Riccati observers for position and velocity bias estimation from either direction or range measurements
For robotic and autonomous systems requiring accurate position estimation from biased sensors, this work provides theoretically grounded observers with guaranteed stability and explicit convergence rates.
This paper proposes Riccati observers for estimating the position and velocity bias of a moving object using velocity and either direction or range measurements, achieving global exponential stability under persistent excitation conditions. The observers guarantee zero estimation errors even with unknown constant velocity bias, with explicit lower bounds on convergence rate.
This paper revisits the problems of estimating the position of an object moving in $n$ ($\geq 2$)-dimensional Euclidean space using velocity measurements and either direction or range measurements of one or multiple source points. The proposed solutions exploit the Continuous Riccati Equation (CRE) to calculate observer gains yielding global exponential stability of zero estimation errors, even in the case where the measured velocity is biased by an unknown constant perturbation. These results are obtained under persistent excitation (p.e.) conditions depending on the number of source points and body motion that ensure both uniform observability and good conditioning of the CRE solutions. With respect to prior contributions on these subjects some of the proposed solutions are entirely novel while others are adapted from existing ones with the preoccupation of stating simpler and more explicit conditions under which uniform exponential stability is achieved. A complementary contribution, related to the delicate tuning of the observers gains, is the derivation of a lower-bound of the exponential rate of convergence specified as a function of the amount of persistent excitation. Simulation results illustrate the performance of the proposed observers.