Abstract: The growing demand for perception in autonomous vehicles, defense, remote sensing, and wireless networks is pushing sensor and radar systems toward higher resolution, wider bandwidth, and operation in increasingly cluttered and contested environments. Conventional model-based signal processing, while interpretable and well-understood, struggles to scale to these regimes, where signal statistics are non-stationary, interference is adaptive, and the underlying physics are only partially known. Artificial intelligence models offer a complementary paradigm: by learning directly from data, they can extract weak targets from noise, suppress clutter and jamming, classify objects from raw or minimally processed returns, and fuse heterogeneous modalities without hand-crafted feature pipelines. This abstract argues that AI will be central to future sensor and radar development along three axes. First, at the front end, learned models enable cognitive and reconfigurable radar that adapts waveforms, beams, and sampling in real time to the scene and mission. Second, in signal and data processing, deep networks improve detection, tracking, super-resolution, and target recognition, particularly for low-observable targets and in low-SNR conditions, while physics-informed and hybrid architectures retain the reliability of model-based methods. Third, at the system level, AI supports distributed multi-sensor fusion, resource management, and edge inference under tight power and latency budgets. Realizing this potential requires addressing open challenges in data scarcity and simulation-to-reality transfer, robustness against adversarial and out-of-distribution inputs, interpretability for safety-critical use, and efficient deployment on constrained hardware. We conclude that the tight integration of AI models with electromagnetic and sensing physics rather than either approach alone will define the performance frontier of the next generation of sensor and radar systems.

Index Terms: AI, Sensor models.