35results about How to "Improve the accuracy of association" patented technology

The invention discloses an enterprise association recognition method and system. The method includes the steps that an enterprise relevant database of a uniform data format is established; enterprise relevant data of a set area are collected through the network technology; relevant data fields of all enterprises in the enterprise relevant database are called, and the data fields are preprocessed; enterprise shareholder data fields are extracted, and whether the data fields belong to persons or the enterprises are judged; data docking is performed on the same enterprise; data association between different enterprises is established; information query of the associated enterprises is performed. The method and system have the advantages that the data fields are preprocessed, enterprise shareholder data fields and the like are intelligently recognized, whether the data fields belong to persons or the enterprises are judged, the correct recognition rate of the system for persons and the enterprises can be increased to the maximum extent, the system accuracy is improved, and enterprise association accuracy is improved.

The invention discloses an adaptive distributed track data fusion method based on a covariance indicator function. The method comprises the following steps: 1) receiving and storing track data, and carrying out pretreatment on the received track data; 2) carrying out space-time registration on the multi-source track data; 3) associating the track data obtained by different sensors and determining correspondence relation between observation data and moving objects; and 4) carrying out fusion on the multi-source track data based on adaptive selection of fusion index, and determining motion state of each moving object. The method can selectively eliminate data having filtering divergence or overlarge system deviation and the like, which hinders improvement of precision of the fusion results, in the fusion process, and keeps the results having good consistency and carries out fusion.

An embodiment mode of the invention discloses an intuitionistic fuzzy association method for ADS-B monitoring data and radar tracks. The method comprises the steps of carrying out association pretreatment on the ADS-B monitoring data and radar tracks to obtain a plurality of primary association radar tracks, obtaining fuzzy intuitionistic indexes corresponding to association attributes between the ADS-B monitoring data and the primary radar tracks, obtaining fuzzy decision grades corresponding to the association attributes according to the fuzzy intuitionistic indexes, and carrying out weighing summation on the fuzzy intuitionistic indexes to obtain final associated radar tracks. The associated attributes comprise the distance, the speed, the speed difference and the course angle difference. The embodiment mode of the invention further discloses an intuitionistic fuzzy association device for the ADS-B monitoring data and the radar tracks. By means of the method, the intuitionistic fuzzy association method for the ADS-B monitoring data and the radar tracks can improve association accuracy between the ADS-B monitoring data and the radar tracks and is easy to achieve.

The invention discloses a nuclear K-mean value track correlation method based on KMDL criteria. The method comprises the following steps that firstly, a typical track correlation scene is established; secondly, the number of target tracks is determined based on the KMDL criteria; thirdly, the nuclear K-mean value algorithm is used for conducting correlation on the track observation scene. According to the nuclear K-mean value track correlation method based on the KMDL criteria, the KMDL criteria and the nuclear K-mean value algorithm are combined to solve the multi-target track correlation problem under the complex environment where clutters are dense, targets are near and the number of the targets is not known. According to the method, the movement state information of the targets is fully utilized, the correlation accuracy rate is effectively increased, the correlation criteria are simple and practicable, the calculated amount is small, the correlation accuracy rate is high, it is not sensitive for target intersection, and the method is suitable for track correlation under the environment where the targets are dense and intersect and applicable to engineering realization.

An SLAM data association method based on fuzzy-self-adaption is disclosed and a fuzzy-self-adaptive data association method based on an error ellipse and an associated threshold value is provided for the deficiencies of an existing SLAM data association method. According to the SLAM data association method, a fuzzy logic rule is applied to data association of a feature observation value and an estimation value, and the threshold value can be adaptively determined according to variations of the environment and noises, so that the data association effect is improved; and in order to effectively prevent a false road sign, a minimum road sign interval in the environment is tracked according to observation data, one, serving as a new virtual road sign, of two observation road signs with a shortest Euclidean distance is added in a system taste, and in addition, data association is carried out on the other observation quantity and the virtual road sign by virtue of the fuzzy rule. A simulation experiment and a real experiment prove that the SLAM data association method is capable of solving the data association problem and well adapting to the variations of the environment and the noises, so that the virtual road sign is effectively prevented and the observation loss rate is reduced.

The invention relates to the technical field of database audit and safety management, and aims at providing a method for improving three-layer correlation accuracy. The method comprises the following steps: obtaining information generated during interaction between a Web server and a front-end accessor and information generated during interaction between the Web server and a background database through a proxy server, and sending the information to a database audit system; and receiving, by the database audit system, the data sent by the proxy server, and carrying out one-to-one correlation to determine the correlation of front-end HTTP access and background database access. According to the method, the efficiency of the one-to-one correlation between the background database access and the front-end accessor is greatly improved, so that the audit efficiency of the database audit system is improved; and meanwhile, the correctness of the one-to-one correlation between the background database access and the front-end accessor is greatly improved, so that the audit correctness of the database audit system is improved.

