436 results about "Wireless positioning" patented technology

The invention discloses a target positioning method and system based on a single reference station, and relates to the field of wireless positioning, and the method comprises the steps: generating vortex electromagnetic waves carrying orbital angular momentum based on a transmitting end of a reference station, and generating a time-varying phase modulation transmitting signal and a metasurface time-varying coding sequence through a programmable metasurface; a time-varying phase modulation emission signal forms a multipath superposition effect in a propagation environment, a receiving end obtains a mixed signal of a direct wave and a reflected wave, and the receiving end carries out OAM mode separation on the mixed signal of the direct wave and the reflected wave through a spiral phase plate and an annular array to obtain signal components in different modes; and based on the signal component and the metasurface time-varying coding sequence, reconstructing an equivalent virtual array response by adopting a compressed sensing algorithm. According to the invention, the orbital angular momentum modal separation technology and the metasurface dynamic coding technology are creatively fused, and high-precision real-time direction finding in a complex multi-path environment is realized under a single base station architecture.

Disclosed are techniques for wireless positioning. In an aspect, a network entity may determine that a positioning reference signal (PRS) processing load for a UE exceeds a PRS processing capacity of the UE. The network entity may send, to the UE, assistance data that reduces the PRS processing load for the UE. In another aspect, a user equipment (UE) may determine, based on information received from a network entity, that one or more PRS measurements is associated with a low-latency location request. The UE may modify one or more parameters associated with the one or more PRS measurements to reduce measurement latency. The UE may perform the one or more PRS measurements associated with the low-latency location request according to the modified one or more parameters.

The invention relates to the cross technical field of satellite navigation and wireless positioning technology, in particular to a real-time dynamic high-precision positioning method based on Beidou and UWB tight coupling, which solves the problem that in the prior art, error coupling in Beidou and UWB observation data is not fully solved, and especially in a high-speed dynamic scene, the positioning accuracy is greatly improved. The mutual influence of the Beidou receiver clock error, the UWB base station clock error and the multi-path error causes the continuous accumulation of the error and the significant reduction of the positioning precision. The invention discloses a real-time dynamic high-precision positioning method based on Beidou and UWB tight coupling. A data preprocessing and space-time alignment module, an error joint estimation model, an ambiguity fast fixing algorithm, a geometric constraint enhanced positioning engine module and a dynamic coordinate conversion and fusion output module are included. According to the mode provided by the invention, a highly integrated multi-model Kalman filtering architecture is adopted, so that synchronous and accurate compensation of the clock error of the Beidou receiver, the time delay of the UWB base station and the multi-path effect is realized.

The invention belongs to the technical field of wireless positioning, and discloses a non-line-of-sight interference-resistant dynamic positioning method and a base station layout method for positioning. Comprising the following steps: calculating original distance measurement results of each initial base station in a label and a sub-region, obtaining current shielding probability and current signal intensity of the initial base station and current geometric accuracy factors of different initial base station combinations in an initial base station set, selecting candidate base stations, calculating information gain of each candidate base station pair, and obtaining the information gain of each candidate base station pair; selecting candidate base stations with information gains meeting preset requirements to construct a target base station subset, inputting an original ranging result of the target base station subset into a Kalman filter based on a motion state space model for processing, and compensating an NLOS time delay error through a sliding window mean value to obtain a positioning result of a label and an NLOS time delay error estimation value, the problem that the positioning result is not accurate enough in a complex environment is solved.

Disclosed are techniques for wireless positioning. In an aspect, a user equipment (UE) receives, from a location server, assistance data for a positioning procedure, obtains a frequency offset measurement of one or more positioning reference signal (PRS) resources transmitted by at least one transmission-reception point (TRP) based on the assistance data, and enables a location of the UE to be determined based, at least in part, on the frequency offset measurement.

