Cooperative sensing fusion tracking method for 6G Internet of Vehicles

Abstract

The invention provides a cooperative communication fusion tracking method for 6G Internet of Vehicles, the method is applied to an Internet of Vehicles system, the Internet of Vehicles system comprises a vehicle, a central processor and a plurality of base stations, and the method comprises the following steps: performing state prediction on the vehicle through each base station to obtain a plurality of prediction state vectors; fusing the plurality of prediction state vectors through a central processing unit, and determining a joint transmission precoding vector of the base station based on the fused prediction state vector; performing state estimation on the vehicle through the base station based on the joint transmission precoding vector so as to obtain a plurality of estimated state vectors; and fusing the plurality of estimation state vectors through the central processing unit, and sending a fused estimation state vector to the base station, so that the base station updates the state based on the fused estimation state vector. According to the method, the precision of the prediction state vector and the estimation state vector can be improved, the vehicle with a complex trajectory can be tracked more accurately, and a high-speed downlink communication service is provided in the vehicle tracking process.

Description

Technical Field

[0001] The present invention relates to the field of communication technologies, and particularly to a collaborative communication and sensing fusion tracking method for 6G vehicle-to-everything (V2X). Background Art

[0002] Vehicle-to-everything (V2X), as a key technology in multiple fields such as automobiles, electronics, and communications, is accelerating the deep integration of industries such as autonomous driving, intelligent transportation, and smart cities. With the continuous development of 6G, the advantages of cellular V2X (C-V2X), as the intelligent roadside network infrastructure, in quickly processing information and interacting with vehicle users (VUs) have become increasingly prominent. However, C-V2X also faces significant challenges. In addition to having a powerful communication ability to meet diverse communication services, the future C-V2X network also needs to have a highly reliable and high-precision active sensing function to support various environmental sensing services. This means that achieving a communication rate of gigabits per second (Gbps) and centimeter-level sensing accuracy will be the goal of the future C-V2X network, and the current C-V2X standard obviously cannot meet these requirements. Therefore, the integrated sensing and communication (ISAC) technology is considered an effective means to solve this problem.

[0003] With the unprecedented surge of new wireless services, spectrum conflicts between communication and sensing will inevitably emerge, leading to an increasing demand for additional spectrum resources. ISAC was initially proposed to release frequency bands previously limited to radar systems for sharing by communication systems. Related research covers Joint Communication and Radar (JCR), Joint Communication and Sensing (JCS), Radar Communication (RadCom), and Dual Function Radar Communication (DFRC). With the continuous in-depth research, the team of Professor Liu Fan from Southern University of Science and Technology officially proposed the concept and scope of ISAC. Compared with previous research, ISAC will redesign the signal model, system architecture, and resource allocation scheme. Obviously, ISAC pursues a deeper integration paradigm, aiming to achieve a high degree of unity between sensing and communication. Compared with dedicated sensing or communication functions, the ISAC design method offers two types of advantages. First, the shared use of limited resources (such as spectrum, energy, and hardware platforms) can improve the spectrum efficiency of sensing and communication and reduce the hardware cost and scale, thus providing an integration advantage. Second, the mutual assistance between sensing and communication can further enhance the dual performance, providing a dual-functional gain. Driven by the above advantages, ISAC has attracted extensive attention from academia and industry. Currently, the technical characteristics of ISAC are highly compatible with the requirements of the C-V2X network and are expected to spawn numerous applications, including new target tracking systems, in this field.

[0004] Although the new tracking systems empowered by ISAC have received increasing attention, most current ISAC tracking systems are studied in single-node scenarios, that is, there is only one Base Station (BS) or Roadside Unit (RSU), and the potential of multi-node cooperation in the C-V2X network has not been fully explored. Driven by the Cloud Radio Access Network (C-RAN) architecture in 6G networks, existing research results show that compared with single-node ISAC systems, multi-node cooperative ISAC systems can achieve higher-rate communication and more accurate sensing, but these works do not focus on the development of tracking systems. In addition, most studies assume that vehicle users perform simple uniform linear motion without considering complex trajectories. Therefore, it is of great value to design a Cooperative ISAC (Co-ISAC) tracking system for vehicle users with complex trajectories. Summary of the Invention

[0005] The object of the present invention is to overcome the deficiencies of the prior art and provide a collaborative communication and sensing fusion tracking method for 6G vehicle-to-everything (V2X).

