104results about How to "Improve Fusion Accuracy" patented technology

The present invention discloses a measurement method on the basis of multi-sensor date fusion. The method provided by the invention comprises the following steps: when a sensor is used for measuring a parameter, the rejection of careless errors of acquired original data according to the Grubbs criterion is performed to minimize the influence on the fusion precision caused by the careless errors; local fusion of measurement data of the same observation object through adoption of the same sensor at different times through a batch estimation method is carried out to obtain local estimated values of relevant parameters of the observation object, and the measurement error of the sensor at this time is obtained through calculation; and weighting coefficients of different sensors are determined through measurement errors of the sensors obtained through the batch estimation, and weighted fusion of the obtained local estimated value of each sensor is performed to obtain final global estimated values of the parameters. The measurement method on the basis of multi-sensor date fusion is applicable to the practical engineering application, and weighting coefficients of data fusion are determined according to prior knowledge.

The invention discloses a ground three-dimensional laser point cloud and ground penetrating radar image fusion method. On the basis of ground penetrating radar image and GPS data synchronization acquisition, an improved electromagnetic wave path propagation model is used for realizing time-depth conversion of the ground penetrating radar image, with a coordinate system of the ground three-dimensional laser data as reference, a geo reference absolute positioning equation is combined to convert two-different source isomerism data to the same coordinate system, and data fusion is realized. According to data features of a ground three-dimensional laser scanner and the ground penetrating radar, a three-dimensional laser point cloud and ground penetrating radar image fusion method is provided. The experiment result and error analysis prove that the process is simple, the fusion precision is high, and the adaptability is wide. Differences between resolutions and data amounts of the two-different source isomerism data are overcome, advantages of each of the three-dimensional laser scanner and the ground penetrating radar are executed thoroughly, and effective fusion of two different typesof data is realized.

The invention relates to a CAN bus-based pig house environmental temperature intelligent monitoring system. The system is characterized in that the intelligent monitoring system is composed of a CAN bus-based pig house environment parameter acquisition and intelligent prediction platform, a pig house environment multi-point temperature fusion model and a pig house environment intelligent prediction model. With the CAN bus-based pig house environmental temperature intelligent monitoring system of the present invention adopted, an existing pig house monitoring system cannot intelligently monitor and predict the temperature of the environment of a pig house according to the non-linearity and large lag of the environmental temperature change of the pig house, the large area of the pig house, complicated temperature change and other characteristics, as a result, control and adjustment of the environmental temperature of the pig house are seriously affected, while, with the CAN bus-based pig house environmental temperature intelligent monitoring system adopted, the above problem can be solved.

The invention provides a multi-sensor fusion farmland environment perception method used for a unmanned agricultural machine in an agricultural machine path control technology field. The method comprises the following steps of step1, before an agricultural machine works, calibrating a camera and fusing a radar and visual sense information in space; step2, during agricultural machine working, adjusting a conversion relation of the radar and a camera coordinate in real time so that the radar and a camera realize space synchronization; step3, resolving received millimeter wave radar data by an industrial control computer, determining an effective target, selecting an area which the radar is interested in at a front portion of an agricultural machine working area, determining a most dangerous target and synchronously collecting camera images; and step4, according to radar information, determining a most dangerous target state, and according to most dangerous target image data collected by the radar and the camera, determining a type of the most dangerous target, and making the agricultural machine carry out corresponding motion by a navigation box. Accuracy of data fusion is high and identification accuracy of an obstacle in front of the agricultural machine is increased.

The invention discloses a networking navigation radar target tracking system based on data fusion. The system comprises at least one radar front end, at least one radar terminal and an Ethernet. The radar front end and the radar terminal are in bidirectional data transmission through the Ethernet. The radar front end comprises an antenna, a transceiver, a data acquisition module, a signal preprocessor module and a control command processing module. The radar terminal comprises a display unit, a signal processing unit and a control unit. The transceiver generates radio-frequency pulses which are radiated to the space through an antenna. The system belongs to a trace point serial processing centralized data fusion structure, has the features of high fusion precision and small system time delay, and meanwhile, has the advantages of eliminating detection blind areas, improving detection probability and improving tracking performance. After the terminal carries out data fusion and comprehensive optimization processing on the data of the plurality of front ends, the problem of blind area of the single radar due to antenna erection location can be effectively solved, marine target detection and tracking accuracy performance is improved, and navigation safety is guaranteed.

