Method and System for Joint Estimation of Base Station Antenna Pointing and User under Discrete Beam Conditions

Abstract

The present invention provides a method and system for jointly estimating the pointing of a base station antenna and a user under discrete beam conditions. The method includes establishing a system model with the base station as an anchor node; using multi-dimensional closed intervals in the state space as "box particles" to initialize user and anchor node box particles respectively; describing the static state of the anchor node through a probability table; implementing the user dynamic tracking process with a box particle filtering framework; updating the user and anchor node states; updating the user state from the user box particles and corresponding weights in the constrained box particle set; statistically calculating the coverage probability of different intervals by the anchor node box particles in the constrained box particle set, and iteratively updating the anchor node probability table and anchor node state based on this; determining whether the number of effective samples in the constrained box particle set and the average volume of the user box particles meet the resampling criteria, and if so, resampling the user and anchor node box particles respectively. The present invention can compensate for base station position and orientation errors and improve user positioning accuracy.

Description

Technical Field

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

[0002] The positioning function has always been one of the important functions provided by wireless networks. In the construction vision of the new generation of wireless communication networks, in addition to the basic communication function, it is also necessary to have highly reliable wireless connections and high-precision and stable sensing capabilities. In a wireless communication network, the wireless communication infrastructure can obtain the channel state information between it and users through channel estimation, and then obtain measurement information such as the signal flight time, the signal arrival angle, and the received signal strength. Thus, 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 position of the base station and the orientation of the antenna array surface. In the application of future wireless networks, the positions of the anchor nodes are not fixed but can be dynamically arranged. The inaccuracy of the anchor node state information will cause error propagation, resulting in the degradation of the positioning performance. In addition, the angular accuracy of the base station receiving antenna beam is limited by the number of antenna elements and has the characteristic of discretization, resulting in a non-Gaussian distribution of the error of the signal arrival angle. Summary of the Invention

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

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

[0006] Another object of the present invention is to propose a system for jointly estimating the base station antenna pointing and users under discrete beam conditions.

[0007] To achieve the above object, on the one hand, the present invention proposes a method for jointly estimating the base station antenna pointing and users under discrete beam conditions, including:

[0008] Taking the base station as an anchor node to establish a system model for the user positioning problem under discrete beam and biased base station position and orientation conditions;

[0009] Regarding the multi-dimensional closed interval in the state space of the system model as a box particle, initializing the user box particle and the anchor node box particle respectively, and describing the static anchor node state through an interval table and a probability table;

[0010] Using the box particle filter method, a constrained box particle set is obtained based on the user box particles and the anchor node box particles, and the weight corresponding to each box particle in the set is calculated to track the user dynamics;

[0011] The user state is updated through the user box particles and the corresponding weights in the constrained box particle set, and the coverage probabilities of the anchor node box particles in the constrained box particle set for different intervals are statistically calculated to iteratively update the probability table and the anchor node state;

[0012] It is judged whether the number of effective box particle samples and the average volume of the user box particles in the constrained box particle set meet the resampling criteria. If so, resampling is performed on the user box particles and the anchor node box particles respectively.

[0013] The method for jointly estimating the base station antenna pointing and the user under the discrete beam condition in the embodiments of the present invention may further have the following additional technical features:

[0014] In an embodiment of the present invention, the base station is used as an anchor node to establish a system model for the user positioning problem under the conditions of discrete beams and biased positions and orientations of the base station, including:

[0015] The position and orientation of the anchor node and the position of the user are used as the system state, and a user state transition model is established through the user motion equation;

[0016] The distance measurement and the angle measurement are used as the system observations, and the angle measurement is described as a uniformly quantized value of a continuous value according to the discrete beam condition.

[0017] In an embodiment of the present invention, the user box particles and the anchor node box particles are initialized respectively, and the static anchor node state is described through an interval table and a probability table, including:

[0018] When initializing the user box particles, the user initial state space is determined through the user initial state and the user initial error bound, and the user initial state space is uniformly divided to generate the user box particles;

[0019] When initializing the anchor node box particles, the anchor node initial state space is determined through the anchor node initial state and the anchor node initial error bound, and the entire anchor node initial state space is used as the anchor node box particles;

[0020] The user box particles and the anchor node box particles are combined to obtain an initialized box particle set;

[0021] Each dimension of the anchor node initial state space is uniformly divided, an anchor node interval table is created to record the division nodes, and an anchor node probability table is created to record the probabilities corresponding to the division results of each dimension.