[0006] To achieve the above object, the present invention adopts the following technical solutions:

[0007] In a first aspect, the present invention provides a collaborative communication and sensing fusion tracking method for 6G V2X. The method is applied to a V2X system, which includes vehicles, a central processor, and multiple base stations. The method includes:

[0008] Each base station performs state prediction on the vehicle to obtain multiple predicted state vectors;

[0009] The central processor fuses the multiple predicted state vectors and determines the joint transmission precoding vector of the base stations based on the fused predicted state vector;

[0010] Based on the joint transmission precoding vector, each base station performs state estimation on the vehicle to obtain multiple estimated state vectors;

[0011] The central processor fuses the multiple estimated state vectors and sends the fused estimated state vector to the base stations so that the base stations update their states based on the fused estimated state vector.

[0012] In some embodiments of the present invention, the step of each base station performing state prediction on the vehicle to obtain multiple predicted state vectors includes:

[0013] Each base station respectively performs linear motion (LM) prediction and turning motion (TM) prediction on the vehicle to obtain a linear motion prediction value and a turning motion prediction value;

[0014] The base station fuses the linear motion prediction value and the turning motion prediction value to obtain the predicted state vector.

[0015] In some embodiments of the present invention, the step of the central processor fusing the multiple predicted state vectors and determining the joint transmission precoding vector of the base stations based on the fused predicted state vector includes:

[0016] The central processor performs weighted least squares calculation on the multiple predicted state vectors to obtain a fused predicted state vector, and determines the predicted azimuth angle and predicted elevation angle of each base station based on the fused predicted state vector and the measurement model. The joint transmission precoding vector is determined according to the predicted azimuth angle and predicted elevation angle.

[0017] In some embodiments of the present invention, the base station performs state estimation on the vehicle based on the joint transmission precoding vector to obtain a plurality of estimated state vectors, including:

[0018] Based on the joint transmission precoding vector, each base station respectively performs linear motion estimation and turning motion estimation on the vehicle, and correspondingly obtains a linear motion estimation value and a turning motion estimation value;

[0019] Based on the joint transmission precoding vector, the base station fuses the linear motion estimation value and the turning motion estimation value to obtain the estimated state vector.

[0020] In some embodiments of the present invention, the step of each base station respectively performing linear motion prediction and turning motion prediction on the vehicle, and correspondingly obtaining a linear motion prediction value and a turning motion prediction value, includes:

[0021] At the base station, obtain the estimated probability corresponding to the first model And the estimated probability corresponding to the second model And calculate to obtain the mixed probability of the first model The mixed probability of the second model The first model is a linear motion model, and the second model is a turning motion model;

[0022] Based on the mixed probability of the first model Calculate to obtain the mixed estimated state vector of the first model And the mixed covariance matrix Based on the mixed probability of the second model Calculate to obtain the mixed estimated state vector of the second model And the mixed covariance matrix

[0023] According to the mixed estimated state vector of the first model And the mixed covariance matrix Predict the deterministic point set of the first model; according to the mixed estimated state vector of the second model And the mixed covariance matrix Predict the deterministic point set of the second model;

[0024] Calculate to obtain the mean weight set of the first model And the covariance weight set The mean weight set of the second model And the covariance weight set

[0025] The deterministic point set and mean weight set based on the first model and covariance weight set Calculate the linear motion prediction value corresponding to the first model; based on the deterministic point set, mean weight set of the second model and covariance weight set Calculate the turning motion prediction value corresponding to the second model. Both the linear motion prediction value and the turning motion prediction value include a predicted state vector and a predicted covariance matrix.

[0026] In some embodiments of the present invention, the linear motion estimation and turning motion estimation are respectively performed on the vehicle by each base station based on the joint transmission precoding vector, and the linear motion estimation value and the turning motion estimation value are correspondingly obtained, including:

[0027] On the base station, calculate the Kalman gain of the first model according to the deterministic point set of the first model, and at the same time, calculate the Kalman gain of the second model according to the deterministic point set of the second model;

[0028] Obtain the linear motion estimation value corresponding to the first model according to the Kalman gain of the first model and the linear motion prediction value; obtain the turning motion estimation value corresponding to the second model according to the Kalman gain of the second model and the turning motion prediction value. Both the linear motion estimation value and the turning motion estimation value include an estimated state vector, an estimated covariance matrix, and a measurement estimation error.

[0029] In a second aspect, the present invention also provides a collaborative communication and sensing fusion tracking device for a 6G vehicle network. The device is applied to a vehicle network system, and the vehicle network system includes a vehicle, a central processor, and multiple base stations. The device includes:

[0030] A state prediction module for performing state prediction on the vehicle by each base station to obtain multiple predicted state vectors;

[0031] A prediction fusion and coding module for fusing the multiple predicted state vectors by the central processor and determining the joint transmission precoding vector of the base station based on the fused predicted state vector;

[0032] A state estimation module for performing state estimation on the vehicle by the base station based on the joint transmission precoding vector to obtain multiple estimated state vectors;

[0033] An estimation fusion module for fusing the multiple estimated state vectors by the central processor and sending the fused estimated state vector to the base station so that the base station updates the state based on the fused estimated state vector.

