A Novel Gamma Distributed Random Variable (RV) Generation Method for Clutter Simulation with NonIntegral Shape Parameters Sea clutter simulation is a wellknown research endeavour in radar detector analysis and design, and many approaches to it have been proposed in recent years, among which zero memory nonlinear (ZMNL) and spherically invariant random process (SIRP) are the most two widely used methods for compound Gaussian distribution. However, the shape parameter of the compound Gaussian clutter model cannot be a noninteger nor nonsemiinteger in the ZMNL method, and the computational complexity of the SIRP method is very high because of the complex nonlinear operation. Although some improved methods have been proposed to solve the problem, the fitting degree of these methods is not high because of the introduction of Beta distribution. To overcome these disadvantages, a novel Gamma distributed random variable (RV) generation method for clutter simulation is proposed in this paper. In our method, Gamma RV with nonintegral or nonsemiintegral shape parameters is generated directly by multiplying an integralshapeparameter Gamma RV with a Beta RV whose parameters are larger than 0.5, thus avoiding the deviation of simulation of Beta RV. A large number of simulation experimental results show that the proposed method not only can be used in the clutter simulation with a noninteger or nonsemiinteger shape parameter value, but also has higher fitting degree than the existing methods.

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.