271 results about "Particle filtering algorithm" patented technology

The invention provides a virtual DPU power plant simulation fault restoration method and system based on digital twinning, and relates to the technical field of digital twinning, and the method comprises the steps: carrying out the preprocessing of collected DPU power plant operation data, including noise reduction, time sequence alignment and abnormal point elimination, carrying out the data quality evaluation, training a fault feature mapping model based on the processed data, and carrying out the fault restoration of the DPU power plant. The model is used for recognizing abnormal clusters in real time, a fault evolution path is searched and determined in combination with a conditional random field and a Monte Carlo tree, an optimal path is determined through a particle filtering algorithm, fault root causes are determined in combination with causal analysis, spectral clustering and a Bayesian network, and a fault diagnosis report is generated.

The invention discloses an electrical equipment operation state monitoring method, system and terminal, full life cycle health management of equipment is realized through multi-dimensional perception and intelligent analysis, a composite sensor network can be constructed from the level of the method, and high-frequency current, ultrahigh frequency, fiber grating temperature vibration, multi-parameter gas and acoustic sensors are integrated. Electromagnetic characteristics, mechanical states, environmental parameters and voiceprint characteristics are covered; the adaptive signal processing technology performs classification and noise reduction on multi-source data, and the three-dimensional digital twin model realizes time-space fusion of a temperature field, a vibration field, an electric field and a sound field; a lightweight space-time convolutional network is deployed to fuse a time domain waveform, a spectrogram and spatial distribution characteristics for diagnosis, and a hidden semi-Markov model and a particle filter algorithm are combined to dynamically predict the residual service life of equipment. The system architecture comprises a self-organizing sensor network with edge computing capability, a time-sensitive industrial communication network and a containerized analysis engine, and supports mixed reality visual interaction.

The invention provides a sea surface target tracking method and system based on infrared and visible light image fusion, and relates to the technical field of ocean monitoring, and the method comprises the steps: collecting visible light and infrared image data of a sea surface target; performing feature extraction and fusion through wavelet transform fusion to generate a comprehensive feature map, and performing target recognition on the comprehensive feature map by using a deep learning target detection model; after target recognition, the system calculates the position of a target based on image data and radar data, performs multi-target matching and association through a Hungary algorithm combined with multi-modal features, predicts the position of the target and updates trajectory information in combination with a Kalman filtering or particle filtering algorithm. The infrared camera and the visible light camera carry out dynamic angle adjustment according to the position and the movement track of the target; whether light information correction is carried out or not is judged based on the light correction threshold value, when light information correction is carried out, light information correction features are constructed based on the visible light compensation model and the infrared compensation model through the image data, and information errors caused by the illumination angle are eliminated.

The invention discloses an atmosphere laser radar data analysis method based on deep learning, and the method comprises the following steps: S1, collecting and preprocessing atmosphere laser radar echo signal data, and generating a time series data set; s2, constructing a long sequence prediction model based on Transform, and forming a preliminary prediction result sequence; s3, optimizing model structure parameters and training parameters by using a particle filtering algorithm to obtain an optimized model; s4, using the optimized model to predict current and future moment data, and generating a final prediction sequence; s5, performing residual analysis on the final prediction sequence and the real-time observation data, and identifying abnormal points; and S6, sudden disturbance is detected, particle filtering optimization is restarted, and the prediction model is dynamically updated. According to the invention, by fusing the deep learning long sequence prediction model and the particle filter optimization algorithm, high-precision prediction and abnormal dynamic identification of atmospheric laser radar data are realized.

The invention discloses a lithium battery health assessment method based on an adaptive extended particle filter algorithm. The method comprises the following steps: constructing a second-order RC equivalent circuit model of a lithium battery; determining all parameters in the lithium battery second-order RC equivalent circuit model by using a recursive least square algorithm with a forgetting factor to obtain a determined lithium battery second-order RC equivalent circuit; estimating SOC parameters of the lithium battery at the moment by combining a particle filtering algorithm with an adaptive extended Kalman filtering algorithm; correcting the terminal voltage of the second-order RC equivalent circuit of the lithium battery at the moment to obtain the terminal voltage of the equivalent circuit; and based on the lithium battery second-order RC equivalent circuit and the corrected terminal voltage, estimating the SOE parameter of the lithium battery at the moment k by using a particle filtering algorithm in combination with an extended Kalman filtering algorithm. The SOC and the SOE of the lithium battery are accurately estimated, and the sampling efficiency and the estimation stability of particle filtering are improved. Cooperative joint estimation of SOC and SOE complements each other, and the accuracy of overall state estimation is further improved.