[0022] 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 through the box particle filtering method, and the weight corresponding to each box particle in the set is calculated to track the user dynamics, including:

[0023] Generating a predicted box particle set through the user velocity observation value, the user motion equation, and the state transition error; wherein, the predicted box particle set includes a user predicted box particle set and an anchor node predicted box particle set;

[0024] Inputting the predicted box particle set into the distance observation function and the angle observation function through the constraint transfer algorithm for iterative constraint to obtain the constrained box particle sets of the user and the anchor node, and calculating the weight corresponding to each box particle.

[0025] In one embodiment of the present invention, generating a predicted box particle set through the user velocity observation value, the user motion equation, and the state transition error includes:

[0026] Performing box particle filtering prediction based on the user box particle set and the user state transition equation to obtain the user predicted box particle set;

[0027] Based on the static anchor nodes, the anchor node box particle set remains unchanged during the prediction process.

[0028] In one embodiment of the present invention, inputting the predicted box particle set into the distance observation function and the angle observation function through the constraint transfer algorithm for iterative constraint to obtain the constrained box particle sets of the user and the anchor node, and calculating the weight corresponding to each box particle includes:

[0029] Calculating the measurement prediction value through the predicted box particle set and the measurement equation;

[0030] Calculating the intersection of the measurement prediction value and The actual measurement value at the moment to obtain the measurement information;

[0031] Constraining the box particles in the predicted box particle set by using the measurement information through the constraint transfer algorithm to obtain the user constrained box particle set and the anchor node constrained box particle set;

[0032] Defining the box particle volume as the product of the interval lengths in each dimension, and calculating the weight corresponding to each box particle through the volume ratio of the constrained box particle to the predicted box particle.

[0033] In one embodiment of the present invention, updating the user state through the user box particles and the corresponding weights in the constrained box particle set, and statistically calculating the coverage probability of the anchor node box particles in the constrained box particle set for different intervals to iteratively update the probability table and the anchor node state, including:

[0034] For each bin particle in the user-constrained bin particle set, multiply the midpoint of each dimension interval by the corresponding weight, and calculate the cumulative sum to obtain the user state estimate value;

[0035] In the anchor node-constrained bin particle set, count the overlapping intervals and overlapping probabilities of each dimension of the anchor node bin particles, use the obtained overlapping intervals and overlapping probabilities as conditional probabilities, use the anchor node probability table at time k-1 as the prior probability, and calculate the anchor node probability density table at time k through Bayesian recursion;

[0036] Multiply the midpoint of each interval in the anchor node interval table by the corresponding value in the anchor node probability density table and calculate the cumulative sum to obtain the anchor node state estimate value.

[0037] In an embodiment of the present invention, determine whether the number of effective bin particle samples and the average volume of user bin particles in the constrained bin particle set meet the resampling standard. If so, resample the user bin particles and the anchor node bin particles respectively, including:

[0038] Calculate the volume of the bin particles by multiplying the lengths of each dimension interval of the bin particles and count the average value. Set a threshold according to the initial volume of the bin particles. Calculate the number of effective bin particle samples in the constrained bin particle set through the weights of the bin particles, and set upper and lower thresholds according to the total number of bin particles. When the average volume of the user bin particles is greater than the threshold or the number of effective bin particle samples in the constrained bin particle set is higher than the threshold, divide the bin particles;

[0039] When the number of effective bin particle samples in the constrained bin particle set is lower than the threshold, generate new user bin particles within a preset range near the user state estimate value with the user state estimate value at the current moment as the benchmark; use the initial state space of the anchor node as the new anchor node bin particles combined with the user bin particles to generate a new set of born bin particles;

[0040] In the original bin particle sequence sorted in descending order of weights, determine whether bin particle division or bin particles are performed in this resampling. If so, select and discard the corresponding number of bin particles from the bottom of the original bin particle sequence to obtain the resampled bin particle set.

[0041] To achieve the above object, on the other hand, the present invention proposes a joint estimation system for the base station antenna pointing and the user under discrete beam conditions, including:

[0042] A system model construction module for taking the base station as an anchor node to establish a system model for the user positioning problem under discrete beam and biased conditions of the base station position and orientation;

[0043] A box particle initialization module, which is used to take a multi-dimensional closed interval in the state space of the system model as box particles, initialize user box particles and anchor node box particles respectively, and describe the static anchor node state through an interval table and a probability table;

[0044] A box particle filtering module, which is used to obtain a set of constrained box particles based on the user box particles and anchor node box particles by the box particle filtering method, and calculate the weight corresponding to each box particle in the set to track the user dynamics;

[0045] A user and anchor node state update module, which is used to update the user state through the user box particles and corresponding weights in the set of constrained box particles, and count the coverage probability of the anchor node box particles in the set of constrained box particles for different intervals to iteratively update the probability table and the anchor node state;

[0046] A resampling module, which is used to judge whether the number of effective box particle samples in the set of constrained box particles and the average volume of user box particles meet the resampling criteria. If so, resample the user box particles and anchor node box particles respectively.

