42 results about "Military systems" patented technology

A miniaturized laser package is provided comprising a standard semiconductor laser package modified to accept a solid state microchip assembly pumped by the diode laser. Standard packages described in the invention include TO and HHL packages all of which are characterized by small dimensions, well sealed housing, robust mounting features, known characterized materials and economical production and assembly techniques characteristic of the semiconductor processing industry. In particular, the microchip lasers are produced using high density techniques that lend themselves to mass production, resulting in very low unit costs. At the same time, the compact laser devices provide a solution to the problem of providing laser radiation at high beam quality and good reliability features with a variety of wavelengths and operational characteristics and low noise features not available from diode lasers yet relying primarily on standardized designs, materials and techniques common to diode laser manufacturing. The devices constructed according to methods taught by the invention can therefore be readily integrated into numerous applications where power, reliability and performance are at a premium but low cost is essential, eventually replacing diode lasers in many existing systems but also enabling many new commercial, biomedical, scientific and military systems.

The invention discloses a low-altitude airship flight control system and a flight control method thereof, belonging to the technical field of aircraft control. The system comprises a front-end data acquisition and processing subsystem, a navigation and flight control and state monitoring subsystem, a rear-end driving execution module subsystem and a flight application subsystem, wherein the front-end data acquisition and processing subsystem is connected with sensing equipment and used for transmitting acquired flight environment and airship state information, the navigation and flight control and state monitoring subsystem is connected with an airship navigation sensor and the rear-end driving execution module subsystem and used for transmitting navigation and flight control information,the rear-end driving execution module subsystem is connected with an airship execution controller and used for transmitting empennage steering engine pulse width modulation signals, propelling rotating speed control voltages and valve blower switch information, and the flight application subsystem is connected with a ground station and used for transmitting flight control instructions and telemetry parameter information through a data radio. The low-altitude airship flight control system disclosed in the invention is suitable for various civil and military systems.

Military systems in particular face two specific problems: field casualties typically increase as unit visibility decreases and it is often difficult for ground stations and / or headquarters to maintain control and visibility of geographically dispersed assets. The system provides satellite communications such as two-way messaging, Voice over Packet, and global positioning information and reporting for fixed and rotary wing aircrafts where traditional methods of communications are not otherwise practical. The system provides communications between remote users and other remote users as well as between remote users and control stations. In some embodiments there is an in-flight transceiver system that includes an antenna, an aviation box, and a switch. The aviation box conforms to a one-half, ½ short Air Transport Rack (ATR) form factor and includes a transceiver, an interface card, and a global positioning system (GPS) unit. The switch is located between the aviation box and the antenna for controlling a data signal between the antenna and the transceiver of the aviation box.

The present invention discloses a polarized SAR image classification method based on the semi-supervised depth distance metric network, and the technical problems that the traditional depth learning only considers the non-linear relationship between the sample characteristics and the classification accuracy is not high when the number of marked samples is relatively small are solved. The method comprises the following steps: inputting to-be-classified polarized SAR image data; solving a neighboring sample of the marked sample; constructing the loss function of the semi-supervised large boundary neighbor algorithm; initializing parameters of the network; pre-training the network; carrying out fine tuning on the network; carrying out classification prediction on the unmarked samples; and outputting a classification result image and classification accuracy of the to-be-classified polarized SAR image. According to the method disclosed by the present invention, by constructing a depth distance metric network, a popular learning regular term is added to the large boundary neighbor algorithm, so that problems of the influence of insufficient marked samples on the classification accuracy and the waste of information of a large number of unmarked samples are overcome; and the characteristics learned in the method of the present invention fully depicts intrinsic attributes of the samples, and the method can be applied to the earth resources survey, military systems and other technical fields.

The invention provides a method for processing motion pattern recognition and action recognition of a moving target based on a light stream direction sequence. The method comprises the following steps of: detecting the target by using background subtraction, tracking the target by using mean shift tracking algorithm, calculating a light flow value, respectively acquiring light stream directions of a rigid body and a non-rigid body, acquiring time series of the light stream directions after quantization, then building a Hidden Markov Model by using the time series, and finally processing the motion pattern recognition and the action recognition by using the Hidden Markov Model. The method provided by the invention can simutaneously process the motion pattern recognition and the action recognition of the moving target through the light stream directions, and can be widely applied to various civil or military systems such as a video monitoring system, a video conference system, an industrial product testing system, a robot visual navigation system, a military target testing classification system, thus having broad market prospect and application value.

