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171 results about "Automatic target recognition" patented technology
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Automatic target recognition (ATR) is the ability for an algorithm or device to recognize targets or other objects based on data obtained from sensors. Target recognition was initially done by using an audible representation of the received signal, where a trained operator who would decipher that sound to classify the target illuminated by the radar. While these trained operators had success, automated methods have been developed and continue to be developed that allow for more accuracy and speed in classification. ATR can be used to identify man made objects such as ground and air vehicles as well as for biological targets such as animals, humans, and vegetative clutter. This can be useful for everything from recognizing an object on a battlefield to filtering out interference caused by large flocks of birds on Doppler weather radar.
System and methods for autonomous tracking and surveillance
ActiveUS20090157233A1Low costReduce riskDigital data processing detailsNavigation instrumentsCamera controlObjective information
A system and methods for autonomously tracking and simultaneously providing surveillance of a target from air vehicles. In one embodiment the system receives inputs from outside sources, creates tracks, identifies the targets and generates flight plans for unmanned air vehicles (UAVs) and camera controls for surveillance of the targets. The system uses predictive algorithms and aircraft control laws. The system comprises a plurality of modules configured to accomplish these tasks. One embodiment comprises an automatic target recognition (ATR) module configured to receive video information, process the video information, and produce ATR information including target information. The embodiment further comprises a multi-sensor integrator (MSI) module configured to receive the ATR information, an air vehicle state input and a target state input, process the inputs and produce track information for the target. The embodiment further comprises a target module configured to receive the track information, process the track information, and produce predicted future state target information. The embodiment further comprises an ownship module configured to receive the track information, process the track information, and produce predicted future state air vehicle information. The embodiment further comprises a planner module configured to receive the predicted future state target information and the predicted future state air vehicle information and generate travel path information including flight and camera steering commands for the air vehicle.
Owner:THE BOEING CO
System and methods for autonomous tracking and surveillance
ActiveUS8718838B2Low costReduce riskDigital data processing detailsAnti-collision systemsCamera controlIntegrator
A system and methods for autonomously tracking and simultaneously providing surveillance of a target from air vehicles. In one embodiment the system receives inputs from outside sources, creates tracks, identifies the targets and generates flight plans for unmanned air vehicles (UAVs) and camera controls for surveillance of the targets. The system uses predictive algorithms and aircraft control laws. The system comprises a plurality of modules configured to accomplish these tasks. One embodiment comprises an automatic target recognition (ATR) module configured to receive video information, process the video information, and produce ATR information including target information. The embodiment further comprises a multi-sensor integrator (MSI) module configured to receive the ATR information, an air vehicle state input and a target state input, process the inputs and produce track information for the target. The embodiment further comprises a target module configured to receive the track information, process the track information, and produce predicted future state target information. The embodiment further comprises an ownship module configured to receive the track information, process the track information, and produce predicted future state air vehicle information. The embodiment further comprises a planner module configured to receive the predicted future state target information and the predicted future state air vehicle information and generate travel path information including flight and camera steering commands for the air vehicle.
Owner:THE BOEING CO
Method of isomorphic singular manifold projection still/video imagery compression
InactiveUS20020176624A1Reduction procedureReduce redundancyCharacter and pattern recognitionImage codingPattern recognitionImage compression
Methods and apparatuses for still image compression, video compression and automatic target recognition are disclosed. The method of still image compression uses isomorphic singular manifold projection whereby surfaces of objects having singular manifold representations are represented by best match canonical polynomials to arrive at a model representation. The model representation is compared with the original representation to arrive at a difference. If the difference exceeds a predetermined threshold, the difference data are saved and compressed using standard lossy compression. The coefficients from the best match polynomial together with the difference data, if any, are then compressed using lossless compression. The method of motion estimation for enhanced video compression sends I frames on an "as-needed" basis, based on comparing the error between segments of a current frame and a predicted frame. If the error exceeds a predetermined threshold, which can be based on program content, the next frame sent will be an I frame. The method of automatic target recognition (ATR) including tracking, zooming, and image enhancement, uses isomorphic singular manifold projection to separate texture and sculpture portions of an image. Soft ATR is then used on the sculptured portion and hard ATR is used on the texture portion.
