In this paper, a two-dimensional (2D) autoregressive (AR) model is employed as a mitigation algorithm for interference in the Frequency Modulated Continuous Wave (FMCW) radars. The AR model, due to its simple structure, can perform super efficiently in multi-dimensional estimation problems and can be a suitable replacement for complex Neural Network (NN) based algorithms. This study addresses the advanced requirements of the 2DAR algorithm for mitigating interference in actual frames collected from real-world experiments. Three approaches to interference generation were incorporated to reproduce and study the most common and likely circumstances of mutual interference. A dynamic sampling direction selection framework is developed to address the unpredictable shapes of interfered segments within a frame. An iterative signal reconstruction algorithm is proposed to reconstruct the damaged areas using clean samples. Finally, the parallelizable processes were vectorized to make them implementable in the real world. The 2DAR mitigator’s performance was assessed using a diverse dataset of frames collected from real-world experiments, each containing unique target, noise, and interference attributes. The derived mitigator improved the signal in all experimental cases, down to the noise floor, and increased the Signal to Interference plus Noise Ratio (SINR) to almost 15 dB. Finally, the performance of different order models was compared in an identical hardware and software environment to provide a scaled indicator of the computation escalation in different model orders.