[0047] The method and system for jointly estimating the base station antenna pointing and the user under discrete beam conditions according to 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 set of box particles and weight allocation, reduce the demand for computing resources and improve the real-time performance of the system; by continuously iteratively updating the states of the user and the anchor node, gradually reduce the error and improve the overall positioning accuracy; by periodically resampling, avoid the phenomenon of particle degradation and ensure that the system always has sufficient diversity.

[0048] The additional aspects and advantages of the present invention will be partially given in the following description, partially become obvious from the following description, or be understood through the practice of the present invention. Description of the Drawings

[0049] The above and / or additional aspects and advantages of the present invention will become obvious and easy to understand from the following description of the embodiments in conjunction with the drawings, where:

[0050] Figure 1 is a flowchart of the method for jointly estimating the base station antenna pointing and the user under discrete beam conditions provided by the embodiments of the present invention;

[0051] Figure 2 is a schematic diagram of the anchor node position, the target motion trajectory sample and the target tracking effect of a single Monte Carlo experiment provided by the embodiments of the present invention;

[0052] Figure 3Schematic diagram of the root mean square error of user location estimation in 100 Monte Carlo experiments provided by an embodiment of the present invention;

[0053] Figure 4 Schematic diagram of the root mean square error of anchor node location estimation in 100 Monte Carlo experiments provided by an embodiment of the present invention;

[0054] Figure 5 Schematic diagram of the root mean square error of anchor node orientation estimation in 100 Monte Carlo experiments provided by an embodiment of the present invention;

[0055] Figure 6 Structural diagram of the base station antenna pointing and user joint estimation system under discrete beam conditions provided by an embodiment of the present invention. Detailed implementation manners

[0056] It should be noted that, without conflict, the embodiments in 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 drawings and in combination with the embodiments.

[0057] In order to enable those skilled in the art to better understand the solution of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0058] The method and system for jointly estimating the base station antenna pointing and the user under discrete beam conditions proposed according to the embodiments of the present invention will be described below with reference to the drawings.

[0059] Embodiment 1

[0060] Figure 1 is a flowchart of the method for jointly estimating the base station antenna pointing and the user under discrete beam conditions according to the embodiments of the present invention. As Figure 1 shown, the method includes:

[0061] S1, taking the base station as an anchor node to establish a system model for the user positioning problem under the conditions of discrete beams and the bias of the base station position and orientation.

[0062] Specifically, taking the anchor node position and orientation and the user position as the system states, establishing a user state transition model through the user motion equation, and taking the distance measurement and the angle measurement as the system observations. According to the discrete beam conditions, the angle measurement is further described as a uniformly quantized value of continuous values.

[0063] In an embodiment of the present invention, the user At The state at a moment is denoted as , the anchor node at The state at a moment is denoted as , where represents the horizontal and vertical coordinates in the two-dimensional plane, represents the orientation of the anchor node. In a system composed of users and anchor nodes, The states of all users at a moment are , The states of all anchor nodes at a moment are , where .

[0064] S2. Use the multi-dimensional 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 states through the interval table and the probability table.

[0065] Specifically, when initializing the user box particles, determine the user initial state space through the user initial state and the error bound, and evenly divide the initial state space to generate a number of user box particles; when initializing the anchor node box particles, determine the anchor node initial state space through the anchor node initial state and the error bound, and use the entire anchor node state space as the anchor node box particle. Combine the user box particles and the anchor node box particles to participate in the subsequent filtering process.

[0066] Evenly divide each dimension of the anchor node state space, and create an anchor node interval table to record the divided nodes; in addition, create an anchor node probability table to initialize the probabilities corresponding to the division results of each dimension.

[0067] In an embodiment of the present invention, use the multi-dimensional closed intervals in the state space as "box particles", and initialize the box particles describing the user states and the anchor node states respectively, including:

[0068] In the box particle filtering process including box particles, given that the user initial state is , the user initial error bound , then the user initial state space is . When initializing the user box particles, evenly divide the user initial state space to generate the user box particle set , thus completing the initialization of the user box particles. Given the anchor node initial state , the anchor node initial error bound , then the anchor node initial state space 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 .

[0069] 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:

[0070] 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 .

[0071] 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.

[0072] 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.

[0073] 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 .

[0074] 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 state transition equation can be obtained:

[0075] ,

[0076] ,

[0077] where describes the transformation relationship of the user's physical position from the moment to the moment, represents the sampling interval, represents the observed value of the user's speed at the moment, represents the observed error of the user's speed at the moment, represents the user state transition error. For any moment , satisfies , . where and are the covariance matrices corresponding to the observed speed error and state transition respectively, while and are the vectors composed of the standard deviations corresponding to the observed speed error and state transition error respectively.