Owner:PHYSICAL OPTICS CORP
Generalized hebbian learning for principal component analysis and automatic target recognition, systems and method
InactiveUS6894639B1Improve robustnessSmall sizeImage enhancementImage analysisKernel principal component analysisPrincipal component analysis
This application discloses a method for distinguishing targets from clutter, comprising the steps inputting data, calculating data statistics from said data and using said data statistics to select target specific feature information to distinguish specific targets from background clutter, generating said target specific feature information from said data statistics, extracting said target specific feature information from said data, using said target specific feature information to distinguish specific targets from background clutter, and outputting target and background clutter information. Classification systems, including hardware and software embodiments, are also disclosed.
Owner:OL SECURITY LIABILITY CO
Automatic target recognition apparatus and process
Apparatus for automatic recognition of a target object from an infrared or visible light image, includes primary segmentation and classification means (4,8,9,10,11,12) in which objects are either recognised or unrecognised and one or more secondary segmentation means (15,21,22,23,12) applicable to primary segmented image areas in which objects which are unrecognised by the primary segmentation means are further classified and either recognised or rejected.
Owner:SELEX GALILEO
System for intelligently controlling a team of vehicles
InactiveUS20070021879A1Analogue computers for vehiclesAutonomous decision making processQuality of serviceGoal recognition
A system collaboratively and autonomously plans and controls a team of vehicles having subsystems within an environment. The system includes a mission management component, a communication component, a payload controller component, and an automatic target recognition component. The mission management component plans and executes a mission plan of the team and plans and executes tasks of the vehicles. The communication component plans communication and networking for the team. The communication component manages quality of service for the team. The communication component directs communication subsystems for the team and for the vehicles. The payload controller component directs and executes sensor subsystems for the team and for the vehicles. The automatic target recognition component processes and fuses information from the sensor subsystems and from the vehicles for use by the mission management component.
Owner:LOCKHEED MARTIN CORP
Radar one-dimensional range profile target recognition method based on depth convolution neural network
ActiveCN109086700AEasy to identifyGood noise robustnessWave based measurement systemsScene recognitionSmall sampleData set
The invention discloses a radar one-dimensional range profile target recognition method based on a depth convolution neural network, includes the following steps: a data set is collected, the collected data is preprocessed, features are extracted from the preprocessed data, the HRRP signal is divided into two parts: low SNR and high SNR, A feature enhancement algorithm based on robust Boltzmann isconstructed, and a HRRP target recognition model based on convolution neural network and bidirectional loop neural network based on LSTM is constructed. The parameters of the network model are fine-tuned by using gradient descent algorithm, and an effective target recognition model is obtained. A radar HRRP automatic target recognition technology with small sample robustness and noise robustnessconstructed by the invention has strong engineering practicability, and a radar one-dimensional range profile target recognition model based on a convolution neural network and a cyclic neural networkis proposed from the aspects of feature extraction and the design of a classifier.
Owner:HANGZHOU DIANZI UNIV
Matching dictionary and compressive sensing based radar range profile object identification method
The invention belongs to the technical field of automatic radar HRRP (high resolution range profile) object identification and particularly relates to compressive sensing based range profile object identification. Range profile object identification includes the steps: constructing a matching dictionary according to a radar echo model, selecting an appropriate test matrix for compressive sensing of a training sample range profile and to-be-identified test sample range profile which are known in type information so as to achieve data dimension reduction; then, subjecting data subjected to compressive sensing to sparse reconstruction so as to obtain sparse coefficients of the training sample range profile and the test sample range profile under the matching dictionary; utilizing the sparse coefficient of the training sample range profile as a pattern vector, and identifying the test sample range profile according to a nearest neighbor method. By the aid of compressive sensing based range profile object identification, since sparse coefficient characteristics of objects under the dictionary are extracted, redundancy is avoided, calculating amount is decreased and unnecessary noise is avoided.