[0034] In a third aspect, the present invention further provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, the above-mentioned method is implemented.

[0035] In a fourth aspect, the present invention further provides a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the above-mentioned method is implemented.

[0036] In a fifth aspect, the present invention further provides a computer program product, including a computer program, and when the computer program is executed by a processor, the above-mentioned method is implemented.

[0037] Advantages of the present invention: The collaborative communication and sensing fusion tracking method for 6G vehicle-to-everything (V2X) provided by the present invention fuses the predicted state vectors of all base stations for a vehicle through a central processor, and determines the joint transmit precoding vector of the base stations based on the fused predicted state vector. After the central processor sends the joint transmit precoding vector to each base station, the base stations perform state estimation on the vehicle, and the central processor fuses the estimated state vectors of all base stations to obtain a fused estimated state vector. Each base station updates its state based on the fused estimated state vector, thereby improving the accuracy of the predicted state vector and the estimated state vector, being able to more accurately track vehicles with complex trajectories, and providing high-rate downlink communication services for vehicles during the tracking process.

[0038] Additional aspects and advantages of the present invention will be given in part in the following description, which will become apparent from the following description, or can be learned through the practice of the present invention. Description of the Drawings

[0039] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0040] Figure 1 It is a schematic structural diagram of a vehicle-to-everything (V2X) system provided by an embodiment of the present invention;

[0041] Figure 2 It is a schematic flowchart of a collaborative communication and sensing fusion tracking method for 6G vehicle-to-everything (V2X) provided by an embodiment of the present invention;

[0042] Figure 3 It is a schematic flowchart of a collaborative communication and sensing fusion tracking method for 6G vehicle-to-everything (V2X) provided by an embodiment of the present invention;

[0043] Figure 4 This is the third flowchart diagram of the collaborative communication and sensing fusion tracking method for 6G vehicle-to-everything (V2X) provided by the embodiments of the present invention. Detailed implementation manners

[0044] The following details the implementation manners of the present invention. The examples of the implementation manners are shown in the accompanying drawings, where the same or similar reference numerals denote the same or similar elements or elements with the same or similar functions throughout. The implementation manners described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention and should not be construed as limiting the present invention.

[0045] Those skilled in the art of the present technology can understand that, unless specifically stated otherwise, the singular forms "a", "an", and "the" used herein may also include the plural forms. It should be further understood that the term "comprising" used in the specification of the present invention means the presence of the features, integers, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or their groups. It should be understood that when we say an element is "connected" or "coupled" to another element, it can be directly connected or coupled to other elements, or there may also be intermediate elements. In addition, the "connection" or "coupling" used herein may include wireless connection or coupling. The phrase "and / or" used herein includes any and all combinations of one or more of the associated listed items.

[0046] Those skilled in the art of the present technology can understand that, unless otherwise defined, all terms (including technical terms and scientific terms) used herein have the same meaning as the general understanding of those of ordinary skill in the art to which the present invention belongs. It should also be understood that terms such as those defined in a general dictionary should be understood to have a meaning consistent with the meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless defined as herein.

[0047] For ease of understanding the embodiments of the present invention, the following will further explain with several specific embodiments as examples in conjunction with the accompanying drawings, and each embodiment does not constitute a limitation to the embodiments of the present invention.

[0048] Embodiment 1

[0049] A collaborative communication and sensing fusion tracking method for 6G vehicle-to-everything (V2X) provided by the embodiments of the present invention is applied to a vehicle-to-everything (V2X) system, as Figure 1 shown. The vehicle-to-everything (V2X) system includes vehicles, a central processing unit, and multiple base stations ( Figure 1 taking 3 base stations as an example), and the base stations are equipped with planar antenna arrays, which sense vehicle users and send messages to vehicle users at the same time. The planar antenna array consists of N t transmitting antennas in P rows and Q columns and Nr is composed of root receiving antennas, and N t = N r . It is assumed that all base stations operate in full-duplex mode without self-interference, and all base stations are connected to the same central processor. The central processor designs a joint transmission precoding vector for multiple base stations to support the implementation of the dual functions of base station communication and sensing. In addition, it is assumed that all base stations are in a perfect time synchronization state, and each base station can not only process its own reflected signals but also process the reflected signals from other base stations.

[0050] As Figure 2 shown in Figure 3 , a collaborative communication and sensing fusion tracking method for 6G vehicle-to-everything (V2X) provided by an embodiment of the present invention specifically includes the following steps:

[0051] S101, predicting the state of the vehicle through each base station to obtain multiple predicted state vectors.