The invention discloses a fusion method of a high-temporal-spatial-resolution vegetation index NDVI. The fusion method comprises the following steps of A, performing downscaling processing on a low-resolution vegetation index NDVI image by utilizing a pixel downscaling algorithm; B, calculating increment NDVI by utilizing the vegetation index NDVI image subjected to downscaling processing and thehigh-resolution vegetation index NDVI image, counting an increment NDVI cumulative distribution histogram, and clustering to construct an increment NDVI classification map; C, constructing a linear hybrid model of the high-resolution increment and the low-resolution increment delta NDVI by utilizing the increment delta NDVI classification diagram, and respectively calculating the high-resolution increment by utilizing a ridge regression method; and D, calculating a high-resolution vegetation index NDVI value at the moment tp. According to the method, the classification graph is generated through incremental NDVI clustering. The NDVI change characteristics are fully utilized. Compared with other methods, the fusion effect of the method is more satisfactory. An effective method is provided for constructing the vegetation index NDVI with the high temporal-spatial resolution.

The invention relates to the field of image processing, and specifically relates to a multi-source image fusion method based on enhanced learning. The method comprises the steps: decomposing a multi-source image to obtain a high-frequency coefficient signal and a low-frequency coefficient signal, and performing weighted reconstruction of the high-frequency coefficient signal and the low-frequencycoefficient signal to obtain a fusion image. The method adopts a Q learning algorithm in enhanced learning for the training of the weights of the high-frequency coefficient signal and the low-frequency coefficient signal, selects a weight which enables the fusion evaluation criteria to be optimal for the weighted fusion, and obtains the fusion coefficient signal. The fusion image is reconstructedto obtain the fusion image. Based on the Q learning algorithm, the method achieves the training of the weights of the high-frequency coefficient signal and the low-frequency coefficient signal obtained through the decomposition of the multi-source image, and improves the reliability and accuracy of weight estimation.

A measurement data fusion method for a two-dimensional radar and infrared sensors at different sites. The method includes the following steps that: step 1, successful measurement target pair information is received; second step, a target observation equation is constructed through transformation; third step, state vectors that satisfy Markov estimation conditions are constructed according to measurement azimuth angle characteristics; fourth step, Markov estimation is performed on a target state vector X; step five, error covariance calculation is performed on the target state vector X; and step six, state vectors on a Z axis and the variance of the state vectors are calculated. The measurement data fusion method is advantageous in simple operation. According to the measurement data fusion method of the invention, the target observation equation is constructed through combining the measurement data of the two-dimensional radar and the infrared sensors at different time points; the state vectors and the covariance thereof of the target observation equation are estimated according to the Markov estimation principle; and therefore, complex time-space alignment calculation of vacant-dimension data can be avoided. The method is easy to implement and apply to engineering practice, and has a target state estimation function as well as fusion and filtering functions, and has excellent real-time performance and high fusion accuracy.

The invention discloses a safety data aggregation method based on dynamic segmentation technology, and relates to a network safety data aggregation method. A wireless sensor network is abstracted as a connected graph G(V, E), and adopts the tree type fusion network topology; all nodes in the network are divided into three types: the BS (Base Station) node, the aggregator node and the leaf node; the aggregator node upward transmits the data received from the sub node to the father node after performing the aggregation operation; the leaf sub-node is only responsible for acquiring data and transmitting to the father node; therefore, the data aggregation function is defined as shown in description; the method comprises the aggregation tree establishment stage, the data mixing stage, the data aggregation stage, and the data aggregation stage. The data aggregation precision is improved, the energy consumption is reduced, the network life cycle is prolonged, the privacy protectiveness of the data is improved, and the insufficiency of the SMART algorithm is effectively solved.