The invention relates to an equipment residual life prediction method based on a physical-data model, and the method comprises the steps: building a degradation model based on a physical degradation mechanism and a standard Wiener process model; obtaining fixed parameters in the degradation model by using a maximum likelihood estimation method in combination with a nonlinear regression method; acquiring an equipment state observation value in real time, and updating random parameters in the degradation model by using a weight optimization particle filter algorithm; and utilizing the degradation model, the fixed parameters in the degradation model and the random parameters in the degradation model to obtain a probability density function of the residual life of the equipment under a random failure threshold value, and performing numerical integration on the probability density function of the residual life of the equipment to obtain an expected value of the residual life of the equipment, and outputting the expected value as the residual life of the equipment. Compared with the prior art, high-precision and high-reliability residual life prediction is realized by fusing a physical mechanism and a data driving method.

The invention relates to the technical field of multi-sensor fusion anomaly detection, in particular to a multi-sensor data fusion pig farm anomaly detection system which comprises the steps that data are continuously collected through sensors, a unique space-time anchor point is bound to an RFID tag, and a particle filter algorithm is utilized to predict the motion trail of a pig in different sensor view fields; local model training is carried out at each node according to own data, then local model parameters are uploaded to a central node for fusion through a gradient aggregation strategy, and a global anomaly detection model is generated; dividing a dynamic sensing network in the hog house, and evaluating the confusion degree and the abnormal possibility of data by calculating entropy values of grid units; a behavior fingerprint database is established for each pig, pig behavior data are monitored in real time, and matching degree calculation is carried out on the behavior fingerprint matching degree. And various types of sensors are widely deployed, so that multi-dimensional information of pigs can be comprehensively acquired, and the condition of the pig farm can be comprehensively sensed.

The invention provides a micro-milling machining parameter identification method considering the random tool wear influence. The method comprises the steps that a cutting force model under the tool wear influence is established by considering tool bounce and a chip separation mechanism; updating the rotating radius of the tool according to the influence of jumping and abrasion on the edge radius value of the tool, and obtaining a tool nose trajectory equation; tool wear data in the actual machining process are collected, the neural network model is trained, and hyper-parameters in the neural network model are optimized through a bidirectional long-short-term memory network; identifying processing parameter values in the neural network model by adopting a particle filtering algorithm; and simulating and calculating cutting forces and wear values under different working conditions by using a neural network model, comparing experimental results, and evaluating the accuracy of machining parameter identification. According to the method, the randomness of tool wear is fully considered in the modeling process, the prediction precision is remarkably improved, and the method has higher practical application value.

The invention discloses an urban particulate matter migration path identification method based on data fusion of a dynamic diffusion model and multi-source sensing. According to the method, firstly, multi-source pollution related data of a fixed monitoring station, a mobile monitoring device, a meteorological observation node and a traffic sensing system are collected, and standardized input is constructed after time synchronization, coordinate mapping and exception handling. And then establishing a hybrid dynamic diffusion model fusing a two-dimensional Gaussian plume model and a Lagrange particle tracking mechanism, and realizing assimilation of monitoring data and model output in combination with an improved particle filtering algorithm to obtain a correction concentration field. Based on concentration gradient analysis and particle trajectory superposition, a pollution migration path is extracted, then indexes such as a migration intensity index (MII), a path stability factor (PSF) and path average correlation are calculated, and recognition and sorting of a migration direction, a pollution source position and path stability are achieved. Meanwhile, an alpha dynamic weight factor is introduced, adaptive weighting is realized between the Gaussian model and the LPDM model, and the path strength and consistency are considered. Experimental results show that the method is superior to a traditional method in average absolute deviation (MAD) and hot spot overlap ratio (IoU) indexes, diffusion and migration rules of particulate matters under complex urban conditions can be more accurately revealed, and the method has important environmental governance and emergency management application value.

