The challenge of avoiding aliasing in the Doppler spectrum for precipitation is addressed. A novel integrative signal processing approach has been proposed to address the research gaps from various disciplines. The proposed approach consists of several steps. First, an aperiodic way of sampling the echoes (aperiodic sampling refers to aperiodic pulse train in the context of radar echoes in slow time) has been proposed by which the maximum unambiguous Doppler frequency (velocity) is enhanced. Second, the Doppler spectrum moment estimation is performed with the help of a parametric form of its covariance. The performance of the moment estimation is assessed by the bias and the variance in the estimated counterparts. The theoretical variance for the parameter estimation is also derived. An aperiodic pulse train design recommendation has been proposed for adequately and unambiguously estimating the Doppler moments for one extended target (like precipitation). Finally, a spectrum reconstruction technique is implemented after the moment estimation on simulated radar echo samples for a realistic precipitation-like event. The comparison with the other approaches proves its superiority for parameter estimation and Doppler spectrum reconstruction.

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Mutual interference between automotive frequency-modulated continuous-wave (FMCW) radar systems has been a concern over recent years. Several interference mitigation (IM) techniques have been proposed to mitigate this phenomenon, which is deemed to grow in severity as more systems are deployed on the road. In this article, an inexpensive technique, based on well-known moving target indicator (MTI) processing, is proposed to separate interference from target signals. It exploits the contrast after stretch processing between uncorrelated FMCW interference (sparse and chirp-like) and beat signals (stable sinusoids). The interference is therefore marked and subtracted. This eliminates interference at the cost of introducing a distortion dependent on the relative radial velocity of the targets. To validate the proposed approach, called MTI-IM, numerical simulations and experiments with commercial-grade radars have been performed, with comparisons between MTI-IM and other IM techniques. Results show good capabilities to fight the uncorrelated interference coming from more than one interfering radar. This is achieved at reduced computational cost, which is a key limiting factor in automotive systems.

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The problem of the limited accuracy of precipitation Doppler spectrum moments estimation measured by fast azimuthally scanning weather radars is addressed. A novel approach for the Doppler moment estimation based on maximum likelihood estimation is proposed. A simplified semianalytical parametric model for the precipitation power spectral density (PSD) as a function of the velocity parameters of the scatterers and the finite radar observation time is derived for typical precipitation-like weather conditions. An inverse problem for estimating the Doppler moments from measurements of the PSD is formulated and solved. It is demonstrated that the variance of the estimation of the Doppler moments approaches the Cramer Rao Lower Bound (CRB) when the observation time approaches infinity. The performance of the proposed approach is compared with some classical techniques and another realization of the maximum likelihood approach based on simulated and experimental data. The results indicate the superiority of the proposed approach, especially for short observation time. Furthermore, a scanning strategy to accurately estimate the Doppler moments based on the true velocity dispersion of the scatterers is provided with the help of the proposed approach.@en

The problem of aliasing in precipitation Doppler spectrum with uniform pulse repetition time is addressed. This work focuses on de-aliasing such Doppler spectra using non-uniform sampling techniques, namely, Log-periodic and Periodic non-uniform sampling. These techniques reduce the ambiguous main lobes caused by aliasing (by going beyond the observable frequency limit) into ambiguous sidelobes that are distinguishable from the original spectra. The SNR is further enhanced by using an Iterative Adaptive Approach (IAA) algorithm. The performance of Doppler moment estimation is presented after applying the IAA algorithm on simulated precipitation-like radar echoes. However, the ambiguous sidelobe suppression is highly dependent on the spectral width of the Doppler spectra.

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The challenge of processing incoherent sets of radar echoes to estimate the Doppler moments and noise standard deviation from precipitation with a fast-scanning radar is addressed. The recently proposed maximum likelihood parametric Doppler spectrum estimator (PSE) is extended to accommodate the noise standard deviation along with the Doppler moments in the parametric model of the Doppler power spectral density (PSD). Its performance in estimating the Doppler moments and the noise standard deviation is compared with another maximum likelihood approach. The proposed approach has a smaller bias in the estimation of the noise standard deviation for a wide range of spectral widths on simulated data. The algorithm is verified on experimental data from a fast-scanning weather radar. @en

In this paper, the full calibration chain of FMCW radar with simultaneous transmission of two orthogonally polarized orthogonal waveforms is considered. Specifically for this type of polarimetric radar, compensation of signals’ biases and equalization of the amplification gains of the parallel polarimetric channels in the receiver are jointly performed using the noise measurements. The calibrations of the absolute complex gains of the transmitter’s polarimetric channels together with complex antenna gains are done using the model-based fit of the measurements of the rotating dihedral reflector. Phase relations between polarimetric channels are treated in the Doppler domain using the unfolded velocity of the target. The performed calibration results in high-accurate measurements of the radar targets’ polarimetric scattering matrix (PSM) in the Doppler domain. All the proposed calibration steps are illustrated using real radar data. @en