Owner:UNIV OF ELECTRONICS SCI & TECH OF CHINA
SAR (Synthetic Aperture Radar) image segmentation method based on dictionary learning and sparse representation
InactiveCN102129573AShorten the timeImage segmentation results are goodCharacter and pattern recognitionSingular value decompositionInverse synthetic aperture radar
The invention discloses a SAR (Synthetic Aperture Radar) image segmentation technique based on dictionary learning and sparse representation, and mainly solves the problems that the existing feature extraction needs a lot of time and some defects exist in the distance measurement. The method comprises the following steps: (1) inputting an image to be segmented, and determining a segmentation class number k; (2) extracting a p*p window for each pixel point of the image to be segmented so as to obtain a test sample set, and randomly selecting a small amount of samples from the test sample set to obtain a training sample set; (3) extracting wavelet features of the training sample set; (4) dividing the training sample set by using a spectral clustering algorithm; (5) training a dictionary by using a K-SVD (Kernel Singular Value Decomposition) algorithm for each class of training samples; (6) solving sparse representation vectors of the test sample on the dictionary; (7) calculating a reconstructed error function of the test sample; and (8) calculating a test sample label according to the reconstructed error function to obtain the image segmentation result. The invention has the advantages of high segmentation speed and favorable effect; and the technique can be further used for automatic target identification of SAR images.
Owner:XIDIAN UNIV
Methods for autonomous tracking and surveillance
ActiveUS9026272B2Low costReduce riskDigital data processing detailsDirection/deviation determining electromagnetic systemsCamera controlIntegrator
Owner:THE BOEING CO
Super broad band land radar automatic target identification method based on information fusion
InactiveCN1595195AImprove signal-to-noise ratioOvercome the shortcomings of high requirements and inability to distinguish similarly shaped targetsRadio wave reradiation/reflectionElectric/magnetic detection for transportPattern recognitionTime lag
It is a ultra-wide band earth-probing radar self object identification method based on information integration, which is the following: first to use the large time lag in the relative object signals to the direct wave in the echo-signal of the earth-probing radar and to filter the direct wave; to use the bandwidth relative process to filter wave and to extract typical data and to improve the signal to noise ratio of the signal and to extract the longitudinal and transverse typical data to object shape identification and to extract typical echo-duct data for Welch power spectrum valuation; to use RBF web to classify the object material and finally to integrate the results of the object shape identification and material identification to achieve the complete and effective automatic identification of the underground object of different materials.
Owner:SHANGHAI JIAO TONG UNIV
SAR image target recognition method based on sparse representation
ActiveCN103984966AImprove recognition rateEasy to identifyCharacter and pattern recognitionFeature vectorImaging processing
The invention discloses an SAR image target recognition method based on sparse representation. The SAR image target recognition method based on sparse representation mainly resolves the problem that an existing method is complex in preprocessing and difficult in estimation of an azimuth angle. The SAR image target recognition method based on sparse representation comprises the steps of (1) extracting partial features of an image and studying a recognizable dictionary through a diversity density function; (2) carrying out sparse encoding on each partial feature through the dictionary, and then carrying out space pooling on each divided sub-area through a space domain pyramid structure to obtain feature vectors of the sub-areas samples of a training set and a test set; (3) weighing the corresponding sub-areas of a test sample according to the sparsity of each sub-area of the test sample; and (4) combining the weighed sub-areas together and recognizing the image through a sparse representation method. Compared with the prior art, the SAR image target recognition method based on sparse representation has high robustness for shielded and partial noise, improves the recognition accuracy of an SAR target without estimating the azimuth angle, and can be used for image processing.
Owner:XIDIAN UNIV
Edge detection method based on fractional-order signal processing
InactiveCN101841642AAdjustment sensitivityEffective filteringTelevision system detailsColor television detailsAlgorithmThresholding
The invention discloses an edge detection method based on fractional-order signal processing, aiming at solving edge detection which is a traditional trouble in the pattern recognition field. The method is a novel algorithm which can conduct gradient operation to all target pixel points in an image by using the fractional-order signal processing to obtain an edge, and comprises that a gray-level matrix is generated from any image, the gradient operation is conducted respectively to each pixel point in the matrix by adopting detection operators to obtain the gradient amplitude of each pixel point, then non-maximum value suppression is conducted to the gradient image and finally a double-threshold method is adopted for judging whether the target pixel points are edge points and are connected with the edge or not. The invention omits the process of smooth filtering preprocessing conducted to the image and utilizes a novel derivative algorithm based on the fractional-order signal processing to conduct the gradient operation. Fractional integrals in the algorithm can suppress the interference introduced during derivation. The entire method has the advantages that the signal-to-noise ratio is good, the edge positioning is accurate and the false edge can be effectively suppressed. The algorithm can be used in the fields such as the automatic target recognition and the like.
