A new signal-processing approach delivers stable, sub-meter satellite positioning for autonomous systems where city interference usually breaks GNSS accuracy.

The proposed approach estimates position using a particle filter without integer ambiguity resolution, while a tightly coupled Kalman filter computes velocity from raw Doppler measurements.

As the final course in the Applied Kalman Filtering specialization, you will learn how to develop the particle filter for solving strongly nonlinear state-estimation problems. You will learn about the ...

Global navigation satellite systems (GNSS) are vital for positioning autonomous vehicles, buses, drones, and outdoor robots. Yet its accuracy often degrades in dense urban areas due to signal blockage ...

As semiconductor devices become more complex and the critical particle size for today’s cutting-edge technology nodes falls into the sub-10 nm size range, controlling and mitigating potentially ...