With the aim of automatic recognition of weak faults in hydraulic systems, this paper proposes an identification method based on multi-scale permutation entropy feature extraction of fault-sensitive intrinsic mode function (IMF) and deep belief network (DBN). In this method, the leakage fault signal is first decomposed by empirical mode decomposition (EMD), and fault-sensitive IMF components are screened by adopting the correlation analysis method. The multi-scale entropy feature of each screened IMF is then extracted and features closely related to the weak fault information are then obtained. Finally, DBN is used for identification of fault diagnosis. Experimental results prove that this identification method has an ideal recognition effect. It can accurately judge whether there is a leakage fault, determine the degree of severity of the fault, and can diagnose and analyze hydraulic weak faults in general.

This paper presents a method of military object detection through the combination of human visual salience and visual psychology, so as to achieve rapid and accurate detection of military objects on the vast and complex battlefield. Inspired by the process of human visual information processing, this paper establishes a salient region detection model based on double channel and feature fusion. In this model the pre-attention channel is to process information on the position and contrast of images, and the sub-attention channel is to integrate information on primary visual features first and then merges results of the two channels to determine the salient region. The main theory of Gestalt visual psychology is then used as the constraint condition to integrate the candidate salient regions and to obtain the object figure with overall perception. After that, the efficient sub-window search method is used to detect and filter the object in order to determine the location and range of objects. The experimental results show that, when compared with the existing algorithms, the algorithm proposed in this paper has prominent advantages in precision, effectiveness, and simplicity, which not only significantly reduces the effectiveness of battlefield camouflage and deception but also achieves the rapid and accurate detection of military objects, thus promoting its application prospect.

Aiming at automatic recognition of hydraulic system’s weak fault, an analysis method based on variable parameter multi-scale permutation entropy (VPMPE) and deep belief network (DBN) is proposed in this paper. The external vibration signals of experimental hydraulic equipment in normal state and three different leakage state (slight, moderate and severe) are taken as research object. By the proposed method, experiment signals are first processed to obtain their multi-scale permutation entropy in different conditions by changing the embedding dimension m and the scale factor s, then the multi-scale permutation entropy under different m and s are combined to form feature vectors, and lastly the DBN classifier is used to identify and analyze the testing samples. Verification test shows that the proposed method has good effects on hydraulic system’s weak fault, which can accurately judge whether there is leakage fault and measure the fault severity.

A feature extraction and fault diagnosis method based on IMF entropy feature fusion was proposed for external vibration signals in five common fault states of hydraulic equipment: normal, leakage, blockage, air cavity and impact. Firstly, all kinds of signals were decomposed by improved EMD based on frequency cutoff, and effective IMF components were screened, then the fusion features of multiple information entropy were extracted, and then the deep learning method of DBN was adopted for feature learning and status recognition. The experimental results show that this method has high recognition accuracy and can effectively realize multi-fault recognition of hydraulic system.

In air combat, the identification of aerial targets serv- es as the foundation and prerequisite for situation assessment and threat estimation. Aiming at the problem of air target recognition, a data fusion recognition method based on improved Dempster-Shafer (D-S) Evidence Theory and fuzzy theory is proposed in this paper. For the target information obtained by radar and other multi-source sensor devices, the members- hip function is constructed by fuzzy set theory, and converted into the Basic Probability Assignment (BPA) function of the target to be identified. Then D-S Evidence Theory is used for conflict evidence synthesis and fusion, and the Pignistic probability decision algorithm is employed for discriminant analysis ultimately, so as to realize the identification of air targets. Simulation results show that the proposed method is correct and effective, and improves the accuracy of target recognition to some extent.

In joint operation, unmanned systems greatly promote the generation of new combat capabilities. The autonomous navigation is a key technology for achieving a new paradigm of warfare. To solve the problem of unmanned systems’ real-time precise localization in GNSS-denied environments, an initialization algorithm for monocular visual inertial navigation is proposed. To handle multi-rate measurements in visual inertial navigation system, the IMU pre-integration technique is utilized to process inertial measurements. And then the visual structure from motion is performed. Finally, the visual inertial loosely coupled method is employed to estimate the initial scale, gravity direction, velocity and gyroscope and acceleration biases, and the mathematical model is addressed in detail. Experimental results indicate that the initialization method can obtain accurate initial states in 15 seconds, which can meet the needs of unmanned systems’ cooperative application in joint operation.