Synthetic Aperture Radar (SAR) imaging is starting to play an essential role in the automotive industry. Its day and night sensing capability, fine resolution, and high flexibility are key aspects ...making SAR a very compelling instrument in this field. This paper describes and compares three algorithms used to combine low-resolution images acquired by a Multiple-Input Multiple-Output (MIMO) automotive radar to form an SAR image of the environment. The first is the well-known Fast Factorized Back-Projection (FFBP), which focuses the image in different stages. The second one will be called 3D2D, and it is a simple 3D interpolation used to extract the SAR image from the Range-Angle-Velocity (RAV) data cube. The third will be called Quick&Dirty (Q&D), and it is a fast alternative to the 3D2D scheme that exploits the same intuition. A rigorous mathematical description of each algorithm is derived, and their limits are addressed. We then provide simulated results assessing different interpolation kernels, proving which one performs better. A rough estimation of the number of operations proves that both algorithms can be deployed using a real-time implementation. Finally, we will present some experimental results based on open road campaign data acquired using an eight-channel MIMO radar at 77 GHz, considering the case of a forward-looking geometry.
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In this paper, we discuss the possibility of generating high-resolution mapping of urban (or extra-urban) environments by the application of synthetic aperture radar (SAR) processing concepts to the ...data collected by mm-wave automotive radars installed on-board commercial vehicles. The study is motivated by the fact that radar sensors are becoming an indispensable component of the equipment of modern vehicles, being characterized by low cost, good performance, and affordable processing; therefore, in the future, nearly every single vehicle could be potentially equipped with radar devices capable of high-resolution imaging, enabled by application of SAR processing methodologies. Throughout this paper, we aim to discuss the role of SAR imaging in the automotive context under a theoretical and experimental perspective. First, we present the resulting benefits in terms of angular resolution and signal-to-noise ratio. Then, we discuss relevant technological aspects, such as suppression of angular ambiguities, fine estimation of platform motion, and SAR processing architectures, and we present a preliminary evaluation of the required computational costs. Finally, we will present a number of experimental results based on open road campaign data acquired using an 8-channel MIMO radar at 77 GHz, considering the cases of side-looking SAR, forward SAR, and SAR imaging of moving targets.
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This paper proposes a method for efficient and accurate removal of grating lobes in automotive Synthetic Aperture Radar (SAR) images. Grating lobes can indeed be mistaken as real targets, inducing in ...this way false alarms in the target detection procedure. Grating lobes are present whenever SAR focusing is performed using data acquired on a non-continuous basis. This kind of acquisition is typical in the automotive scenario, where regulations do not allow for a continuous operation of the radar. Radar pulses are thus transmitted and received in bursts, leading to a spectrum of the signal containing gaps. We start by deriving a suitable reference frame in which SAR images are focused. It will be shown that working in this coordinate system is particularly convenient since it allows for a signal spectrum that is space-invariant and with spectral gaps described by a simple one-dimensional function. After an inter-burst calibration step, we exploit these spectral characteristics of the signal by implementing a compressive sensing algorithm aimed at removing grating lobes. The proposed approach is validated using real data acquired by an eight-channel automotive radar operating in burst mode at 77 GHz. Results demonstrate the practical possibility to process a synthetic aperture length as long as up to 2 m reaching in this way extremely fine angular resolutions.
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Deep learning solutions have recently demonstrated remarkable performance in phase unwrapping by approaching the problem as a semantic segmentation task. However, these solutions lack explainability ...and robustness to unseen conditions, and they often need a large amount of data for training. By contrast, traditional phase unwrapping algorithms, such as PUMA, rely on principled pipelines that estimate the phase through optimization solvers, despite often failing under severe noise conditions. In this work, we show how to exploit the benefits of both approaches by proposing a way to combine deep neural networks with iterative energy minimization algorithms based on graph cuts. We implement a differentiable version of the PUMA algorithm, NeuralPUMA, which we integrate into a traditional deep learning pipeline to implicitly learn to preprocess the wrapped phase into an intermediate representation that improves the algorithm solution. Through extensive experiments, we show that our approach effectively improves the performance of PUMA in noisy conditions and outperforms recent deep learning methods, while also requiring less training data and simpler neural architectures.
Multipath in automotive MIMO SAR imaging Manzoni, Marco; Tebaldini, Stefano; Monti-Guarnieri, Andrea Virgilio ...