Owner:HANGZHOU HENGSHENG ELECTRONICS TECH +4
Automatic target recognition system with online machine learning capability
InactiveUS20160328838A1Quick transitionImprove accuracyImage analysisScene recognitionRadianceGoal recognition
A method and apparatus for real-time target recognition within a multispectral image includes generating radiance signatures from reflectance signatures, sensor information and environment information and detecting targets in the multispectral image with a sparsity-driven target recognition algorithm utilizing set of parameters tuned with a deep neural network.
Owner:APPLIED RES LLC
Real-time measurement system for shield excavation attitude
The invention discloses a real-time measurement system for shield tunneling posture, comprising a total station instrument which is arranged behind a shield machine and is provided with an automatic target recognition function and a plurality of target prisms fixed in the shield machine, wherein, the target prisms are arranged one by one along the longitudinal direction of the shield machine; the system also comprises a double-axis tilt sensor, and the double-axis tilt sensor is arranged on a special mounting base fixed in the shield machine. The system eliminates the contradiction between the improvement of the measurement accuracy and the extension of the continuable observation distance, particularly can adapt to the working environment with worse intervisibility conditions, has the advantages of high measurement accuracy and simple and convenient installation, can adapt to the shield tunneling construction with various conditions, and particularly adapt to the tunnel construction of small diameters and small curvature radii.
Owner:SHANGHAI TUNNEL ENGINEERING CO. LTD.
Vehicular target acquisition and tracking using a generalized hough transform for missile guidance
ActiveUS20050271248A1Improving aimpoint designationEfficient switchingPhotometry using reference valueDirection controllersFire-control systemRounded Rectangle
A generalized Hough transform is used to acquire and track vehicular targets for missile guidance. This is accomplished by recognizing that most vehicles have silhouettes that may be described as a “rounded rectangle”. The position and shape of such rounded rectangles is described in terms of 5 parameters (xc, yc, a, b, θ) where xc, yc are the center coordinates, a,b are the major and minor axis and Θ is the orientation. The computation of a five dimensional Hough transform on an image including such a target will produce the five parameters that provide the “best fit” rounded rectangle to the target. These parameters are then passed to a missile tracker. This capability can be used to improve track gate handoff from the automatic target recognizer (ATR) to the missile tracker, changing aspect ratios of maneuvering targets, limited lock-on after launch (LOAL), aimpoint designation and fire control system to missile seeker handover.
Owner:RAYTHEON CO
Radar signature database validation for automatic target recognition
An information theoretic method for testing and / or validating the suitability of a multi-radar signature database to be used on radar systems having automatic target recognition. The database may include measured data and / or modeled synthetic data. The technique allows measured data to be compared to the synthetic data using modal mutual information. The present invention further includes an information theoretic method for real time calculation of automatic target recognition using modal mutual information calculation.
Owner:THE UNITED STATES OF AMERICA AS REPRESETNED BY THE SEC OF THE AIR FORCE
Integral image-based quick ACCA-CFAR SAR (Automatic Censored Cell Averaging-Constant False Alarm Rate Synthetic Aperture Radar) image target detection method
ActiveCN102968799AImplement object detectionHas target detection capabilityImage analysisPattern recognitionSlide window
The invention provides an integral image-based quick ACCA-CFAR SAR (Automatic Censored Cell Averaging-Constant False Alarm Rate Synthetic Aperture Radar) image target detection method, comprising the following steps of: (1) providing a G0 distribution-based self-adaptive global threshold CFAR pre-segmentation algorithm used for generating a target index matrix by combining the statistical property of data; (2) providing an integral image-based G0 distribution statistical parameter quick estimation method, wherein the statistical parameter can be calculated through simple operations such as addition and subtraction once 2-order and 4-prder integral images of an original image are obtained during the implementation of the method; and (3) giving out a basic implementation process of the ACCA-CFAR SAR image target detection method. Through the integral image-based G0 distribution statistical parameter quick estimation strategy provided by the invention, the time efficiency of the method can be greatly improved and the time complexity of the method is irrelevant to the size of a sliding window; and the requirement of the existing automatic target recognition (ATR) system on the treatment of large-scene data can be met to a great extent.
