The following topics are dealt with: indoor radio; mobile robots; pedestrians; smart phones; position control; radionavigation; wireless LAN; indoor communication; indoor navigation; Kalman filters.

The paper presents an online estimation of parameters of a multi-input renewal Markov process. The underlying model is derived from the physiological generation of intramuscular electromyographic (iEMG) signals, which are recorded by wire electrodes. The iEMG is the sum of several sparse spikes trains and noise. An hidden Markov model, whose parameters express the muscular activity, is developed. The time duration between spikes is modeled with a discrete Weibull distribution, helping us to reduce the complexity of the estimation done with the help of a Bayes filter.

A method is proposed which solves the problem of the Bayes classification of ARMA (autoregressive moving average) signals when the models of classes and samples are not exactly known but only estimated from finite-length data sequences. Justified approximations and the hypothesis lead to decision rules including the variances of the estimations. The results obtained on a large set of simulated data show that this approach is superior to the best classical methods (cepstral distance or Kullback divergence), particularly in the common case where the hypothesis of those methods is not verified (short samples. small training sets. random classes).>