This article addresses the joint estimation of range, velocity and azimuth for multiple fast-moving targets using (ultra-)wideband (UWB) frequency-modulated continuous-wave (FMCW) radar with a phased array in the presence of Doppler ambiguities. The range migration of moving targets is described with the coupling of the fast-time and slow-time (chirp index), leading to the smearing of the target Doppler spectrum. This phenomenon degrades the performance of conventional detection and estimation techniques.As with range-Doppler processing, the estimation accuracy for direction-of-arrival (DOA) with conventional narrowband-based algorithms significantly degrades if a UWB signal is deployed. For the FMCW waveform, the wideband DOA differs from the narrowband DOA due to an extra coupling term, similar to the range migration problem.A novel spectral norm-based algorithm for joint estimation of range, velocity and DOA of fast-moving targets is proposed, taking the appropriate wideband signal model with the coupling terms into account.The proposed spectral norm-based algorithm avoids off-grid peak search and can be easily accelerated withthe power iteration algorithm; it outperforms conventional coherent integration methods in both accuracy and efficiency when using moderate data size. The advantages of the proposed algorithm and its super-resolution capability are validated withnumerical simulations.@en

Separating images of multiple moving extended-targets by means of ultra-wideband (UWB) frequency-modulated-continuous-wave (FMCW) radar is investigated. The difference in motion of closely-spaced extended targets is used to separate and image them when their radar signatures overlap in the range-azimuth domain. A novel auto-focusing criterion based on the entropy of the eigenspectrum and the Fourier spectrum is proposed for target imaging using inverse synthetic aperture radar (ISAR). It allows separating closely spaced targets and estimating their motion parameters. The criterion proposed overcomes the low Doppler resolution of closely-spaced extended targets of conventional techniques. The bearing information is extracted by signal reconstruction for every target separately. With the estimated position and the motion parameters of multiple targets, their images are reconstructed using standard imaging processing algorithms. The performance of the proposed method is validated via numerical simulations.@en

The joint Doppler and Direction-of-Arrival (DOA) estimation of moving targets using an (Ultra-)Wideband (UWB) frequency modulated continuous-wave (FMCW) antenna array radar is investigated. Besides the well-known range migration problem, another concern for wideband signals is the DOA estimation problem. For the first time, both problems are considered in this paper simultaneously, where the wideband DOA is transformed into a second-order coupling system similar to the range migration problem by using the property of the FMCW signal. A novel embedded compensation approach to eliminate the coupling terms caused by range migration and wideband DOA is proposed and 2D multiple signal classification (2D MUSIC) algorithm is subsequently applied with dynamic noise subspace to joint estimation of Doppler and DOA. Further, to reduce the computational load caused by multiple eigendecompositions of large matrices, efficient implementation methods are proposed and their performance in speed, accuracy and robustness is compared. The performance of the proposed methods is validated by the numerical simulations and is compared with Keystone MUSIC. Finally, it is shown that for a small number of targets, the Rayleigh-Ritz is the most efficient approach among them.@en

This paper investigates the joint Doppler and DOA (Direction-of-Arrival) estimation with a wideband phased array in presence of phase residual due to range-Doppler coupling appearing in pulse-Doppler radars. 2D MUSIC algorithm is applied and a compensation approach is developed to eliminate the influence of phase residual. Simulation data validate the improvement of joint Doppler and DOA estimation performance using the proposed method.@en

Ground penetrating radar (GPR) using a 3D antenna array is employed for data acquisition over an area contaminated with landmine simulants. The performance of different processing methods applied in 1D, 2D and 3D radiograms are evaluated on real data obtained from two different test sites. Preliminary results are presented on the effectiveness of 3D GPR and on the applicability and limitations of widely used processing schemes.@en

Ground penetrating radar (GPR) using a 3D antenna array is employed for data acquisition over an area contaminated with landmine simulants. The performance of different processing methods applied in 1D, 2D and 3D radiograms are evaluated on real data obtained from two different test sites. Preliminary results are presented on the effectiveness of 3D GPR and on the applicability and limitations of widely used processing schemes.@en