[0078] Define the measurement value between the user and the anchor node at the moment, where represents the distance measurement, All the measurement values of the system are denoted as

[0079] ,

[0080] ,

[0081] ,

[0082] ,

[0083] where the function represents the method of calculating all the measurement values of the system at the current moment from all the user states and the anchor node states in the system. represents the index of the corresponding element in the state vector, similarly. and They are the distance measurement equation and the angle measurement equation respectively, which is the quantized angle measurement equation. represents the quantization interval. represents the observation error. At any time , satisfies . Among them represents the observation error covariance matrix, while represents the vector composed of the standard deviations of the observation errors.

[0084] At each sampling time, the system state is predicted through the user velocity observation value and the user motion equation, and the anchor node box particles remain unchanged. The two are combined to generate a set of predicted box particles; the discrete angle measurement is described in interval form, and the box particles are constrained through the distance measurement, angle measurement, and observation equation to generate a set of constrained box particles, and the weight corresponding to each box particle is calculated. Further, the user predicted box particle set and the anchor node predicted box particle set are generated from the user velocity observation value, the user motion equation, and the state transition error, including:

[0085] Implement the box particle filter prediction process based on the user box particle set and the user state transition equation to obtain the user predicted box particle set , where represents the calculation result of the user state prediction process. The specific method is:

[0086] ,

[0087] Since the anchor node is static and the anchor node box particle set remains unchanged during the prediction process, the anchor node predicted box particle set is expressed as . Among them represents the calculation result of the anchor node state prediction process.

[0088] Further, the predicted box particle set is substituted into the distance observation function and the angle observation function through the constraint transfer algorithm for iterative constraint to obtain the constrained box particle sets of the user and the anchor node, and the weight corresponding to each box particle is calculated, including:

[0089] First, calculate the measurement prediction value through the predicted box particle set and the measurement equation:

[0090] ,

[0091] Among them has the same meaning as mentioned in the measurement equation. For each measurement prediction value, calculate its difference from the actual measurement value at time The intersection is taken to obtain the measurement innovation:

[0092] ,

[0093] For the user at time and the anchor node the distance measurement between them, based on the distance observation noise and the corresponding standard deviation , take as the actual measurement interval to participate in the calculation of the measurement innovation.

[0094] After obtaining the measurement innovation, the measurement innovation is used to constrain the box particles through the constraint transfer algorithm, aiming to remove the redundant parts that do not meet the constraints in the box particles. Define the results obtained by constraining the box particles and in the user prediction box particle set and the anchor node prediction box particle set as and respectively. The box particle set obtained after constraint should satisfy:

[0095] ,

[0096] For example, for the user and the anchor node , from the distance measurement relationship, we can get:

[0097] ,

[0098] where , and represent the multi-dimensional closed intervals containing the state value at time , and . Using this relationship, the corresponding state value components in the prediction box particles and can be substituted into , and to calculate the value range of the coordinates of the user . Similarly, using the angle measurement relationship, the value range of the coordinates of the user can also be recalculated. After recalculating the coordinates of the user through all the measurement equations related to the user , the intersection of all the calculation results and is taken to obtain the constrained interval . Where Denote the user prediction box particles the user abscissa corresponding component. Denote the constraint result the user abscissa corresponding component.

[0099] Repeat the above process for each dimension of each box particle, and the constrained user box particle set and anchor node box particle set can be obtained .

[0100] The volume of the box particle is defined as the product of the interval lengths of each dimension, denoted as . The weight corresponding to each box particle is calculated by the ratio of the volume of the constrained box particle to the predicted box particle, specifically expressed as:

[0101] .

[0102] where denotes the weight corresponding to the box particle and at time

[0103] S4, Update the user state through the user box particles and corresponding weights in the constrained box particle set, and count the coverage probability of the anchor node box particles in the constrained box particle set for different intervals to iteratively update the probability table and the anchor node state.

[0104] Specifically, in the user constrained box particle set, for each box particle, multiply the midpoint of each dimension interval by the corresponding weight and calculate the cumulative sum to obtain the user state estimate value. In the anchor node constrained box particle set, count the overlapping intervals and their overlapping probabilities for each dimension of 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, and calculate the anchor node probability density table at time k through Bayesian recursion and normalize it. Multiply the midpoint of each interval in the anchor node interval table by the corresponding value in the anchor node probability density table and calculate the cumulative sum to obtain the anchor node state estimate value.

