Moving target motion parameter estimation method and device, electronic equipment and medium

Abstract

The invention relates to a method and device for estimating motion parameters of a moving target, electronic equipment and a medium, and belongs to the technical field of wireless communication signal processing.The method comprises the steps that an MISO system receiving signal model between the moving target and an antenna array is built; the method comprises the following steps: decomposing an MISO system receiving signal model into a plurality of factor nodes, constructing a factor graph based on the plurality of factor nodes, and dividing the factor graph into a plurality of function modules; and performing parameter estimation on a functional module on the factor graph by applying a hybrid message passing algorithm combined with a BP-EP-MF rule to obtain the estimated distance, angle, radial speed and tangential speed from the moving target to the antenna array. According to the method, the MISO system receiving signal model is decomposed into a plurality of factors for mixed message transmission, so that the calculation complexity of the MISO system receiving signal model is reduced.

Description

Technical Field

[0001] This invention relates to the field of wireless communication signal processing technology, and in particular to a method, apparatus, electronic device, and medium for estimating motion parameters of a moving target. Background Technology

[0002] With the deepening of global energy transition and smart grid construction, the power system's demand for the reliability, real-time performance and flexibility of communication networks has increased significantly. Among them, short-distance (1-50km) communication scenarios have become the core pain point area in the construction of power communication networks because they involve key businesses such as new energy grid connection monitoring and transmission line monitoring.

[0003] Traditional wired and conventional wireless communication technologies have significant limitations in terms of adaptability to complex terrain, bandwidth support, and security. Microwave communication, on the other hand, has become a key technology for solving the pain points of short-range power communication due to its advantages such as flexible deployment and good bandwidth adaptability.

[0004] The nonlinear phase and high coupling effect between parameters introduced by the near-field spherical wave model significantly increase the computational complexity of the model. Summary of the Invention

[0005] In view of this, it is necessary to provide a method, device, electronic device and medium for estimating the motion parameters of a moving target, so as to solve the problem of high computational complexity of near-field spherical wave models.

[0006] To address the aforementioned problems, in a first aspect, the present invention provides a method for estimating motion parameters of a moving target, comprising: Construct a model of the received signal of the MISO system between the moving target and the antenna array; The MISO system received signal model is decomposed into multiple factor nodes, and a factor graph is constructed based on the multiple factor nodes. The factor graph is then divided into several functional modules. The hybrid message passing algorithm with joint BP-EP-MF rules is applied to the functional modules on the factor graph to estimate the parameters, and the estimated distance, angle, radial velocity and tangential velocity between the moving target and the antenna array are obtained.

[0007] In one possible implementation, the step of decomposing the MISO system received signal model into multiple factor nodes and constructing a factor graph based on the multiple factor nodes includes: Based on Bayes' theorem, the received signal model of the MISO system is transformed into the observation factor; The observed factors are decomposed into multiple factor nodes, and a factor graph is constructed based on these multiple factor nodes.

[0008] In one possible implementation, the squares in the factor graph represent function nodes, and the circles in the factor graph represent variable nodes.

[0009] In one possible implementation, the plurality of functional modules include: The system includes a noise accuracy estimation module, a channel estimation module for propagation paths between different antennas, a propagation path decomposition module for moving targets to antennas, a radial velocity and radial distance decomposition module for moving targets to a single antenna, and a distance and angle decomposition module for moving targets to the origin antenna.

[0010] In one possible implementation, the parameter estimation is performed by applying a hybrid message-passing algorithm with joint BP-EP-MF rules to the functional modules on the factor graph, including: In the noise accuracy estimation module, the BP rule is used for message passing; in the channel estimation module for propagation paths between different antennas, the EP propagation rule is used for message passing; in the moving target to antenna propagation path decomposition module, the BP-MP rule is used for message passing; in the moving target to a single antenna radial velocity and radial distance decomposition module, the BP-MP rule is used for message passing; and in the moving target to the origin antenna distance and angle decomposition module, the EP propagation rule is used for message passing. Message passing stops when a preset iteration condition is reached, and the parameter value is determined.

