Base station antenna pointing and user joint estimation method and system under discrete beam condition

Abstract

The invention provides a base station antenna pointing and user joint estimation method and system under a discrete beam condition. The method comprises the following steps: establishing a system model by taking a base station as an anchor node; a multi-dimensional closed interval in a state space is used as a'box particle ', and user and anchor node box particles are initialized respectively; describing a static anchor node state through a probability table; realizing a user dynamic tracking process by using a box particle filtering framework; updating user and anchor node states; the user state is updated through the user box particles in the constraint box particle set and the corresponding weights; counting coverage probabilities of anchor node box particles in the constraint box particle set to different intervals, and iteratively updating an anchor node probability table and an anchor node state; and judging whether the number of effective samples in the constraint box particle set and the average volume of the user box particles meet a resampling standard or not, and if so, resampling the user and anchor node box particles respectively. According to the invention, position and orientation errors of the base station can be compensated, and user positioning precision is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of integrated electronic information and wireless network communication perception, and in particular to a method and system for jointly estimating base station antenna pointing and users under discrete beam conditions. Background Art

[0002] Positioning has always been one of the important functions that wireless networks can provide. In the vision of building a new generation of wireless communication networks, in addition to basic communication functions, it is also necessary to have highly reliable wireless connections and high-precision and stable perception capabilities. In a wireless communication network, the wireless communication infrastructure can obtain channel state information between the user through channel estimation, and then obtain measurement information such as signal flight time, signal arrival angle, and received signal strength. In this way, when the base station coordinates are known, the relative position between the base station and the user can be solved to obtain the user coordinates.

[0003] However, in some scenarios, there may be errors in the state information such as the location of the base station and the orientation of the antenna array. In future wireless network applications, the location of the anchor node is not fixed, but can be dynamically arranged. Inaccurate anchor node state information will cause error propagation, resulting in the degradation of positioning performance. In addition, the angular accuracy of the base station receiving antenna beam is limited by the number of antenna array elements, which has the characteristics of discretization, resulting in a non-Gaussian distribution of the signal arrival angle error. Summary of the invention

[0004] The present invention aims to solve one of the technical problems in the related art at least to a certain extent.

[0005] The present invention proposes a method for jointly estimating base station antenna pointing and user under discrete beam conditions, which can compensate for base station position and orientation errors and improve user positioning accuracy.

[0006] Another object of the present invention is to provide a base station antenna pointing and user joint estimation system under discrete beam conditions.

[0007] To achieve the above object, the present invention proposes a method for jointly estimating base station antenna pointing and user under discrete beam conditions, comprising: The base station is used as an anchor node to establish a system model for user positioning under discrete beam and biased base station position and orientation conditions; The multidimensional closed intervals in the state space of the system model are used as box particles, the user box particles and anchor node box particles are initialized respectively, and the static anchor node states are described by interval tables and probability tables; A constrained box particle set is obtained based on the user box particles and the anchor node box particles by a box particle filtering method, and a weight corresponding to each box particle in the set is calculated to track user dynamics; The user status is updated through the user box particles and the corresponding weights in the constraint box particle set, and the coverage probability of the anchor node box particles in the constraint box particle set for different intervals is counted to iteratively update the probability table and the anchor node status; It is determined whether the number of valid box particle samples and the average volume of user box particles in the constrained box particle set meet the resampling standard. If so, the user box particles and the anchor node box particles are resampled respectively.

[0008] The method for jointly estimating base station antenna pointing and user under discrete beam conditions in the embodiment of the present invention may also have the following additional technical features: In one embodiment of the present invention, a base station is used as an anchor node to establish a system model for user positioning under discrete beam and base station position and orientation bias conditions, including: The anchor node position and orientation as well as the user position are taken as the system state, and the user state transfer model is established through the user motion equation; The distance measurement and the angle measurement are taken as the system observation values, and the angle measurement is described as a uniform quantized value of a continuous value according to the discrete beam condition.

[0009] In one embodiment of the present invention, user box particles and anchor node box particles are initialized respectively, and the static anchor node state is described by an interval table and a probability table, including: When initializing the user box particles, the user initial state space is determined by the user initial state and the user initial error limit, and the user initial state space is evenly divided to generate user box particles; When initializing the anchor node box particles, the anchor node initial state space is determined by the anchor node initial state and the anchor node initial error limit, and the entire anchor node initial state space is used as the anchor node box particle; Combine the user box particles and the anchor node box particles to obtain the initialization box particle set; Each dimension of the initial state space of the anchor node is evenly divided, and an anchor node interval table is created to record the divided nodes, and an anchor node probability table is created to record the probability corresponding to each dimension division result.

