Interference control in sliding window detection processes using a Bayesian approach Abstract   Recently a Bayesian approach has been introduced, for the design of noncoherent sliding window detection processes, to operate in Xband high resolution maritime surveillance radar scenarios. The classical problem of comparing a cell under test with a normalised measurement of the clutter level is reinterpreted in terms of a Bayesian predictive inference approach. Viewing unknown clutter parameters as statistical variables, it is possible to construct a Bayesian posterior distribution, using Jeffreys priors. This approach has permitted the construction of detection processes, with the constant false alarm rate property, in situations where this was previously difficult to achieve. However, in the presence of interference in the clutter cells, the detector degrades in performance, as do others constructed in more traditional ways. Here it will be shown how the Bayesian approach can be extended to produce sliding window detectors which are immune to the presence of interference.

Extended Target Frequency Response Estimation Using Infinite Hmm in Cognitive Radars A cognitive radar adapts its waveform to match the extended targetxe2x80x99s frequency response (TFR) for optimized detection performance. In practice, the TFR is unknown and is usually estimated using the Kalman filter assuming a linear Gaussian model. However, this assumption is not always fulfilled and other filters as the particle filter should be used. In all cases, existing approaches require the complete knowledge of the statistical distributions of both the TFR and interference. In this paper, we present a novel formulation of the TFR estimation problem that allows us to use the infinite hidden Markov model (iHMM) to estimate and track the TFR without such prior knowledge. Monte Carlo simulations considering Gaussian and nonGaussian distributions for TFR and interference as well as jamming effects show that the proposed iHMMbased method ameliorates the estimation accuracy compared to the conventional Bayesian filtering techniques.