528 results about "Particle filtering algorithm" patented technology

The described invention allows for rapid geolocation of one or more RF emitters using a single moving collection platform. Inaccuracies of conventional frequency of arrival (FOA) geolocation methods are overcome by solving simultaneously for emitter location and a potential emitter drift associated with an observed emitter frequency. Certain embodiments may utilize particle filtering algorithms to recursively update multimodal state densities that are typical of solutions involving both unknown emitter location and nonstationary emitter carrier drift. Moreover, certain properties of particle filters may be exploited to provide a geolocation solution given a complex multimodal state space composed of emitter location and a non-stationary emitter frequency required for FOA.

The invention discloses a smart phone-based indoor pedestrian positioning and tracking method, comprising the following steps: leading in indoor map information by a smart phone, wherein an indoor map is a vector map; collecting current acceleration information, angular speed information and direction information of the smart phone, and collecting the strength of all indoor WIFI RSS signals; on the basis of PDR, calculating pedestrian steps, pedestrian step lengths and walking directions according to the sampled data; estimating the positions of pedestrians by using a particle filtering algorithm, judging and identifying a swerving scene, a room identification scene and a door detection scene on the basis of WIFI RSS to carry out auxiliary correcting and positioning; and displaying the positions of the pedestrians on the indoor map. The method is high in positioning accuracy and strong in anti-jamming capability, a decimeter-level positioning result can be obtained, the effect on the pedestrian action, caused by the positioning accuracy, is small, the functions of pedestrian swerving, room identification and door detection are effectively achieved, the excessive dependence on the deployment position of a WIFI router is avoided, and the labor consumption in the deployment process is reduced.

The invention relates to a dexterous hand teleoperation control method based on Kinect human hand motion capturing. The hardware for realizing the method comprises a Kinect sensor, a computer and a remote robot dexterous hand system. A mixing movement outline model is used for tracking the outline of the hand, hand posture tracking is realized through a particle filter algorithm, the far-end situation is observed through feedback image information transmitted by a robot in real time, and therefore a specific operation task is implemented. Through the movement outline model combining depth information and YCbCr skin color detection space technology, the problem that a real solution can not be obtained easily when an image contains high noise or a target is provided with a weak boundary is solved. A particle filter posture detection method based on template matching is adopted, and the human hand three-dimensional posture with high-dimensional characteristics can be detected and estimated. Through a master-slave operation combining a manual control mode and a program mode, convenience and rapidness are realized, and the guiding performance and usability of human-machine interaction are represented.

The invention discloses a geomagnetic indoor positioning system and method based on a self-adaptive particle filter algorithm. The geomagnetic indoor positioning system comprises the novel self-adaptive particle filter algorithm and an efficient geomagnetic fingerprint data collecting unit. The efficient geomagnetic fingerprint data collecting unit has the main function of quickly collecting indoor geomagnetic signals by using a mobile phone magnetometer and converting the indoor geomagnetic signals into a geomagnetic fingerprint model to be stored. The novel self-adaptive particle filter algorithm is the improvement to an existing filter algorithm to improve the robustness, the precision and the usability. The key technology of the self-adaptive particle filter improvement algorithm mainly comprises a self-adaptive behavior model, a novel measurement model, a self-adaptive resampling model and a positioning precision estimation and positioning failure detection model. The geomagnetic indoor positioning system is suitable for various smart phones integrated with acceleration sensors, gyroscopes and magnetometers. The geomagnetic indoor positioning system has the advantages that the facing directions, the placing positions and using of the smart phones are not limited, and positioning precision is high.