[0010] In one embodiment of the present invention, a constrained box particle set is obtained based on the user box particles and the anchor node box particles by a box particle filtering method, and a weight corresponding to each box particle in the set is calculated to track user dynamics, including: Generate a prediction box particle set through user speed observation value, user motion equation and state transfer error; wherein the prediction box particle set includes user prediction box particle set and anchor node prediction box particle set; The predicted box particle set is input into the distance observation function and the angle observation function through the constraint transfer algorithm for iterative constraints to obtain the constrained box particle set of the user and the anchor node, and the weight corresponding to each box particle is calculated.

[0011] In one embodiment of the present invention, a prediction box particle set is generated by using the user speed observation value, the user motion equation and the state transfer error, including: Based on the user box particle set and the user state transfer equation, box particle filter prediction is performed to obtain the user prediction box particle set; Based on the static anchor nodes, the set of anchor node box particles remains unchanged during the prediction process.

[0012] In one embodiment of the present invention, the predicted box particle set is input into the distance observation function and the angle observation function through the constraint transfer algorithm for iterative constraint, so as to obtain the constrained box particle set of the user and the anchor node, and calculate the weight corresponding to each box particle, including: Calculate the measurement prediction value through the prediction box particle set and the measurement equation; Calculate the measured predicted value and The intersection of the actual measurement values ​​at each moment is used to obtain the measurement information; The box particles in the prediction box particle set are constrained by using the measurement information through the constraint transfer algorithm to obtain the user constraint box particle set and the anchor node constraint box particle set; The volume of a box particle is defined as the product of the lengths of the intervals of each dimension, and the weight corresponding to each box particle is calculated by the ratio of the volume of the constraint box particle to the predicted box particle.

[0013] In one embodiment of the present invention, the user state is updated by the user box particles and the corresponding weights in the constraint box particle set, and the coverage probability of the anchor node box particles in the constraint box particle set for different intervals is counted to iteratively update the probability table and the anchor node state, including: For each box particle in the user constraint box particle set, take the midpoint of each dimension interval and multiply it by the corresponding weight, and calculate the cumulative sum to obtain the user state estimation value; In the anchor node constraint box particle set, the overlapping interval and overlapping probability of each dimension of the anchor node box particles are counted, and the obtained overlapping interval and overlapping probability are used as conditional probability. The anchor node probability table at time k-1 is used as the prior probability, and the anchor node probability density table at time k is calculated by Bayesian recursion; The midpoint of each interval in the anchor node interval table is multiplied by the corresponding value in the anchor node probability density table and the cumulative sum is calculated to obtain the estimated value of the anchor node state.

[0014] In one embodiment of the present invention, it is determined whether the number of valid box particle samples in the constrained box particle set and the average volume of the user box particles meet the resampling standard. If so, the user box particles and the anchor node box particles are resampled respectively, including: The volume of box particles is calculated by multiplying the lengths of the intervals of each dimension of the box particles and the average value is calculated. The threshold value is set according to the initial volume of the box particles. The number of valid box particle samples in the constrained box particle set is calculated by the box particle weight, and the upper and lower thresholds are set according to the total number of box particles. When the average volume of the user box particles is greater than the threshold value or the number of valid box particle samples in the constrained box particle set is higher than the threshold value, the box particles are divided; When the number of valid box particle samples in the constrained box particle set is lower than the threshold value, a new user box particle is generated within a preset range near the user state estimation value based on the current user state estimation value; the anchor node initial state space is used as a new anchor node box particle and combined with the user box particle to generate a new box particle set; In the original box particle sequence sorted in descending order by weight, determine whether this resampling is box particle division or box particle. If so, select the corresponding number of box particles from the bottom of the original box particle sequence and discard them to obtain the resampled box particle set.

[0015] To achieve the above object, the present invention, on the other hand, provides a base station antenna pointing and user joint estimation system under discrete beam conditions, comprising: A system model building module is used to use the base station as an anchor node to build a system model for user positioning problems under conditions of discrete beams and biased base station positions and orientations; The box particle initialization module is used to use the multidimensional closed intervals in the state space of the system model as box particles, initialize the user box particles and anchor node box particles respectively, and describe the static anchor node state through the interval table and probability table; A box particle filtering module, used for obtaining a constrained box particle set based on the user box particles and the anchor node box particles through a box particle filtering method, and calculating a weight corresponding to each box particle in the set to track user dynamics; A user and anchor node status update module, used to update the user status through the user box particles and corresponding weights in the constraint box particle set, and to count the coverage probabilities of the anchor node box particles in the constraint box particle set for different intervals, so as to iteratively update the probability table and the anchor node status; The resampling module is used to determine whether the number of valid box particle samples in the constrained box particle set and the average volume of the user box particles meet the resampling standard. If so, the user box particles and the anchor node box particles are resampled respectively.