As one of the main sensor in autonomous driving, radar has great advantages over other sensors, especially its capabilities during adverse weather condition and Doppler information extraction. Performance of the radar in terms of accuracy and target resolution strongly depends on radar waveforms transmitted and signal processing algorithms applied. To achieve high range resolution, an ultra-wideband (UWB) signal has to be used for sensing, which introduces difficulties to achieve high Doppler and direction-of-arrival (DOA) estimation simultaneously due to the range migration. To address this problem, in this thesis new signal processing algorithms are proposed, which pave the way to improved performance of the automotive radar sensor. As the frequency-modulated continuous-wave (FMCW) radar are widely used in short-range and middle-range applications due to its low cost and simplicity, FMCW waveform is the main research subject. The FMCW signal model is derived and analysed in Chapter 2 which for the first time takes both the range migration and wideband DOA problems into account at the same time. The point-like moving targets are considered in Chapter 3 where their Doppler velocities are within the maximum unambiguous velocity of the radar. A novel improved multiple signal classification (MUSIC) algorithm with the dynamic noise-subspace method is proposed to address both the range migration and wideband DOA problems. The algorithm releases the great potentials of the conventional MUSIC algorithm in the presence of the range migration. Moreover, an efficient algorithm based-on Rayleigh-Ritz step is introduced for the proposed method resulting in a considerable reduction of computational requirements without any performance degradation. Comparison with the conventional narrow-band MUSIC, Keystone-MUSIC, inversion-MUSIC and corresponding Cramer-Rao bounds (CRB) using simulations, reveals the superiority of the method proposed in terms of accuracy, resolution and efficiency. The problems similar to those considered in Chapter 3 but in the presence of the Doppler ambiguity are considered in Chapter 4. A spectral norm-based algorithm is proposed to address the coupling terms for a single moving point-like target. The algorithm for the first time abandons the integration-based method for ambiguous velocity estimation. The spectral-norm based algorithm provides a new tool to resolve the ambiguity problem which outperforms the conventional integration-based algorithm by avoiding the off-grid problem with limited data size. Moreover, combined with the modified CLEAN techniques and Greedy algorithm, the proposed algorithm can be extended to multiple moving targets. Furthermore, the power iteration algorithm is smartly adopted for an efficient implementation of the proposed method. After addressing the point-like targets, the moving extended targets are studied in Chapter 5 especially when multiple extended targets cannot be separated both in range and beam profile. The Doppler difference is used to recognise them and inverse synthetic aperture radar (ISAR) concept is adopted to split and image the targets separately. The conventional entropy minimisation approach is applied to the signal model for not only the Fourier spectrum but also the eigenspectrum as well for the first time. The Fourier spectrum has a relatively high resolution in higher-order motion (e.g. acceleration) while eigenspectrum has a better resolution in Doppler separation. The advantages of both spectra are utilised to separate multiple extended targets by a simple but powerful combination. Via numerical simulation, the applicability of the algorithm in the automotive application is demonstrated. Last in Chapter 6, by processing the experimental data from automotive radar, we present a novel and fast imaging algorithm for slow-moving targets which provides super-resolution on DOA. The range information is processed via fast Fourier transform (FFT) for efficiency while the DOA is estimated by the MUSIC algorithm for super-resolution. Since the MUSIC spectrum is pseudo-spectrum and can not represent the correct dynamic range of the imaging results, a novel normalisation method is introduced to vividly indicate the energies of different targets. In comparison with conventional FFT-BF, a cleaner range-azimuth image is obtained with the proposed algorithm demonstrating higher angular resolution and without strong side-lobes. Although the research presented in this thesis is served for automotive application, some of the algorithms and ideas can be easily generalised for a broad spectrum of diverse applications. @en

This paper investigates the joint range, Doppler and Direction-of-Arrival (DOA) estimation with a wideband phased array in the presence of phase residual which brought by the range-Doppler couplings. 3D MUSIC algorithm is adopted and a compensation approach is proposed to eliminate the influence of the phase residual on the estimation accuracy. Tensor Decomposition (TD) is applied to obtain the noise subspace. Therefore, the spatial smoothing technique can be avoided. Simulation data validate the improvement of joint parameters estimation performance using the proposed method.@en

Radar imaging using ultra-wideband (UWB) automotive radar is investigated. To overcome poor cross-range resolution of traditional beamforming (BF) algorithms, super resolution technique is applied for the Direction of Arrival (DOA) estimation. Both super-resolution algorithm and conventional BF method are used to process experimental data collected with UWB frequency modulated continuous wave (FMCW) automo- tive radar. Algorithm performances are thoroughly compared.@en