The invention belongs to the technical field of unmanned aerial vehicles, and particularly relates to an intelligent aerial 3D printing and spraying system and method based on an unmanned aerial vehicle cluster, and the system comprises a spatial three-dimensional laser ranging scanning modeling module for generating a real-size scene model, a three-dimensional positioning network module, and a three-dimensional positioning network composed of a plurality of laser radar arrays. Wherein the laser radar or the wireless positioning beacon array is arranged on a working plane to construct a three-dimensional space coordinate reference system; the printing module is integrated on the unmanned aerial vehicle, compared with traditional high-altitude operation, the construction efficiency is improved, the constraint of tedious procedures such as scaffold building and manual high-altitude operation is thoroughly eliminated, the unmanned aerial vehicle cluster can be rapidly deployed in a short time, and operation tasks such as large-area single-color or full-color spraying, repairing, modeling and printing are efficiently completed with high precision; the project construction period is greatly shortened, the comprehensive construction cost is reduced, the safety construction level is improved, the engineering income is improved, and the construction accident risk is reduced.

The invention relates to the technical field of evacuation intelligent simulation, in particular to a fire-fighting evacuation path dynamic planning method and system based on digital twinning. In the fire-fighting evacuation path dynamic planning method and system based on digital twinning, a digital twinning environment containing multi-dimensional real-time sensing data such as temperature, smoke and heat radiation intensity is constructed; the method can dynamically analyze space-time evolution characteristics such as fire spreading trend and heat accumulation, further carries out accurate quantitative evaluation on the danger level of each segment of an evacuation path, and meanwhile, integrates video monitoring and wireless positioning information to grasp the specific positions and moving tracks of people in a building in real time, thereby improving the safety and reliability of the evacuation path. The actual flow bottleneck is identified based on the flow direction consistency, the distribution density and the congestion condition of the crowd, and finally, the dynamic danger of the fire environment and the macroscopic flow trend of the crowd are combined, and the passage cost value of each path section is subjected to dual dynamic correction, so that the flow rate of the crowd is improved. And an evacuation path planning result giving consideration to fire hazard avoidance and crowd congestion evacuation is generated, so that the real-time performance and safety of evacuation decision making are effectively improved.

The invention relates to the technical field of wireless positioning, and provides a high-speed rail user positioning method and system based on trajectory compensation, and the method comprises the steps: recognizing high-speed rail users, carrying out the data preprocessing, constructing high-speed rail grids, and generating a signal fingerprint database; preliminarily screening out a plurality of candidate grids according to RSRP and TA signal features reported by high-speed rail users in real time; based on the service cell switching sequence, a hidden Markov model or particle filtering is adopted for motion trail modeling, and the advancing direction and speed of a train are recognized; introducing a multi-source information fusion and grid compensation strategy; and selecting a final positioning result from the candidate grids in a weighted voting mode, and outputting grid IDs and latitude and longitude coordinates. Through multi-source data fusion, track compensation and intelligent decision making, the positioning precision and reliability of the high-speed rail user in a complex mobile environment are remarkably improved, and the method has important technical popularization value.

The invention relates to the technical field of wireless positioning, in particular to an intelligent positioning analysis method and system for a safety tool, and the method comprises the steps: collecting the video data and voice data of a high-altitude operation site in real time through a video collection module worn by an operator; the method comprises the following steps: acquiring far-end high-altitude operation video data through a ground camera, and when the far-end high-altitude operation video data is lower than a preset value, acquiring three-dimensional position data of personnel in real time through a wireless positioning module; establishing a three-dimensional electronic fence model on the monitoring platform, and synchronizing the three-dimensional position data to the three-dimensional electronic fence model; when the monitoring platform detects that the three-dimensional position data exceed the three-dimensional electronic fence, early warning is triggered; after early warning is triggered, video data and voice data of the aerial work site in the corresponding time period are obtained, personnel posture information in the video data is analyzed, and abnormal types are recognized in combination with voice keywords. Therefore, personnel supplementary positioning under the condition of low visibility can be realized, and the reliability of the positioning monitoring system is improved.

The application provides an angle of arrival measurement method, an electronic device, a readable storage medium and a chip, and relates to the technical field of wireless positioning. The method is applied to an electronic device, and the electronic device comprises a first antenna and a second antenna. The antenna radiation field patterns of the first antenna and the second antenna are similar within a first angle range around the electronic device and are not similar within a second angle range around the electronic device. The method comprises the following steps: determining the difference between target parameters measured by the first antenna and the second antenna according to a wireless signal; determining the angle of arrival range of the wireless signal according to the difference; wherein the angle of arrival range is the first angle range or the second angle range; and measuring the angle of arrival of the wireless signal by using the first antenna and the second antenna according to the angle of arrival range. The technical scheme provided in the embodiment can improve the accuracy of angle of arrival measurement.