[0105] In an embodiment of the present invention, updating the user state by the user state and corresponding weights in the constrained box particle set includes:

[0106] In the calculation process of the user state, each box particle in the user constrained box particle set is replaced by taking the midpoint of each dimension interval, denoted as . The user state estimate value at time

[0107] .

[0108] In one embodiment of the present invention, the coverage probabilities of the anchor node box particles in the statistical constraint box particle set for different intervals are counted, and the anchor node probability table and the anchor node state are iteratively updated, including:

[0109] In the anchor node constraint box particle set, the number of interval overlaps of each dimension of the anchor node box particle is counted by the interval scanning line algorithm, and is normalized by the total number of box particles to the overlap probability. The overlapping intervals are arranged in descending order of the overlap probability, and the top intervals with the most overlap times are recorded in the table, and the corresponding overlap probabilities are recorded in the table. is a parameter representing the statistical accuracy of the overlapping intervals. For any , its meaning can be understood as: the probability that the true value of the anchor node is included in is .

[0110] It can be known from the Bayesian recursion formula that the posterior probability of the anchor node state satisfies:

[0111] ,

[0112] where represents all system measurement values from time 1 to k. can be represented by the overlapping interval table and the overlapping probability table . Since the anchor node is stationary, can be represented by the anchor node interval table at time and the anchor node probability table . The update process of the anchor node probability table is specifically expressed as:

[0113] ,

[0114] ,

[0115] where represents the result after the partition of the anchor node state space, represents the weight corresponding to this partition result at time . The function of

[0116] is to calculate the interval length. The probabilities corresponding to the non-intersecting parts of the anchor node probability table and the overlapping interval table are set to a very small value, aiming to prevent the samples from having large deviations at individual times and missing the true value of the anchor node state.After the update of the anchor node probability table is completed, the midpoints of each dimension interval are used to replace each interval in the anchor node interval table, denoted as . The estimated value of the anchor node state at the moment is expressed as:

[0117] .

[0118] S5. Determine whether the number of effective bin particle samples in the constrained bin particle set and the average volume of user bin particles meet the resampling criteria. If they meet, resample the user bin particles and the anchor node bin particles respectively.

[0119] Specifically, calculate the volume of the bin particles by multiplying the lengths of each dimension interval of the bin particles and calculate the average value, and set the threshold according to the initial volume of the bin particles. Calculate the number of effective samples in the constrained bin particle set through the weights of the bin particles, and set the upper and lower thresholds according to the total number of bin particles. When the average volume of the user bin particles is greater than the threshold or the number of effective samples in the constrained bin particle set is higher than the threshold, divide the bin particles; when the number of effective samples in the constrained bin particle set is lower than the threshold, generate new user bin particles within a certain range near the estimated value of the user state based on the estimated value of the user state at the current moment; use the initial state space of the anchor node as the combination of the newly generated anchor node bin particles and the user bin particles to generate a new set of bin particles. In the sequence of bin particles sorted in descending order of weights, according to whether bin particle division and bin particle generation are performed in this resampling, select and discard the corresponding number of bin particles from the bottom of the sequence to keep the total number of bin particles unchanged before and after resampling.

[0120] In an embodiment of the present invention, determining whether the number of effective samples in the constrained bin particle set and the average volume of user bin particles meet the resampling criteria includes:

[0121] Calculate the number of effective samples in the constrained bin particle set through the weights of the bin particles , and the calculation method is:

[0122] ,

[0123] Set the upper and lower bounds of the number of effective samples in the constrained bin particle set according to the total number of bin particles , , which are respectively used to initiate resampling when the bin particle distribution is too concentrated and when the bin particles degenerate.

[0124] Calculate the average volume of the user bin particles :

[0125] ,

[0126] According to the initialized volume of the user bin particles Set the average volume upper limit , which is used to start resampling when the volume of the bin particles is too large.

[0127] In an embodiment of the present invention, resampling is respectively performed on the bin particles describing the user state and the bin particles describing the anchor node state, including:

[0128] Set the bin particle division ratio and the bin particle newborn ratio .

[0129] When or , select ratio of bin particles for division: sort the user bin particles in descending order of weight, select ratio of bin particles from the top of the sequence, and perform uniform division on a random dimension of them. After division, ratio of bin particles generate ratio of bin particles. According to the anchor node probability table, discard the parts at both ends in the anchor node interval table , and use the remaining part as the anchor node bin particles to combine with each divided user bin particle. After the bin particle division is completed, select and discard ratio of bin particles from the bottom in the original bin particle sequence sorted in descending order of weight to obtain the divided bin particle set.

