Constraints-Enhanced Resilient Estimation Against Random Sensor Failures and Colored Measurement Noise

This letter proposes a novel scheme for resilient estimation under random sensor failures and colored measurement noise, without requiring hardware-redundant designs or complex adaptive mechanisms. The key insight is to leverage prior constraints to increase information redundancy. Resilience against both disturbances is achieved through a Kalman-Petovello filter, which is derived by whitening the colored noise via measurement differencing and incorporating the prior constraints as pseudo-measurements. Theoretical analysis and numerical simulations demonstrate the efficacy of the proposed scheme.