The invention discloses a self-adaptive cascade classifier training method based on online learning, which comprises the following steps: (1), preparing a training sample set with a small quantity of samples, and training an initial cascade classifier HC(x) in a cascade classifier algorithm; (2), using the HC(x) for traversal of image frames to be detected, extracting areas with sizes identical to the sizes of the training samples one by one, calculating a feature value set, classifying the areas with the initial cascade classifier, and judging whether the areas are target areas, thereby completing target detection; (3) tracking the detected targets in a particle filtering algorithm, verifying the target detection results through tracking, marking detection with errors as a negative sample for online learning, obtaining different attitudes of real targets through tracking and extracting a positive sample for online learning; and (4) carrying out online training and updating for the initial cascade classifier HC(x) in a self-adaptive cascade classifier algorithm when an online learning sample is obtained, thereby gradually improving the target detection accuracy of the classifier.

The invention discloses a multi-lane-line detection method. Color images of continuous frames are obtained, graying processing is carried out on a current frame of image, and inverse perspective transformation is carried out on a grey-scale map based on a camera parameter and a set grid map size, thereby obtaining a grid map of a road part; according to priori information of a control point, region division is carried out on the grid map by using a Thiessen polygon and image binaryzation processing is carried out on the divided regions; region grouping is carried out based on priori information of lane lines and pixel coordinates of non-zero pixel values is recorded; curve fitting is carried out on a grouped point set by using an improved RANSAC algorithm, a current image control point is predicted by combining a lane line equation and a particle filter algorithm, a final lane line equation is calculated and e perspective transformation is carried out on the lane line equation, and thus a lane line equation in an original image is obtained. With the method, lane line detection precision and robustness are high; and multi-lane-line detection can be completed simultaneously.

The invention discloses a binocular vision positioning and three-dimensional mapping method for an indoor mobile robot. A binocular vision positioning and three-dimensional mapping system for the indoor mobile robot is mainly composed of a two-wheeled mobile robot platform, an odometer module, an analog camera, an FPGA core module, an image acquisition module, a wireless communication module, a storage module, a USB module and a remote data processing host computer. The FPGA core module controls the image acquisition module to collect information of a left image and a right image and sends the information of the left image and the right image to the remote data processing host computer. Distance information between images and the robot is obtained on the remote data processing host computer according to the information of the left image and the right image in a fast belief propagation algorithm, a three-dimensional environmental map is established in the remote data processing host computer, recognition of a specific marker is realized in an improved SIFT algorithm, and then the position of the mobile robot is determined through information integration in a particle filter algorithm. The binocular vision positioning and three-dimensional mapping system is compact in structure and capable of having access to an intelligent space system so that the indoor mobile robot can detect the environment in real time and provide more and accurate services according to the corresponding position information at the same time.

Disclosed is a high-speed train driver alertness detecting method based on face image and eye movement analysis. The method includes steps of acquiring images of the head of a driver by a primary camera and a secondary camera simultaneously, extracting characteristics of the head of the driver step by step by the aid of wavelet multi-scale, recognizing the head of the driver via a neural network training method, and detecting the face of the driver via an AdaBoost arithmetic; respectively starting a Harr characteristic and a two-dimensional orthogonal Log-Gabor filtering phase characteristic to detect human eyes under different illuminations; building an adaptive non-linear level-log model of the driver; realizing eye movement tracking analysis for the driver via a strong tracking particle filter arithmetic; and finally realizing weight sum for an eye movement characteristic fatigue factor value and six face images including PERCLOS, pupilla, blinking, nodding, yawning and face inclination so as to obtain an alertness value of the driver. The calculated alertness value is high in accuracy and robustness.

The invention discloses a lithium ion battery remaining service life prediction method based on long and short term memory LSTM and particle filter PF, and belongs to the field of new energy electricvehicle lithium ion battery remaining service life prediction. The method comprises the following specific steps: analyzing performance degradation characteristic parameters of the lithium ion batteryextracted from voltage, current and temperature of the lithium ion battery, fusing the characteristic parameters by utilizing an improved principal component analysis method to serve as a health index of the lithium ion battery, and fully characterizing performance degradation characteristics of the lithium ion battery without redundant information; training a lithium ion battery capacity prediction model based on a long-and-short-term memory neural network to predict the capacity of the lithium ion battery; and taking the capacity prediction value of the LSTM prediction model as the observation value of the particle filtering prediction model, adjusting and updating the capacity prediction value in each iteration process of the particle filtering algorithm, and comparing the capacity prediction value with the capacity failure threshold so as to predict the residual service life of the lithium ion battery. The method can effectively monitor and predict the performance degradation process of the lithium ion battery.