The invention relates to a tunnel construction progress model generation method based on artificial intelligence. The method comprises the following steps: acquiring multi-source heterogeneous data such as tunnel field equipment operation and geological change, and performing multi-modal fusion to form a unified data set; based on this, a particle filter algorithm is used to correct equipment positioning deviation, and a real-time position optimization result is generated. Comparing the time and space with the progress plan, and identifying and quantifying deviation points through a critical path method and a deviation analysis algorithm to form deviation data. And constructing a construction progress model by combining construction basic parameters and adopting a genetic algorithm, generating an adjustment scheme by taking a construction period and resources as targets, and obtaining an optimal scheme through compliance screening and fitness calculation iterative optimization. By the adoption of the method, efficient integration of heterogeneous data can be achieved, the equipment positioning precision is improved to the centimeter level, multi-dimensional quantification of progress deviation is achieved, progress deviation recognition and resource allocation efficiency is improved, and a technical scheme is provided for tunnel intelligent construction.

The invention discloses a ten-thousand-square-meter muddy deep foundation pit supporting structure deformation prediction method which comprises the following steps: establishing a soil-supporting interaction rheological model, simulating stress relaxation and creep effects of muddy soil in combination with a Burgers rheological model, and realizing dynamic evolution prediction of a supporting structure deformation field and an internal force field. According to the method, the particle filter algorithm is adopted to carry out denoising processing on real-time monitoring data, model parameters are updated through the Bayesian method, and the prediction precision is improved. Meanwhile, nonlinear mapping of the deformation rate and the internal force increment is established by using a random forest algorithm, and instability risk probability distribution is generated. According to the method, multi-stage collapse early warning thresholds are set, monitoring data and prediction results are fused by adopting a weighted average method, and abnormal points of a high-risk area are identified. For a high-risk area, a genetic algorithm is applied to optimize the rigidity and prestress distribution of a supporting system, effective control over deformation and internal force of a supporting structure is achieved by dynamically adjusting construction parameters, and construction safety of the deep foundation pit is ensured.

The invention belongs to the technical field of automatic driving, and particularly relates to an automatic driving decision-making method and system with dynamic risk perception and attention focusing and a vehicle, and the method comprises the steps: predicting the track of a surrounding vehicle in real time through a multi-feature Gaussian weighted particle filtering algorithm, and improving the prediction precision through combining a vehicle kinematic model and resampling optimization; constructing a comprehensive evaluation model fusing transverse and longitudinal risks, and dynamically quantifying the collision risk of the vehicle and surrounding vehicles; and inputting the risk value as a key state feature into a double-depth Q network based on attention mechanism enhancement, focusing key information through a feature attention distribution mechanism, and generating an optimal driving decision in combination with a multi-target reward function. Compared with the prior art, the method solves the problems of insufficient quantification of uncertainty factors, incomplete risk assessment and low decision-making efficiency of automatic driving in a complex dynamic environment, and significantly improves the risk perception capability and decision-making safety of the automatic driving vehicle.

The invention relates to the technical field of attitude control, and discloses a method for realizing self-adaptive active leveling compensation, which comprises the following steps: acquiring real-time data of a vehicle, the data being provided by a plurality of sensors; processing the real-time data through a data fusion algorithm; according to the inclination state and the motion parameters of the vehicle, hydraulic supporting leg adjusting parameters are calculated through a multi-objective optimization algorithm; according to the calculated hydraulic supporting leg adjusting parameters, the action of the hydraulic supporting leg is adjusted through a PI D controller for vehicle balance recovery; the stability of the hydraulic supporting leg adjusting system is verified through the Lyapunov stability theory, and the hydraulic supporting leg adjusting system is used for enabling the adjusting process of the hydraulic supporting leg to be completed in all operation states; and the vehicle inclination state is fed back in real time. By fusing multi-sensor data and adopting a Kalman filtering or particle filtering algorithm, high-precision perception of the real-time inclination state and dynamic parameters of the vehicle is realized, and a more accurate and reliable vehicle attitude judgment effect is obtained.

