The automotive industry is in a race to bring more advanced driver-assistance systems (ADAS), such as active cruise control, emergency braking, and lane keeping to its vehicles. The increasing sophistication of these systems has, in turn, driven increasing demand for on-vehicle radar systems, to provide the information they need to make sense of their environment.
How do they do this? An automotive radar system works by transmitting a continuous, high-frequency signal, and then measuring the propagation delay and Doppler frequency shifts of its reflection. This allows the calculation of distances to other objects and vehicles, and their radial velocities. Advanced radar systems with array antennas can also measure the azimuth (angle in the horizontal plane) between one vehicle’s path and a detected object, as well as the elevation (angle in the vertical plane) between one vehicle and other objects.
It is important that automotive radar systems work accurately and reliably, since the data they provide is utilized by a vehicle’s control systems to understand its environment and make real-time decisions about how it should maneuver. Bad data will lead to bad decisions. For example, if a radar system misreports the angle between two vehicles by 1 degree when they are 100 m apart, when they converge, their paths could be 1.5 m, or a car’s width, closer to each other than expected. That distance makes the difference between two vehicles passing safely in their lanes and a head-on crash.
Form Frustrates Function
Radar sensors are usually hidden behind the emblems on a vehicle’s radiator grill, or in its bumpers. This improves vehicle aesthetics, at the cost of introducing attenuating materials into the path of the radar signals. These “radar domes,” or radomes, become part of the RF system of the radar sensor, altering the transmitted signal in a way that can affect its detection performance and accuracy.
What impact does this have? The inverse square law tells us that the power of the reflected signal received by a radar sensor will be 1/r4 of its transmitted power. If, for example, a 77 GHz radar with a 3 W output power and a 25 dBi antenna gain must detect a target of 10 m² and a minimum detectable signal of -90 dBm, its maximum radar range will be 109.4 m. If adding a radome causes a two-way attenuation of a further 3 dB, the radar’s detection range will be cut by 16 percent, to 92.1 m—in practical terms, a difference of several car-lengths.
Radomes create other challenges. There may be an RF mismatch between the base material and the radar signal. Plastic moldings often have non-uniform material properties, which can cause unpredictable signal distortions. The same is true of RF scattering caused by metallic surface finishes. The resultant interference reduces the detection sensitivity of the radar receiver. One way of fixing this is to mount the radome so that the emitted radar signal is not reflected directly back into the receiver. However, this can limit the vehicle designer’s options and does not overcome the issue of parasitic reflections.
Achieving Systemic Performance Through Systemic Calibration
The challenge for radar system designers is that they need to achieve a certain level of performance, despite the multiple uncertainties inherent in their systems. Anything that reduces systemic uncertainties makes achieving that performance easier.
For example, radar sensor manufacturers can calibrate their products. RF system designers, however, know that manufacturers can’t predict what kind of radome their sensors will be mounted under, how it will be painted, the variability of the material it is made of, and so on. So calibrated sensors can only be a part of the solution to achieve the necessary systemic performance.
Another step toward greater certainty is for radome suppliers to test and validate the properties of their offerings, so that radar system designers know what they are working with. The alternative of testing and adjusting each radome’s performance on the vehicle production line is much too costly.
Radome suppliers, therefore, need access to detailed, reliable production-level testing. To date, this has often involved testing a reference radar system in a static environment that includes a number of radar reflectors. Measurements are taken at various distances and angles from the source to create a reference specification, after which each radome is measured in the same way. A radome passes the test when its measurements are within specified tolerances of the reference measurements.
A more detailed version of this test involves mounting the radar and radome on a turntable, facing a single reflector. Measurements are then taken at various angles as it rotates. This can create a more detailed set of reference measurements, but is too slow for use in production tests.
A Better Radome Tester
Given the increasing importance of radar system performance in automotive systems, any method to test an automotive radome during production would need to complete in seconds rather than minutes. An innovative test solution using an array of hundreds of radar transmitter and receiver antennas has been developed to solve this industry-wide challenge. It provides measurements of much higher resolutions than turntable-based options using a commercial radar solution.
The system provides reliable test data at a cost and speed that is practical for use in production environments. Using this method, the radome’s impact on the test signal can be visualized. The resulting image can also be analyzed to extract quality parameters, which can then be processed into a simple pass/fail metric.
The tester can also assess transmissivity, revealing the frequency matching and attenuation of the material and hence its suitability for use at particular frequencies. A calibrated transmission unit behind the device under test (Figure 1) sweeps a selected frequency span and the antenna array receives the signals, enabling a precise assessment of the radome’s transmission frequency response. This response also shows how well the radome will form an RF match with the radar signal at the intended operating frequency.
Assuring the Accuracy and Reliability of Safety-Critical Data
Autonomous driving assistance systems need access to high quality and reliable data from the multiple radar systems they use to sense their environments. The quality and reliability of this data can be undermined by the introduction of radomes of variable properties in the RF signal path. Since it is too complex, costly, and time consuming to check and adjust the properties of radomes on the vehicle production line, manufacturers will have to test and validate the RF performance of their radomes as standalone parts.