The invention discloses a power battery SOC (state of charge) estimation method based on an expansion Kalman particle filter algorithm. A conventional method has the problems of unsatisfied on-line estimation needs, large accumulation errors, diverging filtering and the like. An expansion Kalman filtering method and a particle filter method are combined and important density functions are generated through expansion Kalman filtering, so that a particle degeneration phenomenon is avoided and the estimation precision is enhanced. By using the provided method, the SOC of a battery can be effectively estimated and the precision is quite high, thereby being suitable for the SOC estimation of various batteries.

An autonomous global relocation method for robots is provided. The method comprises steps that existing maps are rasterized; the grids with obstacles and without obstacles are assigned to different values; multiple distance data and angle data corresponding to the distance data obtained by a lidar sensor with the same position of a robot scanning external obstacles are marked on the map with the simulation robot position as the origin in the form of laser point location; and the assignment of the grid of the laser point location corresponding to each simulation robot position is integrated, robot positions are screened out, and the correct position of the robot is calculated from the initially selected simulation robot positions by a particle filtering algorithm. Through the differential assignment of grids with obstacles and without obstacles, the position of the laser point location in the map is described, and the matching degree between the lidar sensor information and the electronic map is quickly calculated, the most divergent transfer point is found as the transfer target point, and the self position can be rapidly and accurately found in a known map.

The invention discloses a road surface construction robot environment perception system and method based on a multi-source sensor. A camera and a laser radar are arranged on a robot body; an embedded industrial control computer is arranged in the robot body; the embedded industrial control computer is used for obtaining and fusing class information of picture data and distance information of the laser radar based on a data fusion multi-target detection ranging algorithm, then, tracking a target by adopting a particle filter algorithm in the two directions of an image and point cloud, obtaining dynamic estimation change of an obstacle target in continuous time, and finally, obtaining the advancing speed and the motion direction of an obstacle in a construction area through the dynamic estimation change; road surface environment perception on the construction area is realized by drawing a risk graph; therefore, the adaptive capacity of the system for different environments is obviously improved; furthermore, the shielding condition between obstacles has some improvements on multi-target detection and a tracking algorithm; therefore, the effective operation of the construction robot is ensured; and thus, the system and the method have good application prospect.

The invention provides an unmanned plane target tracking method combining a mean-shift algorithm and a particle-filter algorithm. The unmanned plane target tracking method comprises the following steps of: constructing a mean-shift tracking algorithm based on a bandwidth matrix according to the bandwidth matrix during a mean-shift tracking process, and updating a target-scale window in a self-adaptive manner during the tracking process; establishing a weighting and data-fusion target positioning method according to detected results of the mean-shift tracking algorithm and the particle-filter algorithm; determining an unmanned plane target position according to the weighting and data-fusion target positioning method; sampling particles in the particle-filter algorithm according to a target re-convergence method to generate the particle-filter algorithm based on the target re-convergence; and obtaining a target expanding and searching strategy according to the target re-convergence particle-filter algorithm and tracking the target. The embodiment of the invention can realize real-time positioning and tracking of the target under complex conditions including a dynamic scene, illumination change, scale change, shielding and the like, and therefore, the unmanned plane target tracking method has the advantages of being good in real-time performance, strong in adaptability, good in expandability and the like.

The invention provides a novel robot self-positioning and target recognition algorithm based on omni-vision aiming at the problem of self-positioning and target recognition of service robots in the family environment. According to the method, the advantages of the two positioning algorithms used most frequently currently, namely the FastSLAM particle filter algorithm and the matching optimization positioning algorithm are combined to make up the defects of each other, so that high-precision self-positioning is achieved in real time and effective global positioning and obstacle avoidance path planning are achieved, and the positioning algorithm is made to be highly robust to various dynamic factors, such as severe blocking of a visual system, high-speed dynamic confrontation of the robot and changing light conditions, of the environment.