The invention relates to the technical field of unmanned aerial vehicle control, and particularly discloses a wrist-worn unmanned aerial vehicle countering control method and system, through cooperative work of a radar sensor, an infrared sensor, an optical sensor and a GPS sensor, flight state data of an unmanned aerial vehicle can be obtained, and state prediction and target tracking are performed on the unmanned aerial vehicle by using a particle filtering algorithm; a deviation coefficient between an actual flight trajectory and a preset trajectory is calculated through a dynamic time warping algorithm, and the system can judge whether the unmanned aerial vehicle deviates from the trajectory and adjust an interference strategy in time; the frequency, the intensity and the transmitting direction of the interference signal are dynamically optimized according to the flight state of the unmanned aerial vehicle so as to form an efficient countering strategy; and by comprehensively evaluating the countering response time and the success rate, a countering precision coefficient is calculated, so that data support is provided for optimization of a subsequent countering strategy.

The invention provides an indoor positioning method and system based on dynamic mode switching and data fusion, and relates to the technical field of visible light indoor positioning, and the method comprises the following steps: deploying a VLC transmitting device and an auxiliary positioning point, constructing a VLC fingerprint database, carrying out dynamic signal detection and mode conversion, carrying out VLC fingerprint positioning, carrying out IMU data processing, and carrying out main positioning mode data fusion. VLC signal intensity distance estimation, ultrasonic ranging, IMU relative displacement estimation and standby positioning mode data fusion are carried out. According to the invention, by deploying the visible light communication transmitting device and the auxiliary positioning point, the strength and the stability of the VLC signal are monitored in real time, and dynamic positioning mode switching is realized through fuzzy logic reasoning. In the main positioning mode, VLC fingerprint matching is combined with IMU data, and accurate positioning is carried out through a Kalman filtering algorithm; and when the VLC signal is weak or unstable, a standby positioning mode is switched, and accurate positioning is carried out by fusing data through a particle filter algorithm according to VLC signal intensity distance estimation, ultrasonic ranging and I MU data.

The invention discloses an automatic control gradient LED lamp energy-saving illumination method, which comprises the following steps: S1, asynchronous sampling is carried out through an illumination sensor A and an illumination sensor B, ambient light change data is collected, a sampling timestamp is recorded, a time difference weight is obtained based on the timestamp, data fusion is carried out through the time difference weight, and ambient light intensity data is obtained; s2, performing data calibration on the ambient light intensity data through a state space model, and performing optimization through a weighted moving average method to obtain an ambient light intensity change rate; and S3, acquiring a light change condition based on an environment illumination intensity change rate, adjusting a brightness duty ratio through a probability model based on Bayesian reasoning to output an initial control variable, performing optimization based on the initial control variable in combination with a particle filtering algorithm to obtain a final control scheme, and performing optimization through the probability model and the algorithm to obtain a final control scheme. A relatively accurate brightness adjustment scheme can still be given, and the intelligent level of lamp control is improved.

The invention provides a flow battery energy storage power station full life cycle health degree assessment management method. The method comprises the following steps: constructing an initial health reference library, collecting electrochemical parameters through a reference electrode, collecting electrolyte physical parameters and environmental parameters through a sensor, synchronously recording operation and maintenance data, and carrying out time-space synchronization processing; introducing a hierarchical fusion assessment mechanism, assessing data integrity and constructing an attention mechanism model, calculating a dynamic weight according to a parameter relative change rate and a health degree correlation coefficient, generating a weighted feature vector, and predicting a short-term health degree index by adopting a bidirectional long-short-term memory network Bi-LSTM; a particle filter (PF) algorithm is introduced to be combined with a physical parameter drift distance to correct a long-term health degree index, and then a real-time health degree index is output through fuzzy comprehensive evaluation. The method can achieve the linkage evaluation of the health state in the operation period and the full life cycle trend, corrects the long-term SOH through the PF algorithm in combination with the physical parameter drift distance, and reduces the error of long-term prediction.

