An algorithm can double the resolution of a radar system without replacing the equipment, writes Nick Flaherty.

In Korea, the algo developed by researchers at Daegu Gyeongbuk Institute of Science and Technology enables precise object recognition using existing hardware specifications without the need for bandwidth expansion.

This approach also enables the precise identification of objects outside the vehicle with lower-cost hardware or more resolution for autonomous vehicles.

Frequency-modulated continuous-wave (FMCW) radar systems for automotive and aerospace applications require resolution-enhancement technologies to improve object recognition. The answer typically involves boosting bandwidth or using ultra high-resolution algorithms with significant complexity, but this increases both costs and system complexity.

The range resolution of FMCW radars is determined by bandwidth – its size being proportional to the observation window (OW). If the OW is sufficiently large, multiple frequencies with small differences between sinusoids can be well estimated. However, if the OW is insufficient, multiple sinusoids with similar frequencies may be incorrectly assumed to be a single frequency.

The research team discovered that additional information embedded in the envelope of radar signals could be used. The algorithm analyses the features of received signals from two targets – the beats of the signal envelope – to improve the resolution of target detection using the same bandwidth. This achieves nearly twice the resolution through signal processing on existing radar hardware.

The algorithm is based on the fact that the envelope of the beat signal contains information about the difference between two frequencies. This is used to estimate the frequencies missed due to the insufficient size of the observation window.

Specifically, it uses the Fast Fourier Transform (FFT) results that were incorrectly estimated as a single target, along with the FFT results of the envelope of the beat signal.