Radars are an indispensable sensing modality for autonomous navigation, vehicular networking and beyond, with features complementary to visible light sensing systems. Traditional radar signal processing estimates the range and radial velocity of objects in direct line-of-sight to the radar, i.e., objects directly illuminated by the radar that scatter the illumination back to the radar. However, line-of-sight signal processing limits radar performance in three ways. First, in radar systems with highly directional signal transmissions, e.g., those in the millimeter-wave and terahertz frequency bands, line-of-sight processing limits the field-of-view over which objects can be detected/sensed. Second, real-world signal propagation is rarely limited to line-of-sight propagation, and signals undergo significant multipath due to secondary reflections in the environment. Line-of-sight processing in presence of multipath results in the formation of false targets, a.k.a. ``ghosts,'' at physically incorrect locations, degrading accurate target detection and localization capabilities. Third, line-of-sight Doppler processing prevents radars from estimating the tangential velocities of moving objects, making it challenging to distinguish between objects that are stationary versus those that are moving tangentially to the radar.

This thesis tackles all three limitations by rethinking the role of multipath in radar signal processing. The three parts of this thesis demonstrate how the three limitations can be overcome by treating multipath as an opportunity to leverage - by explicitly incorporating multipath into radar signal processing - rather than as a nuisance. The first part of this thesis theoretically demonstrates that leveraging multipath for radar imaging can improve radar resolution when multipath provides new looks'' of the imaging scene beyond those provided by line-of-sight, effectively forming a multi-look'' synthetic aperture without requiring any physical aperture extension. The second and third parts of this thesis translate this theoretical idea into practice. The second part of this thesis utilizes the additional looks" provided by multipath to sense beyond-field-of-view objects that are imperceptible with line-of-sight processing, e.g., objects behind the radar or around-corners, without having to contend with the problem of multipath ghosts''. The final part of this thesis in turn uses multipath from static features in the environment (building pillars, walls, etc.), that may be known a-priori or estimated via beyond-field-of-view processing, to estimate both the tangential and radial velocities of line-of-sight moving objects.

Overall, this thesis advocates for novel modeling and signal processing approaches to improve and unlock new sensing capabilities with existing radar systems. The methods proposed in this thesis are implementation-agnostic and are compatible with existing radar sensing and communication pipelines across different waveform choices and frequency bands. Hence, the results presented in this thesis are applicable to multiple use-cases, such as autonomous navigation, vehicular networking, emergency services, spatial computing, joint radar sensing and cellular communication, etc.