[0016] The method and system for jointly estimating base station antenna pointing and users under discrete beam conditions of the embodiments of the present invention can more accurately reflect the state of the base station in the actual environment, thereby improving the accuracy of user positioning; can better capture the uncertainty of the system, thereby improving the accuracy of estimation; by effectively managing the box particle set and weight distribution, the demand for computing resources is reduced and the real-time performance of the system is improved; by continuously iteratively updating the state of users and anchor nodes, the error is gradually reduced and the overall positioning accuracy is improved; by periodic resampling, particle degradation is avoided to ensure that the system always has sufficient diversity.

[0017] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which: Figure 1 A flowchart of a method for joint estimation of base station antenna pointing and user under discrete beam conditions provided by an embodiment of the present invention; Figure 2 A schematic diagram of the anchor node position, target motion trajectory samples and target tracking effect of a Monte Carlo experiment provided in an embodiment of the present invention; Figure 3 A schematic diagram of the root mean square error of user position estimation in 100 Monte Carlo experiments provided in an embodiment of the present invention; Figure 4 A schematic diagram of the root mean square error of anchor node position estimation in 100 Monte Carlo experiments provided in an embodiment of the present invention; Figure 5 A schematic diagram of the root mean square error of anchor node orientation estimation in 100 Monte Carlo experiments provided in an embodiment of the present invention; Figure 6 A structural diagram of a base station antenna pointing and user joint estimation system under discrete beam conditions provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0019] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0020] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0021] The following describes a method and system for joint estimation of base station antenna pointing and user under discrete beam conditions proposed in accordance with an embodiment of the present invention with reference to the accompanying drawings.

[0022] Example 1 Figure 1 is a flow chart of a method for joint estimation of base station antenna pointing and user under discrete beam conditions according to an embodiment of the present invention. Figure 1 As shown, the method includes: S1, taking the base station as the anchor node, to establish a system model for user positioning problem under the conditions of discrete beam and biased base station position and orientation.

[0023] Specifically, the anchor node position and orientation as well as the user position are taken as the system state, the user state transition model is established through the user motion equation, and the distance measurement and angle measurement are taken as the system observation value. According to the discrete beam condition, the angle measurement is further described as a uniform quantized value of the continuous value.

[0024] In one embodiment of the present invention, the user exist The state at the moment is recorded as , anchor node exist The state at the moment is recorded as ,in represents the horizontal and vertical coordinates of the two-dimensional plane, Indicates the direction of the anchor node. Users and In a system consisting of anchor nodes, The status of all users at this moment is , The status of all anchor nodes at this moment is ,in .

[0025] S2, taking the multidimensional closed intervals in the state space of the system model as box particles, initialize the user box particles and anchor node box particles respectively, and describe the static anchor node state through interval tables and probability tables.

[0026] Specifically, when initializing user box particles, the user initial state space is determined by the user initial state and error limit, and the initial state space is evenly divided to generate several user box particles; when initializing anchor node box particles, the anchor node initial state space is determined by the anchor node initial state and error limit, and the entire anchor node state space is used as the anchor node box particles. The user box particles are combined with the anchor node box particles to participate in the subsequent filtering process.

[0027] Each dimension of the anchor node state space is evenly divided, and an anchor node interval table is created to record the divided nodes; in addition, an anchor node probability table is created to initialize the probability corresponding to each dimension division result.

[0028] In one embodiment of the present invention, a multidimensional closed interval in the state space is used as a "box particle", and box particles describing the user state and the anchor node state are initialized respectively, including: Including In the box particle filtering process of box particles, it is known that the user's initial state is , user initial error bound , then the user's initial state space is When initializing the user box particles, the user initial state space is evenly divided to generate a user box particle set , thus completing the initialization of the user box particles. The initial state of the known anchor node , the initial error bound of the anchor node , then the initial state space of the anchor node is The entire initial state space of the anchor node is initialized as a box particle, that is, the anchor node box particle set is Each anchor node box particle is combined with each different user box particle by replication to generate an initialization box particle set. In the initialization box particle set, the weight corresponding to each box particle is set to .

[0029] In one embodiment of the present invention, a probability table is used to describe the static anchor node state, each dimension of the anchor node state space is evenly divided, the division results are recorded and the corresponding probabilities are initialized, including: Each dimension of the initial state space of the anchor node is evenly divided into share, Indicates the accuracy of the anchor node approximation during the filtering process, which can be adjusted according to actual needs. And create a Anchor node interval table In addition, create a node of size Anchor node probability table , record the probability corresponding to the result after each dimension division. Since the anchor nodes are evenly distributed in the initial state space, each element of the anchor node probability table is initialized to .

[0030] S3, obtaining a constrained box particle set based on the user box particles and the anchor node box particles through a box particle filtering method, and calculating a weight corresponding to each box particle in the set to track user dynamics.