The invention discloses a semi-supervised deep learning method based on semi-supervised sparse filtering. The method solves a technical problem that a conventional deep learning method is complex in parameter regulation and low in classification precision when having low label data, and comprises steps of: inputting polarized SAR image data to be classified; extracting a training sample and a testing sample; solving the Wishart neighbor sample of the training sample; initializing the parameter of a deep sparse filtering network; pre-training the deep sparse filtering network; fine regulating the deep sparse filtering network; predicting the classification of the testing sample; and outputting the classification image and the classification precision of the polarized SAR image to be classified. The method, by constructing the novel deep sparse filtering network and adding a semi-supervised regular term to a pre-training process, reduces parameter regulation complexity of the deep learning network, increases polarized SAR image terrain classification, and can be used in the technical fields of environment monitoring, earth resource exploration, military systems and the like.

Video frequency face tracking identify method based on appearance model belongs to video frequency dispose technology field. In the training video frequency, get person face through sample tracker and handy dispose and project training picture of each object to low-dimensional space by local linearity inlay technology. In the low-dimensional space, take means clustering to divide person faces into some species based on different gesture or expression. During each of group of pictures, take robust local reserved mapping to approximate the non-linear submanifold with linearity character space and learn the dynamic character. Carry the tracking identify of person faces in the testing video frequency. The tracking identify takes same appearance model and greatly enhances the tracking and identify capability of video frequency person face. It abroad uses in all kind of public residence and military system, such as the optic controlling, video frequency inspect controlling, video frequency meeting controlling, robot optic navigation system and military affairs goal tracking identify system.

The invention provides a natural color night vision realization method based on a single band infrared image. The method is characterized by providing a characteristic vector which is based on multi-scale and spatial context information and used for analyzing pixel points. The method comprises the following steps of: training a characteristic-vector-based natural color model by adopting a sample study method first; and then, establishing the color distribution of an infrared night vision image by using the trained natural color model to realize a process of automatic colorization. The method has the advantages that: the method can work in a single band infrared image wherein a conventional night vision image colorization method cannot work, and the single band infrared image can be endowed with natural color automatically so as to improve the accuracy and the efficiency of target identification and scene understanding. The method can be applied to various types of civil and military systems such as a night driving assisting system of a vehicle, a video monitoring system, a military target tracking and identifying system and the like.

The invention discloses a chaotic characteristic parameter-based motion mode video segmentation and traffic condition identification method, which comprises the following steps of: 1, calculating a characteristic vector matrix; 2, segmenting a video; 3, comparing clustering results; 4, retrieving the video; and 5, classifying the video. Characteristic parameters of the video are extracted, and new characteristic vectors are formed, so that the dynamic information of the video can be well described, and the method can be widely applied to various civil and military systems such as video monitoring systems, video conference systems, industrial product detection systems, robot visual navigation systems and military target detection and classification systems, and has broad market prospect and high application value.

The invention is an elastic multi-dimension damping desk based on sires and parallel pair parallel mechanism; it is made up of the parallel mechanism of the whole rotation pair, the active rotating pair uses the traditional type, the other rotating pair uses the reed, in order to eliminate the gap among pairs. A torsion spring and a rotary controllable damper are added at the active rotating pair, which is used to self-adaptable power balance and absorb the vibration energy, achieves the aim of high sensitivity multi-dimension damping, overcomes the deficiency that the stress performance of the moving pair is bad, it can be applied to ambulance damping frame, car plate, engine, base, tank, ship in military system, each kind of precise instruments and satellite antenna, radar antenna, space telescope, space interrupter, aiming sight, and transmission of crushable, explosive and harmful materials.

The invention relates to a machining device for a curvature radius-adjustable aspheric concave lens, and belongs to the field of precision machining. Through the macro-motion / micro-motion combined feed mode of a DC (direct-current) servo motor and a piezoelectric actuator, by combining a directional motion joint of revolution motion unit, the precise adjustment of a machining turning radius can be realized; the realization of the revolution motion of a spherical grinding head depends on the rotational degree of freedom, in two orthogonal directions, of a swing support frame and a spinning motion motor; meanwhile, through the precision follow-up adjustment of the vertical height of a concave lens test piece, the adjustment of the curvature radius of a machined lens can also be realized. As a vertical structure is adopted, the machining device disclosed by the invention is small in size and compact in structure and has small overall dimensions; the machining efficiency of the existing aspheric optical lens grinding technology can be effectively increased; the machining device is suitable for the precision machining of optical devices of a mini-type aspheric concave lens so as to broaden the application range of the mini-type aspheric lens in the field of space flight and aviation, military systems, optical communication and the like.