The invention discloses a semi-supervised learning positioning method based on channel mapping, belongs to the field of signal processing, and is applied to the problem of wireless positioning of user equipment in a communication and sensing integrated network. According to the method, mass unlabeled channel data which is easy to obtain is fully utilized, and the implicit relationship between the channel and the position is learned from the unlabeled data and the labeled data together. The semi-supervised learning scheme provided by the invention can be divided into two stages: firstly, pre-training an unsupervised dual-branch channel mapping twin network by using an unlabeled data set, and then performing supervised training fine tuning on the dual-branch channel mapping twin network and an overall positioning network of a positioning head module by using a labeled data set. According to the method, only a small part of labeled data is used, and robustness and positioning performance superior to a supervised learning algorithm and other baseline algorithms are obtained.

This disclosure provides methods, devices and systems for wireless positioning. An example method includes determining a first received signal strength indicator (RSSI) signature, the first RSSI signature including a respective RSSI associated with each access point (AP) of a first plurality of APs, where the first plurality of APs form at least a portion of a mesh network of APs, comparing the first RSSI signature with each reference RSSI signature of a first plurality of reference RSSI signatures, each reference RSSI signature of the first plurality of reference RSSI signatures including a respective RSSI associated with each AP of the first plurality of APs, and identifying a first position of the wireless STA based on the comparing of the first RSSI signature.

The invention provides an unmanned aerial vehicle positioning method and system based on a wireless positioning beacon and a program product, and relates to the technical field of unmanned aerial vehicle positioning, and the unmanned aerial vehicle positioning method comprises the steps: obtaining the first position information of the wireless positioning beacon, the second position information of an unmanned aerial vehicle, and the measurement distance information between the wireless positioning beacon and the unmanned aerial vehicle; determining a relative error vector of the unmanned aerial vehicle relative to the wireless positioning beacon based on the measurement distance information, the first position information and the second position information; and determining relative position information between the unmanned aerial vehicle and the wireless positioning beacon based on the relative error vector.

The invention discloses a high-precision indoor positioning system based on a smart bracelet, and the system comprises a data processing module which is used for obtaining and preprocessing multi-source sensing data collected by the smart bracelet; the gait reckoning module is used for executing gait event detection and pedestrian dead reckoning processing; the wireless observation module is used for carrying out preprocessing and credibility evaluation on indoor wireless positioning observation data; the fusion positioning module is used for carrying out credibility weighted fusion; the trajectory constraint module is used for applying trajectory rationality constraint and correcting abnormal position points; the error correction module is used for constructing a positioning error dynamic correction model and executing online correction processing; and the track output module is used for carrying out smoothing and time resampling processing and extracting a human body position determination result set. The method is based on the multi-source perception fusion and federated Kalman dynamic correction method, achieves the indoor high-precision positioning of the smart bracelet, and has the advantages of continuous track, controllable error and high environmental adaptability.

The invention discloses an ultra wide band (UWB) robust positioning method for a non-line-of-sight (NLOS) interference environment, and belongs to the technical field of wireless positioning. According to the method, firstly, all three-base-station combinations are generated through a permutation and combination method, the initial position of each group of base stations is solved through a trilateral positioning method, and a multi-dimensional coarse positioning point cloud is formed; then grouping the coarse positioning points according to the resolving participation condition of the base stations, acquiring representative position points of the base stations by adopting a central position estimation algorithm, and constructing a base station space distribution model; a mean vector and a covariance matrix of the point cloud are calculated, after a pseudo-inverse matrix is solved through SVD decomposition, the mahalanobis distance from each representative point to the distribution center is calculated, a dynamic threshold value is set according to the 95% confidence coefficient of chi-square distribution, a base station with the distance exceeding the threshold value is judged as an NLOS abnormal node, and data of the base station is abandoned; and finally, constructing a dual-stage anomaly suppression mechanism: screening reliable base stations based on geometric consistency at a measurement stage, dynamically adjusting the Kalman gain through an exponential gain constraint factor at a residual stage, constructing a dynamic noise model by fusing a geometric precision factor, and realizing position calculation by adopting improved extended Kalman filtering. According to the method, the positioning error can be effectively reduced in the NLOS interference environment, and a centimeter-level reliable positioning solution is provided for the fields of industrial Internet of Things or indoor positioning and the like.

