Automotive functions such as advanced driver assistance (ADAS) and autonomous driving (AD) lead to the requirement of deploying radars in vehicles. These automotive radars interfere with each other when vehicles are in traffic, called mutual interference. Interference causes a decrease in target detection probability and an increase in false alarm rate simultaneously. This can make automotive functions dependent on automotive radars unreliable. Mitigating interference is therefore important for efficient deployment of automotive radars. In this paper, we introduce a novel method for interference mitigation based on principal component analysis (PCA) using the Alternating Least Square (ALS) algorithm. The method is tested successfully with a set of signals obtained from the radar measurement in the server interference environment.