[0031] Specifically, at each sampling moment, the system state is predicted by the user speed observation value and the user motion equation, and the anchor node box particles remain unchanged. The two are combined to generate a predicted box particle set; discrete angle measurement is described in the form of intervals, and the predicted box particles are constrained by distance measurement, angle measurement and observation equations to generate a constrained box particle set, and the weight corresponding to each box particle is calculated.

[0032] It can be understood that the box particle filter is a Monte Carlo state estimation method based on interval analysis theory. The box particle filter process uses "box particles" composed of multidimensional closed intervals instead of multidimensional vectors to describe the system state, and uses interval operations instead of real number arithmetic operations to realize state transfer and measurement update processes, and function operations are replaced by interval inclusion functions. For example, the function The interval inclusion function is recorded as ,satisfy .

[0033] The user box particles and anchor node box particles after time resampling are recorded as The user's movement satisfies certain physical rules, from which the user's state transfer equation can be obtained: , , in Describes the user Time has come The transformation relationship between physical positions at different times, represents the sampling interval, express The user speed observation value at the moment, express The user speed observation error at each moment, Represents the user state transfer error. For any time , satisfy , .in and are the covariance matrices corresponding to the velocity observation error and state transition, respectively, and and are vectors consisting of the standard deviations of the velocity observation error and the state transfer error respectively.

[0034] definition Moment User With anchor node The measured value between ,in Indicates distance measurement, represents angle measurement. All measurement values ​​of the system are recorded as The relationship between the system measurement value and the system state can be described by the measurement equation, namely: , , , , The function Indicates the status of all users in the system and anchor node status A method for calculating all measurement values ​​of the system at the current moment. represents the index of the corresponding element in the state vector, Same reason. and They are the distance measurement equation and the angle measurement equation, is the quantized angle measurement equation. Indicates the quantization interval. Represents the observation error. For any time , satisfy .in represents the observation error covariance matrix, and Represents a vector of standard deviations of the observation errors.

[0035] At each sampling moment, the system state is predicted by the user speed observation value and the user motion equation. The anchor node box particles remain unchanged, and the two are combined to generate a prediction box particle set; the discrete angle measurement is described in the form of intervals, and the box particles are constrained by distance measurement, angle measurement and observation equations to generate a constrained box particle set, and the weight corresponding to each box particle is calculated. Furthermore, the user prediction box particle set and the anchor node prediction box particle set are generated by the user speed observation value, the user motion equation and the state transfer error, including: Based on the user box particle set and the user state transfer equation, the box particle filter prediction process is realized to obtain the user prediction box particle set ,in Represents the calculation result of the user status prediction process. The specific method is: , Since the anchor node is static, the anchor node box particle set remains unchanged during the prediction process, so the anchor node prediction box particle set is expressed as .in Represents the calculation result of the anchor node state prediction process.

[0036] Furthermore, the predicted box particle set is substituted into the distance observation function and the angle observation function through the constraint transfer algorithm for iterative constraints, and the constraint box particle set of the user and the anchor node is obtained, and the weight corresponding to each box particle is calculated, including: First, by predicting the box particle set And the measurement equation calculates the measurement prediction value: , in The meaning of For each measured predicted value, calculate its Actual measured value at the moment The intersection of , we get the new measurement information: , for Moment User With anchor node Distance measurement between , based on the distance observation noise and the corresponding standard deviation ,Pick As the actual measurement interval, it participates in the calculation of new measurement information.

[0037] After obtaining the measurement information, the box particles are constrained by the constraint propagation algorithm using the measurement information to remove the redundant parts of the box particles that do not meet the constraints. Define the box particles in the user prediction box particle set and the anchor node prediction box particle set and The results obtained by applying the constraints are and The box particle set obtained after constraint Should meet: , For example, for user and anchor nodes , through the distance measurement relationship we can get: , in , and Representatives include Status value at the moment , and Using this relationship, we can transform the prediction box particles into and Substitute the corresponding state value components into , and Counting users Similarly, the angle measurement relationship can also be used to The coordinate range is recalculated. All measurement equations related to recalculation of the user After the coordinates are obtained, all the calculated results are compared with Take the intersection and get the constrained interval .in Represents the user prediction box particle Medium User The horizontal axis The corresponding amount. Represents the result of the constraint Medium User The horizontal axis The corresponding amount.

[0038] Repeat the above process for each dimension of each box particle to obtain the constrained user box particle set and anchor node box particle set. .

[0039] The volume of a box particle is defined as the product of the lengths of the intervals in each dimension, denoted as The weight corresponding to each box particle is calculated by the volume ratio of the constraint box particle to the predicted box particle, which is specifically expressed as: .

