Detecting Signalsin a Non-stationary EnvironmentModeled by aTVAR Process,from Data Corrupted by an Additive White Noise

In this paper, a method to detect unknown signals ina non-stationaryenvironmentis proposed. In addition, due to the sensor, the data are corrupted by an additive measurement stationary zero-mean white noise.Our approach, which can be useful in a wide range of situations such as the analysis of the object passing by, anomaly detection and digital communications, operates in three steps.Firstly, the nonstationaryenvironmentis assumed to be modeled by a time-varying autoregressive (TVAR) process.Secondly, the TVAR parameters and both the variances of the additive measurement white noise and the driving process are estimated by an evolutive method based on an errors-in-variables (EIV) approach. Thirdly, signal detection consists in studying the normalized prediction-error process of the TVAR model. Simulation results point out the relevance of the approach. Key-Words: -Signal detection, non-stationary noise, time-varying autoregressive model, parameter estimation, evolutivemethod, errors-in-variable approach, prediction-error process.