171 results about "Automatic target recognition" patented technology

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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 (x_c, y_c, a, b, θ) where x_c, y_c 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.

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.

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.

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.

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.

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.

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.

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.

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.

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.