[0011] In one possible implementation, the received signal expression of the MISO system received signal model is:

[0012] In the formula, The parameter represents the channel gain, t represents the transmission time, and the parameter is... Indicates the first The distance between each base station antenna and the target Indicates the target is in the connection. Velocity components along the straight line between the antenna and the target, noise This represents additive white Gaussian noise, and , ,in and The target distance and angle are measured from the first antenna of the uniform linear array. and These are the radial and lateral velocities of a moving target relative to the same antenna.

[0013] In one possible implementation, Taylor expansion is used to derive the phase variable expression during the message passing process of the distance and angle decomposition module from the moving target to the origin antenna.

[0014] Secondly, the present invention also provides an apparatus for estimating motion parameters of a moving target, comprising: The model building module is used to build a model of the received signal of the MISO system between a moving target and an antenna array. The factor graph construction module is used to decompose the received signal model of the MISO system into multiple factor nodes, construct a factor graph based on the multiple factor nodes, and divide the factor graph into several functional modules. The parameter estimation module is used to perform parameter estimation on the functional modules on the factor graph by applying a hybrid message passing algorithm with joint BP-EP-MF rules, and obtain the estimated distance, angle, radial velocity and tangential velocity between the moving target and the antenna array.

[0015] Thirdly, the present invention also provides an electronic device, including a memory and a processor, wherein, The memory is used to store programs; The processor, coupled to the memory, is used to execute the program stored in the memory to implement the steps in the method for estimating motion parameters of a moving target as described in any of the above implementations.

[0016] Fourthly, the present invention also provides a computer-readable storage medium for storing a computer-readable program or instructions, which, when executed by a processor, can implement the steps in the method for estimating motion parameters of a moving target as described in any of the above implementations.

[0017] The beneficial effects of this invention are as follows: This invention provides a method for estimating motion parameters of a moving target. First, a MISO system received signal model between the moving target and the antenna array is constructed. Then, the MISO system received signal model is decomposed into multiple factor nodes, and a factor graph is constructed based on these factor nodes. This factor graph is further divided into several functional modules. Finally, a hybrid message passing algorithm combining BP-EP-MF rules is applied to these functional modules for parameter estimation. Different functional modules correspond to different message passing rules, thereby improving computational efficiency and obtaining the estimated distance, angle, radial velocity, and tangential velocity between the moving target and the antenna array. This invention reduces the computational complexity of the MISO system received signal model by decomposing it into multiple factors for message passing, thus improving the efficiency of parameter estimation. Attached Figure Description

[0018] Figure 1 A flowchart illustrating an embodiment of a method for estimating motion parameters of a moving target provided by the present invention; Figure 2A detailed flowchart of an embodiment of a method for estimating motion parameters of a moving target provided by the present invention; Figure 3 A schematic diagram of linear array signal transmission and reception in one embodiment of a method for estimating motion parameters of a moving target provided by the present invention; Figure 4 A schematic diagram of model velocity in one embodiment of the method for estimating motion parameters of a moving target provided by the present invention; Figure 5 A schematic diagram of a factor graph in one embodiment of a method for estimating motion parameters of a moving target provided by the present invention; Figure 6 A comparison diagram of four algorithms for an embodiment of the method for estimating motion parameters of a moving target provided by the present invention; Figure 7 A comparison diagram of four algorithms after adding antennas to an embodiment of the method for estimating motion parameters of a moving target provided by the present invention; Figure 8 An iterative graph of parameter r in an embodiment of a method for estimating motion parameters of a moving target provided by the present invention; Figure 9 An iterative graph of parameter θ in an embodiment of a method for estimating motion parameters of a moving target provided by the present invention; Figure 10 An embodiment of the method for estimating motion parameters of a moving target provided by the present invention includes parameters in the method. The iterative graph; Figure 11 A schematic flowchart of an embodiment of the motion parameter estimation device for a moving target provided by the present invention; Figure 12 A schematic diagram of an embodiment of the electronic device provided by the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0020] In the description of the embodiments of the present invention, unless otherwise stated, "multiple" means two or more. "And / or" describes the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone.

