Frequency-Diverse Computational Automotive Radar Technique for Debris Detection

Abstract

Frequency-diversity is a computational imaging technique that can offer all-electronic imaging systems by leveraging spatio-temporally incoherent radiation patterns as an enabling technology. This approach exhibits a significant contrast to conventional imaging modalities, such as synthetic aperture radars (SAR) and phased arrays in that the raster scanning requirement of a scene to be imaged (mechanical or electronic) can be broken and replaced by a quasi-random interrogation of the scene. This aspect of frequency-diverse computational imaging systems significantly simplifies the physical hardware requirements of conventional radars. Despite this advantage, the application of the frequency-diversity technique has been mostly limited to static imaging scenarios, where the position of the scene to be imaged remains fixed over the data acquisition cycle. This limitation hinders the frequencydiverse computational radars from being deployed for applications where the scene dynamics may vary over the data acquisition cycle, such as in automotive radars. In this paper, we demonstrate that by modifying the sensing matrix to account for the movement of the radar platform, frequency-diverse computational imaging radars can be successfully used in debris detection on roads. We show that operating within the frequency band of 77-81 GHz, the presented dynamic frequency-diverse radar technique can produce high fidelity point spread function (PSF) patterns eliminating the distortions caused by the motion of the radar. We also prove that the PSF patterns of the radar are in excellent agreement with theoretical diffraction limited resolution limits.

Hardware Enabled Acceleration of Near-Field Coded Aperture Radar Physical Model for Millimetre-Wave Computational Imaging

“…These reconstructions are from simulated data using the developed physical model given in Equations ( 1)-( 5) only. Using the imaging setup as shown in Figure 1(a), the total electric field of the transmitting and receiving apertures is calculated according to Equations (3) and (4). These values are used to calculate the sensing matrix in Equation (2), and the scene is reconstructed from the calculated sensing matrix in Equation (5).…”

“…Combined with the hardware simplification facilitated by the CI technique, FPGAparallelization of the CI physical model exhibits a significant potential for reducing the computational latency of CI radars. It is worth mentioning that in spite of being shown for radar imaging, the validity of the presented approach is independent of the type of specific application considered, and this technique can be readily adopted across a wide application spectrum in CI, from security-screening [22,33,43] to automotive radars [4] and computational remote sensing techniques [6]. This is because the compression of the back-scattered data on the physical layer is the same for such applications.…”

“…They are also used for imaging purposes. Imaging at millimetre-waves (mmW) has many applications such as security screening [1][2][3], automotive radars [4,5], coincidence imaging [6], autonomous robotics [7][8][9], remote sensing [10][11][12], and concealed weapon detection [13][14][15]. In comparison to optical modalities, mmW signals can penetrate most optically opaque materials and operate in all-weather conditions.…”

Coded-Aperture Computational Millimeter-Wave Image Classifier Using Convolutional Neural Network

“…This makes it possible for the model to be used in applications requiring real-time decision making. One such particular application example for this can be automotive radars for debris detection [81]. In [81], a frequency-diverse computational radar system has been developed for detecting debris on roads.…”

“…One such particular application example for this can be automotive radars for debris detection [81]. In [81], a frequency-diverse computational radar system has been developed for detecting debris on roads. This computational radar system can be integrated with the classification scheme developed in this paper.…”