IEEE transactions on geoscience and remote sensing,
01/2023, Volume:
61
Journal Article
Peer reviewed
This paper discusses the effect of multipath in automotive radar imaging under different sensor configurations. The study is motivated by the fact that radar technologies are becoming indispensable ...in the automotive scenario. Many applications such as collision avoidance systems, assisted parking, and driving assistance systems take advantage of radar technologies to accomplish their task. However, one of the main concerns about automotive radars is the possibility of detecting false targets due to multiple signal reflections. In this paper, we show how different sensor layouts experience multipath differently. In particular, we demonstrate that with Multiple-Input Multiple-Output (MIMO) radars, what really matters is the physical positions of the transmitting and receiving antennas. The monostatic/bistatic equivalent configurations cannot be used to design a system and to simulate an acquisition in the presence of multipath. We also demonstrate how vehicle-based MIMO-Synthetic Aperture Radar (MIMO-SAR) imaging can generate a bi-dimensional aperture which significantly reduces multipath effects in the focused image, avoiding the detection of false targets. All the theoretical analyses are supported by several simulations where different sensor layouts are tested, and the capability of MIMO-SAR to reject multipath is validated.
Coherent multistatic radio imaging represents a pivotal opportunity for forthcoming wireless networks, which involves distributed nodes cooperating to achieve accurate sensing resolution and ...robustness. This paper delves into cooperative coherent imaging for vehicular radar networks. Herein, multiple radar-equipped vehicles cooperate to improve collective sensing capabilities and address the fundamental issue of distinguishing weak targets in close proximity to strong ones, a critical challenge for vulnerable road users' protection. We prove the significant benefits of cooperative coherent imaging in the considered automotive scenario in terms of both probability of correct detection, evaluated considering several system parameters, as well as resolution capabilities, showcased by a dedicated experimental campaign wherein the collaboration between two vehicles enables the detection of the legs of a pedestrian close to a parked car. Moreover, as coherent processing of several sensors' data requires very tight accuracy on clock synchronization and sensor's positioning-referred to as phase synchronization -(such that to predict sensor-target distances up to a fraction of the carrier wavelength), we present a general three-step cooperative multistatic phase synchronization procedure, detailing the required information exchange among vehicles in the specific automotive radar context and assessing its feasibility and performance by hybrid Cramér-Rao bound.
The oldest tide gauge observations date back to the 18th century. Although, globally, they are available in limited number, these centuries-old sea level time series are the only data records ...providing information on the long-period rates of change of the mean ocean surface. Knowledge of the past sea level behavior can contribute key insights to the understanding of climate change impacts. We highlight the greatest importance of monitoring sea-level changes at all spatial scales, from global to local, using terrestrial and space techniques and outline the physical processes, natural and man-induced, responsible for such changes. In general, tide gauge data are made available through different archiving facilities serving both international and national developments. Tide gauges measure local sea-level relative to a benchmark on land, hence, correctly interpreting these observations is challenging since it demands, among other requirements, a proper knowledge of vertical land motions at the stations. In general, it is not easy to find well documented historical data; moreover, benchmarks were not frequently leveled. For more than two decades, space geodetic techniques, such as GNSS (Global Navigation Satellite System) and InSAR (Interferometric Synthetic Aperture Radar), have provided the opportunity to accurately position points in the surroundings of tide gauge sites, potentially giving rise to a large amount of information. However, despite the availability of these techniques, the evolution of the international efforts aiming at realizing consistent observational infrastructures for sea level networks is undergoing only a slow development. In the Mediterranean area, there are a few centennial tide gauge records. Our study focuses on the time series of Alicante, in Spain, Marseille, in France, Genoa, Marina di Ravenna (formerly Porto Corsini), Venice and Trieste, in Italy. After briefly reviewing the gauge types presently in use for sea level measurements, a comprehensive historical description is given for each time series, which may assist understanding an assessment of the problems these stations have experienced over more than one century of operations. Two Italian stations, Marina di Ravenna and Venice, are affected by both natural and anthropogenic subsidence, the latter was particularly intense during a few decades in the 20th century because of ground fluid withdrawal. For these two stations, we have retrieved leveling data of benchmarks close to the tide gauges from the end of the 19th century and, for the last couple of decades, we have evaluated GPS and InSAR heights in close proximity to the stations. The GPS (Global Positioning System) and SAR results were carefully compared. Modeling of the long-period non-linear behavior of subsidence was successfully accomplished by using an ensemble of leveling, GPS and SAR data. After removing the vertical land motions in Venice and Marina di Ravenna, and the inverted barometer effect at all the sites, the linear long period sea-level rates were estimated. The results are in excellent agreement ranging between +1.2 and +1.3mm/year for the overall period from the last decades of the 19th century till 2012. The associated errors, computed by accounting for serial autocorrelation, are of the order of 0.2–0.3mm/year for all stations, except Alicante, for which the error turns out to be 0.5mm/year.