The invention discloses a pedestrian navigation method and system based on intelligent fusion of optical signals and inertia. The method comprises the following steps: calculating the distance between a receiving end and a transmitting end according to the positions of a visible light transmitting end and a receiving end and visible light original data collected by the receiving end; calculating the collected inertial data by adopting a pedestrian dead reckoning method to obtain position information of a receiving end; and carrying out tight combination fusion on the distance between the receiving end and the transmitting end and the position information of the receiving end based on a particle filtering algorithm to obtain a predicted pedestrian position. According to the method, the pedestrian dead reckoning positioning result and the visible light ranging information are fused, the defects of a single positioning technology are overcome, the method has good performance on a fusion positioning system of visible light and other signals susceptible to environmental interference and inertial and other high-noise signals, the adverse effect of environmental noise on the fusion system is weakened, and the positioning accuracy is improved. And the migration capability and the adaptive capability of the fusion system are effectively improved.

The invention relates to a radar point cloud real-time modeling water conservancy earthwork measurement method and device, equipment and a medium. According to the method, a vegetation root system interference area is scanned and recognized through a dual-frequency polarization radar, a coordinate set of the vegetation root system interference area is obtained, an elevation compensation model is constructed based on dielectric constant difference and fractal dimension to generate a compensation parameter set, and original point cloud is processed by combining a bare soil reference curved surface and adopting a dynamic constraint particle filtering algorithm to obtain real ground point cloud. Then, a corrected terrain curved surface is generated through parasitic energy field constraint optimization, and finally, compensation parameters are fused to realize high-precision earth volume calculation in a root system interference environment, so that point cloud distortion and a terrain curved surface systematic lifting effect caused by a vegetation root system are greatly reduced, and an earth measurement result of a complex vegetation coverage area meets an engineering acceptance standard.

The invention relates to the field of substation operation and maintenance, and provides an urban rail transit substation operation and maintenance method and system based on an inspection robot, and the method comprises the steps: obtaining the motion posture data of the inspection robot, and collecting environment point cloud data, visual image data and substation equipment state data in a substation in real time through the inspection robot; by fusing the environment point cloud data, the visual image data and the moving posture data, constructing a substation internal map by adopting a particle filtering algorithm; based on a Dijkstra algorithm and the internal map of the substation, planning an inspection path of the inspection robot; enabling the inspection robot to advance along the inspection path, obtaining the coordinates of the obstacle in real time through the environment point cloud data, and carrying out dynamic obstacle avoidance based on an artificial potential field method; constructing an equipment fault diagnosis model, and identifying the substation equipment state data to obtain a substation equipment anomaly category; and pushing the maintenance notification and the maintenance suggestion to a manager. According to the invention, the operation and maintenance safety and working efficiency of the substation equipment are improved.

The invention discloses a spacecraft sensor abnormal data recovery method based on spatio-temporal information fusion, and relates to a spacecraft sensor abnormal data recovery method. The method aims at solving the problems that in an existing spacecraft sensor abnormal data recovery technology, the signal-to-noise ratio is deteriorated due to environment interference, and multi-dimensional feature modeling has limitation and is insufficient in self-adaptive capacity. The method comprises the following steps: step 1, preprocessing original sensor data through a sliding mean filter; 2, constructing a time-space dual-scale feature extraction network based on Transform, mining a sensor time sequence dependency relationship from a time dimension, and analyzing multi-sensor association features from a space dimension; 3, introducing a particle filter algorithm to carry out optimal fusion on space-time dual-channel output, and quantifying the uncertainty of a data recovery result; and step 4, by comparing the actually measured data with the uncertainty confidence interval, abnormal detection and data recovery of sensor anomalies are realized. The invention belongs to the technical field of satellite sensor abnormal data recovery.