[0040] in express Time Box Particles and The corresponding weight.

[0041] S4, updating the user status through the user box particles and corresponding weights in the constraint box particle set, and counting the coverage probabilities of the anchor node box particles in the constraint box particle set for different intervals, so as to iteratively update the probability table and the anchor node status.

[0042] Specifically, in the user constraint box particle set, for each box particle, take the midpoint of each dimensional interval and multiply it with the corresponding weight, and calculate the cumulative sum to obtain the user state estimate. In the anchor node constraint box particle set, count the overlapping intervals and overlapping probabilities of each dimension in the state space; use the obtained overlapping intervals and overlapping probabilities as conditional probabilities, and the anchor node probability table at time k-1 as the prior probability. Calculate the anchor node probability density table at time k through Bayesian recursion and normalize it. Take the midpoint of each interval in the anchor node interval table and multiply it with the corresponding value in the anchor node probability density table and calculate the cumulative sum to obtain the anchor node state estimate.

[0043] In one embodiment of the present invention, updating the user state based on the user state and the corresponding weight in the constraint box particle set includes: In the process of calculating the user state, each box particle in the user constraint box particle set is replaced by the midpoint of each dimension interval, denoted as . The estimated value of the user state at a given moment is expressed as: .

[0044] In one embodiment of the present invention, the coverage probability of anchor node box particles in the constraint box particle set for different intervals is statistically analyzed to iteratively update the anchor node probability table and the anchor node status, including: In the anchor node constraint box particle set, the interval scanning line algorithm is used to count the number of interval overlaps for each dimension of the anchor node box particle, and the total number of box particles is calculated. Normalized to the overlap probability. Arrange the overlapping intervals in descending order according to the overlap probability, and take the one with the largest number of overlaps. The intervals are recorded in In the table, the corresponding overlap probability is recorded in In the table. is a parameter representing the statistical accuracy of overlapping intervals. , which means: the true value of the anchor node is contained in The probability of .

[0045] From the Bayesian recursive formula, we can know that the posterior probability of the anchor node state satisfies: , in Represents all system measurement values ​​from time 1 to time k. The overlapping interval table And the overlap probability table Since the anchor node is stationary, Can be Time anchor node interval table And anchor node probability table The updating process of the anchor node probability table is specifically expressed as: , , in, represents the result after the anchor node state space is divided, express The weight corresponding to the division result at this moment, . The function of is to calculate the interval length. The probability corresponding to the part where the anchor node probability table and the overlapping interval table do not intersect is set to a very small value, in order to prevent the sample from missing the true value of the anchor node state due to large deviations at individual moments.

[0046] After completing the update of the anchor node probability table, replace each interval in the anchor node interval table with the midpoint of each dimension interval, recorded as . The estimated value of the anchor node state at time is expressed as: .

[0047] S5, judging whether the number of valid box particle samples in the constraint box particle set and the average volume of the user box particles meet the resampling standard, if so, resampling the user box particles and the anchor node box particles respectively.

[0048] Specifically, the volume of the box particles is calculated by multiplying the lengths of the intervals of each dimension of the box particles and the average value is calculated, and the threshold value is set according to the initial volume of the box particles. The number of valid samples in the constrained box particle set is calculated by the box particle weight, and the upper and lower thresholds are set according to the total number of box particles. When the average volume of the user box particles is greater than the threshold value or the number of valid samples in the constrained box particle set is higher than the threshold value, the box particles are divided; when the number of valid samples in the constrained box particle set is lower than the threshold value, the user state estimate value at the current moment is used as the reference, and a new user box particle is generated within a certain range near the user state estimate value; the initial state space of the anchor node is used as a new anchor node box particle and combined with the user box particle to generate a new box particle set. In the box particle sequence sorted in descending order by weight, according to whether the box particle division and box particle regeneration are performed in this resampling, the corresponding number of box particles are selected from the bottom of the sequence and discarded to keep the total number of box particles before and after resampling unchanged.

[0049] In one embodiment of the present invention, determining whether the number of valid samples in the constraint box particle set and the average volume of the user box particles meet the resampling standard includes: Calculate the number of valid samples in the constraint box particle set by box particle weight , the calculation method is: , According to the total number of particles in the box Set the upper and lower bounds of the number of valid samples in the constraint box particle set , , which are used to start resampling when the box particle distribution is too concentrated and the box particles are degenerate.

[0050] Calculate the average volume of particles in the user box : , According to the initialized user box particle volume Set an upper limit on average volume , used to start resampling when the box particle volume is too large.

[0051] In one embodiment of the present invention, the box particles describing the user state and the box particles describing the anchor node state are resampled respectively, including: Set the box particle division ratio And the ratio of newborn particles in the box .

