Improvements in the application of stochastic estimation algorithms--Parameter jump detection

The paper discusses the problem of recursive algorithms for estimating parameters which are subject to random jumps. A new method is presented which consists of detecting these parameter jumps and, should a detection occur, reinitializing the estimation gain sequence. New variables required by this method are calculated by means of diffusion approximations. Some simulation results illustrate the improved adaptation capabilities offered by the method.