[0130] When , according to the user state estimation value at time , select a certain range of the state space around it for uniform division to generate new user bin particles. Use the initial state space of the anchor node as the newborn anchor node bin particles to combine with the user bin particles to generate the newborn bin particle set. The ratio of the newborn bin particles to the total number of bin particles is denoted as . After obtaining the newborn bin particle set, select and discard ratio of bin particles from the bottom in the original bin particle sequence sorted in descending order of weight; if bin particle division is performed in this resampling, additionally select and discard ratio of bin particles from the bottom of the original bin particle sequence to obtain the resampled bin particle set.

[0131] After resampling, the total number of bin particles remains unchanged. After resampling, normalize the weight of each bin particle to:

[0132] .

[0133] Embodiment 2

[0134] The present invention will be further described below in conjunction with MATLAB simulation.

[0135] Assume that there are 3 anchor nodes in a two-dimensional plane, and 2 users are tracked through distance measurement and quantized angle measurement. The movement of the users in the plane follows a constant turn rate model. The method for jointly estimating the base station antenna pointing and the users under the discrete beam condition of the present invention may further include the following steps:

[0136] S201, taking the base station as an anchor node, establish a system model for the user positioning problem under the conditions of discrete beam and the bias of the position and orientation of the base station.

[0137] The state of the anchor node at time is expressed as , where represents the horizontal and vertical coordinates of the anchor node, and represents the orientation of the anchor node. The state of the user at time is expressed as , where represents the horizontal and vertical coordinates of the user. Let represent the states of all anchor nodes and the states of all users.

[0138] The initial states and actual states of the anchor nodes and users are shown in Table 1.

[0139] Table 1

[0140]

[0141] The initial state error bound of the anchor node is , and the initial state error bound of the user is . That is, the true states of the anchor node and the user satisfy , .

[0142] S202, initialize the user and anchor node box particles respectively, and describe the static anchor node states through probability tables.

[0143] Specifically, initialize the user box particles, evenly divide each dimension of the user initial state space into 6 parts, and generate user box particles. Take the anchor node initial state space as the combination of the anchor node box particles and each user box particle to generate an initial box particle set. The weight of each box particle is initialized to .

[0144] Divide each dimension of the anchor node state space into parts, and establish an anchor node interval table and an anchor node probability table 。And initialize each element in the anchor node probability table to 。

[0145] S203. Implement the user dynamic tracking process with the box particle filter framework.

[0146] 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 user state prediction process of the box particle filter. In the measurement equation, take the user and the base station The standard deviation of the observation noise between them is 。In the angle measurement quantization equation, take the quantization interval 。Implement the measurement update and box particle constraint process of the box particle filter to obtain the filtered box particle set and the corresponding weights.

[0147] S204. Update the user state from the user box particles and the corresponding weights in the constrained box particle set; statistically calculate the coverage probability of the anchor node box particles in the constrained box particle set for different intervals, and iteratively update the anchor node probability table and the anchor node state accordingly.

[0148] Specifically, estimate the user state from the user box particles after box particle filtering and the corresponding weights. In the anchor node state estimation, statistically calculate the number of interval overlaps through the interval scan line algorithm, and record the intervals before the number of overlaps for updating the anchor node probability table. After completing the update of the anchor node probability table, calculate the anchor node state estimation value.

[0149] S205. Determine whether the number of effective samples in the constrained box particle set and the average volume of the user box particles meet the resampling criteria. If so, resample the box particles describing the user state and the box particles describing the anchor node state respectively.

[0150] Specifically, set the upper and lower bounds of the number of effective samples in the constrained box particle set to be , 。Set the upper bound of the average volume of the user box particles to be 。Sort the user box particles in descending order according to the weights.

[0151] When the number of effective samples in the constrained box particle set and the average volume of the user box particles meet ,select box particles from the top of the sequence for partitioning, and discard box particles from the bottom of the sequence.

[0152] When it is time, according to the estimated value of the user state at the current moment select a range of box particles, evenly divide each dimension into three parts to generate new user box particles. And use the initial state space of the anchor node as the new anchor node box particles to combine with each user box particle. The proportion of the newly born box particles in the total number of box particles is . Finally, additionally discard user box particles from the bottom of the sequence. Before and after resampling, the total number of box particles remains unchanged.

[0153] After resampling, normalize the weight of each box particle to .

[0154] Conduct 100 Monte Carlo experiments on the trajectory containing 100 samples according to the method provided by the present invention. For the target tracking and anchor node estimation effects, refer to the simulation results in Figures 2 - 5 . Figure 2 is a schematic diagram of the anchor node position, target motion trajectory, and one Monte Carlo target tracking effect provided by the embodiment of the present invention. Among them, the solid line represents the actual trajectory of the user's movement, and the dotted line represents the trajectory obtained through the target tracking algorithm. To avoid the influence of trajectory overlap on the intuitiveness of the target tracking effect, Figure 2 only the first 35 samples are taken for the trajectory in Figure 3 , Figure 4 , Figure 5 are respectively schematic diagrams of the root mean square errors of the 100 Monte Carlo user position estimation, anchor node position estimation, and anchor node orientation estimation provided by the embodiment of the present invention.

