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Automotive Radars

FMCW radar EM simulation setup

 
Radar algorithms are often verified by means of simulations, which reduces the cost of prototyping and testing. While modeling the radar systems, the targets and channels under consideration are assumed to be ideal. The targets are modeled as objects with perfect reflectivity, and the signals are assumed to propagate through unobstructed paths. To verify the viability of various radar estimation algorithms in the real world, it is necessary to use computational EM software to simulate potential target RCSs and channels.
 
A realistic simulation setup should include radiation patterns of the transmit and receive antenna elements, which count for the direction dependent scaling of the transmitted and reflected signals according to the geometry of the system. In addition, EM waves undergo reflection, diffraction, and scattering, depending on the shape and size of the target with respect to its wavelength. To incorporate these phenomena, Maxwell’s equations with appropriate boundary conditions must be solved. Along with numerical computing, software packages such as MATLAB or MATHEMATICA and EM simulators such as ADS [60], FEKO [61] or Xpatch [62] can be used for the accurate modeling of the automotive radar imaging. The effect of RF impairments such as phase noise, local oscillator leakage, and in-phase and quadrature imbalance can be modeled either in MATLAB or an EM simulator such as ADS.

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 stamping. 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 component of these autonomous systems is the automotive radar, which has become feasible due to prospering mm-wave circuit technology. Concurrently, sophisticated signal processing techniques have gained momentum to efficiently utilize the automotive radar hardware. In this article, we have presented various signal processing aspects of automotive radars, starting from basics of range and velocity estimation to complex 3-D end-to-end EM simulation. The target location estimation techniques are explained with sufficient mathematical details and illustrative examples so that the article may also serve as a tutorial. For briefly touched-on advanced topics in the field, we have pointed to relevant literature, which readers can pursue according to their interests. This review article should help researchers and engineers take a first step forward in developing novel automotive radar signal processing techniques.

Acknowledgment

 
This work was, in part, supported by Semiconductor Research Corporation through the Texas Analog Center of Excellence at the University of Texas at Dallas under task 1836.150.

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