The invention discloses a pig house environment temperature detection system based on a CAN (controller area network) bus. The pig house environment temperature detection system is characterized by comprising a CAN bus based pig house environment parameter collection platform and a pig house environment multipoint temperature fusion model, wherein pig house environment factor parameters are detected, adjusted and monitored by the CAN bus based pig house environment parameter collection platform, and the pig house environment multipoint temperature fusion model realizes pig house environment multipoint temperature fusion on the basis of support degree fusion weight and grey relational degree fusion weight which are obtained by a support degree matrix and a grey relational degree matrix of temperature fuzzy values of multiple detection point temperature sensors of the pig house environment as well as linear combination weight. According to the system, the problem that regulation and control for the temperature of the pig house environment is greatly affected by the fact that a conventional pig house monitoring system does not detect the temperature of the pig house environment according to the characteristics of nonlinearity and large lag of the pig house environment temperature change, large area of a pig house, complicated temperature change and the like is effectively solved.

The invention discloses a WSN data layered fusion method for plant growth monitoring. According to the method, a wireless sensor network is divided into a plurality of fixed layers, data transmission volume can be decreased, and errors can be reduced layer by layer. The method includes the following steps that: gross errors in a single node datum are eliminated by using Grubbs criterion, and the fusion value of the node datum per unit time is obtained through batched estimation; data adaptively weighted fusion at a sub layer level is carried out; and finally, data weighted adaptively fusion at a base station level is carried out. As indicated by a simulation experiment result, the fusion result of the method of the invention is closer to a true value compared with an arithmetic average method and a single adaptive weighting algorithm. With the method adopted, data transmission volume can be reduced. The method has high expansibility.

The invention discloses a dinner plate edging method based on vision control. The method comprises the following steps: clamping and locating a dinner plate to be edged, and acquiring images of the dinner plate by virtue of two cameras which are symmetrically arranged at the two sides of the dinner plate; integrating the images through pre-treatment, image registration and fusion processing, and then extracting contour features to generate a complete dinner plate contour drawing; comparing the dinner plate contour drawing with a theoretical CAD model of the dinner plate, and obtaining a central position and a deviation angle of the current clamping and locating; and carrying out coordinate offset in a numerical control system according to the central position and the deviation angle, and controlling an edging unit to realize edging for the dinner plate by the numerical control system after the coordinate offset according to G code corresponding to the theoretical CAD model. According to the method disclosed by the invention, the two cameras which are symmetrically distributed at the two sides of the dinner plate are used for carrying out contour acquisition on the edge of the dinner plate, at a machining station, comprehensive acquisition and control for the contour of the dinner plate can be realized, and the G code is finally used for machining; and the method has the advantages of high edging quality, high machining efficiency and the like.

The invention discloses a particle filter centralized tracking method for multi-base radar out-of-sequence measurement fusion, which belongs to the technical field of data fusion of multi-base radar systems. According to the invention, out-of-sequence measurement is sequentially used to update all states between the out-of-sequence measurement generation time and the latest sequential filter time,and particle filtering is used to achieve target tracking; the problem of deterioration of target fusion tracking performance, which is caused by radar measurement out-of-sequence caused by differentdata processing time of a radar station and communication link delay, is overcome; whether the current measurement is sequential measurement is judged; if the current measurement is sequential measurement, sequential measurement update is carried out, and otherwise out-of-sequence measurement fusion is carried out; the problem of out-of-sequence measurement of multiple arbitrary timing sequences,which is caused by different data preprocessing time of each radar station and communication link delay in a multi-base radar system is effectively solved; and a universal solution is proposed for the problem of out-of-sequence measurement of multiple arbitrary timing sequences, which has higher fusion precision than direct neglect of out-of-sequence measurement.