Owner:BEIHANG UNIV
SAR automatic target recognition method based on multi-view deep learning framework
ActiveCN108038445AQuick and accurate identificationFlexible Data Augmentation MethodScene recognitionNeural architecturesPattern recognitionMulti input
The invention discloses an SAR automatic target recognition method based on a multi-view deep learning framework. The method is applied to the field of radar target recognition and aims to solve the problem that a deep neural network is hard to be trained effectively under the condition of a small quantity of original SAR images. According to the method, a small quantity of original SAR images areutilized to generate a large quantity of multi-view combination samples by use of different views of the original SAR images in combination with data collection cost and performance recognition requirements in actual application, and effective recognition information contained in the samples is increased; and based on the deep learning theory, a multi-input parallel deep neural network is constructed, effective features of different views are automatically extracted, and a category prediction result is given, so that rapid and precise recognition of an SAR target is realized. The method has the advantages of being flexible, accurate, efficient and high in generalization capability.
Owner:UNIV OF ELECTRONICS SCI & TECH OF CHINA
ATR trajectory tracking system (A-Track)
A-Track is a tracking process that is driven by automatic target recognition techniques (ATR), mobility analysis using digital maps and exploitation of other constraining information. It is structured as an optimization problem amenable to the formalism from classical relaxation labeling algorithm. The novel combination of techniques in A-Track provides a new approach to the temporal problem of establishing and extending target tracks through a sliding time window involving a sequence of multiple data frames. A data frame is a collection of sensor reports taken from one scan of a predetermined surveillance area that is being systematically and repeatedly scanned over time. The relative time differences between reports within a frame are generally much smaller than the time difference between different frames. A-Track is especially designed to handle cases having relatively large time intervals between frames that cause problems for conventional tracking algorithms based on predictive models.
Owner:THE UNITED STATES OF AMERICA AS REPRESETNED BY THE SEC OF THE AIR FORCE
System and method for target separation of closely spaced targets in automatic target recognition
The Target Separation Algorithms (TSAs) are used to improve the results of Automated Target Recognition (ATR). The task of the TSAs is to separate two or more closely spaced targets in Regions of Interest (ROIs), to separate targets from objects like trees, buildings, etc., in a ROI, or to separate targets from clutter and shadows. The outputs of the TSA separations are inputs to ATR, which identify the type of target based on a template database. TSA may include eight algorithms. These algorithms may use average signal magnitude, support vector machines, rotating lines, and topological grids for target separation in ROI. TSA algorithms can be applied together or separately in different combinations depending on case complexity, required accuracy, and time of computation.
Owner:RAYTHEON CO
SAR sequence image classification method based on space-time joint convolution
ActiveCN110781830AImprove effectivenessOvercome the problem of destroying the time information of sequence imagesScene recognitionNeural architecturesTime informationGoal recognition
The invention discloses an SAR (Synthetic Aperture Radar) sequence image classification method based on space-time joint convolution, which mainly solves the problems of insufficient time informationutilization and low classification accuracy due to the fact that only single image features are utilized in the existing SAR target recognition technology. The method comprises the following steps: 1)generating a sample set, and generating a training sequence sample set and a test sequence sample set from the sample set; 2) constructing a space-time joint convolutional neural network; 3) traininga space-time joint convolutional neural network by using the training sequence sample set to obtain a trained space-time joint convolutional neural network; and 4) inputting the test sequence sampleset into the trained space-time joint convolutional neural network to obtain a classification result. According to the method, the space-time joint convolutional neural network is utilized to extractthe change characteristics of the time dimension and the space dimension of the SAR sequence image, and the accuracy of SAR target classification and recognition is improved. The method can be used for automatic target identification based on SAR sequence images.