The invention relates to a multi-target passive tracking method based on a wireless sensor network. The technical characteristics are that the multi-target passive tracking method based on the wireless sensor network comprises the following steps that: (1) a measurement model is established according to the receiving signal intensity of the sensor network; (2) according to the established measurement model from the step (1), the variable multi-target positioning and tracking is realized under the indoor environment through the combination of a multi-target Bernoulli filter algorithm and a particle filter algorithm. The multi-target passive tracking method based on the wireless sensor network provided by the invention is reasonable in design; the established measurement model has high accuracy under the indoor environment, and the model forecast value is approximate to the actual observation value; the target detecting and tracking algorithm has high accuracy and stability, and can detect and track a plurality of targets; and the measurement model and the target algorithm are appropriate in calculation complexity, and thus guaranteeing the running real-time performance of the tracking system.

The invention discloses a short-term wind speed prediction method of Gaussian process regression and particle filtering, thereby realizing on-line dynamic detection and correction of abnormal values and improving wind speed prediction accuracy. According to the method, an input variable set having the highest correlation with a wind speed value at a to-be-predicted time is determined by using a partial autocorrelation function, a state vector is determined, and a proper training sample set is constructed; a Gaussian-process-regression-based short-term wind speed prediction model is establishedin the training sample set and a fitting residue during the training process is given; on the basis of combination of the state vector and the Gaussian process regression model, a particle filteringstate space equation is established and state estimation is carried out on a current measurement value by using a particle filtering algorithm; and the estimation value and the measurement value residual of particle filtering are analyzed, determination is carried out based on a 3 sigma principle, and an abnormal value is corrected. According to the method provided by the invention, the abnormal value can be detected and corrected effectively; the short-term wind speed prediction precision is improved; and a wind speed prediction problem of the power system is solved.

The invention discloses a robot map building method suitable for a large-scale transformer substation environment. A particle filter algorithm is used for building a map, discrete particles are used for approximating posterior probability distribution of a robot posture, each particle independently maintains a map of the transformer substation, for the problem of large calculation amount and insufficient power of the robot, an SLAM algorithm is optimized, a rosbag software package based on ROS is combined to be changed, only speedometer and laser data packets are recorded in the field of the transformer substation, if the power of the robot is used up in the process, the robot can be restarted after being charged, data of the transformer substation can be stored as a plurality of data packets, the speedometer among the data packets is unnecessarily continuous, and after the data packets are completely recorded, the map is built in a way of playing back the data packets by virtue of rosbag. By adopting the method, a high-precision raster map of a large-scale transformer substation environment can be built, and the probability of map building failure caused by the positioning mistake can be reduced.

The invention provides a combined positioning method based on inertial navigation, wi-fi or bluetooth signal fingerprints and an indoor electronic map, and can be widely applied in indoor positioning and seamless combination of indoor and outdoor positioning. The combined positioning method is characterized by comprising the steps: 1) coarse positioning is performed via the wifi or bluetooth network, then through an inertial navigation system of an intelligent terminal and with combination of the map, a precise position of a customer is obtained, and a walking track of the customer is real-timely and continuously obtained; 2) based on formation and use of the electronic map of an indoor planar graph, an indoor electronic map labeling method is included; 3) a particle filtering algorithm combining the several factors together includes detection of a moving state, detection and adjustment of the step length, the step direction and the step frequency, adaption for the electronic map and the wifi (bluetooth) signal fingerprints and the like; 4) based on a map matching algorithm, a correct and accurate walking path is guaranteed; and 5) the terminals not only include intelligent mobile phones and panel personal computers and also include intelligent wearable devices.