The invention discloses a multi-base-station combined wireless positioning method and system based on a self-attention mechanism, and relates to the technical field of deep learning and wireless communication, and the method comprises the following steps: collecting channel observation data of distributed base stations, and forming a multi-base-station combined channel matrix; performing Gaussian normalization processing on each dimension of data of the multi-base-station joint channel matrix to obtain a normalized multi-base-station joint channel matrix, and performing multi-channel convolution to obtain a feature fusion matrix; performing self-attention value calculation on the feature fusion matrix to obtain a distributed attention coding matrix; and constructing a long-short-term memory recurrent neural network for the distributed attention coding matrix, and decoding the spatial position coordinates of the target to be perceived. According to the method, the ideas of distributed base station sensing, multi-base station channel information fusion and self-attention coding and decoding are innovatively provided, the channel feature extraction mode in an existing wireless positioning algorithm is improved, and the base station sensing precision in a large-range wireless communication scene is improved.

The invention discloses a dynamic positioning precision optimization system based on a self-learning fusion model, and relates to the technical field of wireless positioning. Comprising the steps that an acquisition and synchronization module synchronously acquires AOA, RSSI and TOA signals from a plurality of anchor points and generates a synchronous multi-source signal flow; the discrimination module extracts multi-dimensional instability characteristics such as fluctuation entropy, angle jitter and delay abrupt change rate of the signal flow, and classifies the current channel environment into predefined modes such as high occlusion, non-line-of-sight error or stable line-of-sight; the mapping module calls a corresponding weight distribution strategy according to the modal identifier to generate a real-time fusion weight vector; the adaptive positioning module couples the weight vector to an unscented Kalman filter observation model, dynamically adjusts the weight of each signal source in state updating, and outputs an estimated position and a positioning confidence coefficient; the optimization module monitors the positioning confidence and iteratively optimizes the weight strategy online based on the deviation of the positioning confidence from the target value. The method effectively improves the positioning precision and robustness in a complex dynamic scene.

The invention relates to a scraper conveyor chain fault detection system and method, the system comprises a motor state detection unit, a chain operation state detection unit and a chain fault positioning unit, the motor state detection unit comprises a motor state data acquisition module, a motor state data wireless transmission module and a motor state data processing module; the chain running state detection unit comprises a chain state data acquisition module, a chain state data wireless transmission module and a chain state data processing module; the chain fault positioning unit comprises a chain strain data acquisition module, a wireless positioning module and a chain fault positioning data processor. The fault type of the chain is judged according to the power of the motor of the scraper conveyor; and the specific position of the chain fault is correspondingly obtained according to the chain strain value of the fault point on each flat ring. The chain breaking, chain clamping and chain jumping fault types of the chain can be detected, the chain breaking, chain clamping and chain jumping positions can be accurately positioned, rapid maintenance is achieved, and the maintenance efficiency is improved.

Disclosed are techniques for wireless positioning. In an aspect, a user equipment (UE) receives, from a network entity, a configuration of one or more positioning reference signal (PRS) resources to measure during a positioning session, the one or more PRS resources having a PRS periodicity TPRS and a PRS occasion length LPRS, and measures the one or more PRS resources during a measurement period, wherein the measurement period is based on a number of measurement instances of the one or more PRS resources the UE is expected to process multiplied by a periodicity parameter, wherein the periodicity parameter is based on a PRS processing window of ‘T’ milliseconds, the PRS periodicity TPRS, and a measurement gap periodicity of at least one measurement gap.

The invention relates to the technical field of wireless positioning, in particular to a dynamic-dynamic TDOA (time difference of arrival) cooperative positioning method based on an unmanned aerial vehicle-mounted mobile base station. The method comprises the following steps: constructing a cooperative sensing network comprising at least four unmanned aerial vehicles carrying unmanned aerial vehicle-mounted base stations, and realizing microsecond-level high-precision time synchronization of the whole network through a bidirectional distance measurement technology in combination with an introduced clock error model. All the base stations receive signals transmitted by the moving target to be measured under the unified time base, the arrival time is recorded, and a TDOA measurement value is calculated with the main node base station as the reference. And based on the group of TDOA measurement values, tracking and positioning the to-be-measured moving target by adopting an extended Kalman filtering algorithm, and estimating and outputting the three-dimensional position and speed of the to-be-measured moving target in real time by linearizing the nonlinear model. The method effectively overcomes the technical problems of difficult time synchronization and serious error coupling in a dynamic-dynamic scene, and has the advantages of being independent of ground infrastructure, high in positioning precision, strong in robustness and remarkable in cooperative gain.