This article focuses on the experimental validation of probing signals designed to enable radar operation in spectrally crowded environments using an S-band software defined radar (SDR). The tested waveforms ensure spectral coexistence between the sensing system and frequency-overlaid emitters, while optimizing radar performance. This is achieved through a bespoke notching of the radar signal spectrum to control the amount of interference injected by the radar in each shared frequency interval. In addition, some relevant features of the probing signal that influence radar performance are controlled via a similarity index, describing the maximum allowable distance between the spectrally notched waveform and a prescribed radar signal. In a first stage, the study is aimed at verifying whether the transmit and receive chains of the SDR system impair the spectral and temporal features of the designed waveforms. Subsequently, the tested signals are radiated into the environment to investigate their effectiveness to detect targets in the illuminated scene, as well as to ensure spectral coexistence in the presence of frequency-overlaid emitters. The results demonstrated that by exploiting the designed radar probing signals, the SDR system is capable of sharing spectrum with other radio frequency wireless systems while also allowing to detect both stationary and moving targets.

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The challenge of reconstructing the Doppler spectrum of a precipitation-like event observed by a fast-scanning weather radar is addressed. A novel method is proposed where the echo sequence in time is assumed to be a complex Gaussian process with a known covariance structure. It is a two-step approach where the first step is the estimation of the hyperparameters of the covariance function with a maximum likelihood approach, and the second step is the reconstruction of the spectrum directly in the time or spectral domain. The proposed approach is applied to simulated data for hyper-parameter estimation performance analysis and real radar data for the complete Doppler spectrum reconstruction. @en

Starting from numerical simulation and comparative analysis of different polarimetric detector algorithms using the proposed Gain of Detectability measure, this paper has validated the feasibility and accuracy of polarimetric detectors in scenarios with homogeneous clutter. These algorithms’ application to real radar data with non-homogeneous clutter also shows that detection quality can be seriously improved using detectors that use a priori knowledge of the expected target and clutter polarimetric characteristics. A new application of the Polarimetric Whitening Filter and the Optimal Polarimetric Detector for the classification/mapping of targets and ground-based clutter has been proposed and demonstrated.@en

A dedicated signal and data processing chain is proposed for a fully polarimetric Doppler surveillance S-band radar to extract the polarimetric signatures of moving targets. To extract the target’s polarimetric features, detection, clustering, and tracking steps are realized for a multi-target environment in the range-Doppler domain. A dedicated data fusion method for all four polarimetric radar channel signals is implemented to take full advantage of the additional polarimetric information and improve the detection performance. While tracking each particular target, polarization information is collected and used to describe their polarization scattering characteristics. Using the polarimetric H/A/α decomposition technique, the polarimetric features of moving automotive targets are extracted and investigated. The developed processing chain has been applied to the signals scattered from vehicles moving in a highway. By employing both time averaging and spatial averaging of the statistical coherency matrix, the polarimetric signatures of both moving vehicles and static clutter have been presented in the two-dimensional H/α plane. It has been found that the spatial averaging approach results in polarimetric signatures of moving vehicles that give the opportunity to directly and without consideration of the motion of the targets compare the polarization features of moving targets and static clutter. Therefore, this method can be used to improve the performance of target detection or target classification.@en

This paper investigates the possibility of transmitting waveforms designed to enable spectral coexistence between radar and other Radio Frequency (RF) wireless systems via a Software Defined Radar (SDR). The design technique tested in this study nominally enables the placement of notches in the spectrum of the synthesized probing radar signal. Their widths and depths are set during the design stage so as to accounting for the interference into each shared frequency interval, allowing for spectral coexistence. At the assessment stage, the synthesized signal is used with the PARSAX radar system, an SDR capable of operating in the S frequency band. The analysis first focuses on studying the compliance of the signal generated by the PARSAX radar with its theoretical counterpart. Subsequently, open-air experiments are conducted in the presence of stationary and moving targets. The results show that the spectral characteristics of the probing radar signal adhere well to the theoretical spectral mask, and prove the system ability to detect both stationary and moving targets.

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At the airports, fast-scanning phased array radars are used to detect point-like targets, e.g., birds and drones. However, these radars do not possess Doppler processing and thus are not used for observation of the weather targets like precipitation events. A series of steps need to be performed, such as beamforming in elevation, target masking for weather-like targets, and Doppler processing to complete the signal processing chain for this kind of radar for weather-like targets. This paper focuses only on beamforming in elevation which is the first step in the direction of 3D wind field estimation for a fast-scanning phased array radar. The reiterative Minimum Mean Square Error (rMMSE) beamforming technique is proposed in this paper and has been used on real radar observations. The performance is analysed for the first time when noise is under or overestimated. The rMMSE beamformer has been compared with the Fourier (FR) and Capon (CP) beamformers.@en

The paper presents the statistical analysis of trihedral corner reflectors RCS in presence of mass production and installation errors. It is shown that the degradation of RCS from its nominal value can be modeled by Beta distribution. The derived probability density functions (PDF) of corner reflector RCS is further exploited to design an optimal procedure for the radar power calibration technique, taking the aforementioned effect into account. This procedure can be used for real-time estimation of radar sensor healthiness parameter that characterises the sensing quality for awareness of human driver or automated driving system.@en