[0155] According to the method for jointly estimating the base station antenna pointing and the user under discrete beam conditions in the embodiment of the present invention, by combining the deviation modeling of the base station position and orientation, the box particle filtering method, and an effective resampling strategy, the base station position and orientation errors can be compensated, and the user positioning accuracy can be improved. It not only improves the positioning accuracy but also enhances the robustness and adaptability of the system.

[0156] To implement the above embodiments, as Figure 6 shown, a system 10 for jointly estimating the base station antenna pointing and the user under discrete beam conditions is further provided in this embodiment, including:

[0157] A system model construction module 100, which is used to take the base station as an anchor node to establish a system model for the user positioning problem under discrete beam and the biased conditions of the base station position and orientation;

[0158] The bin particle initialization module 200 is used to initialize the multi-dimensional closed intervals in the state space of the system model as bin particles, initialize the user bin particles and the anchor node bin particles respectively, and describe the static anchor node states through the interval table and the probability table;

[0159] The bin particle filtering module 300 is used to obtain a set of constrained bin particles based on the user bin particles and the anchor node bin particles by the bin particle filtering method, and calculate the weights corresponding to each bin particle in the set to track the user dynamics;

[0160] The user and anchor node state update module 400 is used to update the user state through the user bin particles and the corresponding weights in the set of constrained bin particles, and count the coverage probabilities of the anchor node bin particles in the set of constrained bin particles for different intervals to iteratively update the probability table and the anchor node states;

[0161] The resampling module 500 is used to determine whether the number of effective bin particle samples in the set of constrained bin particles and the average volume of the user bin particles meet the resampling criteria. If so, resample the user bin particles and the anchor node bin particles respectively.

[0162] According to the base station antenna pointing and user joint estimation system under the discrete beam condition of the embodiment of the present invention, by combining the deviation modeling of the base station position and orientation, the bin particle filtering method, and an effective resampling strategy, the base station position and orientation errors can be compensated, and the user positioning accuracy can be improved. It not only improves the positioning accuracy but also enhances the robustness and adaptability of the system.

[0163] In the description of this specification, the description with reference to terms such as "an embodiment", "some embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials, or characteristics described in connection 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 can be combined in any one or more embodiments or examples in a suitable manner. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0164] In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the quantity of the indicated technical features. Thus, the features defined with "first" and "second" can explicitly or implicitly include at least one of the features. In the description of the present invention, "a plurality" means at least two, such as two, three, etc., unless otherwise specifically defined.

Claims

1. A method for jointly estimating the pointing of a base station antenna and a user under discrete beam conditions, characterized in that Including: Taking the base station as an anchor node to establish a system model for the user positioning problem under the conditions of discrete beams and biased base station position and orientation; Regarding the multi-dimensional closed interval in the state space of the system model as box particles, initializing user box particles and anchor node box particles respectively, and describing the static anchor node state through an interval table and a probability table; Obtaining a set of constrained box particles based on the user box particles and the anchor node box particles by means of box particle filtering, and calculating the weight corresponding to each box particle in the set to track the user dynamics; Updating the user state through the user box particles and the corresponding weights in the set of constrained box particles, and statistically calculating the coverage probability of the anchor node box particles in the set of constrained box particles for different intervals to iteratively update the probability table and the anchor node state; Judging whether the number of effective box particle samples and the average volume of user box particles in the set of constrained box particles meet the resampling criteria. If so, resample the user box particles and the anchor node box particles respectively; Obtaining a set of constrained box particles based on the user box particles and the anchor node box particles by means of box particle filtering, and calculating the weight corresponding to each box particle in the set to track the user dynamics, including: Generating a set of predicted box particles through the user velocity observation value, the user motion equation, and the state transfer error; wherein, the set of predicted box particles includes a user predicted box particle set and an anchor node predicted box particle set; Inputting the set of predicted box particles into the distance observation function and the angle observation function through the constraint transfer algorithm for iterative constraint to obtain a set of constrained box particles for the user and the anchor node, and calculating the weight corresponding to each box particle; Updating the user state through the user box particles and the corresponding weights in the set of constrained box particles, and statistically calculating the coverage probability of the anchor node box particles in the set of constrained box particles for different intervals to iteratively update the probability table and the anchor node state, including: For each box particle in the set of user constrained box particles, multiplying the midpoint of each dimension interval by the corresponding weight and calculating the cumulative sum to obtain the user state estimation value; In the set of anchor node constrained box particles, statistically calculating the overlapping intervals and overlapping probabilities of each dimension of the anchor node box particles, taking the obtained overlapping intervals and overlapping probabilities as conditional probabilities, taking the anchor node probability table at the k - 1 moment as the prior probability, and calculating the anchor node probability density table at the k moment through Bayesian recursion; Multiplying the midpoint of each interval in the anchor node interval table by the corresponding value in the anchor node probability density table and calculating the cumulative sum to obtain the anchor node state estimation value.