[0021] The terms "first," "second," etc., used in the embodiments of this invention are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a technical feature defined with "first" or "second" may explicitly or implicitly include at least one of that feature.

[0022] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0023] Before demonstrating the embodiments, the following terms will be explained.

[0024] MISO (Multiple Input Single Output) systems are signal processing systems widely used in wireless communication, radar, sonar, and other fields. Their core characteristic is that the transmitting end has multiple input signals, while the receiving end has only one output signal. Multi-antenna technology is used to improve data transmission efficiency and system reliability.

[0025] This invention provides a method, apparatus, electronic device, and medium for estimating motion parameters of a moving target, which will be described below.

[0026] Figure 1 A schematic flowchart of an embodiment of the method for estimating motion parameters of a moving target provided by the present invention is shown below. Figure 1 As shown, the methods for estimating the motion parameters of a moving target include: S101. Construct a MISO system received signal model between the moving target and the antenna array; It should be noted that the MISO system has multiple input signals at the transmitter and only one output signal at the receiver.

[0027] S102. Decompose the MISO system received signal model into multiple factor nodes, construct a factor graph based on the multiple factor nodes, and divide the factor graph into several functional modules. S103. Apply the hybrid message passing algorithm with joint BP-EP-MF rules to the functional modules on the factor graph to perform parameter estimation, and obtain the estimated distance, angle, radial velocity and tangential velocity between the moving target and the antenna array.

[0028] It should be noted that the hybrid message passing algorithm combining BP-EP-MF rules can achieve dynamic and static parameter estimation, transforming complex computational steps into a progressive iterative problem solution.

[0029] Compared with existing technologies, this embodiment provides a method for estimating motion parameters of a moving target. First, it constructs a MISO system received signal model between the moving target and the antenna array. Then, it decomposes the MISO system received signal model into multiple factor nodes and constructs a factor graph based on these nodes. The factor graph is further divided into several functional modules. Finally, a hybrid message passing algorithm combining BP-EP-MF rules is applied to these functional modules for parameter estimation. Different functional modules correspond to different message passing rules, thereby improving computational efficiency. The estimated distance, angle, radial velocity, and tangential velocity between the moving target and the antenna array are obtained. This invention reduces the computational complexity of the MISO system received signal model by decomposing it into multiple factors for message passing, thus improving the efficiency of parameter estimation.

[0030] It should be noted that the application scenario of this invention is short-range (1-50km) communication scenarios.

[0031] In specific embodiments of the present invention, such as Figure 2 As shown, on the left is a vertically arranged column of triangular symbols, representing an antenna array. These antennas are evenly spaced along a straight line. Distribution. From bottom to top, the positions are: ,in This is an integer used to indicate the antenna's position number. (See diagram:)

[0032] In the formula, This indicates the distance from the moving target to the first antenna. Indicates the movement target to the first The distance between the first and second antennas is θ, which represents the angle between the direction of the line connecting the target to the first antenna and the direction of the antenna array.

[0033] In specific embodiments of the present invention, such as Figure 2 As shown, multiple velocity vectors are displayed, among which... and Representing the radial velocity components, corresponding to the first antenna (reference point) and the second antenna (reference point), respectively. Radial velocity at the root antenna and This represents the corresponding tangential velocity component. Velocity and Representing the distance from the target to the reference point and the first... The radial velocity vector at the root antenna can be decomposed into: , .in .

[0034] Let q denote the subscript of the transmitting antenna, then . Represents the time of symbol transmission. The subscript representing the receiving antenna. This represents the total number of antennas.

[0035] From the radial velocity of the target with respect to the line connecting the q-th and m-th antennas, we can solve for the radial and tangential velocities of the target with respect to the 1st antenna (reference point). This can be obtained from the following formula:

[0036] By obtaining the estimated radial velocity of the target to each antenna line and the estimated radial and tangential velocities of the target relative to the first antenna, the arithmetic mean of the estimated values ​​for each path can be obtained, as shown in the following formula:

[0037] Then the target at time t reaches the first The radial distance of each antenna is calculated as follows: .