[0052] S102, fusing the multiple predicted state vectors by the central processor and determining the joint transmission precoding vector of the base stations based on the fused predicted state vectors.

[0053] S103, estimating the state of the vehicle by the base stations based on the joint transmission precoding vector to obtain multiple estimated state vectors.

[0054] S104, fusing the multiple estimated state vectors by the central processor to obtain a fused estimated state vector.

[0055] The collaborative communication and sensing fusion tracking method for 6G V2X provided by an embodiment of the present invention fuses the predicted state vectors of the vehicle from all base stations by the central processor and determines the joint transmission precoding vector of the base stations based on the fused predicted state vectors. After the central processor sends the joint transmission precoding vector to each base station, the state of the vehicle is estimated by the base stations, and the estimated state vectors of all base stations are fused by the central processor to obtain a fused estimated state vector. Each base station updates its state based on the fused estimated state vector, thereby improving the accuracy of the predicted state vector and the estimated state vector, being able to more accurately track a vehicle with a complex trajectory, and providing a high-rate downlink communication service for the vehicle during the tracking process.

[0056] In some embodiments of the present invention, as Figure 4 shown, the predicting the state of the vehicle through each base station to obtain multiple predicted state vectors includes:

[0057] Each base station performs linear motion prediction and turning motion prediction on the vehicle respectively, and obtains a linear motion prediction value and a turning motion prediction value correspondingly.

[0058] The base station fuses the linear motion prediction value and the turning motion prediction value to obtain the predicted state vector.

[0059] Specifically, the u-th base station performs the following calculations:

[0060] First, determine that the motion model corresponding to linear motion prediction and estimation is the first model g (1) (·), and the motion model corresponding to turning motion prediction and estimation is the second model g (2) (·). Although the prediction and estimation steps of the first model and the second model are the same, and both are calculated based on the Unscented Kalman Filter (UKF), the motion state evolution processes of the first model and the second model are different. In addition, the calculation results output by the first model and the second model will interact in the initial and final steps of the Interacting Multiple Model (IMM).

[0061] Based on the respective estimation probabilities of the above first model and second model at the (n - 1)-th time slot and the mixed probability of the first model and the mixed probability of the second model can be calculated respectively as:

[0062]

[0063] where is the element in the j1-th column and j2-th row of the state transition matrix T. The state transition matrix T is determined by the motion model and the road structure, and has nothing to do with the filter algorithm.

[0064] Next, the u-th base station calculates the inputs (i.e., the mixed estimated state vector and the mixed covariance matrix) of the unscented Kalman filters of the first model and the second model respectively based on the mixed probability of the first model and the mixed probability of the second model. The specific expressions are:

[0065]

[0066] where are the mixed estimated state vectors of the first model and the second model respectively, are the mixed covariance matrices of the first model and the second model respectively, They are the initial estimated state vectors of the first model and the second model, respectively. They are the initial estimated covariance matrices of the first model and the second model, respectively.

[0067] Both the first model and the second model can be predicted based on the corresponding mixed estimated state vector and the mixed covariance matrix and using the standard unscented Kalman filter. Specifically, first, the unscented transform is used to calculate the deterministic point set for each point in:

[0068]

[0069] where (·) l represents the l-th column of the matrix, ζ is a scaling factor set to reduce the prediction error, L is the dimension of the mixed estimated state vector , j = 1, 2. When j = 1, the deterministic point set corresponds to the first model. When j = 2, the deterministic point set corresponds to the second model.

[0070] Then, according to the scaling factor and the dimension of the mixed estimated state vector , calculate the respective mean weight sets and covariance weight sets for the first model and the second model for each weight value:

[0071]

[0072] Based on the respective deterministic point sets , mean weight sets and covariance weight sets of the first model and the second model, calculate the respective predicted state vectors and predicted covariance matrices The specific process is as follows:

[0073]

[0074] where Q s,n represents the state noise covariance matrix of VU, g (j) (·) is the state transition function of model j, is the predicted deterministic point set, is each point in.

[0075] Furthermore, the u-th base station will send the and of the first model and the and are fused to obtain a predicted state vector and a predicted covariance matrix P u,n|n-1 :

[0076]

[0077] Then each base station will send its own and P u,n|n-1 to the central processor.

[0078] In some embodiments of the present invention, the central processor fuses the multiple predicted state vectors and determines the joint transmission precoding vector of the base stations for the fused predicted state vector, including:

[0079] The central processor performs weighted least squares calculation on the multiple predicted state vectors to obtain a fused predicted state vector, and determines the predicted azimuth angle and predicted elevation angle of each base station based on the fused predicted state vector and the measurement model, and determines the joint transmission precoding vector according to the predicted azimuth angle and predicted elevation angle.