The invention discloses an unmanned aerial vehicle monitoring method and system in a complex electromagnetic environment, and the method comprises the steps: extracting a signal feature from a spectrum signal collected in real time, and obtaining a first feature vector; adopting a pre-constructed random forest model to classify the first feature vectors and match the first feature vectors with known unmanned aerial vehicle information to obtain a first model and a first serial number of the unmanned aerial vehicle; according to the coordinates of the at least three measurement stations, calculating the three-dimensional coordinates of the unmanned aerial vehicle by adopting a time difference of arrival positioning technology and an angle of arrival measurement technology; calculating the radial speed of the unmanned aerial vehicle relative to each measurement station by adopting a Doppler effect, and calculating a three-dimensional speed vector of the unmanned aerial vehicle according to the radial speed; and according to the three-dimensional coordinates and the three-dimensional speed vector of the unmanned aerial vehicle, predicting the change trend of the three-dimensional coordinates and the three-dimensional speed vector of the unmanned aerial vehicle in a preset time window by adopting a particle filtering algorithm. By adopting the embodiment of the invention, the identification accuracy, the positioning precision and the anti-interference capability of unmanned aerial vehicle monitoring in a complex electromagnetic environment can be improved.

The invention discloses an unmanned bus high-precision positioning method based on an SLAM map, and relates to the technical field of unmanned driving, and the method comprises the following steps: obtaining laser radar data, inertial measurement data, odometer data and visual data of an unmanned bus; analyzing a vehicle control instruction, simulating the movement track of the unmanned bus through a particle filter algorithm, and generating a pose hypothesis set; performing position matching on the laser radar data and a pre-constructed SLAM map, and performing auxiliary positioning in a low-visibility scene to obtain a position matching result; initial static map construction and dynamic obstacle detection are carried out, and a dynamic and static fusion SLAM map is generated; and state monitoring is carried out, and a multi-stage fault recovery strategy is triggered. According to the method, through dynamic fusion of timestamp alignment and Kalman filtering, severe environment parameter adjustment and a multi-stage fault recovery mechanism, the positioning accuracy and the system stability of the driverless bus are remarkably improved.

The invention relates to the technical field of intelligent transportation, and discloses an airport self-service baggage check-in intelligent identification system, which comprises a sensor module, a data processing module, a data processing module and a storage module, and is characterized in that the sensor module is used for acquiring physical state data of baggage; the data processing module is used for receiving the signal from the sensor and executing data preprocessing; the control module is used for generating position feedback according to the real-time luggage tracking data and providing a visual interface and abnormal alarm information; and the feedback module is used for transmitting feedback information to the control module and ensuring automatic processing of luggage tracking and abnormal management, and the data processing module predicts and updates the state of the luggage through Kalman filtering, Bayesian filtering and particle filtering algorithms. The luggage state is estimated in real time by adopting multi-sensor data fusion and advanced algorithms such as Kalman filtering, Bayesian filtering and particle filtering, and the system can accurately predict the position, speed and acceleration of luggage by integrating data of various sensors.

The invention discloses an electronic device residual life online prediction method and system under an operation condition, a storage medium and electronic equipment, and the method comprises the steps: building a Gamma state space model, and carrying out the processing through an expectation maximization algorithm, and obtaining an initial parameter estimation value of the Gamma state space model; and based on the initial parameter estimation value and pre-acquired real-time monitoring data of the field operation device, dynamically updating the real health state of the electronic device through a kernel smoothing particle filter algorithm, and estimating new parameter estimation of the Gamma state space model by using an expectation maximization algorithm to obtain a final Gamma state space model. According to the method, the degradation information is extracted and the initial degradation model is established based on the accelerated failure experiment data, and then the degradation information and the model parameters are dynamically updated by adopting the kernel smoothing particle filter algorithm in combination with the field operation data, so that the real-time life prediction under the field operation condition is realized.

The invention discloses a three-dimensional underwater target tracking method based on improved particle filtering, which belongs to the technical field of underwater wireless sensor networks, and comprises the following steps: collecting three-dimensional target tracking data of an underwater target; constructing a target tracking model and a measurement model according to the three-dimensional target tracking data; the method comprises the following steps: improving a particle filtering algorithm based on unscented Kalman filtering, constructing an importance density function based on the improved particle filtering algorithm, a target tracking model and a measurement model, and re-sampling particles; a dynamic adaptive hierarchical weight factor is introduced to correct different particle weights, and normalization processing is performed on the corrected particle weights; performing anomaly detection on the three-dimensional target tracking data based on an optimized Grubbs criterion; and fusing the data of the trusted nodes by adopting an information entropy weighting strategy to obtain a fused target state estimation value, and realizing three-dimensional tracking of the underwater target. According to the invention, high-precision and high-reliability underwater target three-dimensional tracking can be realized.