[0038] The linear array here uses full-duplex communication mode. To simplify the process, we will use the MISO signal transmission system as an example, that is, by The signal is transmitted by the m-th antenna, reflected by the moving target, and received by the m-th antenna. In some embodiments of the present invention, the received signal expression of the MISO system signal receiving model is:

[0039] In the formula, This represents the received signal of the m-th antenna. This represents the modulated transmitted signal, where Indicates the number of antennas, where This represents white noise during transmission.

[0040] In some embodiments of the present invention, the step of decomposing the MISO system received signal model into multiple factor nodes and constructing a factor graph based on the multiple factor nodes includes: Based on Bayes' theorem, the received signal model of the MISO system is transformed into the observation factor; The observed factors are decomposed into multiple factor nodes, and a factor graph is constructed based on these multiple factor nodes.

[0041] In some embodiments of the present invention, the squares in the factor graph represent function nodes, and the circles in the factor graph represent variable nodes.

[0042] In some embodiments of the present invention, the plurality of functional modules include: The system includes a noise accuracy estimation module, a channel estimation module for propagation paths between different antennas, a propagation path decomposition module for moving targets to antennas, a radial velocity and radial distance decomposition module for moving targets to a single antenna, and a distance and angle decomposition module for moving targets to the origin antenna.

[0043] In a specific embodiment of the present invention, the signal transmission of the MISO system, i.e., by... The signal is transmitted by the m-th antenna, and after passing the moving target, it is received by the m-th antenna. To simplify the expression, let's denote... , , , .here Indicates the subscript of the transmitting antenna. In this system, let Let represent the emission vector, where It is the first One transmitting antenna The radiated digital modulation symbol. The transmitted signal employs Orthogonal Frequency Division Multiplexing (OFDM) technology, which exhibits excellent performance in resisting multipath interference, adapting to channel variations, and supporting high data rates. After classical cyclic prefix removal and Fast Fourier Transform (FFT) processing, the baseband echo signal received at the m-th antenna and reflected by the moving target can be expressed as follows:

[0044] In the formula, The parameter represents the channel gain, t represents the transmission time, and the parameter is... Indicates the first The distance between each base station antenna and the target Indicates the target is in the connection. Velocity components along the straight line between the antenna and the target, noise This represents additive white Gaussian noise, and , ,in and The target distance and angle are measured from the first antenna of the uniform linear array. and These are the radial and lateral velocities of a moving target relative to the same antenna.

[0045] Based on the OFDM modulation method, signal propagation delay, and the Doppler effect caused by target motion, the expression for the received signal and the observation factor are used. It is decomposed into several smaller factors. The intermediate variables and operational rules introduced are specifically defined in Table 1.

[0046]

[0047]

[0048] Table 1: Definitions of Variables and Functions

[0049] Figure 5 The factorization in the formula is represented by a factor graph, where squares and circles represent function nodes and variable nodes, respectively.

[0050] In some embodiments of the present invention, the application of a hybrid message-passing algorithm with joint BP-EP-MF rules to the functional modules on the factor graph for parameter estimation includes: In the noise accuracy estimation module, the BP rule is used for message passing; in the channel estimation module for propagation paths between different antennas, the EP propagation rule is used for message passing; in the moving target to antenna propagation path decomposition module, the BP-MP rule is used for message passing; in the moving target to a single antenna radial velocity and radial distance decomposition module, the BP-MP rule is used for message passing; and in the moving target to the origin antenna distance and angle decomposition module, the EP propagation rule is used for message passing. Message passing stops when a preset iteration condition is reached, and the parameter value is determined.

[0051] In some embodiments of the present invention, Taylor expansion is used to derive the phase variable expression during the message passing process of the distance and angle decomposition module from the moving target to the origin antenna. By deriving the phase variable expression through Taylor expansion, a dynamic linear approximation is introduced to resolve the high coupling effect between variables, thereby improving the accuracy and efficiency of the calculation.

