  Automotive radar emerges as a crucial sensor for autonomous vehicle
perception. As more cars are equipped radars, radar interference is an
unavoidable challenge. Unlike conventional approaches such as interference
mitigation and interference-avoiding technologies, this paper introduces an
innovative collaborative sensing scheme with multiple automotive radars that
exploits constructive interference. Through collaborative sensing, our method
optimally aligns cross-path interference signals from other radars with another
radar's self-echo signals, thereby significantly augmenting its target
detection capabilities. This approach alleviates the need for extensive raw
data sharing between collaborating radars. Instead, only an optimized weighting
matrix needs to be exchanged between the radars. This approach considerably
decreases the data bandwidth requirements for the wireless channel, making it a
more feasible and practical solution for automotive radar collaboration.
Numerical results demonstrate the effectiveness of the constructive
interference approach for enhanced object detection capability.