The invention relates to the technical field of hybrid power system power distribution, and provides a hybrid power operation platform power distribution method suitable for narrow lane operation. A state acquisition module, a fuzzy preprocessing unit, a particle filter estimation unit, a model prediction control unit and a self-adaptive control unit are designed, vehicle inclination angle, load and wheel speed data are acquired in real time, and terrain uncertainty factors are output through fuzzy logic processing; estimating hidden state variables such as the wheel slippage rate and the energy margin by adopting a particle filtering algorithm; performing multi-stage power distribution optimization by applying a model predictive control technology to realize coordinated control of the diesel engine and the electric pump; and finally, self-adaptive feedback and switching are executed, wherein the power modes are switched according to the uncertainty factors, inclination angle and load safety protection is implemented, and braking energy is recycled. The terrain adaptability, the power distribution precision and the energy utilization efficiency of the working platform in the narrow lane complex environment are effectively improved, and meanwhile the operation stability and the environment friendliness are enhanced.

The invention provides a ranging error compensation method for indoor positioning precision time measurement, and aims to improve the precision and robustness of an indoor positioning system. The error type identification method under the LOS / NLOS mixed environment is established by combining the difference between LOS and NLOS signals, RSSI (received signal strength indication) information and a power attenuation model so as to accurately distinguish LOS and NLOS error signals, and corresponding error compensation models are respectively established. Meanwhile, the particle filter algorithm is introduced, so that the adaptability and robustness of the positioning algorithm to complex signal conditions are improved. The error compensation methods are not only suitable for a precision time measurement (FTM) technology, but also can be expanded to other indoor positioning technologies based on time measurement. Through experimental verification, the indoor positioning precision can be effectively improved, and the method has important application value in scenes with high positioning precision requirements.

The invention belongs to the technical field of navigation and positioning, and particularly discloses an unmanned aerial vehicle cooperative positioning method and system in a satellite denial environment. According to the invention, relative distance information between cluster unmanned aerial vehicles is utilized to design a cluster unmanned aerial vehicle cooperative positioning system architecture based on relative information assistance, and through the architecture, communication between an inertial navigation system and an unmanned aerial vehicle group can provide more reliable positioning service; and the positioning precision is optimized in combination with a selective correction adaptive particle filter algorithm. According to the method, the inertial navigation system is used for obtaining the position of each auxiliary unmanned aerial vehicle, the external source relative distance sensor is used for measuring the relative distance relative to the to-be-assisted unmanned aerial vehicle, then the observation position of the to-be-assisted unmanned aerial vehicle is obtained, and the result is substituted into the improved particle filter for filtering. The method is used for correcting the drift error of the inertial navigation system, so that the positioning precision of cooperative positioning in the denial environment is improved.

The invention provides a self-adaptive particle filtering method for a laser SLAM task. The problems that a traditional particle filtering algorithm is bottleneck in calculation efficiency, insufficient in dynamic environment adaptability and the like in a complex environment are solved. In the particle filtering prediction stage, particle swarm optimization (PSO) is introduced, particle distribution is optimized by using the global search capability of the PSO, and the inertia weight and the acceleration factor of the PSO are dynamically adjusted to enhance the adaptability of the algorithm in a complex dynamic environment, so that the algorithm is effectively prevented from falling into a local extreme value; in the resampling stage, KLD sampling and a selective resampling method are combined, a sampling strategy is dynamically switched according to the effective particle number (ESS) and the KL divergence, and the GPU is used for accelerating the resampling step to improve the calculation efficiency. The method has remarkable advantages in the aspects of improving the positioning precision and the mapping efficiency, and is suitable for robot navigation and autonomous movement tasks in a complex environment.