The invention discloses a dynamic texture identification method based on a chaos invariant. The dynamic texture identification method based on the chaos invariant comprises the steps of: (1) calculating a characteristic vector matrix, to be specific, regarding each pixel with the position changeable along with the time of a video as a chaos time sequence, calculating an embedding dimension of each chaos time sequence, delaying the embedding time, forming a characteristic vector by a box dimension, a information dimension, a box dimension, a mean value and a variance, representing each pixel point position of the video by the characteristic vector to obtain a characteristic vector matrix; and (2) performing EDM (Electronic Distance Measurement) identification or BOW (Browsable On-air Witness) identification on the characteristic vector matrix obtained in the step (1). By extracting the characteristic vector of the video and composing a new characteristic vector, the dynamic texture identification method based on the chaos invariant, disclosed by the invention, has the advantages of describing the dynamic texture video well, being widely applied to various civil and military systems such as a dynamic texture identification system, a dynamic texture detection system, a dynamic texture retrieval system, a military target detection and classification system, and obtaining a wide market foreground and an application value.

A method for rudder roll stabilization having two-feedback-path nonlinear dynamic compensation (NDC) is described. The high-order, Nyquist-stable control system having NDC hereof is absolutely stable and will provide a 20%-40% improvement in performance over existing roll reduction designs when lower performance steering mechanisms are employed, and is superior to linear controllers. That is, the present invention will be effective rudder roll stabilization in commercial vessels having slower rudders as well as in vessels having steering machines representing the best performance currently available, such as military systems. Since no ship hardware modifications are required, the present roll control technology will be able to be economically implemented.

An alpha-beta Ti alloy having improved mechanical and ballistic properties formed using a low-cost composition is disclosed. In one embodiment, the Ti alloy composition, in weight percent, is 4.2 to 5.4% aluminum, 2.5 to 3.5% vanadium, 0.5 to 0.7% iron, 0.15 to 0.19% oxygen and balance titanium. The exemplary Ti alloy exhibits a tensile yield strength of at least about 120,000 psi and an ultimate tensile strength of at least about 128,000 psi in both longitudinal and transverse directions, a reduction in area of at least about 43%, an elongation of at least about 12% and about a 0.430-inch-thick plate has a V50 ballistic limit of about 1936 fps. The Ti alloy may be manufactured using a combination of recycled and / or virgin materials, thereby providing a low-cost route to the formation of high-quality armor plate for use in military systems.

Military systems in particular face two specific problems: field casualties typically increase as unit visibility decreases and it is often difficult for ground stations and / or headquarters to maintain control and visibility of geographically dispersed assets. The system provides satellite communications such as two-way messaging, Voice over Packet, and global positioning information and reporting for fixed and rotary wing aircrafts where traditional methods of communications are not otherwise practical. The system provides communications between remote users and other remote users as well as between remote users and control stations. In some embodiments there is an in-flight transceiver system that includes an antenna, an aviation box, and a switch. The aviation box conforms to a one-half, ½ short Air Transport Rack (ATR) form factor and includes a transceiver, an interface card, and a global positioning system (GPS) unit. The switch is located between the aviation box and the antenna for controlling a data signal between the antenna and the transceiver of the aviation box.

The invention specifically relates to a dynamic texture recognition method based on multi-task learning and is designed so as to improve dynamic texture recognition accuracy. The dynamic texture recognition method based on multi-task learning is characterized by, to begin, carrying out chaotic feature vector extraction on each pixel time sequence in a dynamic texture video, so that the video is changed into a chaotic feature vector matrix; then, carrying out video modeling through a bag-of-word model to obtain histogram features; and converting the identification problem into group sparse representation, and calculating through an ADMM method. The method can carry out dynamic texture recognition through a multi-task learning method, can be widely applied to various civilian and military systems, such as a video monitoring system, a video conference system, an industrial product detection system, a robot vision navigation system and a military target detection and classification system,and has a wide market prospect and application value.

The present disclosure relates to hunting, law enforcement, and military systems and the like. The present disclosure includes a hunting apparatus comprising a base; a frame coupled to the base. The base is rotatable around said base and the base provides concealment for a user therein.

