and Peak-to-Average Power Reduction (COSPAR)' which embodies a parametric window function as a spectral weighting to control the autocorrelation sidelobes and a Peak-to-Average-Power-Ratio (PAPR) reduction technique inspired by a phase retrieval algorithm is proposed to synthesize tailored noise waveforms for enhanced Low Probability of Intercept (LPI) radar operation. The proposed COSPAR algorithm is recommended for the generation of infinitely many orthogonal low PAPR noise-like signals suitable to feed up noise radar waveform libraries. Furthermore, a visual analysis tool based on Spectral Kurtosis in the Time-Frequency domain is proposed to assess the noise-like behaviour and pertinent LPI characteristics of the signal. In the second algorithm, we pose the waveform design task as a nonlinear large-scale optimization problem and propose a novel computational approach utilizing a nonlinear optimization technique i.e. Limited Memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) recursion to synthesize unimodular sequences with good correlation and spectral stopband properties. The proposed algorithm, named 'L-BFGS based Sequence Design (LBSD)', includes a modified search direction and step length rule to facilitate faster convergence and aims to accelerate the overall waveform generation process. As a result, the proposed method is viable for the agile generation of spectrally compatible waveforms that are essential for cognitive radars. Finally, the benefits and good features of the proposed COSPAR signals are shown in field trials using an experimental noise radar demonstrator system, and some results from these experiments are reported in the thesis. Additionally, the performance of the proposed LBSD algorithm is shown via numerical examples in comparison to existing state-of-art waveform design algorithms.