[0080] Specifically, on the central processor, the multiple predicted state vectors are fused based on the weighted least squares algorithm to obtain a fused covariance matrix P n|n-1 and a fused predicted state vector The specific steps are as follows:

[0081]

[0082] where U is the total number of base stations.

[0083] Then the central processor uses the fused predicted state vector combined with the measurement model h u (·) to obtain the predicted azimuth angle and predicted elevation angle of each base station, and then obtains the multi-base station joint transmission precoding vector w n :

[0084]

[0085] In the formula, a t is the steering vector, and P lim is the transmission power of each base station.

[0086] In some embodiments of the present invention, as Figure 4 shown, based on the joint transmission precoding vector, the base stations perform state estimation on the vehicle to obtain multiple estimated state vectors, including:

[0087] Based on the joint transmission precoding vector, each base station performs linear motion estimation and turning motion estimation on the vehicle respectively, and obtains a linear motion estimation value and a turning motion estimation value correspondingly.

[0088] Based on the joint transmission precoding vector, the base station fuses the linear motion estimation value and the turning motion estimation value to obtain the estimated state vector.

[0089] Specifically, in the base station, the following state estimation of the vehicle is performed based on the joint transmission precoding vector:

[0090] First, for the first model and the second model, according to the predicted deterministic point set mean weight set and covariance weight set calculate the Kalman gain K, and the specific steps are as follows:

[0091]

[0092] In the formula, is the predicted measurement point set, is the predicted measurement vector, P zz is the measurement vector covariance matrix, P xz is the state-measurement covariance matrix, is the residual covariance matrix, Q m,u,n is the measurement noise covariance matrix.

[0093] As can be seen from the above, it is found that the Kalman gain K is closely related to the measurement noise covariance matrix Q m,u,n closely related.

[0094] Among them, and respectively represent the measurement variances of azimuth, elevation, distance and speed. The magnitudes of these variances will be affected by the bandwidth and the transmission precoding vector, and can be expressed as:

[0095]

[0096] Among them, δ1, δ2 and δ3 are closely related to specific system settings, signal design and matched filtering algorithms, is the perceived signal-to-noise ratio of the vehicle user by the u-th base station in time slot n, and B is the transmission bandwidth.

[0097] Based on the Kalman gain, the estimated state vectors estimated covariance matrix and measurement estimation error Their expressions are respectively:

[0098]

[0099] Where q u,n is the measurement vector actually measured by the u-th base station, is the predicted state vector, is the predicted covariance matrix.

[0100] Each base station, based on the measurement estimation error combines the estimated state vector and the estimated covariance matrix of the first model with the estimated state vector and the estimated covariance matrix of the second model to obtain the estimated state vector and the estimated covariance matrix P u,n , and the specific steps are as follows:

[0101]

[0102] Where is the likelihood ratio of model j, is the residual covariance matrix of model j, is the estimated probability of model j, is the estimated state vector of model j.

[0103] Finally, each base station sends its respective estimated value to the central processor for a second fusion.

[0104] In some embodiments of the present invention, the central processor fuses the multiple estimated state vectors and sends the fused estimated state vector to the base station so that the base station updates its state based on the fused estimated state vector, including:

[0105] The central processor will again use the weighted least squares algorithm to obtain the fused estimated state vector and the fused estimated covariance matrix P n , that is:

[0106]

[0107] Then the central processor sends the fused estimated state vector back to all base stations, and each base station performs the following update:

[0108]

[0109] Where is the estimated state vector of the first model obtained by the u-th base station at the n-th time slot, is the estimated state vector of the u-th base station for the second model obtained in the n-th time slot.

[0110] In some embodiments of the present invention, the above-mentioned collaborative communication and sensing fusion tracking method for 6G vehicle-to-everything also implements based on the following model.

[0111] I. Motion model

[0112] 1) State evolution model: Define the motion state of a vehicle user within an extremely short time interval ΔT as n represents the n-th time interval (time slot). Specifically, x n represents the x-direction coordinate of the vehicle user, and y n represents the y-direction coordinate of the vehicle user, represents the x-direction speed of the vehicle user, represents the y-direction speed of the vehicle user, and Ω n represents the turning rate of the vehicle user. The larger Ω n is, the greater the degree of curvature of the vehicle user's trajectory.

[0113] The present invention considers that the vehicle user may have two state evolution models. The first is the uniform rectilinear motion model, and the specific formula includes:

[0114]

[0115] The second is the uniform turning motion model, and the specific formula includes:

[0116]

[0117] a = sin(ΔTΩ n-1 ), b = cos(ΔTΩ n-1 ),

[0118]

[0119] Based on the above uniform rectilinear motion model and uniform turning motion model, the state evolution process of the vehicle user in time slot n can be written in a more compact form:

[0120] e n = g (i) (e n-1 ) + ω s , i ∈ {1, 2},

[0121] where is the state noise vector, and each element in the state noise vector follows a zero-mean Gaussian distribution. e n is the motion state vector of the vehicle user in time slot n.