Our estimated rates for the northern Mediterranean, a relatively small regional sea, are slightly lower than the global mean rate, +1.7±0.2mm/year, recently published in the IPCC AR5 (Intergovernmental Panel on Climate Change 5th Assessment Report) (Church et al., 2013), but close enough, if uncertainties are taken into account. It is known that Mediterranean stations had always had lower trends than the global-average ones. Our regional results, however, are in close agreement with the global mean rate, +1.2mm/year, published by Hay et al. (2015) which is currently being discussed by the oceanographic community (see, for example, Hamlington and Thompson, 2015). The six time series were also analyzed by means of the EOF (Empirical Orthogonal Functions) technique over the 1934–2012 common period. As a result, about 50% of the total variance is explained by the first mode, which is characterized by a coherent behavior of the six stations.
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With the advent of self-driving vehicles, autonomous driving systems will have to rely on a vast number of heterogeneous sensors to perform dynamic perception of the surrounding environment. ...Synthetic Aperture Radar (SAR) systems increase the resolution of conventional mass-market radars by exploiting the vehicle's ego-motion, requiring very accurate knowledge of the trajectory, usually not compatible with automotive-grade navigation systems. In this setting, radar data are typically used to refine the navigation-based trajectory estimation with so-called autofocus algorithms. Although widely used in remote sensing applications, where the timeliness of the imaging is not an issue, autofocus in automotive scenarios calls for simple yet effective processing options to enable real-time environment imaging. This paper aims at providing a comprehensive theoretical and experimental analysis of the autofocus requirements in typical automotive scenarios. We analytically derive the effects of navigation-induced trajectory estimation errors on SAR imaging, in terms of defocusing and wrong targets' localization. Then, we propose a motion estimation and compensation workflow tailored to automotive applications, leveraging a set of stationary Ground Control Points (GCPs) in the low-resolution radar images (before SAR focusing). We theoretically discuss the impact of the GCPs position and focusing height on SAR imaging, highlighting common pitfalls and possible countermeasures. Finally, we show the effectiveness of the proposed technique employing experimental data gathered during open road campaign by a 77 GHz multiple-input multiple-output radar mounted in a forward-looking configuration.
This paper analyzes the concept of multipath in automotive radar imaging, particularly in MIMO-SAR imaging. In a typical automotive environment, the radar signal may experience multiple reflections, ...which leads to the presence of ghost targets in the focused image. These targets may trigger undesired maneuvers from an advanced driving assistance system (ADAS), resulting in possible accidents. In this paper, we show how the position and brightness of the ghost targets are inherently related to the radar's physical layout, including the number of transmitting and receiving elements and their positions. Accordingly, we strongly recommend avoiding the usage of any monostatic or bistatic equivalence in simulation software since they will result in entirely erroneous results. We also show how MIMO-SAR, if implemented with the MIMO aperture orthogonal to the SAR aperture, is intrinsically robust to double bounces resulting in the suppression of ghost targets due to this effect. A set of simulations representing typical automotive scenarios support the theoretical analysis.
Abstract
The main interest in using synthetic aperture radar (SAR) technology in automotive scenarios is that arbitrarily long arrays can be synthesized by exploiting the natural motion of the ego ...vehicle, enabling finer azimuth resolution and improved detection. All of this is achieved without increasing the hardware complexity in terms of the number of physical antennas. In this paper, we start by discussing the application of SAR imaging in the automotive environment from both theoretical and experimental perspectives. We proceed by describing an efficient processing workflow and we derive the rough number of operations required to focus an image proving the real-time imaging capability of the system. The experimental results are based on open road data acquired using an eight-channel radar at 77 GHz, considering side-looking SAR and forward SAR. The results confirm the idea that SAR imaging can be successfully and routinely used for high-resolution mapping of urban environments in the near future.
