On the Sensitivity of a Fixed-Point Smoothing Algorithm

This paper presents a simple approach to the derivation of sensitivity measures of smoothing algorithms. It is proposed that the known sensitivity results of the Kalman filtering algorithm be utilized along with the state augmentation approach for this purpose. The sensitivity measures so obtained are easier to compute than the ones available in the literature. It is then shown that the fixed-point smoothing algorithm, derived recently by Biswas and Mahalanabis [1], [2], is less sensitive to model parameter variations than the algorithm studied by Griffin and Sage [7], [8]. The case of a satellite tracking problem is presented by way of illustrating the results.