[0122] 2) Measurement model: Define the measurement vector q obtained when the u-th base station observes the vehicle user u,n as:

[0123]

[0124] where θ u,n 、 d u,n and v u,n represent the azimuth angle, elevation angle, distance, and speed of the vehicle user relative to the u-th base station at time slot n, respectively.

[0125] According to the geometric relationship, the measurement model h u (·) can be constructed as follows:

[0126]

[0127] where, [x u ,y u ,z u T is the position of the u-th base station, where x u represents the x-direction coordinate of the base station, y u represents the y-direction coordinate of the base station, and z u represents the z-direction coordinate of the base station.

[0128] Furthermore, the measurement vector q u,n can be:

[0129]

[0130] where, represents the measurement noise vector, and each element in the vector also follows a zero-mean Gaussian distribution, represents the set of all base stations.

[0131] II. Signal model

[0132] At time slot n, define the transmit precoding vector corresponding to the u-th base station as Then the transmit signal of the u-th base station can be constructed as x u,n = w u,n s n , where s n is the downlink communication and sensing fusion data sent to the vehicle user. Accordingly, the received signal r n at the vehicle user can be expressed as:

[0133]

[0134] where n0 is Gaussian white noise, with a mean of 0 and a variance of σ​2 Complex Gaussian distribution. h n is the joint channel vector, w n is the joint transmit precoding vector of multiple base stations.

[0135] In the case of only considering the direct-path channel, the downlink channel h from the u-th base station to the vehicle user u,n can be modeled as:

[0136]

[0137] where, μ u,n = 2d u,n / c and τ u,n = 2v u,n / λ are the Doppler frequency shift and time delay respectively, and can be compensated by using a matched filter. c is the speed of light, and λ is the wavelength of the center frequency f c . δ(t) is the Dirichlet function. κ represents the transmission gain, is the steering vector of the planar antenna array, and α u,n is the path loss coefficient.

[0138] Immediately afterwards, the echo signal r received by the u-th base station at time slot n u,n can be expressed as:

[0139]

[0140] where, n u is the Gaussian white noise at the base station, following a Gaussian distribution with a mean of 0 and a variance of . f u,n is the receive precoding vector constructed according to the predicted angle, and the specific expression is

[0141] is the receive steering vector of the planar antenna array, and and are the predicted azimuth angle and predicted elevation angle determined based on the fusion prediction state vector and the measurement model respectively. x i,n is the transmit signal of the i-th base station.

[0142] The cascaded receive channel g from the i-th base station to the vehicle user and then to the u-th base station i,u,n can be expressed as:

[0143]

[0144] where, is the transmit-receive gain. Thanks to the deployment of the cloud radio access network and the assumption of perfect time synchronization, μ i,u,n and τ i,u,nIt can also be compensated at the base station. β i,u,n is the reflection coefficient.

[0145] In summary, the perceived signal-to-noise ratio of the u-th base station for the vehicle user in time slot n can be deduced as follows:

[0146]

[0147] where is the received noise power of the u-th base station, and w i,n is the transmit precoding vector of the i-th base station.

[0148] The collaborative communication and sensing fusion tracking method for 6G vehicle-to-everything (V2X) provided by the embodiments of the present invention fuses the predicted state vectors of all base stations for the vehicle through a central processing unit, and determines the joint transmit precoding vector of the base stations based on the fused predicted state vector. After the central processing unit sends the joint transmit precoding vector to each base station, the base stations perform state estimation on the vehicle, and the central processing unit fuses the estimated state vectors of all base stations to obtain a fused estimated state vector, and each base station updates the state based on the fused estimated state vector. In addition, during the prediction and estimation of the state vector, the prediction and estimation are respectively based on a linear motion model and a turning motion model. The prediction results and estimation results of the two models of each base station are combined respectively, thereby improving the accuracy of the predicted state vector and the estimated state vector, being able to more accurately track vehicles with complex trajectories, and providing high-rate downlink communication services for vehicles during the tracking process.

