Francisco Alvarez-Vaquero

Nonparametric rank detectors under K-distributed clutter in radar applications

This correspondence deals with a comparative analysis of parametric detectors versus rank ones for radar applications, under K-distributed clutter and nonfluctuating and Swerling II target models. We show that the locally optimum detectors (LODs) (optimum for very low signal-to-clutter ratio (SCR)) under K-distributed clutter are not practical detectors; on the contrary, asymptotically optimum detectors (optimum for high SCR) are the practical ones. The performance analysis of the parametric log-detector and the nonparametric (linear rank) detector is carried out for independent and identically distributed (IID) clutter samples, correlated clutter samples, and nonhomogeneous clutter samples. Some results of Monte Carlo simulations for detection probability (P/sub d/) versus SCR are presented in curves for different detector parameter values.

Signal processing algorithms by permutation test in radar application

A hypothesis H is parametric if every distribution from the process defined by H belongs to a family of distributions characterized by a finite number of parameters; on the other hand, if the distribution can not be defined by a finite number of parameters, the hypothesis is nonparametric. In this paper, we analyze a detector based on the optimum permutation test, applied to nonparametric radar detection which provide good performances without a large computational work, and we compare it with the parametric test and rank test in the Neyman-Pearson sense. The computational complexity of the detector is high and its implementation in real time is difficult, due to the number of operations increase with the factorial of the number of samples. Also, we present an algorithm that reduces the computational work required. We also present the detectability characteristic of the optimum permutation test against rank test and parametric test under Gaussian noise environments and different types of target models (nonfluctuating, Swerling I and Swerling II). The detection probability versus signal-to-noise ratio is estimated by Monte-Carlo simulations for different parameter values (N pulse, M reference samples and false alarm probability Pfa).

Permutation tests for nonparametric detection

In this paper, the authors provide a methodology to design nonparametric permutation tests and, in particular, nonparametric rank tests for applications in detection. In the first part of the paper, the authors develop the optimization theory of both permutation and rank tests in the Neyman-Pearson sense; in the second part of the paper, they carry out a comparative performance analysis of the permutation and rank tests (detectors) against the parametric ones in radar applications. First, a brief review of some contributions on nonparametric tests is realized. Then, the optimum permutation and rank tests are derived. Finally, a performance analysis is realized by Monte-Carlo simulations for the corresponding detectors, and the results are shown in curves of detection probability versus signal-to-noise ratio.

Complexity analysis of permutation test versus rank test for nonparametric radar detection

In this paper, we analyze the complexity of an optimal algorithm for realizing the permutation test applied to nonparametric radar detection against the complexity of rank test realization. For a primitive permutation test algorithm, the computational work is very high and its implementation in real-time is difficult, due to the number of operations increases with the number of reference samples (M) to the power of the number of integrated pulses (N) (i.e. MN). We propose new permutation test and rank test algorithms, and analyze the complexity with respect to N and M for a given false-alarm probability (Pfa); also, the detection probability (Pd) will be evaluated for each case. Optimum values of N and M for a given Pfa will be given for the permutation test and the rank test, resulting in similar values of computational complexity of both of them, i.e. computational complexity proportional to N (DOT) M. We also show the detectability curves of the optimum permutation test versus optimum rank test under Gaussian noise environments for different values of N and M and different target models.

Permutation Detectors under Nonhomogeneous K-Distributed Clutter in Radar Applications

In this paper, we analyze the performance of some permutation tests (PTs) under a nonhomogeneous K-distributed clutter model, and nonfluctuating and Swerling II target models. Also, we compare the PTs results against their parametric counterparts under the same conditions. We shall analyze the detector performances in terms of detection probability versus signal-to-clutter ratio for different parameter values: the number of integrated pulses, the clutter reference samples, the false alarm probability, the shape parameter of the K-distributed clutter and the power deviation of the clutter.

Adaptive Importance Sampling Technique for Neural Detector Training

In this paper, we develop the use of an adaptive Importance Sampling (IS) technique in neural network training, for applications to detection in communication systems. Some topics are reconsidered, such as modifications of the error probability objective function (Pe), optimal and suboptimal IS probability density functions (biasing density functions), and adaptive importance sampling. A genetic algorithm was used for the neural network training, having utilized an adaptive IS technique for improving Pe estimations in each iteration of the training. Also, some simulation results of the training process are included in this paper.