María A. González Huici

Safe operation of driver assistance systems, especially at higher speeds, remains a challenge. For this automotive application, we propose a long-range coherent LiDAR system concept with two-dimensional micro electro-mechanical system (MEMS) scanning and sparse sensing capability. We address system design challenges for the transmitter, the fiber-based optics and coherent receiver, and the impact on MEMS mirror design. Furthermore, we compare compressed sensing and deep learning techniques to implement sparse sensing and we provide preliminary results of the systems performance with a simplified setup.

This chapter addressed the challenges and opportunities that come along with the introduction of micro -Doppler signatures in automotive radar applications. Here, a major interest lies in the development of methods to protect vulnerable road users, i.e. pedestrians, cyclists, etc. As has been shown in various studies, micro -Doppler signatures enable a way to identify these targets, thereby making active protection measures possible. Apart from the well -studied single -target scenario, this chapter focused on the far more realistic multi -target scenario and presented an experimentally validated method for simultaneous micro -Doppler signature extraction and classification using a 94 GHz FMCW radar. Central to any multi -target classification system is a tracking algorithm as it links signatures with dynamic targets. Furthermore, it has been shown that by solely using micro -Doppler information, the motion of a pedestrian (Section 12.3.3.1, Case 2) can be determined and separated from other vulnerable road users (Section 12.3.3.1, Case 1). Future work might include the fusion of tracking and micro Doppler signature information to increase the overall classification performance. In the scope of this chapter, it was not feasible to cover every approach in equal depth. The presented approach is adequate but by no means the only one. Therefore, throughout the chapter other solutions are mentioned and referenced.

Radar sensors are one of the key elements in highly automated driving systems. Their performance is robust independently of weather and visibility conditions, being able to detect targets up to hundreds of meters distance. In vehicles, radar sensors are required to estimate the range, doppler shift and direction of arrival of the reflected wave. In this contribution we will focus on the direction of arrival (DoA) estimation, which, while being crucial for environmental perception, still has considerable room for improvement. In particular, we aim to measure the performance of Compressive Sensing (CS) based algorithms applied for reconstruction of the DoA. In contrast to the traditional FFT approach, these algorithms are able to exploit sparse antenna configurations with a reduced number of Tx and Rx channels and a large effective aperture. For this purpose, a series of relevant metrics are defined and applied to measurements in representative open-air driving scenarios acquired with an automotive 4x8 MIMO radar operating at 77GHz. The results show that, when compared with the FFT, these algorithms display an overall enhanced angular estimation accuracy, resolution and false alarm ratio

The ground penetrating radar (GPR) has demonstrated good potential for the remote imaging of surface-laid or shallow-buried landmine-like objects (typically ~ 5-30 cm) and its use is currently receiving much attention. It has turned out to be a promising alternative technology for low dielectric contrast objects, a difficult detection situation that is often encountered in practise (e.g. detection of plastic mines in dry or sandy soils environment). This paper examines numerically the imaging of buried objects using ultrawide-band (UWB) time domain radar. We develop a simplified model to characterize the system, air-ground-buried targets-antennas and simulate the electromagnetic wave propagation and scattering at a bandwidth of 0.5-2.5 Ghz. All the elements need to be modelled simultaneously in order to obtain an accurate estimation of our radar performance and surface response to the incoming radar pulses. The final goal is to improve the detection rate of plastic antipersonnel mines, reducing the false alarm level. We show some simulated results in 2D and 3D assuming plane waves and afterwards we introduce a model for our GPR antennas to characterize the real source.

In this paper, we explore the radiation performance of an experimental Ground Penetrating Radar (GPR) antenna system intended for cavity detection up to a few meters depth. To completely characterize the antenna in frequency and time domain we carried out a series of free space measurements in an anechoic chamber. The obtained results were in satisfactory agreement with full-wave simulations. Then, we analyzed via simulation the electromagnetic energy coupling into the soil for different antenna elevations. We also investigated the influence of nearby receivers over the antenna fingerprint and radiation pattern. Finally, we summarize the obtained results to select the optimum arrangement for the final design.

Ground penetrating radar (GPR), which can detect shallow buried low dielectric contrast objects in a variety of soils by non-invasive subsurface sensing, is a promising technology for imaging low-metal or non-metallic landmines. In this paper we present a complete model of a complex GPR scenario which is solved via finite element method, and includes the actual impulse GPR system, interface, soil and targets. We obtain time domain signatures for different testmines and small objects under different soil conditions which are satisfactorily correlated with measurements. The simulated responses give us a broad understanding about the factors which control the electromagnetic scattering by small objects and are used to interpret the characteristics of the signatures according to the target and background parameters. These obtained waveforms may be applied to reduce the false alarm.