[0052] when or When Divide the box particles according to the proportion: sort the user box particles in descending order according to the weight size, and select The box particles of the ratio are evenly divided in one random dimension. Proportional box particle generation According to the anchor node probability table, the anchor node interval table is discarded Middle and ends The remaining part is used as the anchor node box particle to be combined with each user box particle after the division. After the box particle division is completed, in the original box particle sequence sorted in descending order by weight, select The box particles with a certain proportion are discarded to obtain the divided box particle set.

[0053] when When, according to Estimated value of user status at the moment , select a certain range of state space around it for uniform division, and generate new user box particles. The initial state space of the anchor node is used as a combination of the new anchor node box particles and the user box particles to generate a new box particle set. The proportion of new box particles to the total number of box particles is recorded as After obtaining the new box particle set, select from the bottom of the sequence in the original box particle sequence arranged in descending order of weight The proportion of box particles is discarded; if the box particles are divided in this resampling, additional box particles are selected from the bottom of the original box particle sequence The box particles with a certain proportion are discarded to obtain the resampled box particle set.

[0054] After resampling, the total number of bin particles remains unchanged. After resampling, the weight of each bin particle is normalized to: .

[0055] Example 2 The present invention is further described below in conjunction with MATLAB simulation.

[0056] Assuming that there are three anchor nodes in a two-dimensional plane, two users are tracked by distance measurement and quantized angle measurement, and the movement of the users in the plane obeys a uniform turning model. The method for jointly estimating the base station antenna pointing and the user under discrete beam conditions of the present invention may also include the following steps: S201, using the base station as an anchor node, establish a system model for user positioning problem under the conditions of discrete beam and biased base station position and orientation.

[0057] Time Anchor Node The state is represented by ,in Indicates the horizontal and vertical coordinates of the anchor node. Indicates the orientation of the anchor node. Moment User The state is represented by ,in Indicates the user's horizontal and vertical coordinates. Indicates the status of all anchor nodes and all users.

[0058] The initial status and actual status of anchor nodes and users are shown in Table 1.

[0059] Table 1

[0060] The initial state error bound of the anchor node is , the user's initial state error limit is That is, the anchor node and the user's real state satisfy , .

[0061] S202, initialize the user and anchor node box particles respectively, and describe the static anchor node state through a probability table.

[0062] Specifically, initialize the user box particles and convert the user initial state space Each dimension of is evenly divided into 6 parts, generating user box particles. The anchor node initial state space As the anchor node box particle, it is combined with each user box particle to generate an initial box particle set. The weight of each box particle is initialized as .

[0063] Each dimension of the anchor node state space is divided into Create an anchor node interval table And anchor node probability table . And each element in the anchor node probability table is initialized to .

[0064] S203, implementing the user dynamic tracking process using a box particle filter framework.

[0065] Specifically, in the user's state transition equation, take the sampling interval ,user The standard deviation of the state transition noise is ,user The standard deviation of the velocity observation noise is . And implement the box particle filter user state prediction process. In the measurement equation, take the user With base station The standard deviation of the observation noise between In the angle quantization equation, take the quantization interval The box particle filter measurement update and box particle constraint process are implemented to obtain the filtered box particle set and the corresponding weights.

[0066] S204, updating the user status by the user box particles in the constraint box particle set and the corresponding weights; and counting the coverage probabilities of the anchor node box particles in the constraint box particle set for different intervals, thereby iteratively updating the anchor node probability table and the anchor node status.

[0067] Specifically, the user box particles after box particle filtering and the corresponding weights are used to estimate the user state. In the anchor node state estimation, the interval scanning line algorithm is used to count the number of interval overlaps, and the number of overlaps before and after is recorded. After completing the update of the anchor node probability table, the anchor node state estimation value is calculated.

[0068] S205, judging whether the number of valid samples in the constraint box particle set and the average volume of the user box particles meet the resampling standard, and if so, resampling the box particles describing the user state and the box particles describing the anchor node state respectively.

[0069] Specifically, set the number of valid samples in the constraint box particle set The upper and lower bounds of , . Set the upper limit of the average volume of user box particles to . Sort the user box particles in descending order according to their weights.

[0070] When the number of valid samples in the constraint box particle set and the average volume of the user box particles meet When selecting from the top of the sequence The box particles are divided and discarded from the bottom of the sequence Box particles.

[0071] when When the user status is estimated at the current moment choose The box particles in the range divide each dimension evenly into 3 parts to generate new user box particles. And the initial state space of the anchor node As a new anchor node box particle, it is combined with each user box particle. The ratio of the newly generated box particles to the total number of box particles is Finally, we remove an additional The total number of box particles remains unchanged before and after resampling.

[0072] After resampling, the weight of each box particle is normalized to .