The invention relates to an indoor man-vehicle conflict early warning method, and the method comprises the steps: assembling a UWB chip tag on an in-station forklift, enabling an in-station worker to wear the UWB chip tag, and setting a UWB wireless positioning base station at the same time; obtaining a UWB broadband signal of the UWB wireless positioning base station, and carrying out signal denoising reconstruction processing; calculating a signal emission position through a hybrid positioning algorithm, wherein the hybrid positioning algorithm is a Chan algorithm based on a time difference of arrival / angle ranging hybrid positioning mode; and the safety fields of the forklift and the workers are divided respectively, and whether anti-collision early warning is carried out or not is judged according to the signal emission position. According to the scheme provided by the invention, the UWB chip is assembled to be matched with the UWB wireless positioning base station, the UWB broadband signal transmitting and receiving information is obtained, the signal transmitting position is calculated through signal denoising reconstruction processing and a hybrid positioning algorithm, whether anti-collision early warning is carried out or not is judged, and the probability of occurrence of safety accidents is reduced.

The invention relates to a multi-source positioning and inertial navigation fused work card high-precision positioning method. The method comprises the following steps: acquiring observation data of at least two heterogeneous wireless positioning modules and inertial navigation data of an IMU (Inertial Measurement Unit) in real time; based on observation data quality evaluation and motion state recognition, a fusion strategy defining the weight of each data source is generated in a self-adaptive mode; performing space-time alignment and fusion on the data according to the strategy, and outputting a positioning result; and finally, performing two-stage compensation and closed-loop feedback on IMU accumulated drift by using the result. According to the invention, through an environment adaptive fusion strategy and closed-loop error compensation, the problems of insufficient positioning precision, poor stability and short endurance in a complex industrial scene are effectively solved, and the positioning effects of high precision, high robustness and low power consumption are realized.

The invention relates to an indoor fingerprint positioning method based on terahertz channel characteristics, and belongs to the technical field of wireless positioning. The method comprises the following steps: firstly, collecting terahertz channel response information at different positions, extracting channel characteristic parameters with stability and distinction degree, and constructing an indoor positioning fingerprint database; using a DBSCAN algorithm to form a clustering fingerprint database; terahertz channel information of a to-be-positioned terminal is obtained in real time, channel features of the same type are extracted and matched with samples in a fingerprint database, and therefore position estimation is achieved. According to the method, the high resolution, the multipath characteristic and the position sensitivity of the terahertz channel in the indoor propagation process are fully utilized, the precision and the robustness of a positioning system are improved, and the method is suitable for high-precision positioning requirements in various complex indoor environments.

The present invention discloses a multi-base station joint wireless positioning method and system based on a self-attention mechanism, which relates to the field of deep learning and wireless communication technology and includes the following steps: collecting channel observation data from distributed base stations to form a multi-base station joint channel matrix; performing Gaussian normalization on the various dimensions of the multi-base station joint channel matrix to obtain a normalized multi-base station joint channel matrix and performing multi-channel convolution to obtain a feature fusion matrix; performing self-attention value calculation on the feature fusion matrix to obtain a distributed attention coding matrix; constructing a long short-term memory recurrent neural network for the distributed attention coding matrix to decode the spatial position coordinates of the target to be sensed. The present invention innovatively proposes the ideas of distributed base station perception, multi-base station channel information fusion, and self-attention encoding and decoding, improving the channel feature extraction method in existing wireless positioning algorithms and improving the base station perception accuracy in large-scale wireless communication scenarios.

The invention provides an unmanned aerial vehicle positioning method and system based on a wireless positioning beacon and a program product, and relates to the technical field of unmanned aerial vehicle positioning, and the unmanned aerial vehicle positioning method comprises the steps: obtaining the first position information of the wireless positioning beacon, the second position information of an unmanned aerial vehicle, and the measurement azimuth angle information of the unmanned aerial vehicle relative to the wireless positioning beacon; based on the first position information and the second position information, calculating azimuth angle information of the unmanned aerial vehicle relative to the wireless positioning beacon is determined; determining corrected azimuth angle information of the unmanned aerial vehicle relative to the wireless positioning beacon based on the measured azimuth angle information and the calculated azimuth angle information; and determining relative position information between the unmanned aerial vehicle and the wireless positioning beacon based on the corrected azimuth angle information.