The invention belongs to the technical field of automatic production lines, and particularly relates to a fault prediction and health management method applied to an automatic production line. The method comprises the following steps of preparing a plurality of automatic production lines to be tested, carrying out heating aging and vibration testing on the bearing equipment in automatic productionlines to be tested to obtain training data and storing the training data in a database, carrying out FMECA analysis to obtain a training sample, training a neural network by using a particle filtering algorithm and placing into a test chip, monitoring the bearing equipment in the automatic production lines in the working state in real time by using a testing chip, and calculating residual servicelife and conducting health management. The running state of the bearing equipment in an automatic production line can be monitored in real time, and the fault time is predicted. The occurrence probability of sudden faults is reduced, and a plurality of potential safety hazards are avoided when the sudden faults occur, so that properties can be protected, and the maintenance cost is reduced.

The invention relates to an omnibearing vision based method for identifying and positioning a dynamic target, belonging to the technical field of dynamic image analysis. The method comprises the following steps of: 1, acquiring an omnibearing vision sequence image, and preprocessing the omnibearing vision sequence image to obtain a binary image separating a moving target and a background area; 2, searching a local area by an optical flow method, matching feature points between adjacent frames of the image, and detecting the moving target of an image sequence; 3, estimating the moving state of the moving target by a particle filtering algorithm, and predicting the parameter of the moving target in a subsequent frame so as to complete a tracking process. The invention can obviously reduce the calculating amount and enhance the accuracy by identifying and positioning the dynamic target by the method.

The invention provides a multi-sensor-based attitude determination system, which comprises a sensitive subsystem, a signal acquisition and preprocessing subsystem and an integrated processing subsystem, wherein the sensitive subsystem integrates four classes of sensitive parts such as star sensitive probes, sun sensitive probes, fiber optic gyros and magnetic field probes; the signal acquisition and preprocessing subsystem is realized by an FPGA chip and a peripheral circuit thereof, parallelly receives output data of the four classes of sensitive probes and completes the smoothing and preprocessing of the data; and finally, the integrated processing subsystem taking a DSP (or ARM) chip as a core is utilized to realize the high-precision attitude determination of the system by an ant colony particle filter-based combined attitude determination method according to sunlight information, starlight information, inertial information and geomagnetic information after smoothing and preprocessing. The multi-sensor-based attitude determination system with high precision, integration level and reliability is realized, and has important practical significance for high-resolution earth observation of new-generation earth observation satellites.

The invention provides an MCAUPF based target tracking method. An MCAUPF is used to complete state estimation in the target tracking process. In the target tracking process, target tracking equation and measurement equations are reconstructed into a nonlinear recursion model, in the framework of unscented particle filtering, a significance probability density function needed by filtering particlesgenerated by maximum correntropy unscented Kalman filtering is used, a Kullback-Leibler distance resampling method is used to resample the generated particles, the state of the tracking target is estimated according to a UPF algorithm flow, and the target is tracked in real time. The MCAUPF method is applied to target tracking in which outliers occur in noise measurement, the precision is highercompared with that of present PF, improved PF and robust filtering, and the computing complexity is lower than that of a present improved particle filter algorithm.

The invention discloses an aero-engine gas path component health diagnosis method based on particle filtering. The aero-engine gas path component health diagnosis method includes the steps that a nonlinear mathematical model of an engine is established; a particle filtering algorithm is designed based on significance weight value adjustment of a neural network; finally, a gas path component health diagnosis is achieved based on the nonlinear model of the engine by the adoption of the designed algorithm. The nonlinear mode is that on the basis of a physical equation reflecting the aerothermodynamics performance of the engine, a shared working equation set among the components is established, and by the adoption of a Newton Laphson interactive algorithm, the nonlinear equation set is solved to obtain working parameters of the cross section of the engine; the particle filtering algorithm based on the significance weight value adjustment of the neural network is that a BP neural network algorithm and a typical sampling algorithm are combined, on the basis of a standard particle filtering algorithm, two steps of weight value splitting and particle adjustment are added, and therefore the phenomena of particle degradation and sample depletion are effectively avoided. The health diagnosis of gradual performance degradation and sudden faults of the gas path components within the service life of the engine can be achieved.