[0073] According to the method provided by the present invention, 100 Monte Carlo experiments are performed on a trajectory containing 100 samples. The target tracking and anchor node estimation effects can be seen in Figure 2-5 The simulation results are shown in Figure 2. Figure 2 This is a schematic diagram of the anchor node position, target motion trajectory, and a Monte Carlo target tracking effect provided by an embodiment of the present invention, where the solid line represents the actual trajectory of the user's motion, and the dotted line represents the trajectory obtained by the target tracking algorithm. To avoid trajectory overlap that affects the intuitiveness of the target tracking effect, Figure 2 The trajectories in only take the first 35 samples. Figure 3 , Figure 4 , Figure 5 Schematic diagrams of the root mean square error of 100 Monte Carlo user position estimation, anchor node position estimation and anchor node orientation estimation provided in embodiments of the present invention.

[0074] According to the method for jointly estimating base station antenna pointing and user under discrete beam conditions in an embodiment of the present invention, by combining deviation modeling of base station position and orientation, box particle filtering method and effective resampling strategy, it is possible to compensate for base station position and orientation errors and improve user positioning accuracy. This not only improves positioning accuracy, but also enhances the robustness and adaptability of the system.

[0075] In order to implement the above embodiment, Figure 6As shown, this embodiment also provides a base station antenna pointing and user joint estimation system 10 under discrete beam conditions, including: The system model building module 100 is used to use the base station as an anchor node to establish a system model for user positioning problems under the conditions of discrete beams and base station positions and orientations being biased; The box particle initialization module 200 is used to use the multidimensional closed intervals in the state space of the system model as box particles, initialize the user box particles and the anchor node box particles respectively, and describe the static anchor node state through the interval table and the probability table; The box particle filtering module 300 is used to obtain a constrained box particle set based on the user box particles and the anchor node box particles through a box particle filtering method, and calculate the weight corresponding to each box particle in the set to track the user dynamics; The user and anchor node status update module 400 is used to update the user status through the user box particles and corresponding weights in the constraint box particle set, and to count the coverage probabilities of the anchor node box particles in the constraint box particle set for different intervals, so as to iteratively update the probability table and the anchor node status; The resampling module 500 is used to determine whether the number of valid box particle samples in the constrained box particle set and the average volume of the user box particles meet the resampling standard. If so, the user box particles and the anchor node box particles are resampled respectively.

[0076] According to the discrete beam condition base station antenna pointing and user joint estimation system of the embodiment of the present invention, by combining the deviation modeling of base station position and orientation, box particle filtering method and effective resampling strategy, it is possible to compensate for the base station position and orientation errors and improve the user positioning accuracy. This not only improves the positioning accuracy, but also enhances the robustness and adaptability of the system.

[0077] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0078] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

Claims

1. A method for joint estimation of base station antenna pointing and user under discrete beam conditions, characterized in that: include: The base station is used as an anchor node to establish a system model for user positioning under discrete beam and biased base station position and orientation conditions; The multidimensional closed intervals in the state space of the system model are taken as box particles, the user box particles and anchor node box particles are initialized respectively, and the static anchor node states are described by interval tables and probability tables; A constrained box particle set is obtained based on the user box particles and the anchor node box particles by a box particle filtering method, and a weight corresponding to each box particle in the set is calculated to track user dynamics; The user status is updated through the user box particles and the corresponding weights in the constraint box particle set, and the coverage probability of the anchor node box particles in the constraint box particle set for different intervals is counted to iteratively update the probability table and the anchor node status; It is determined whether the number of valid box particle samples and the average volume of user box particles in the constrained box particle set meet the resampling standard. If so, the user box particles and the anchor node box particles are resampled respectively.

2. The method according to claim 1, characterized in that: The base station is used as an anchor node to establish a system model for user positioning under discrete beam and base station position and orientation bias conditions, including: The anchor node position and orientation as well as the user position are taken as the system state, and the user state transfer model is established through the user motion equation; The distance measurement and the angle measurement are taken as the system observation values, and the angle measurement is described as a uniform quantized value of a continuous value according to the discrete beam condition.

3. The method according to claim 2, characterized in that Initialize the user box particles and anchor node box particles respectively, and describe the static anchor node state through interval tables and probability tables, including: When initializing the user box particles, the user initial state space is determined by the user initial state and the user initial error limit, and the user initial state space is evenly divided to generate user box particles; When initializing the anchor node box particles, the anchor node initial state space is determined by the anchor node initial state and the anchor node initial error limit, and the entire anchor node initial state space is used as the anchor node box particle; Combine the user box particles and the anchor node box particles to obtain the initialization box particle set; Each dimension of the initial state space of the anchor node is evenly divided, and an anchor node interval table is created to record the divided nodes, and an anchor node probability table is created to record the probability corresponding to each dimension division result.