Defining military refuel orbit using unchanged commercial aircraft FMS capability includes uplinking military refuel orbits using an existing commercial FMS datalink function. Refuel orbits are defined as commercial NavDB STAR procedures using RF and TF legs. STAR procedures can also be stored within company routes. Company route names and STAR procedures are stored within a NavDB used in a military unique system. The military system creates a flight plan with custom, place / bearing / distance, place bearing place bearing, or latitude / longitude waypoints to define the military refuel pattern ARIP and ARCP. This is combined with the refuel orbit stored in the military NavDB as a STAR procedure or company route name to define a refuel pattern flight plan. The military system uplinks the flight plan using the existing commercial FMS datalink function. The FMS extracts the appropriate refuel orbit from the commercial FMS NavDB using the uplinked STAR procedure or company route name.

The invention discloses an image defect recognition method and system, electronic equipment and a storage medium. The method comprises the following steps of: respectively taking each row of pixels and each column of pixels in an image as chaotic time sequences and calculating a chaotic feature of each chaotic time sequence; respectively a chaotic feature vector for the chaotic feature of each chaotic time sequence; clustering a chaotic feature vector matrix of a training sample by utilizing a clustering algorithm so as to obtain a code book; calculating histograms of each training image and each test image according to a bag of words mode for code books; establishing group sparse models for the histograms of the training images and the test images through a multi-task learning method; calculating the group sparse models by utilizing an alternating direction multiplier algorithm; and classifying defect images by utilizing reconstruction errors. According to the method, surface defectscan be better recognized and the correctness is high; and the method can be applied to various civil and military systems such as face recognition and military target tracking recognition systems, andhas wide market prospect and application value.

A limited redundancy discrete wavelet transform method is used in the field of image processing, image fusion and signal processing. The invention firstly designs the filter, which is used respectively in the first class and the subsequent dissociation class, and then takes different sub-sampling rules according to different channels during the process of wavelet dissociation to, and uses the filter corresponding to its positive transformation and super-sampling rules during the process of inverse transformation. The invention, which uses different filter design methods in different class of discrete wavelet transform, and sub-sampling, super-sampling strategies according to different channels, can make the wavelet coefficients to have good invariance and appropriate redundancy. The invention can be applied to image processing, image fusion, signal processing and transformation, which can be used in radar, voice, geographic information processing and other home and military systems.

The invention belongs to the technical field of target detection and tracking, and particularly discloses a pre-detection tracking method for a weak extension target. Under a Bayesian filtering framework, a particle approximation means is used for recursion prediction and updating of a target state, so that the detection and tracking of the weak extension target are realized; in the updating step,intensity measurement data of resolution units occupied by the target is accumulated through particles; after the accumulated length reaches a set value, the power of each resolution unit occupied bythe target is estimated; and the weights of the particles are updated by taking the power as parameters of a likelihood function. According to the method, the weak extension target can be detected and tracked without target prior information, so that the complicated nonlinear parameter estimation process of a traditional method is avoided; meanwhile, no specific requirement is raised for the typeof the intensity measurement data; and the method can be widely applied to various civilian and military systems such as a video monitoring system, a robot navigation system, a military target detection and tracking system and the like, and has a wide market prospect and application value.

The invention discloses a military benefit-based GSO frequency track resource efficiency evaluation method. The method comprises the following steps of calculating a military coverage area satisfaction degree A1 of to-be-evaluated data; calculating military frequency coverage A2; calculating a service support degree A3; calculating the support degree A4 of the earth station; calculating the effective coverage A5 of the military application; calculating the cooperation degree A6 with the existing military system; taking the to-be-evaluated indexes A1-A6 as first-level indexes, and giving a judgment matrix of the first-level indexes; calculating weights based on the judgment matrix, obtaining weight vectors of the first-level indexes, and calculating the fuzzy satisfaction degree of each first-level index; calculating ambiguity vectors for the obtained first-level index values, and generating an evaluation matrix R; and selecting the maximum value in the matrix C as an efficiency evaluation value based on the weight vector of the first-level index. According to the method, the quality of the stored military frequency orbit resources can be improved, all indexes are clear, and the method has high engineering realizability.