The invention provides a multi-sensor data synchronization method and device thereof, and the method is applied to a data synchronization controller and comprises the steps: obtaining the state information reported by all sensors; if it is determined that each sensor is in a normal state according to the state information, sending a time synchronization command to each sensor while obtaining the reference time, so that each sensor is synchronized with the reference time and a beat timer is started; and sending a data acquisition command to each sensor, so that each sensor acquires data after receiving the data acquisition command, determines a current timestamp according to the reference time and the beat number of the beat timer, and reports the acquired data associated with the current timestamp to a data fusion processor. Therefore, the sensors work under the same time reference to acquire data, and the subsequent data fusion accuracy is improved.

The invention provides a point cloud correction method and device and a readable storage medium,relating to technical field of unmanned driving. The method comprises the steps of obtaining first pointcloud data and second point cloud data at the same moment, mapping the first point cloud data and the second point cloud data to the same coordinate system, wherein the overlapped area of the first point cloud data and the second point cloud data is third point cloud data; constructing a multi-stage error equation according to the third point cloud data; calculating a correction result, obtainingthe corrected first point cloud data and/or the corrected second point cloud data according to the correction result so that the fusion precision of the laser point cloud and the visual point cloud is improved, then the overall precision of the output fusion point cloud is improved, and a stable and reliable data source is provided for a follow-up technical scheme of unmanned driving.

The invention discloses a networking radar tracking method and system based on conversion measurement reconstruction, and belongs to the technical field of radar target tracking. The method comprisesthe following steps: S1, initializing a filter; S2, predicting a target state; S3, reconstructing a conversion measurement model; S4, applying a CMKF filter; S5: estimating a target state. According to the method, an angle prediction value is estimated based on state prediction, then angle measurement and prediction are introduced into conversion measurement, and the proportion of the two is adjusted according to the proportion of the angle measurement error to the prediction error, so that a new conversion measurement model is constructed; in the reconstruction conversion measurement model, the cosine term of the angle error is effectively reduced, and the radial estimation performance is improved accordingly; and in the networking radar, information of a plurality of radars is fused in sequence based on the reconstructed conversion measurement model, so that for target tracking estimation, fusion precision is greatly improved compared with an original method, a satisfactory result isobtained; and the fusion method can be expanded to application of 2D radar networking tracking.

The invention discloses a single out-of-order measurement centralized fusion method of an arbitrary delay step of a nonlinear system. The generalized Gaussian smoothing method is used to solve a smoothing probability density function of the arbitrary nonlinear system, and the particle filter algorithm is used to achieve update. Firstly, it is determined that the measurement is sequential or not, asequential posterior state is obtained by using the particle filter algorithm if so, otherwise, out-of-order measurement fusion is performed, i.e., the generalized Gaussian smoothing method is used to solve the smoothing probability density function at the generation time of the out-of-order measurement, then the smoothing probability density function and a local likelihood function are combinedto solve an asynchronous likelihood function, and finally, the asynchronous likelihood function is used to update the sequential posterior state at the previous time to obtain the fusion posterior state, and the fusion posterior state represented by particle samples is approximately stored as Gaussian parameters, which effectively solves the problem that the out-of-order measurement data in asynchronous sensor networks are difficult to be fused due to data processing and communication delay.

The invention provides an improved evidence dissimilarity index and an improved evidence fusion algorithm. The improved evidence dissimilarity index and the improved evidence fusion algorithm comprisethe following steps 1-6. The method comprises the following steps: step 1, calculating dissimilarity indexes among different evidence bodies based on proposed improved evidence dissimilarity; step 2,calculating the support degree between different evidence bodies according to the evidence dissimilarity degree, and constructing a support degree matrix; step 3, calculating a characteristic value and a characteristic vector of the support degree matrix; step 4, taking the feature vector corresponding to the maximum feature value as a weight coefficient of each evidence body; step 5, except forthe evidence with the maximum weight, correcting the rest evidence bodies by taking the weight coefficient as a discount factor; and step 6, performing evidence fusion on the corrected evidence body through a D-S combination formula, and completing a final decision. According to the method, the conflict degree between different evidence bodies can be evaluated more effectively, and the accuracy and precision of evidence fusion are improved, so that the decision risk is reduced.