[0149] Embodiment 2

[0150] Based on Embodiment 1, Embodiment 2 of the present invention provides a collaborative communication and sensing fusion tracking device for 6G vehicle-to-everything (V2X). The collaborative communication and sensing fusion tracking device for 6G vehicle-to-everything (V2X) corresponds to the above-mentioned collaborative communication and sensing fusion tracking method for 6G vehicle-to-everything (V2X). The device is applied to a vehicle-to-everything (V2X) system, and the vehicle-to-everything (V2X) system includes vehicles, a central processing unit, and multiple base stations. The device includes:

[0151] A state prediction module, configured to perform state prediction on the vehicle through each base station to obtain multiple predicted state vectors;

[0152] A prediction fusion and coding module, configured to fuse the multiple predicted state vectors through the central processing unit, and determine the joint transmit precoding vector of the base stations based on the fused predicted state vector;

[0153] A state estimation module, configured to perform state estimation on the vehicle through the base stations based on the joint transmit precoding vector to obtain multiple estimated state vectors;

[0154] An estimation fusion module, configured to fuse the multiple estimated state vectors through the central processor and send the fused estimated state vector to the base station, so that the base station updates the state based on the fused estimated state vector.

[0155] For specific details, refer to the description in the section of the collaborative communication and sensing fusion tracking method for 6G vehicle-to-everything (V2X) network, which will not be elaborated here.

[0156] Embodiment 3

[0157] Embodiment 3 of the present invention provides an electronic device, including a memory and a processor, where the processor and the memory communicate with each other. The memory stores program instructions executable by the processor, and the processor invokes the program instructions to execute the collaborative communication and sensing fusion tracking method for 6G vehicle-to-everything (V2X) network. The method includes the following process steps:

[0158] The method is applied to a vehicle-to-everything (V2X) network system, which includes vehicles, a central processor, and multiple base stations. The method includes:

[0159] Each base station performs state prediction on the vehicle to obtain multiple predicted state vectors;

[0160] The central processor fuses the multiple predicted state vectors and determines the joint transmit precoding vector of the base station based on the fused predicted state vector;

[0161] Based on the joint transmit precoding vector, each base station performs state estimation on the vehicle to obtain multiple estimated state vectors;

[0162] The central processor fuses the multiple estimated state vectors and sends the fused estimated state vector to the base station, so that the base station updates the state based on the fused estimated state vector.

[0163] Embodiment 4

[0164] Embodiment 4 of the present invention provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the collaborative communication and sensing fusion tracking method for 6G vehicle-to-everything (V2X) network. The method includes the following process steps:

[0165] The method is applied to a vehicle-to-everything (V2X) network system, which includes vehicles, a central processor, and multiple base stations. The method includes:

[0166] Each base station performs state prediction on the vehicle to obtain multiple predicted state vectors;

[0167] The central processor fuses the multiple predicted state vectors and determines the joint transmission precoding vector of the base station based on the fused predicted state vector;

[0168] Based on the joint transmission precoding vector, the base station performs state estimation on the vehicle to obtain multiple estimated state vectors;

[0169] The central processor fuses the multiple estimated state vectors and sends the fused estimated state vector to the base station so that the base station updates its state based on the fused estimated state vector.

[0170] Embodiment 5

[0171] Embodiment 5 of the present invention provides a computer program product, which includes a computer program. When the computer program is executed by a processor, it implements a collaborative communication and sensing fusion tracking method for 6G vehicle-to-everything (V2X) networks. The method includes the following process steps:

[0172] The method is applied to a V2X network system, which includes vehicles, a central processor, and multiple base stations. The method includes:

[0173] Each base station performs state prediction on the vehicle to obtain multiple predicted state vectors;

[0174] The central processor fuses the multiple predicted state vectors and determines the joint transmission precoding vector of the base station based on the fused predicted state vector;

[0175] Based on the joint transmission precoding vector, the base station performs state estimation on the vehicle to obtain multiple estimated state vectors;

[0176] The central processor fuses the multiple estimated state vectors and sends the fused estimated state vector to the base station so that the base station updates its state based on the fused estimated state vector.

[0177] Those of ordinary skill in the art can understand that the drawings are only schematic diagrams of an embodiment, and the modules or processes in the drawings are not necessarily essential for implementing the present invention.

[0178] Each embodiment in this specification is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other. Each embodiment focuses on the differences from other embodiments. In particular, for method or system embodiments, since they are basically similar to method embodiments, they are described relatively simply. For related parts, reference can be made to the corresponding parts of the method embodiments. The method and system embodiments described above are only illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. A person of ordinary skill in the art can understand and implement it without creative work.

[0179] The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed by the present invention should be covered by the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims

1. A collaborative communication and sensing fusion tracking method for 6G vehicle-to-everything (V2X) networks, characterized in that, The method is applied to a vehicle networking system, which includes vehicles, a central processor, and multiple base stations. The method includes: Each base station performs state prediction on the vehicle to obtain multiple predicted state vectors; The central processor fuses the multiple predicted state vectors and determines the joint transmission precoding vector of the base stations based on the fused predicted state vector; Based on the joint transmission precoding vector, each base station performs state estimation on the vehicle to obtain multiple estimated state vectors; The central processor fuses the multiple estimated state vectors and sends the fused estimated state vector to the base stations, so that the base stations update the state based on the fused estimated state vector.

