Vol 61, No 9 (2018)

Modern radars have adopted adaptive processing techniques to mitigate the deleterious effects of unwanted clutter and jammer. In this situation, the CFAR algorithms play a vital role in achieving the heterogeneous detection of fluctuating targets. In this regard, while the CA-CFAR processor has the top homogeneous performance, the OS and TM techniques have been suggested to provide robust estimates of the threshold in heterogeneous situations. In order to simultaneously exploit the merits of CA and OS or TM processors, some their hybrid versions have been recently introduced. They are termed as CAOS and CATM models. Practically, the frequency diversity between noncoherent sweeps is widespread in actual radar systems. Additionally, the pulse integration strategy is often used in radar systems to improve the target signal-to-noise ratio and correspondingly the system detection performance. For this reason, this paper is focusing on analyzing these new models in the case where the radar receiver noncoherently integrates M-pulses to handle its detection. Closed-form expression is derived for their nonhomogeneous performance. The tested as well as the spurious targets are assumed to follow χ²-distribution with four degrees of freedom in their fluctuations. Our simulation results reveal that the new version CATM exhibits a homogeneous performance that outweighs that of the classical Neyman-Pearson (N-P) procedure, which is employed as a baseline comparison for other strategies in the field of adaptive detectors.

In this paper there are researched regularizing properties of discretization in a space of output signals for some linear operator equation with noisy data. The essence of proposed method is selection of discretization level which is a parameter of the regularization in this context by the principle of equality of random and deterministic components of the input signal recovering error. It is shown the method, i.e. the solution which is discrete by input signal is stable to small inaccuracies in input signal. At that in case of definite level of output signal measurements inaccuracy the recovering error of input signal is unambiguously defined by input signal sampling increment that allows to select reasonably the regularization parameter for specific criterion, for example, for definite measurements inaccuracy. Specific calculations and examples are represented in explicit form for single-dimension case but this does not restricts generality of proposed method.