The performance and limitations of the Doppler processing of the scattered signals from extended meteorological objects (precipitation) are analysed in the case of radar with fast azimuthal scanning. The classical method of the Discrete Fourier Transform (DFT) has been applied to simulated weather radar signals to estimate the Doppler velocity spectrum and characterise it with the mean Doppler velocity and the Doppler spectrum width. The accuracy and resolution of these estimations have been analysed as a function of the scanning radar rotation speed. Finally, the performances of the 2D wind field retrieval are analysed in relation to the accuracy and resolution of Doppler spectra estimations. The wind field retrieval has been done using the classical velocity azimuthal display (VAD) retrieval technique that gives an overall/average estimate of the wind field over an observation region. A few possible approaches for improving the accuracy and resolution of a fast scanning weather radar Doppler signal processing are proposed and analysed based on simulated scanning radar data.@en

Polarimetric radar responses from moving automotive targets are studied aiming at target classification using the polarimetric H/A/α-decomposition technique. A signal- and data processing chain has been proposed for the detection and tracking of targets in a multi-target environment in the range-Doppler domain. Polarimetric information of the vehicles is collected during tracking and is analyzed by the H/A/α-decomposition technique. Employing both time averaging and spatial averaging of the statistical coherency matrix, the polarimetric signatures of both vehicles and static clutter have been presented in the two-dimensional H/α-plane. It has been found that the spatial averaging approach results in a polarimetric signature that can be very helpful to distinguish automotive vehicles from static clutter.@en

This paper presents algorithms for joint signal processing of data from two radars located on the rooftop of TU Delft: PARSAX, operating in S-band, and MESEWI, operating in X-band [1]. In particular, the problem of data alignment in space (2D map) and time is addressed by observing moving targets of opportunity in the high-resolution mode. After the data alignment procedure, the detection algorithms for optimal fusing of dual-polarization and multi-frequency data are proposed. The detection results are considered the input for moving target (an auto) tracking and its signature extraction. The developed techniques were tested on the data records in experimental scenarios.@en

This paper presents an analysis of a new method of automotive radar self-calibration which uses targets of opportunity. While conventional offline calibration of a phased array antenna requires accurate knowledge of the positions of calibration targets relative to the radar, such information is not available in a dynamic scenario. To compensate for this, we have developed an estimation procedure based on an extended Kalman filter (EKF) to address the challenge of simultaneous localisation, mapping and calibration. The proposed technique makes it possible to compensate for moderate errors of amplitude and phase in the phased array response with just a few measured frames. A significant reduction in the sidelobe rejection of the array response, plus the ability to correct for angular steering errors, are demonstrated via numerical simulations and real data processing.

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Classical saw-tooth Frequency Modulated Continuous Wave (FMCW) radars experience a coupling between the maximum unambiguous Doppler-velocity interval, maximum operational range, range-resolution and processing gain. Operationally, a trade-off is often necessarily made between these parameters. In this paper, we propose a waveform and a processing method that decouples the aforementioned parameter dependencies at the price of using multiple receiver channels within the radar. The proposed method exploits the fact that beat-frequency signals have the same baseband frequency, even if the transmitted and received chirps occupy different radio frequency bands, and have different center-frequencies. We concatenate those baseband signals in the time-frequency domain to restore the range-resolution and processing gain. An overview of FMCW parameters trade-off for related waveforms and a feasibility and limitations analysis of implementing the proposed processing method are presented. The method is verified by simulations and experiments with an FMCW radar for stable, moving and extended-moving targets. We additionally have highlighted its non-idealities in the simulations and experiments. We found that the proposed method indeed alleviates the trade-off between FMCW operational parameters and allows the extension of the Doppler ambiguity interval without compromising on those parameters.@en

Wind turbines usually cause significant interference in the conventional radar operations which might degrade the detection capabilities. Wind turbines do not only block the radar beam focusing on a specific target, thus creating shadowing effects, but also impose Doppler spectra contamination due to the continuous blade rotation. Therefore in order to identify, detect and possibly mitigate the presence of Wind Turbine Clutter (WTC), fundamental features of the blades rotation need to be estimated, such as rotation (angular) velocity. This information can be directly extracted by initially evaluating the angular displacement of the blades between successive radar measurements. In this paper, a method to estimate this rotation angle is proposed which is based on both radar polarimetry and estimation theory.

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This paper proposes to perform radar recognition of multi-propeller drones using micro-Doppler linear spectral pattern in long Doppler coherent processing interval (CPI) circumstances. It focuses on the investigation of the influence of geometry design and motion variables, such as blade number, blade shape, drone’s design geometry and propeller synchronisation, on the micro-Doppler spectral pattern. We propose suitable scalar features for the characterisation of these patterns measured within a long (relatively to propellers rotation period) CPI. A thin-wire model was used to simulate drones micro-Doppler spectra for various input variables. Proposed features are extracted from the simulated micro-Doppler spectra for further processing in a support vector machine (SVM), with the purpose of demonstrating the radar recognition of multi-propeller drones based on these features.@en