The small error approximation is used to derive a linear relationship between the source parameters (i.e. power levels and directions of arrival or DOA's) and a measurement of the covariance error matrix, defined as the difference between a nonparametric consisten estimate of the spectral density matrix and a covariance model from the scenario parameters (power level and directions of arrival). The resulting framework allows the disign of a recursive algorithm which provides a simultaneous and adaptive estimation of the sources parameters no matter what is the source waveform or modulation. Due to its similarities with the Kalman filter structure [1], good performance is expected, mainly in the presence of sensors' misfunctioning, low signal-to-noise ratio (SNR), etc.

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