The invention discloses a high-precision plant personnel and equipment positioning system resistant to a complex industrial environment, belongs to the technical field of high-precision wireless positioning, and aims at high efficiency and safety requirements of plant personnel and equipment management and stereoscopically positioning plant personnel, equipment and foreign visitors by utilizing technologies such as UWB (Ultra Wideband) and the like. The system comprises a multi-scene applied positioning label, a positioning base station and a 24-hour automatic monitoring and positioning management and control platform. According to the invention, the positioning label can be accurately positioned by using a bidirectional distance measurement method and a multi-base-station fusion differential positioning algorithm, so that corresponding personnel and equipment are positioned, more accurate positioning data are obtained, deep services such as key area alarm, automatic attendance checking, SOS one-key calling for help and factory planning upgrading can be provided on the basis of the positioning data, and the positioning accuracy is improved. The aim of personnel management efficiency can be achieved with low cost, and powerful safety guarantee can be provided for high-risk field work.

The invention discloses a screen candid shooting prevention monitoring method and system based on multi-mode model fusion, and relates to screen privacy protection.The method comprises the steps that firstly, multi-source data from a video mode, a wireless positioning mode, an environment light intensity mode, an audio mode and a peripheral behavior mode are acquired through a data acquisition module; and the data enhancement module is used for enhancing the source data. Then, feature extraction is carried out on the enhanced multi-modal data, and corresponding multi-modal feature representation is obtained; and inhibiting inter-modal redundant information through an adaptive feature enhancement module, highlighting significant features related to candid photographing behaviors, and generating optimized fusion features. And on the basis of the optimized fusion features, a multi-task learning architecture module is adopted to simultaneously predict a device category, a user behavior category and whether a secret photographing behavior exists or not. According to the method, multi-modal data can be fully fused, the accuracy and robustness of screen candid shooting prevention detection are improved, and the method is suitable for real-time security monitoring of multiple scenes and multiple types of terminals.

The invention discloses a camera data labeling method based on UWB and target tracking, and belongs to the technical field of computer vision and wireless positioning, and the method mainly comprises the steps: dynamically generating a camera priority score based on the absolute three-dimensional coordinates of a target provided by the UWB in combination with motion trend prediction; selecting the camera with the highest score for scheduling according to the priority scores of the cameras in the overlapping region scheduling scene; when visual tracking fails, UWB coordinates are directly used as spatial anchor points to keep track continuity; when the vision is available, UWB positioning and visual observation are fused into a smooth track through extended Kalman filtering, and structured annotation data including the identity, the position, the timestamp and the appearance characteristics of the target are output in real time. According to the method, a real-time scheduling and labeling framework of UWB-visual depth fusion is constructed, and a high-precision and high-robustness data basis is provided for scenes such as intelligent monitoring and robot cooperation.

The invention relates to the technical field of wireless positioning, and discloses a wireless personnel positioning method and system through a positioning tag card. The method comprises the following steps: acquiring an original measurement value of a multi-band signal in a positioning tag card, and extracting an interfered band combination based on the original measurement value; carrying out fluctuation modeling on the frequency band combination by adopting field intensity detection, generating a fluctuation feature vector, carrying out weather type classification based on the fluctuation feature vector, and determining a rainy day attenuation calculation mode; adjusting the signal intensity according to a rainy day attenuation calculation mode, obtaining adjusted signal intensity data, calculating a distance estimation value, and generating a distance parameter set; and performing triangulation positioning according to the distance parameter set, generating a real-time position coordinate, matching the real-time position coordinate with historical trajectory data, adjusting a compensation coefficient of the compensation model, and generating an optimized positioning result by using the adjusted compensation model. According to the invention, the continuity and accuracy of wireless positioning are improved.

An information interaction apparatus, configured in a first device, includes: a transmitter configured to transmit to a second device a model-related information request and / or an entity-related information request for optimizing a wireless positioning AI / ML model; and a receiver configured to receive model-related information feedback and / or entity-related information feedback transmitted by the second device.