The invention relates to a marine target motion estimation method based on multi-source SAR satellites. The method comprises the steps of: detecting a marine target by using a multi-source SAR satellite image obtain a target position and an image slice, and then performing parameter extraction by using the image slice to obtain a target length; performing fuzzy correlation by using the target position and the target length to solve the attribution problem of the same target in different satellite images; eliminating heading fuzziness by the time and the positions of the target in different satellite images to obtain the accurate heading of the marine target, and then obtaining the accurate speed of the marine target by speed iterative estimation. The method accurately solves the heading and the speed of the marine target through the target correlation between the SAR satellites, and can realize the effective fusion of the multi-source SAR satellite information.

The invention discloses an SLAM (Simultaneous Localization and Mapping) data association method based on maximum-minimum ant system optimization. The SLAM data association method based on maximum-minimum ant system optimization includes the steps: utilizing examination threshold filtering to reduce the solution space range associated with data, and improving the search efficiency of an algorithm; converting a data association problem into an optimal path solution problem, and utilizing an explanation tree model to stand for an association path node; and combining a maximum-minimum ant system with a combination maximum likelihood criterion, and taking a combination maximum likelihood value assumed by data association as a target function to perform path search. The SLAM data association method based on maximum-minimum ant system optimization reduces the association solution space of data association, can improve the search efficiency of the algorithm, and can effectively make up the defect that the ant colony algorithm is low in the rate of convergence, and stagnates in search and is easy to fall into local optimum.

The invention discloses a heterogeneous sensor combined track association and tracking method, which comprises the steps of S1, initializing system parameters; S2, mapping a plurality of data sourcesto a unified Cartesian coordinate system through Gauss Kruger projection; S3, performing coarse association on the data source message through hash coding; S4, performing Kalman filtering processing on the message track; S5, performing time alignment on the filtered message trace points through linear extrapolation and interpolation; S6, carrying out trace point association through a nearest neighbor algorithm; and S7, carrying out track association through a (M, C) / N logic judgment criterion. The method has the advantages of low calculation complexity, high association accuracy and the like.

The invention relates to a ship target track association method for a stationary orbit staring satellite remote sensing image, belongs to the technical field of satellite remote sensing, introduces ship automatic identification system information, firstly realizes association of cooperative ship targets, effectively reduces the number of association matching combinations, and then is divided into two association stages to associate non-cooperative targets. The number of matching combinations in the association algorithm is reduced, the ship target track association speed is improved, the influence of medium-low resolution and low frame rate conditions on interrupted track association of the ship target is overcome, association complexity is reduced, association accuracy is improved, finally, the associated interrupted tracks are connected, and ship target track association efficiency is improved; and integrity of ship target situation display is improved.

The invention provides an address tracing method and device. The method comprises the following steps: taking a minimum unrelated degree sum obtained by accumulating unrelated degrees between each log record and a target detail record corresponding to each log record as evidence and determining the target detail record corresponding to each log record; and carrying out correlation treatment on each log record and the target detail record corresponding to each log record so as to carry out IP (Internet Protocol) address tracing. The correlation treatment is carried out due to that a second standard moment corresponding to the log record for indicating is not at a first standard moment corresponding to the target detail record corresponding to the log record, which is used as a starting moment, and a time length is the unrelated degree of the possibility in a standard time length. Therefore, the technical problems that the relation accuracy of the detail records and the log records in the prior art is lower so that the IP address tracing accuracy is lower are solved.

The present invention belongs to the technical field of radars or similar systems special for application, and discloses a lag track correlative fusion method, system and application based on a randomdistance. The concrete flows of the method comprise: inputting a first-class reference track and multiple other-class tracks; selecting windows with sizes of n; calculating the lag correlation; finding a track set meeting the correlation threshold value; performing verification of the random distance for the track correlation in the set; determining the correlative track and performing track fusion; and calculating the incidence relation between the tracks with lag correlation, ranking the incidence relation, finding the track with the maximum incidence relation and taking the track with themaximum incidence relation as an correlative track, and employing a least square method to start fusion from the track lag moment. The lag track correlative fusion method, system and application basedon the random distance are high in classification precision and low in operation time complexity, can effectively solve the fusion problem of lag and interrupted tracks in the track correlation fusion, can improve the track correlation correction and effectively improve the integration of target feature portrayal description through fusion.

