Multiframe weak target track-before-detect based on pseudo-spectrum in mixed coordinates

Traditional multiframe Track-Before-Detect(TBD) may incur adverse integration loss resulting from model mismatch in sensor coordinates. Its suboptimal integration strategy may cause target envelope degradation. To address these issues, a pseudo-spectrum-based multiframe TBD in mixed coordinates is proposed firstly. The data search for energy integration is conducted based on an accurate model in the x-y plane while target energy is integrated based on pseudo-spectrum in sensor coordinates. The a...     

A new target tracking filter based on deep learning

At present, current filters can basically solve the filtering problem in target tracking, but there are still many problems such as too many filtering variants, too many filtering forms, loosely coupled with the target motion model, and so on. To solve the above problems, we carry out crossapplication research of artificial intelligence theory and methods in the field of tracking filters. We firstly analyze the computation graphs of typical a-β and Kalman. Through analysis, it is concluded that ...     

An intelligent approach for flight risk prediction under icing conditions

Flight risk prediction is significant in improving the flight crew’s situational awareness because it allows them to adopt appropriate operation strategies to prevent risk expansion caused by abnormal conditions, especially aircraft icing conditions. The flight risk space representing the nonlinear mapping relations between risk degree and the three-dimensional commanded vector(commanded airspeed, commanded bank angle, and commanded vertical velocity) is developed to provide the crew with practi...     

Intrinsic component filtering for fault diagnosis of rotating machinery

Fault diagnosis of rotating machinery has always drawn wide attention. In this paper, Intrinsic Component Filtering (ICF), which achieves population sparsity and lifetime consistency using two constraints: $l_{1/2}$ norm of column features and $l_{3/2}$ -norm of row features, is proposed for the machinery fault diagnosis. ICF can be used as a feature learning algorithm, and the learned features can be fed into the classification to achieve the automatic fault classification. ICF can also be used as a filter training method to extract and separate weak fault components from the noise signals without any prior experience. Simulated and experimental signals of bearing fault are used to validate the performance of ICF. The results confirm that ICF performs superior in three fault diagnosis fields including intelligent fault diagnosis, weak signature detection and compound fault separation.     

Investigations of entrainment characteristics and shear-layer vortices evolution in an axisymmetric rear variable area bypass injector

In this study,firstly,for the axisymmetric RVABI,the change-rule of adverse pressure gradient caused by radial velocity during the transition of internal flow mode in variable geometry is summarized,and a Bypass Ratio(BR) iterative algorithm based on the empirical correlation of non-equilibrium pressure is proposed.The algorithm can estimate the nonlinear relationship between area ratio and BR,with an error range falling below 6.5%.Then,we discuss the favorable effect of uniform mixing on the th...