2. The method according to claim 1, wherein Taking the base station as an anchor node to establish a system model for the user positioning problem under the conditions of discrete beams and biased base station position and orientation, including: Regarding the anchor node position and orientation and the user position as the system state, and establishing a user state transfer model through the user motion equation; Regarding the distance measurement and the angle measurement as the system observation values, and describing the angle measurement as a uniformly quantized value of a continuous value according to the discrete beam condition.

3. The method according to claim 2, wherein Initializing user box particles and anchor node box particles respectively, and describing the static anchor node state through an interval table and a probability table, including: When initializing user box particles, determine the user initial state space based on the user initial state and the user initial error bound, and uniformly divide the user initial state space to generate user box particles; When initializing anchor node box particles, determine the anchor node initial state space based on the anchor node initial state and the anchor node initial error bound, and use the entire anchor node initial state space as the anchor node box particles; Combine the user box particles and the anchor node box particles to obtain the initialized box particle set; Uniformly divide each dimension of the anchor node initial state space, create an anchor node interval table to record the division nodes, and create an anchor node probability table to record the probabilities corresponding to the division results of each dimension.

4. The method according to claim 3, wherein Generate a predicted box particle set through the user speed observation value, the user motion equation, and the state transition error, including: Perform box particle filter prediction based on the user box particle set and the user state transition equation to obtain the user predicted box particle set; Based on the static anchor nodes, the anchor node box particle set remains unchanged during the prediction process.

5. The method according to claim 1, wherein Input the predicted box particle set into the distance observation function and the angle observation function through the constraint transfer algorithm for iterative constraint to obtain the constrained box particle set of the user and the anchor nodes, and calculate the weight corresponding to each box particle, including: Calculate the measurement prediction value through the predicted box particle set and the measurement equation; Calculate the intersection of the measured prediction value and the actual measured value at the moment to obtain measurement information; Use the measurement information to constrain the box particles in the predicted box particle set through the constraint transfer algorithm to obtain the user constrained box particle set and the anchor node constrained box particle set; Define the box particle volume as the product of the interval lengths of each dimension, and calculate the weight corresponding to each box particle through the ratio of the volume of the constrained box particle to the volume of the predicted box particle.

6. The method according to claim 1, wherein Judge 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, resample the user box particles and the anchor node box particles respectively, including: Calculate the box particle volume by multiplying the interval lengths of each dimension of the box particle and calculate the average value. Set a threshold according to the initial volume of the box particle. Calculate the number of valid box particle samples in the constrained box particle set through the box particle weights, and set upper and lower thresholds according to the total number of box particles. When the average volume of the user box particles is greater than the threshold or the number of valid box particle samples in the constrained box particle set is higher than the threshold, divide the box particles; When the number of valid box particle samples in the constrained box particle set is lower than the threshold, generate new user box particles within a preset range near the user state estimation value at the current moment as the benchmark; use the anchor node initial state space as the new anchor node box particles to combine with the user box particles to generate a new-born box particle set; In the original box particle sequence sorted in descending order of weights, judge whether box particle division or box particles are performed in this resampling. If so, select and discard the corresponding number of box particles from the bottom of the original box particle sequence to obtain the resampled box particle set.

7. A base station antenna pointing and user joint estimation system under discrete beam conditions adopting the method according to claim 1, characterized in that, Including: A system model construction module for using the base station as an anchor node to establish a system model for the user positioning problem under the conditions of discrete beams and biased positions and orientations of the base station; The box particle initialization module is used to take the multi-dimensional 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 states through the interval table and the probability table; The box particle filtering module is used to obtain a set of constrained box particles based on the user box particles and the anchor node box particles by the box particle filtering method, and calculate the weights corresponding to each box particle in the set to track the user dynamics; The user and anchor node state update module is used to update the user state through the user box particles and the corresponding weights in the set of constrained box particles, and count the coverage probabilities of the anchor node box particles in the set of constrained box particles for different intervals to iteratively update the probability table and the anchor node states; The resampling module is used to determine whether the number of effective box particle samples and the average volume of the user box particles in the set of constrained box particles meet the resampling criteria. If so, resample the user box particles and the anchor node box particles respectively.

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