Interference in Automotive Radar Systems: Characteristics, mitigation techniques, and current and future research

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Interference in Automotive Radar Systems: Characteristics, mitigation techniques, and current and future research

Stephen Alland; Wayne Stark; Murtaza Ali; Manju Hegde

This article examines the problem of interference in automotive radar. Different types of automotive radar as well as mechanisms and characteristics of interference and the effects of interference on radar system performance are described. The interference-to-noise ratio (INR) at the output of a detector is a measure of the susceptibility of a radar to interference. The INR is derived from different types of interfering and victim radars and depends on the location of both as well as parameters such as transmit power, antenna gain, and bandwidth. In addition, for victim radar with beamscanning, INR depends on the location of the target the victim radar is attempting to detect. Analysis is presented to show the effects of various interference scenarios on the INR. A review of the current state of the art in interference mitigation techniques previously deployed as well as areas of research currently being addressed is then provided. Finally, important future research directions are suggested.

Vehicular sensors

Sensors for vehicular (automobiles, buses, trucks, and so on) applications are an important area of R&D. There are many different types of currently used sensors being considered for use in future vehicles, such as radars, cameras, lidars, and ultrasonics. Each of these sensors has strengths and weaknesses; good engineering judgment indicates that a combination of sensors, which complement the strengths and weaknesses of one with another, is required to maintain the integrity of safety-critical systems. For instance, radars are the best sensors for detecting range and radial velocity and have “all-weather” capability, but are weak for classification and angular resolution. Lidars, in general, have good angular resolution and range, but are limited in field of view (FOV) and have limited ability in adverse weather. Cameras have excellent color perception and classification capabilities but are limited in estimating velocity and range. Cameras also have difficulty in dark or adverse weather.

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