State estimation in systems with multiple simultaneous measurements

Considers the problem of estimating the state of a discrete-time, linear stochastic system given a number of distinct, noisy measurements at each time instant. The observation process consists of a finite set of known, linear measurement models with additive white noise. The number of measurements may vary with time and the correspondence of the measurements with the models is unknown. The authors derive a recursive, suboptimal filter that provides an effective solution to this multi-measurement association and filtering problem