Radar waveform diversity has received considerable attention in recent years due to increasing spectral congestion and the burgeoning capabilities of digital waveform generation. The promise of waveform diversity is far greater utilization of available degrees of freedom to enhance sensing performance and to even facilitate new operating modes. This tutorial provides an overview of this very broad topic, from the basic principles upon which it is founded to the myriad different areas being explored in research for practical sensing applications.

Overview of radar waveform diversity

The 6G vision of creating authentic digital twin representations of the physical world calls for new sensing solutions to compose multi-layered maps of our environments. Radio sensing using the mobile communication network as a sensor has the potential to become an essential component of the solution. With the evolution of cellular systems to mmWave bands in 5G and potentially sub-THz bands in 6G, small cell deployments will begin to dominate. Large bandwidth systems deployed in small cell configurations provide an unprecedented opportunity to employ the mobile network for sensing. In this paper, we focus on the major design aspects of such a cellular joint communication and sensing (JCAS) system. We present an analysis of the choice of the waveform that points towards choosing the one that is best suited for communication also for radar sensing. We discuss several techniques for efficiently integrating the sensing capability into the JCAS system, some of which are applicable with NR air-interface for evolved 5G systems. Specifically, methods for reducing sensing overhead by appropriate sensing signal design or by configuring separate numerologies for communications and sensing are presented. Sophisticated use of the sensing signals is shown to reduce the signaling overhead by a factor of 2.67 for an exemplary road traffic monitoring use case. We then present a vision for future advanced JCAS systems building upon distributed massive MIMO and discuss various other research challenges for JCAS that need to be addressed in order to pave the way towards natively integrated JCAS in 6G.

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Joint Design of Communication and Sensing for Beyond 5G and 6G Systems

Although the beginning of research on automotive radar sensors goes back to the 1960s, automotive radar has remained one of the main drivers of innovation in millimeter wave technology over the past two decades. Today, millions of sensors are produced each year, which was made possible by inexpensive and mature millimeter wave technology. The technology maturity, in turn, enables research to be carried out on systems that are considerably more complex and powerful than was possible just a few years ago. The focus of research has thus shifted from purely hardware-oriented and device-level topics to sophisticated millimeter wave systems and RF signal processing topics. This opens up new research topics such as digital modulation schemes, radar networks, radar imaging, and machine learning. In this review paper, we sketch the path from the very beginning through the state of the art with sophisticated multiple-input multiple-output (MIMO) antenna arrays and mature assembly and interconnect concepts to today's key research topics of automotive radar.

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Automotive Radar — From First Efforts to Future Systems

Automotive radar is the most promising and fastest-growing civilian application of radar technology. Vehicular radars provide the key enabling technology for the autonomous driving revolution that will have a dramatic impact on everyone's day-to-day lives. They play a central role in the autonomous sensing suit because of the significant progress in the radio-frequency (RF) CMOS technology that enables high-level radaron-chip integration and thus reduces the automotive radar cost to the level of consumer mass production. However, this would not be sufficient without high spatial resolution performance, which can be obtained by multiple-input, multiple-output (MIMO) and cognitive approaches at a lower cost.

The Rise of Radar for Autonomous Vehicles: Signal processing solutions and future research directions

The ongoing automation of driving functions in cars results in the evolution of advanced driver assistance systems (ADAS) into ones capable of highly automated driving, which will in turn progress into fully autonomous, self-driving cars. To work properly, these functions first must be able to perceive the car's surroundings by such means as radar, lidar, camera, and ultrasound sensors. As the complexity of such systems increases along with the level of automation, the demands on environment sensors, including radar, grow as well. For radar performance to meet the requirements of self-driving cars, straightforward scaling of the radar parameters is not sufficient. To refine radar capabilities to meet more stringent requirements, fundamentally different approaches may be required, including the use of more sophisticated signal processing algorithms as well as alternative radar waveforms and modulation schemes. In addition, since radar is an active sensor (i.e., it operates by transmitting signals and evaluating their reflections) interference becomes a crucial issue as the number of automotive radar sensors increases. This article gives an overview of the challenges that arise for automotive radar from its development as a sensor for ADAS to a core component of self-driving cars. It summarizes the relevant research and discusses the following topics related to highperformance automotive radar systems: 1) shortcomings of the classical signal processing algorithms due to underlying fundamental assumptions and a signal processing framework that overcomes these limitations, 2) use of digital modulations for automotive radar, and 3) interference-mitigation methods that enable multiple radar sensors to coexist in conditions of increasing market penetration. The overview presented in this article shows that new paradigms arise as automotive radar transitions into a more powerful vehicular sensor, which provides a fertile research ground for further investigation.

