A new Bayesian filtering technique for estimating signal parameters directly from discrete-time sequences is introduced. The so called probabilistic instantaneous matching algorithm recursively updates the probability density function of the parameters for every received sample and, thus, provides a high update rate up to the sampling rate with high accuracy. In order to do so, one of the signal sequences is used as part of a time-variant nonlinear measurement equation. Furthermore, the time-variant nature of the parameters is explicitly considered via a system equation, which describes the evolution of the parameters over time. An important feature of the probabilistic instantaneous matching algorithm is that it provides a probability density function over the parameter space instead of a single point estimate. This probability density function can be used in further processing steps, e.g. a range based localization algorithm in the case of time-of-arrival estimation.