4. The method according to claim 3, characterized in that A constrained box particle set is obtained based on the user box particles and the anchor node box particles through a box particle filtering method, and a weight corresponding to each box particle in the set is calculated to track user dynamics, including: Generate a prediction box particle set through user speed observation value, user motion equation and state transfer error; wherein the prediction box particle set includes user prediction box particle set and anchor node prediction box particle set; The predicted box particle set is input into the distance observation function and the angle observation function through the constraint transfer algorithm for iterative constraints to obtain the constrained box particle set of the user and the anchor node, and the weight corresponding to each box particle is calculated.

5. The method according to claim 4, characterized in that Generate a prediction box particle set through user velocity observations, user motion equations, and state transfer errors, including: Based on the user box particle set and the user state transfer equation, box particle filter prediction is performed to obtain the user prediction box particle set; Based on the static anchor nodes, the set of anchor node box particles remains unchanged during the prediction process.

6. The method according to claim 4, characterized in that The predicted box particle set is input into the distance observation function and the angle observation function through the constraint transfer algorithm for iterative constraint to obtain the constraint box particle set of the user and the anchor node, and the weight corresponding to each box particle is calculated, including: Calculate the measurement prediction value through the prediction box particle set and the measurement equation; Calculate the measured predicted value and The intersection of the actual measurement values ​​at each moment is used to obtain the measurement information; The box particles in the prediction box particle set are constrained by using the measurement information through the constraint transfer algorithm to obtain the user constraint box particle set and the anchor node constraint box particle set; The volume of a box particle is defined as the product of the lengths of the intervals of each dimension, and the weight corresponding to each box particle is calculated by the ratio of the volume of the constraint box particle to the predicted box particle.

7. The method according to claim 6, characterized in that The user state is updated through the user box particles and the corresponding weights in the constraint box particle set, and the coverage probability of the anchor node box particles in the constraint box particle set for different intervals is counted to iteratively update the probability table and the anchor node state, including: For each box particle in the user constraint box particle set, take the midpoint of each dimension interval and multiply it by the corresponding weight, and calculate the cumulative sum to obtain the user state estimation value; In the anchor node constraint box particle set, the overlapping interval and overlapping probability of each dimension of the anchor node box particles are counted, and the obtained overlapping interval and overlapping probability are used as conditional probability. The anchor node probability table at time k-1 is used as the prior probability, and the anchor node probability density table at time k is calculated by Bayesian recursion; The midpoint of each interval in the anchor node interval table is multiplied by the corresponding value in the anchor node probability density table and the cumulative sum is calculated to obtain the estimated value of the anchor node state.

8. The method according to claim 7, characterized in that Determine whether the number of valid box particle samples in the constraint box particle set and the average volume of the user box particles meet the resampling standard. If so, resample the user box particles and the anchor node box particles respectively, including: The volume of box particles is calculated by multiplying the lengths of the intervals of each dimension of the box particles and the average value is calculated. The threshold value is set according to the initial volume of the box particles. The number of valid box particle samples in the constrained box particle set is calculated by the box particle weight, and the upper and lower thresholds are set according to the total number of box particles. When the average volume of the user box particles is greater than the threshold value or the number of valid box particle samples in the constrained box particle set is higher than the threshold value, the box particles are divided; When the number of valid box particle samples in the constrained box particle set is lower than the threshold value, a new user box particle is generated within a preset range near the user state estimation value based on the current user state estimation value; the anchor node initial state space is used as a new anchor node box particle and combined with the user box particle to generate a new box particle set; In the original box particle sequence sorted in descending order by weight, determine whether this resampling is box particle division or box particle. If so, select the corresponding number of box particles from the bottom of the original box particle sequence and discard them to obtain the resampled box particle set.

9. A base station antenna pointing and user joint estimation system under discrete beam conditions, characterized in that: include: A system model building module is used to use the base station as an anchor node to build a system model for user positioning problems under conditions of discrete beams and biased base station positions and orientations; The box particle initialization module is used to use the multidimensional closed intervals in the state space of the system model as box particles, initialize the user box particles and anchor node box particles respectively, and describe the static anchor node state through the interval table and probability table; A box particle filtering module, used for obtaining a constrained box particle set based on the user box particles and the anchor node box particles through a box particle filtering method, and calculating a weight corresponding to each box particle in the set to track user dynamics; A user and anchor node status update module, used to update the user status through the user box particles and corresponding weights in the constraint box particle set, and to count the coverage probabilities of the anchor node box particles in the constraint box particle set for different intervals, so as to iteratively update the probability table and the anchor node status; The resampling module is used to determine whether the number of valid box particle samples in the constrained box particle set and the average volume of the user box particles meet the resampling standard. If so, the user box particles and the anchor node box particles are resampled respectively.

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