[0052] In a specific embodiment of the present invention, problems such as summation, product, and variance estimation among intermediate variables are solved by flexibly selecting the corresponding factor nodes as BP, EP, and MF message update rule nodes. This represents the set of all factor nodes, which is then divided into two disjoint subsets.

[0053] and Then the variable node .

[0054] Divide into three disjoint subsets: , , .

[0055] This message passing algorithm, which combines BP-EP-MF rules, offers greater flexibility and optimizes the performance for solving complex computational problems.

[0056] To facilitate subsequent discussion, Figure 5 The factor graph is divided into five functional modules: noise accuracy estimation, channel estimation of propagation paths between different antennas, propagation path decomposition from the target to two antennas, radial velocity and radial distance decomposition from the target to a single antenna, and distance and angle decomposition from the target to the origin antenna.

[0057] These functional modules are Figure 5 The dashed boxes in the figure, labeled (i)-(v), represent message passing algorithms that operate iteratively. Each iteration includes the forward message passing process and the backward message passing process shown in the figure. Indicates from node To the node The message being transmitted is The function. For Gaussian messages. Its mean and variance The arrow at the top indicates the direction of message passing. Additionally, using... Representing variables The prior probabilities. Note that if forward computation requires backward messages, the relevant messages from the previous iteration are used by default.

[0058] The factor graph is divided into five modules. This section details the calculation of forward and backward messages on each edge.

[0059] Factor graph module calculation Message Passing Module (I)

[0060] According to the belief propagation (BP) rule, the input message is: It can be calculated as:

[0061]

[0062] Using the message obtained from the previous iteration (This will be updated in equation (18) later), regarding The confidence level is expressed as:

[0063]

[0064]

[0065] Given information as well as The prior accuracy of the message noise follows a gamma distribution, with an expected value of 0 and a variance of . .

[0066] obey

[0067] but The mean can be calculated when there are With one receiving antenna and one user, the situation is as follows:

[0068] When there is a single receiving antenna, the case for one user is as follows:

[0069] According to the mean field (MF) rule, the message It can be represented as

[0070] B. Message Passing Module (II) Assuming the message:

[0071]

[0072]

[0073]

[0074]

[0075] The input message can then be calculated as:

[0076]

[0077]

[0078]

[0079] C. Message Passing Module (III)

[0080] The solution is obtained using the hybrid message passing rule BP-MF method, assuming that the given information is known.

[0081]

[0082] From the previous update

[0083]

[0084]

[0085] MF-like rules for handling variables Received news :

[0086]

[0087]

[0088] The message can be known using BP rules. It has a Gaussian mixture form and is calculated as follows:

[0089] From node To the node The message can be updated as follows:

[0090]

[0091] From node To the node The message can be updated as follows:

[0092]

[0093] Message Passing Module (IV)

[0094] The BP-MF hybrid message passing rules, BP-like rules on variables integral.

[0095]

[0096] MF-like rule processing variables Received news This is because it involves integration over complex exponential expressions.

[0097]

[0098]

[0099]

[0100]

[0101]

[0102]

[0103]

[0104]

[0105] Similarly, it can be calculated

[0106]

[0107]

[0108]

[0109]

[0110] The following distribution is obtained from the expression of the variables after Taylor expansion:

[0111]

[0112] from arrive The transmitted message can be updated as follows:

[0113]

[0114] Message Passing Module (V) from arrive The message can be calculated as

[0115] where

[0116] Then, The belief can be expressed as:

[0117] Similarly, we can obtain from arrive Message passing:

[0118]

[0119] Then, the channel components can be obtained. The beliefs are as follows:

[0120]

[0121] This allows for updating from arrive Message passing:

[0122]

[0123] Among them, using These represent the Hermitian transpose, transpose, and conjugate of the matrix and vector, respectively. Representation matrix The Middle Line number Column elements. Symbol This represents taking the absolute value of each element. (Regarding the probability density function) The expectation operator is used express. The mean is variance is The complex Gaussian probability density function, where ⊙ represents the element-wise multiplication at corresponding positions.

