The successful implementation of autonomous driving in an urban setting depends on the ability of the environment perception system to correctly classify vulnerable road users such as pedestrians and bicyclists in dense, complex scenarios. Self-driving vehicles include sensor systems such as cameras, lidars, and radars to enable decision making. Among these systems, radars are particularly relevant due to their operational robustness under adverse weather and night light conditions. Classification of pedestrian and car in urban settings using automotive radar has been widely investigated, suggesting that micro-Doppler signatures are useful for target discrimination. Our objective is to analyze and study the micro-Doppler signature of bicyclists approaching a vehicle from different directions in order to establish the basis of a classification criterion to distinguish bicycles from other targets including clutter. The micro-Doppler signature is obtained by grouping individual reflecting points using a clustering algorithm and observing the evolution of all the points belonging to an object in the Doppler domain over time. A comparison is then made with simulated data that uses a kinematic model of bicyclists’ movement. The suitability of the micro-Doppler bicyclist signature as a classification feature is determined by comparing it to those belonging to cars and pedestrians approaching the automotive radar system.
In recent years, the automotive industry has experienced an evolution toward more powerful driver assistance systems that provide enhanced vehicle safety. These systems typically operate in the optical and microwave regions of the electromagnetic spectrum and have demonstrated high efficiency in collision and risk avoidance. Microwave radar systems are particularly relevant due to their operational robustness under adverse weather or illumination conditions. Our objective is to study different signal processing techniques suitable for extraction of accurate micro-Doppler signatures of slow moving objects in dense urban environments. Selection of the appropriate signal processing technique is crucial for the extraction of accurate micro-Doppler signatures that will lead to better results in a radar classifier system. For this purpose, we perform simulations of typical radar detection responses in common driving situations and conduct the analysis with several signal processing algorithms, including short time Fourier Transform, continuous wavelet or Kernel based analysis methods. We take into account factors such as the relative movement between the host vehicle and the target, and the non-stationary nature of the target’s movement. A comparison of results reveals that short time Fourier Transform would be the best approach for detection and tracking purposes, while the continuous wavelet would be the best suited for classification purposes.
KEYWORDS: Target detection, Detection and tracking algorithms, Data modeling, Wavelets, Dielectrics, Wave propagation, Signal processing, Radio propagation, Ground penetrating radar, General packet radio service
Ground Penetrating Radars (GPR) process electromagnetic reflections from subsurface interfaces
to characterize the subsurface and detect buried targets. Our objective is to test an inversion
algorithm that calculates the intrinsic impedance of subsurface media when the signal transmitted
is modeled as the first or second derivative of a large bandwidth Gaussian pulse. For this
purpose we model the subsurface as a transmission line with multiple segments, each having
different propagating velocities and characteristic impedances. We simulate the propagation and
reflection of the pulse from multilayered lossless and lossy media, and process the received
signal with a rectifier and filter subsystem to estimate the impulse response. We then run the
impulse response through the inversion algorithm in order to calculate the relative permittivity of
each subsurface layer. We show that the algorithm is able to detect targets using the primary
reflections, even though secondary reflections are sometimes required to maintain inversion
stability. We also demonstrate the importance of compensating for geometric spreading losses
and conductivity losses to accurately characterize each substrate layer and target. Such
compensation is not trivial in experimental data where electronic range delays can be arbitrary,
transmitted pulses often deviate from the theoretical models, and limited resolution can cause
ambiguity in the range of the targets.