The invention relates to the technical field of military system engineering, and further discloses a multi-field equipment capability evaluation and analysis method based on radar map. The invention provides a multi-field weapon equipment capability evaluation and analysis method based on a radar map. The satisfaction degree of the field capability is expressed through the function relationship between the actual magnitude and the expected magnitude of the field capability of the weaponry; the domain capability satisfaction degree and the domain capability weight are taken as the basis, a radar map method is adopted to carry out comparative analysis on capability satisfaction degrees of multiple fields of weaponry; decision reference is provided for accurately mastering importance and relative satisfaction degree of abilities in various fields in weaponry ability composition. The problems that due to the complexity of weapon equipment capacity composition, the overall level and the quality condition of the weapon equipment capacity are often difficult to accurately grasp only according to the comprehensive evaluation value of the weapon equipment capacity, and scientific evaluationon the weapon equipment capacity is not facilitated are solved.

The invention discloses a simulation modeling method for a military complex system based on multiple views and relates to the technical field of a method for constructing a computer virtual system. The method comprises the following steps: defining a military field question set and confirming a modeling focus point; for the focus point, establishing a corresponding view set for summarizing; confirming a relation between the views; establishing a conceptual model system under the views. According to the method, a complex system is decomposed into a plurality of basic small systems and the complex system is analyzed by analyzing the small systems and the relations thereof, so that the requirements and aspirations of various related personnel can be conveniently reflected, the general description for the military system can be easily formed and the characteristics of the military complex system can be accurately and comprehensively reflected.

An improved unmanned aerial vehicle target tracking algorithm comprises four steps of S1, S2, S3 and S4. A new deep neural network structure is firstly designed, and a deep neural network structure capable of well representing object features is obtained through offline learning. Then, online tracking is carried out, firstly, a motion model between two adjacent frames is obtained through feature points, then target features are extracted through the deep neural network structure obtained through offline learning, a deep neural network feature with good target edge information description capability and certain degree of robustness to noise and geometric deformation is provided by using a focus loss function, a generative tracking method is designed by performing motion compensation on adjacent frames, and the method can obtain a good tracking result under various scenes such as camera rapid motion, video jitter, light ray change and scale change, and is suitable for various civil and military systems such as face tracking, unmanned aerial vehicle tracking and military target tracking systems.

The invention specifically relates to a dynamic texture recognition method based on a bipartite graph and is designed for improving the accuracy of dynamic texture recognition. According to the dynamic texture recognition method based on a bipartite graph, firstly a chaotic feature vector is extracted from each pixel time sequence in a dynamic texture video, and thus the video becomes a chaotic feature vector matrix. Then, the hybrid Gauss method is used to segment the chaotic feature matrix, a similarity degree between two dynamic textures is compared with a metric, then a graph model is established through the similarity comparison. Finally, the Hungarian algorithm is used to achieve the purpose of dynamic texture recognition. The method can be applied to various civil and military systems such as face recognition and military target tracking and recognition systems, and the method has a broad market prospect and application value.

The invention discloses a semi-supervised deep learning method based on semi-supervised sparse filtering. It solves the technical problem of complex parameter adjustment of traditional deep learning methods and low classification accuracy when there are low-label data. The steps include: inputting polarimetric SAR image data to be classified; extracting training samples and test samples; calculating the training sample Wishart nearest neighbor samples; initialize the parameters of the deep sparse filter network; pre-train the deep sparse filter network; fine-tune the deep sparse filter network; predict the category of the test sample; output the classification image and classification accuracy of the polarimetric SAR image to be classified. By constructing a novel deep sparse filter network model and adding a semi-supervised regular term in the pre-training process, the present invention reduces the complexity of deep learning network parameter adjustment and improves the classification accuracy of polarimetric SAR images. It can be used in technical fields such as environmental monitoring, earth resource surveying and military systems.

The invention provides a method for processing motion pattern recognition and action recognition of a moving target based on a light stream direction sequence. The method comprises the following steps of: detecting the target by using background subtraction, tracking the target by using mean shift tracking algorithm, calculating a light flow value, respectively acquiring light stream directions of a rigid body and a non-rigid body, acquiring time series of the light stream directions after quantization, then building a Hidden Markov Model by using the time series, and finally processing the motion pattern recognition and the action recognition by using the Hidden Markov Model. The method provided by the invention can simutaneously process the motion pattern recognition and the action recognition of the moving target through the light stream directions, and can be widely applied to various civil or military systems such as a video monitoring system, a video conference system, an industrial product testing system, a robot visual navigation system, a military target testing classification system, thus having broad market prospect and application value.