[0124] The message passing algorithm is summarized in Algorithms 1 and 2. Iteration can terminate when the preset maximum number of iterations is reached or the difference between two consecutive iteration estimates is less than a threshold.

[0125]

[0126] In summary, this embodiment achieves parameter estimation of distance, angle, radial velocity, and tangential velocity simultaneously without pre-setting the target's trajectory. A factor graph of the parameter estimation model for moving target perception is established, and reasonable message passing modules and factor nodes are selected. Effective solutions are provided for computational difficulties arising from variables located in the exponential part and high coupling between variables in the estimation algorithm. Taylor expansion and dynamic linear approximation techniques are employed to improve the accuracy and efficiency of the calculation. Practical results show that this method simplifies complex expressions and calculation processes, achieves rapid estimation, and exhibits good performance.

[0127] To better illustrate the technical effects of the method in this embodiment, simulation experiments are conducted below.

[0128] The experimental design process is shown in the figure below. First, a simulated transmission signal is generated, which is then modulated. Parameters are generated by simulating the motion of an object. Next, the receiving antenna, in conjunction with motion parameters, completes the path transmission and noise addition process, demodulating the analog received signal to obtain the final signal. Finally, the HMP algorithm is used for factorization. Based on the formula derivation of the factor graph module, the motion parameters are calculated iteratively according to the stopping conditions. This enables parameter estimation.

[0129] Various simulation results are provided to verify the performance of the proposed algorithm. The system settings are as follows: a uniform linear antenna array with half-wavelength spacing and a center frequency fc = 70GHz is used. We assume a distance range of [5m, dmax] and an angle range of [30, 150]. In different comparative experiments, the parameter values ​​are all guaranteed to keep the target within the near field. The specific parameter settings are shown in Table 2 below.

[0130] Table 2: Experimental Parameter Settings

[0131] The method of this embodiment is compared with three benchmarks: Maximum Likelihood Estimation (LS) is a method that utilizes near-field spherical wave characteristics for modeling and jointly optimizes channel gain and velocity parameters. This method achieves efficient solution through gradient iteration and is suitable for real-time tracking and communication in highly dynamic scenarios.

[0132] The 2DFFT+MUSIC algorithm employs multi-stage signal processing to determine target range, velocity, and azimuth. Ordered Statistical Cell Average Constant False Alarm Rate (OSCA-CFAR) is used to detect range-velocity pairs on the 2D-FFT results (range-Doppler image, RDM). The received signal covariance matrix is ​​spatially smoothed and decorrelated, and then the music algorithm is used for angle estimation.

[0133] CP-NFL Algorithm By grouping data into subarrays, tensor decomposition is performed on the received data from each subarray to obtain a factor matrix. Intermediate parameters such as time delay and velocity path difference are estimated from the factor matrix. The non-convex optimization problem is transformed into a convex optimization problem using Taylor expansion. The target coordinates are solved through convex optimization, and the velocity components are estimated using geometric relationships and the least squares method.

[0134] We use the root mean square error (RMSE) metric to quantify the estimation performance:

[0135] First, the parameter estimation accuracy of the four methods under different signal-to-noise ratio conditions is compared, with the actual motion parameter set as distance. 5m, angle Take 1.656 rad, radial velocity and tangential velocity The value is set to 20 m / s; the number of antennas, Nt = 50, is selected to ensure the target is in the near field. Experimental results are as follows: Figure 6 As shown.

