CFAR DETECTORS FOR HIGH RESOLUTION RADAR IN NON-GAUSSIAN CLUTTER

For high resolution radar, the echoes of target come from several points rather than one point as in low resolution radar. In this case the target is called an extended target. This paper presents two CFAR detectors for such a target in non Gaussian clutter, which are CA CFAR and OS CFAR detectors. The detection performances of the two detectors are evaluated.

The Effect of Integrating Inductive Approach and Deductive Approach With Multimedia Assistance Into Acquisition of Subjunctive Mood

This study applied both inductive approach and deductive approach with multimedia assistance into an English grammar class on the acquisition of subjunctive mood. It aimed to investigate whether this kind of teaching approach, as a general grammar pedagogy, would improve the efficiency of students＇ acquisition of certain grammar points. This study results from comparison and contrast between one same class on the acquisition of subjunctive mood to 70 students in total, employing three different teaching methods： inductive approach with multimedia assistance （method 1）, deductive approach with multimedia assistance （method 2）, and both inductive approach and deductive approach with multimedia assistance （method 3）, based on self-reported reflection on the experiment, observation of students＇ learning process, students＇ pre-test and after-class evaluation test results, and surveys. Two identical experiments were conducted to two groups of students of different levels of language proficiency to increase the generalizability of the results. Findings revealed that the evaluation test score of the grammatical points taught with method 3 is much higher than those taught with the other two methods, and most students felt positive about method 3. Students nevertheless encounter obvious difficulties in inductive approach, indicating certain lack of self-learning skills in Chinese students

Studies on quasi-Newton methods in timedomain multiscale full waveform inversion

The time-domain multiscale full waveform inversion(FWI)mitigates the influence of the local minima problem in nonlinear inversion via sequential inversion using different frequency components of seismic data.The quasi-Newton methods avoid direct computation of the inverse Hessian matrix,which reduces the amount of computation and storage requirement.A combination of the two methods can improve inversion accuracy and efficiency.However,the quasi-Newton methods in time-domain multiscale FWI still cannot completely solve the problem where the inversion is trapped in local minima.We first analyze the reasons why the quasi-Newton Davidon–Fletcher–Powell and Broyden–Fletcher–Goldfarb–Shanno methods likely fall into the local minima using numerical experiments.During seismic-wave propagation,the amplitude decreases with the geometric diffusion,resulting in the concentration of the gradient of the velocity model in the shallow part,and the deep velocity cannot be corrected.Thus,the inversion falls into the local minima.To solve this problem,we introduce a virtual-source precondition to remove the influence of geometric diffusion.Thus,the model velocities in the deep and shallow parts can be simultaneously completely corrected,and the inversion can more stably converge to the global minimum.After the virtual-source precondition is implemented,the problem in which the quasi-Newton methods likely fall into the local minima is solved.However,problems remain,such as incorrect search direction after a certain number of iterations and failure of the objective function to further decrease.Therefore,we further modify the process of timedomain multiscale FWI based on virtual-source preconditioned quasi-Newton methods by resetting the inverse of the approximate Hessian matrix.Thus,the validity of the search direction of the quasi-Newton methods is guaranteed.Numerical tests show that the modified quasi-Newton methods can obtain more reasonable inversion results,and they converge faster and entail lesser computational resources than the gradient method.