Owner:XIDIAN UNIV
Method and system of automatic target recognition tracking under complex scene
ActiveCN103065131AShorten the timeFast recognitionCharacter and pattern recognitionPattern recognitionGoal recognition
The invention discloses a method and a system of automatic target recognition tracking under a complex scene. An embedded image processor and high-speed field programmable gate array (FPGA) processor architecture are adopted. The function of target automatic recognition is finished by utilization of the embedded image processor under the complex scene, and real-time tracking and communication module function are finished by utilization of the high-speed FPGA processor architecture. The invention further discloses a method of the automatic recognition tracking under the complex scene based on the system. Not only is quick recognition of a target under the complex scene guaranteed, but also the accuracy of target recognition and tracking is guaranteed meanwhile. Therefore, the problem that currently, quick recognition of the target can not be finished on an embedded device under the complex scene is solved, and application values are great and application prospects are good in terms of a precision guided weapon.
Owner:CHINA AEROSPACE TIMES ELECTRONICS CORP
Data fusion analysis for maritime automatic target recognition
ActiveUS20150347871A1Character and pattern recognitionRadio wave reradiation/reflectionAlgorithmConfidence metric
A system and method for performing Automatic Target Recognition by combining the outputs of several classifiers. In one embodiment, feature vectors are extracted from radar images and fed to three classifiers. The classifiers include a Gaussian mixture model neural network, a radial basis function neural network, and a vector quantization classifier. The class designations generated by the classifiers are combined in a weighted voting system, i.e., the mode of the weighted classification decisions is selected as the overall class designation of the target. A confidence metric may be formed from the extent to which the class designations of the several classifiers are the same. This system is also designed to handle unknown target types and subsequent re-integration at a later time, effectively, artificially and automatically increasing the training database size.
Owner:RAYTHEON CO
Method for automatic target recognition of synthetic aperture radar (SAR)
ActiveCN102902979AConforming to the nonlinear distribution structureEasy to sortCharacter and pattern recognitionRadio wave reradiation/reflectionHat matrixRadar
The invention discloses a method for automatic target recognition of an SAR. The automatic target recognition of the SAR mainly comprises three steps such as SAR image preprocessing, feature extraction and target classification, and the method is applicable to feature extraction and target classification of the automatic target recognition of the SAR and solves the problem that effective identification information can not be extracted from high-dimensional SAR images. According to the method for the automatic target recognition of the SAR, a manifold structure theory is introduced, and the method is based on a neighborhood identification embedding criterion. The method comprises the steps of A, initializing; B, constructing a similarity matrix and a difference matrix; C, calculating a target matrix on the basis of a maximum margin criterion; D, calculating a projection matrix; E, conducting feature extraction on training samples according to the projection matrix to obtain training sample features; E, conducting feature extraction on SAR images to be classified to obtain test sample features; and F, classifying SAR images to be tested according to a nearest neighbor classifier, wherein Step A-Step E belong to the feature extraction phase, and Step F belongs to the target classification phase. By the aid of the method, the probability of correct identification of targets can be improved.
Owner:UNIV OF ELECTRONICS SCI & TECH OF CHINA
Multi-classifier system-based synthetic aperture radar automatic target recognition method
InactiveCN101894269AReduce space complexityImprove recognition rateImage analysisCharacter and pattern recognitionSupport vector machineFeature extraction
The invention discloses a synthetic aperture radar automatic target recognition method which belongs to the target recognition field and mainly solves the problem that the space complexity of the existing synthetic aperture radar automatic target recognition technology is higher and single classifier has low recognition rate. The method comprises the following recognition steps: preprocessing, extracting characteristics, training classifiers and identifying target, wherein the step of extracting characteristics is to extract PCA characteristics of the synthetic aperture radar image, elliptic Fourier descriptor and two-dimensional Fourier transform; the step of training classifiers is based on the extracted three characteristics to separately use K-nearest neighbor method, support vector machine and MINACE filter theory to train three classifiers; and the step of identifying target is to input the extracted synthetic aperture radar image to be identified in the trained three classifiers for classification and finally adopting the Dempster-Shafer evidence theory to fuse the recognition results of the three classifiers. The method has the advantages of high recognition rate and low space complexity and can be used in the target tracking of the military or civilian field.