[0136] This set of figures compares the root mean square error (RMSE) of different algorithms (HMP, RCRB, LS, 2DFFT+MUSIC, CP-NFL) for estimating radial velocity (Vr) and angular velocity (Vtheta) under different signal-to-noise ratios (SNR). RCRB (orange curve) represents the theoretical performance upper limit (Cramé-Rao lower bound), and its RMSE decreases significantly and continuously with increasing SNR, clearly demonstrating the rule that "the higher the SNR, the better the theoretical accuracy of parameter estimation." The LS and 2DFFT+MUSIC algorithms have relatively high RMSE values, with similar trends. LS (yellow) has a lower RMSE than 2DFFT+MUSIC in most parameter and SNR regions, indicating slightly higher accuracy. Compared to LS, it is based solely on the principle of minimizing the sum of squared errors between observed and predicted values. Tensor decomposition, by decomposing the high-dimensional coupled expression tensor into a low-dimensional factor matrix product, decouples multi-dimensional information and jointly optimizes to estimate parameters, thus the tensor decomposition algorithm performs better. Compared to other algorithms, HMP utilizes statistical dependencies for information passing approximation and approximates the optimal solution through an iterative process of estimation and error correction. It outperforms other algorithms in estimating all four parameters.

[0137] Secondly, the parameter estimation accuracy of the four methods is compared under different aperture scales, with the actual motion parameter set as distance. 5m, angle Take 1.656 rad, radial velocity and tangential velocity The values ​​are all 20 m / s, and the signal-to-noise ratio (SNR) is selected as SNR=50. To ensure the target is in the near field and the antenna spacing remains constant, the aperture is enlarged by increasing the number of antennas. We selected the number of antennas Nt=75, 150, 250, 350, 500. The experimental results are as follows. Figure 7 As shown.

[0138] Figure 2 The two figures show a comparative experiment of root mean square error (RMSE) as a function of the number of receiving antennas (Nt), used to evaluate the performance of different algorithms (HMP, LS, 2DFFT+MUSIC, Tensor, etc.) in parameter estimation (theta, Vtheta) tasks. As the aperture increases, the RMSE of both angle and tangential velocity gradually decreases. The figures show that the HMP algorithm exhibits superior performance.

[0139] Plot the parameter estimation results for each iteration in the 250 iterations, as shown below. Figure 8 , Figure 9 as well as Figure 10 As shown.

[0140] As the iterations progress, the estimated value gradually approaches and stabilizes towards the true value. In the initial iterations (close to 0 iterations), the estimated value deviates significantly from the true value (red line). As the number of iterations increases (the horizontal axis increases), the blue estimated value curve drops rapidly and gradually catches up with the red line. Most parameters are close to the true value around 100 iterations, indicating that the algorithm converges stably and is relatively fast.

[0141] Table 1 illustrates the differences in complexity among different algorithms, i.e., the trend of execution time and space as the input size increases. CP-NFL has cubic terms ( , (etc.), with a slightly larger input size, the computational load increases dramatically, resulting in high complexity. The complexity of the LS method increases with... The computational complexity increases quadratically with a large number of antennas. DFFT+music incorporates the logarithmic complexity of FFT (…). ) and the cubic complexity of matrix operations ( Our method mainly consists of linear product terms (without higher powers). As the input size increases, the complexity increases relatively slowly, which is theoretically more suitable for large-scale scenarios (such as multiple antennas).

[0142] In comparison, CP-NFL has the highest complexity and is suitable for scenarios with small parameters; LS and 2DFFT+music fall in between, depending on the specific variables. The method proposed in this paper has the lowest complexity (no high powers) and is more suitable for scenarios such as large-scale MIMO and multi-symbol transmission.

[0143] To better implement the method for estimating motion parameters of a moving target in this embodiment of the invention, based on the method for estimating motion parameters of a moving target, correspondingly, as follows: Figure 11 As shown, this embodiment of the invention also provides a device for estimating motion parameters of a moving target. The device 1100 for estimating motion parameters of a moving target includes: Model building module 1101 is used to build a model of the received signal of the MISO system between the moving target and the antenna array; The factor graph construction module 1102 is used to decompose the MISO system received signal model into multiple factor nodes, construct a factor graph based on the multiple factor nodes, and divide the factor graph into several functional modules. The parameter estimation module 1103 is used to apply a hybrid message passing algorithm with joint BP-EP-MF rules to the functional modules on the factor graph to perform parameter estimation, and obtain the estimated distance, angle, radial velocity and tangential velocity between the moving target and the antenna array.