The invention discloses an interrupted track correlation method of a maritime target of a high-orbit optical satellite. The method comprises the steps of: performing backward extrapolation on a previous first track and performing forward extrapolation on a latter second track; performing preliminary correlation on the first track and the second track whose state estimation at the same moment meetsconstraint conditions; performing further correlation on the tracks in a preliminary correlation set to obtain a further correlation set; solving the further correlation set by using a two-dimensional optimal allocation principle to obtain a first stage correlation set with the minimal global cost; solving a possible correlation segment pair of a complementary set of the first stage correlation set in the preliminary correlation set by using distance two-dimensional optimal allocation; detecting and screening an intermediate correlation set by using an amplitude relationship to obtain a second stage correlation set; and fitting correlated tracks in the first stage correlation set and the second stage correlation set to obtain a continuous track. By adopting the interrupted track correlation method of the maritime target of the high-orbit optical satellite disclosed by the invention, the performance of track correlation is improved.

The invention belongs to the field of data association, and particularly relates to a line feature data association method of a robot based on improved ant colony optimization. The method comprises the following steps: constructing a global coordinate system and a local coordinate system, and setting a sensor working mode; establishing a line feature observation model; extracting environmental line features using an improved segmentation-aggregation method; designing a line feature data association method combining an improved ICNN method and an improved ant colony optimization method; and inputting the environmental line feature information and the mobile robot pose information obtained by the sensor into a line feature data association method based on improved ant colony optimization, carrying out data association to obtain an association pair set, and updating the map feature set. Firstly, environment line characteristics are extracted based on laser sensor data, then an improved ICNN algorithm and an improved ant colony optimization algorithm are combined, the correlation accuracy of a data correlation algorithm is improved, and an accurate environment map is obtained.

The invention belongs to the data association field and especially relates to a mismatching determination method of indoor environment robot line characteristic ICNN data association. The invention provides an improved ICNN data association method of a decision rule of mismatching and mismatching prevention based on a line segment position relationship. The method is characterized by extracting anenvironmental line characteristic based on laser sensor data, re-selecting a line characteristic parameter, and summarizing line segment position relationships in a same two-dimensional plane; and for a problem that mismatching is easy to generate during line segment parallel or collineation, giving a mismatching evaluation model; and based on that, providing an improved ICNN algorithm. In the improved algorithm, computational time similar to the time of a standard algorithm is used to achieve higher correlation accuracy.

The invention relates to an active and passive track motion feature association method based on sequential processing. For the problems that an existing association method lacks effective and accurateestimation of statistical information of a heterogeneous sensor and lacks a reliable cleaning means for a to-be-associated track, so that the existing active and passive association based on the target motion feature has the problems of high association error rate, high processing resource consumption and the like, the invention provides an active and passive track motion feature association method based on sequential processing, and efficient association of massive observation tracks is completed; and the method comprises the specific steps of sector track traversal pairing, nearest neighbortime extrapolation registration, active sensor target motion feature statistical property estimation, passive sensor target motion feature statistical property estimation and association judgment based on motion feature association, and therefore, the requirement for high-reliability association application of mass observation data in a complex detection environment is met.

The invention discloses a code scanning correlation method for small boxes and bar boxes of cigarettes, capable of eliminating accumulative mistakes and relates to the technical field of cigarette package production. Through addition and configuration of a small box code reading module, a bar box code reading module and an industrial control computer on a cigarette package machine, the industrialcontrol computer can correlate small box identification codes identified by the small box code reading module with bar box identification codes identified by the bar box code reading module accordingto the correlation method disclosed by the invention; whether the amount of the identification codes in the bar boxes is consistent with a set amount is judged according to a correlation result, and thus whether an abnormity occurs is judged; and a prompt is made for an abnormal result if the abnormity occurs. The method disclosed by the invention has the beneficial effects that through correlation of identification mistakes or data mistakes which occur in a course, a mistake scope can be controlled in a scope as small as possible, so that the accumulative mistakes are eliminated, influences on subsequent correlation are avoided, and accuracy and stability of correlation are enhanced.

The invention discloses an adaptive distributed track data fusion method based on a covariance indicator function. The method comprises the following steps: 1) receiving and storing track data, and carrying out pretreatment on the received track data; 2) carrying out space-time registration on the multi-source track data; 3) associating the track data obtained by different sensors and determining correspondence relation between observation data and moving objects; and 4) carrying out fusion on the multi-source track data based on adaptive selection of fusion index, and determining motion state of each moving object. The method can selectively eliminate data having filtering divergence or overlarge system deviation and the like, which hinders improvement of precision of the fusion results, in the fusion process, and keeps the results having good consistency and carries out fusion.