Owner:XIDIAN UNIV
Method for identifying, tracking and converting target based on confidence degree and multi-frame judgement
InactiveCN102163280AReliable conversionImage analysisCharacter and pattern recognitionMultiple frameGoal recognition
The invention discloses an integrated method for identifying and tracking a target based on confidence degree and multi-frame judgement, comprising the following steps of: (1) inputting an image; (2) carrying out histogram drawing on a grey-scale image; (3) identifying the position of the target in the image, and giving the confidence degree of the identification result; (4) determining whether the position of the identified target is consistent or not, and classifying the identification results with the same position into a class; (5) calculating the correct probability that a certain position belongs to the target according to the confidence degrees of the identification results and continuous multi-frame identification results by utilizing the Binomial distribution theory; and (6) selecting one image with the highest correct probability as an initial reference frame in a tracking stage and taking the identification result of the image as the reference position of the target. In the invention, the confidence degree of target identification results and the identification results of continuous multiple frames are subjected to comprehensive judgement, thus the operating mode of an automatic target identification system can be automatically, accurately and reliably converted into the target tracking stage from a target identifying stage.
Owner:HUAZHONG UNIV OF SCI & TECH
Semi-autonomous motorized weapon systems
InactiveUS20190137219A1Improve system efficiencyImproved weapon system accuracySafety arrangementWeapon control systemsEngineeringWeapon system
Various techniques are described herein for controlling autonomous and semi-autonomous motorized weapons systems. In various embodiments, semi-autonomous motorized weapons systems may perform automated target identification, selection and prioritization techniques. Dynamic target tracking may be performed, for both primary and secondary targets, in cases of stationary and moving targets and weapon systems. A motorized weapon system then may be actuated automatically toward a firing solution target point, during which the operator-controlled firing mechanism may be enabled or disabled based on the projected point of impact of the weapon in comparison to a determined boundary area associated with the target.
Owner:AIMLOCK INC
Automatic millimeter wave image target identification method and device
ActiveCN106529602ASolve the problem that it is difficult to obtain good detection resultsImprove target recognition abilityCharacter and pattern recognitionGoal recognitionIdentification device
The invention discloses an automatic millimeter wave image target identification method and a device. The method comprises steps that (1), sub image blocks of a to-be-identified target millimeter wave image are acquired; (2), based on a trained convolutional neural network, target containing probability values of the sub image blocks are acquired; (3), based on the probability values, a probability cumulative graph of the to-be-identified target millimeter wave image is acquired; and (4), based on the probability cumulative graph, target marking is carried out so as to accomplish target identification of the to-be-identified target millimeter wave image. The invention further discloses an automatic millimeter wave image target identification device. The device and the method are advantaged in that the device and the method are suitable for automatic millimeter wave image target identification, the excellent target identification effect is realized, a problem that employing traditional manual design characteristics for the millimeter wave image can not realize the excellent detection effect in the prior art is solved, precise target positioning is realized, false alarms are reduced, safety check efficiency is improved, and manpower cost is reduced.
Owner:SHANGHAI INST OF MICROSYSTEM & INFORMATION TECH CHINESE ACAD OF SCI
Automatic target recognition system with Elliptical Laplacian Pyramid filter
InactiveUS7274801B2High Pd : Pfd ratioEfficiently effectively identifyingTelevision system detailsCharacter and pattern recognitionImage resolutionEllipse
Automatic target recognition system and / or method which employs an elliptical Laplacian pyramid based pre-screen image data filter. The filter is a resolution sequence matched filter, where the elliptical Laplacian operator reflects the size, spatial characteristics and image intensity level of prospective, candidate targets embedded in the image data. Targets which exhibit a rectangular or elliptical shape at various aspect angles are more therefore more effectively and efficiently identified and located.
Owner:LOCKHEED MARTIN ELECTRONICS & MISSILES +1
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