High-Performance Automotive Radar: A review of signal processing algorithms and modulation schemes

The general requirement in the automotive radar application is to measure the target range R and radial velocity v
 r
 simultaneously and unambiguously with high accuracy and resolution even in multitarget situations, which is a matter of the appropriate waveform design. Based on a single continuous wave chirp transmit signal, target range R and radial velocity v
 r
 cannot be measured in an unambiguous way. Therefore a so-called multiple frequency shift keying (MFSK) transmit signal was developed, which is applied to measure target range and radial velocity separately and simultaneously. In this case the radar measurement is based on a frequency and additionally on a phase measurement, which suffers from a lower estimation accuracy compared with a pure frequency measurement. This MFSK waveform can therefore be improved and outperformed by a chirp sequences waveform. Each chirp signal has in this case very short time duration T
 chirp
. Therefore the measured beat frequency f
 B
 is dominated by target range R and is less influenced by the radial velocity v
 r
. The range and radial velocity estimation is based on two separate frequency measurements with high accuracy in both cases. Classical chirp sequence waveforms suffer from possible ambiguities in the velocity measurement. It is the objective of this paper to modify the classical chirp sequence to get an unambiguous velocity measurement even in multitarget situations.

New chirp sequence radar waveform

An FMCW radar system, which transmits a sequence of rapid chirp signals, is considered to measure target range and radial velocity simultaneously even in multitarget situations. The main outcome of the coherent processing procedure applied to the received echo signal is the two-dimensional range-Doppler-matrix (RDM), which is the basis for an adaptive constant false alarm rate (CFAR) target detection technique. The well-known one-dimensional cell averaging (CA) CFAR procedure suffers from masking effects in all multitarget situations and is additionally very limited in the proper choice of the reference window length. In contrast the ordered statistic (OS) CFAR is very robust in multitarget situations but requires a high computation power. Therefore two-dimensional CFAR procedure based on a combination of OS and CA CFAR is proposed. In this case any masking effects in multitarget situations can be avoided without increasing the computation complexity. In addition the size of the reference window can be adjusted in order to optimize the detection performance.

Fast Two-Dimensional CFAR Procedure

A Continuous Wave (CW) radar with a linear Frequency Modulation (FM) and a very short chirp duration is considered in this paper. A single target range and Doppler frequency measurement consits of a sequence of L chirp signals with the same chirp duration. The maximum unambiguously measurable radial velocity in this case is limited by a minimum chirp duration. Therefore fast moving targets will cause ambiguous measurement results, that have to be resolved. The technique of resolving ambiguities on the basis of several subsequent measurements with different ambiguity interval lengths is well known as Chinese Remainder Theorem (CRT). It has been extensively described in literature [2]. Two different signal processing approaches to apply this technique for the resolution of ambiguities are compared in this paper. These do not differ in terms of the ambiguity resolution itself, because they are based on the same principle. The detection performance however is different.

Radar target detection and Doppler ambiguity resolution

For all radar applications it is required to measure the target range R and radial velocity v r simultaneously and unambiguously with high accuracy and resolution even in multi target situations. In this paper, a new radar waveform is proposed, which is based on a chirp sequence. In the considered case, each chirp signal has a very short duration T chirp . Therefore, the measured beat frequency f B is dominated by the target range R and is less influenced by the radial velocity v r . However, the classical chirp sequence waveform suffers from possible ambiguities in the velocity measurement. It is, therefore, the objective of this paper to modify the classical chirp sequence based on an additional frequency modulation from chirp to chirp to get an unambiguous velocity measurement even in multi target situations.

New radar waveform based on a chirp sequence

Driving a car seems to be a safe action. However, there are about 5,000 fatalities on German streets every year, which is absolutely too much. All drivers have strong limitations when measuring the distance and the relative velocity of other cars, especially under bad weather conditions which are the reason for several accidents. Therefore, some technical assistance is highly welcome to every driver. The European Union has called all car manufacturers to intensify their research activities in protecting vulnerable road users and increasing the traffic safety. An automotive radar sensor in the 24 GHz frequency domain measures target range, radial velocity and azimuth angle simultaneously with high accuracy and resolution even in multitarget situations. The all-weather capability is an important additional feature of all radar systems. Therefore, this chapter considers automotive radar sensors as a basis for reliable driver assistant systems. The technical challenge is the simultaneous measurement of target range, radial velocity and azimuth angle. This task has a direct and strong relevance to the waveform design process. Therefore, the objective of this chapter is the waveform design for continuous wave radar systems. Several transmit signals are considered in detail and different proposals are discussed and compared, especially for automotive applications. Usually, the lateral velocity component cannot be measured by a radar sensor and a single measurement. This task can be of high importance in typical city traffic situations where the track direction of a car needs to be known in a very short time frame. A signal processing scheme will be shown in this chapter which indeed allows the additional measurement of the lateral velocity component based on a single observation.

Matthias Kronauge

Papers

New chirp sequence radar waveform

Fast Two-Dimensional CFAR Procedure

Radar target detection and Doppler ambiguity resolution

New radar waveform based on a chirp sequence

Continuous waveforms for automotive radar systems