Controlled Sensing: A Myopic Fisher Information Sensor Selection Algorithm
For researchers working on state estimation with observation control, this work offers a computationally efficient alternative to optimal control strategies, though it is incremental.
The paper proposes a suboptimal, lower-complexity Fisher information-based sensor selection algorithm for state tracking in dynamical systems with controlled observations, achieving near-optimal performance in a physical activity tracking application.
This paper considers the problem of state tracking with observation control for a particular class of dynamical systems. The system state evolution is described by a discrete-time, finite-state Markov chain, while the measurement process is characterized by a controlled multi-variate Gaussian observation model. The computational complexity of the optimal control strategy proposed in our prior work proves to be prohibitive. A suboptimal, lower complexity algorithm based on the Fisher information measure is proposed. Toward this end, the preceding measure is generalized to account for multi-valued discrete parameters and control inputs. A closed-form formula for our system model is also derived. Numerical simulations are provided for a physical activity tracking application showing the near-optimal performance of the proposed algorithm.