2. The method according to claim 1, wherein The step of each base station performing state prediction on the vehicle to obtain multiple predicted state vectors includes: Each base station respectively performs linear motion prediction and turning motion prediction on the vehicle to obtain a linear motion prediction value and a turning motion prediction value correspondingly; The base station fuses the linear motion prediction value and the turning motion prediction value to obtain the predicted state vector.

3. The method according to claim 1, wherein The step of the central processor fusing the multiple predicted state vectors and determining the joint transmission precoding vector of the base stations for the fused predicted state vector includes: The central processor performs weighted least square calculation on the multiple predicted state vectors to obtain the fused predicted state vector, and determines the predicted azimuth angle and the predicted elevation angle of each base station based on the fused predicted state vector and the measurement model, and determines the joint transmission precoding vector according to the predicted azimuth angle and the predicted elevation angle.

4. The method according to claim 1, characterized in that The step of, based on the joint transmission precoding vector, each base station performing state estimation on the vehicle to obtain multiple estimated state vectors includes: Based on the joint transmission precoding vector, each base station respectively performs linear motion estimation and turning motion estimation on the vehicle to obtain a linear motion estimation value and a turning motion estimation value correspondingly; Based on the joint transmission precoding vector, the base station fuses the linear motion estimation value and the turning motion estimation value to obtain the estimated state vector.

5. The method according to claim 2, characterized in that, The step of each base station respectively performing linear motion prediction and turning motion prediction on the vehicle to obtain a linear motion prediction value and a turning motion prediction value correspondingly includes: On the base station, obtain the estimated probability corresponding to the first model and the estimated probability corresponding to the second model and calculate to obtain the mixed probability of the first model and the mixed probability of the second model The first model is a linear motion model, and the second model is a turning motion model; The mixing probability based on the first model Calculate the mixed estimated state vector of the first model And the mixed covariance matrix The mixing probability based on the second model Calculate the mixed estimated state vector of the second model And the mixed covariance matrix The mixed estimated state vector according to the first model and the mixed covariance matrix predict the deterministic point set of the first model; according to the mixed estimated state vector of the second model and the mixed covariance matrix predict the deterministic point set of the second model; The mean weight set of the first model is calculated based on the dimension L of the mixed estimation state vector and the scaling factor ζ and the covariance weight set The mean weight set of the second model and the covariance weight set The deterministic point set and mean weight set based on the first model and covariance weight set Calculate the predicted value of the linear motion corresponding to the first model; based on the deterministic point set, mean weight set and covariance weight set Calculate the predicted value of the turning motion corresponding to the second model. Both the linear motion predicted value and the turning motion predicted value include a predicted state vector and a predicted covariance matrix.

6. The method according to claim 5, wherein The step of, based on the joint transmission precoding vector, each base station respectively performing linear motion estimation and turning motion estimation on the vehicle to obtain a linear motion estimation value and a turning motion estimation value correspondingly includes: On the base station, the Kalman gain of the first model is calculated according to the deterministic point set of the first model, and at the same time, the Kalman gain of the second model is calculated according to the deterministic point set of the second model; Obtain the linear motion estimation value corresponding to the first model according to the Kalman gain of the first model and the linear motion prediction value; obtain the turning motion estimation value corresponding to the second model according to the Kalman gain of the second model and the turning motion prediction value, where both the linear motion estimation value and the turning motion estimation value include an estimated state vector, an estimated covariance matrix, and a measurement estimation error.

7. A collaborative communication and sensing fusion tracking device for 6G vehicle-to-everything network, characterized in that, The device is applied to a vehicle networking system, and the vehicle networking system includes a vehicle, a central processor, and a plurality of base stations. The device includes: A state prediction module, configured to perform state prediction on the vehicle through each base station to obtain a plurality of predicted state vectors; A prediction fusion and encoding module, configured to fuse the plurality of predicted state vectors through the central processor, and determine a joint transmission precoding vector of the base stations based on the fused predicted state vectors; A state estimation module, configured to perform state estimation on the vehicle through the base stations based on the joint transmission precoding vector to obtain a plurality of estimated state vectors; An estimation fusion module, configured to fuse the plurality of estimated state vectors through the central processor, and send the fused estimated state vectors to the base stations so that the base stations update the state based on the fused estimated state vectors.

8. An electronic device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the program, the method according to any one of claims 1-6 is implemented.

9. A computer-readable storage medium, characterized in that, It stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1-6 is implemented.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, the method according to any one of claims 1-6 is implemented.

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