The invention relates to a maritime target association system and method based on high and low orbit optical satellite observation, including: a target detection and tracking module, a target detection and motion feature extraction module, and a target multi-level multi-feature association module; the target detection and tracking module uses The high-orbit optical satellite image sequence detects and tracks maritime targets, and obtains the trajectory information of maritime targets; the target detection and motion feature extraction module uses low-orbit images for detection and motion feature extraction of maritime targets, and obtains the position and heading of the target information; the target multi-level multi-feature association module, based on the target detection and tracking module to obtain the motion trajectory information and the target detection and motion feature extraction module to obtain the position and heading information, to perform multi-level multi-feature association of maritime targets. The invention can remarkably improve the monitoring capability of sea targets, has a high accuracy rate of target association under complex observation backgrounds, and is easy to implement.

The invention relates to a marine target motion estimation method based on multi-source SAR satellites. The method comprises the steps of: detecting a marine target by using a multi-source SAR satellite image obtain a target position and an image slice, and then performing parameter extraction by using the image slice to obtain a target length; performing fuzzy correlation by using the target position and the target length to solve the attribution problem of the same target in different satellite images; eliminating heading fuzziness by the time and the positions of the target in different satellite images to obtain the accurate heading of the marine target, and then obtaining the accurate speed of the marine target by speed iterative estimation. The method accurately solves the heading and the speed of the marine target through the target correlation between the SAR satellites, and can realize the effective fusion of the multi-source SAR satellite information.

The invention discloses a kernel K-means track association method based on KMDL criterion criterion, comprising the following steps: Step 1: constructing a typical track association scene; Step 2: using KMDL criterion criterion to determine the number of target tracks; Step 3: Correlate with the kernel K-means algorithm for the track observation scene. The disclosed kernel K-means track correlation method based on the KMDL criterion criterion of the present invention, based on the target state information, combines the KMDL criterion criterion and the kernel K-means algorithm to solve the complex environment (intensive clutter, close to the target, The multi-target track association problem with unknown number of targets). This method makes full use of the target's motion state information, effectively improving the correlation accuracy rate, the correlation criterion is simple and easy to implement, the calculation amount is small, the correlation accuracy rate is high, and it is not sensitive to target crossing, so it is suitable for navigation in dense and cross-target environments. Trace correlation, suitable for engineering implementation.

The invention provides a graph matching association method and system for multi-radar dense targets. The method comprises the following steps: constructing a vector set formed by radar tracking targets; analyzing the influence of errors on approximate congruency, and forming a vector similarity matrix; giving a single-moment graph matching algorithm calculation process, and determining a triangle with the highest approximate congruency as an alternative triangle in a vector similarity matrix by using a greedy algorithm; the vertexes and the basic edges are screened from other targets to form alternative associated triangles; and designing an association relationship maintaining method under continuous moments, judging successfully associated triangles at historical moments and current moments by using a DS evidence theory, and determining a target corresponding to a final successfully associated triangle at the current moment, so that the association relationship maintaining stability and the association accuracy are improved. The method has the advantages of being low in time complexity, high in convergence speed and high in real-time performance, and applications such as multi-radar target fusion track generation can be effectively supported by establishing the stable and correct target incidence relation.

The invention belongs to the technical field of sonar, provides a shell sonar and towed linear array sonar target track association method, and aims to solve the problem that the target track association performance is easy to decline due to the fact that the shell sonar and the towed linear array sonar are large in direction finding precision characteristic difference and only board angle similarity is adopted for target track association. According to the method, multi-dimensional features such as the side angle, the signal-to-noise ratio and the DEMON spectrum of the target are extracted, joint association processing is carried out, the robustness of target track association of the shell sonar and the towed linear array sonar can be effectively improved, and the association accuracy is improved.

The invention provides an SLAM data association method based on a self-adaptive local and grouping association strategy. The method comprises the following specific steps: firstly, adaptively determining a local data association region according to the position of the mobile robot at the current moment, the measurement range of a sensor and the distribution condition of observation values at the current moment; secondly, adaptively determining the grouping number of the observation values according to the local association region and the number and distribution of the current observation values, and grouping the current observation values by using a Gaussian mixture clustering algorithm on the basis of the grouping number; thirdly, completing data association between the observed value ineach group and the feature points in the local area by using a joint compatible branch and bound algorithm, and screening out an optimal association solution of each group; and finally, integrating each group of optimal solutions to obtain a final data association result. Experimental results prove that the method can reduce the association calculation amount while ensuring high association accuracy.