Signal Processing - March 2017 - 33

Coupler

FMCW/
CW
Source

To
Baseband
A/D

Phase
Shifter

Target
Channel Model (Reflectivity
Model)

50 m

RX
LPF

Mixer

1 × 8 Element Patch
Antenna Array

LNA

(b)

(a)

Choose Appropriate Method of EM Simulation
According to Size of the Targets
Run FEKO from MATLAB
Read the Power and Phase at Each Antenna
Element from .out File of FEKO Using MATLAB
and Apply Imaging Algorithms

0
-10
Normalized Power (dB)

Define System Specifications
(Geometry, Tx-Rx Antenna Pattern
Wavelength, and Bandwidth)
Using CADFEKO

-20

Beamforming
MUSIC

-30
-40
-50
-60

Estimated Location of the Target

-90 -75 -60 -45 -30 -15 0 15 30 45 60 75 90
Angle (°)

(c)

(d)

Figure 10. (a) An RF block diagram showing various aspects of realistic radar simulations. (b) The antenna array, channel, and target modeling using ray
tracing in FEKO. (c) The algorithm for FEKO and MATLAB integration. (d) Azimuthal angle estimation for the scenario in Figure 10(b) using MUSIC and
beamforming algorithms; it shows the effect of directional RCS of the car.

We demonstrate a realistic automotive radar simulation setup
based on FEKO and MATLAB implementations, as illustrated
in Figure 10.

Data fusion and challenges
The automotive radar output is often combined with outputs
from other sensors such as lidar, camera vision, and ultrasound.
Lidar and vision sensors can help enhance discrimination capabilities and reduce computation costs by delivering faster
response. Independent observations from other sensors must be
combined with radar systems to increase the reliability. For
example, the lidar provides improved target detection on curved
roads. Radar offers superior speed measurements, as they rely
on the Doppler effect as opposed to lux measurement in lidar
[63]. Moreover, lidar is more sensitive to environmental factors
such as snow, fog, dust, and rain [64].
When multiple sensors are in operation, all measurements
should be synchronized to a common clock using time stamp-

ing. Observations from individual sensors are typically combined
together to form global sensor data. The relative placement, orientation, and mathematical models of each sensor should be considered. Details about fusion techniques such as object-list-level,
track-to-track, low-level, and feature-level fusion are discussed in
[65] and [66]. More information about real-time object detection
using learning algorithms can be found in [67].
Another important aspect of automotive radars is the interference between two vehicles [68]. Analytical studies point out
reduced radar sensitivity in such cases. Null steering, tracking,
coded sequences, and interleaving are among several techniques
used for interference mitigation. An additional feature of the
intelligent transportation system can include vehicle-to-vehicle
communication, which can also help to avoid collision [69], [70].

Conclusions
As we progress toward fully autonomous driving, many challenges and innovative solutions will emerge. The fundamental

IEEE SIgnal ProcESSIng MagazInE

March 2017

Table of Contents for the Digital Edition of Signal Processing - March 2017