[0144] The motion parameter estimation device 1100 for a moving target provided in the above embodiments can realize the technical solution described in the above embodiments of the motion parameter estimation method for a moving target. The specific implementation principle of each module or unit can be found in the corresponding content in the above embodiments of the motion parameter estimation method for a moving target, and will not be repeated here.

[0145] like Figure 12 As shown, the present invention also provides an electronic device 1200. The electronic device 1200 includes a processor 1201, a memory 1202, and a display 1203. Figure 12 Only some components of the electronic device 1200 are shown, but it should be understood that it is not required to implement all the components shown, and more or fewer components may be implemented instead.

[0146] In some embodiments, processor 1201 may be a central processing unit (CPU), microprocessor or other data processing chip, used to run program code stored in memory 302 or process data, such as a method for estimating motion parameters of a moving target in this invention.

[0147] In some embodiments, processor 1201 may be a single server or a group of servers. The server group may be centralized or distributed. In some embodiments, processor 1201 may be local or remote. In some embodiments, processor 1201 may be implemented on a cloud platform. In some embodiments, the cloud platform may include a private cloud, public cloud, hybrid cloud, community cloud, distributed cloud, internal cloud, multi-cloud, etc., or any combination thereof.

[0148] In some embodiments, memory 1202 may be an internal storage unit of electronic device 1200, such as a hard disk or memory of electronic device 1200. In other embodiments, memory 1202 may also be an external storage device of electronic device 1200, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc. equipped on electronic device 1200.

[0149] Furthermore, the memory 1202 may include both internal storage units of the electronic device 1200 and external storage devices. The memory 1202 is used to store application software and various types of data installed on the electronic device 1200.

[0150] In some embodiments, display 1203 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen. Display 1203 is used to display information from electronic device 1200 and to display a visual user interface. Components 1201-1203 of electronic device 1200 communicate with each other via a system bus.

[0151] In one embodiment, when the processor 1201 executes a program for estimating motion parameters of a moving target stored in the memory 1202, the following steps can be implemented: Construct a model of the received signal of the MISO system between the moving target and the antenna array; The MISO system received signal model is decomposed into multiple factor nodes, and a factor graph is constructed based on the multiple factor nodes. The factor graph is then divided into several functional modules. The hybrid message passing algorithm with joint BP-EP-MF rules is applied to the functional modules on the factor graph to estimate the parameters, and the estimated distance, angle, radial velocity and tangential velocity between the moving target and the antenna array are obtained.

[0152] It should be understood that when the processor 1201 executes a program for estimating the motion parameters of a moving target in the memory 1202, in addition to the functions mentioned above, it can also perform other functions, as detailed in the description of the corresponding method embodiments above.

[0153] Furthermore, the embodiments of the present invention do not specifically limit the type of the electronic device 1200 mentioned. The electronic device 1200 can be a mobile phone, tablet computer, personal digital assistant (PDA), wearable device, laptop computer, or other portable electronic device. Exemplary embodiments of portable electronic devices include, but are not limited to, portable electronic devices running iOS, Android, Microsoft, or other operating systems. The aforementioned portable electronic device can also be other portable electronic devices, such as a laptop computer with a touch-sensitive surface (e.g., a touch panel). It should also be understood that in some other embodiments of the present invention, the electronic device 1200 may not be a portable electronic device, but rather a desktop computer with a touch-sensitive surface (e.g., a touch panel).

[0154] Those skilled in the art will understand that all or part of the processes of the methods described in the above embodiments can be implemented by a computer program instructing related hardware, and the program can be stored in a computer-readable storage medium. The computer-readable storage medium may be a disk, optical disk, read-only memory, or random access memory, etc.

[0155] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for estimating motion parameters of a moving target, characterized in that, include: Construct a model of the received signal of the MISO system between the moving target and the antenna array; The MISO system received signal model is decomposed into multiple factor nodes, and a factor graph is constructed based on the multiple factor nodes. The factor graph is then divided into several functional modules. The hybrid message passing algorithm with joint BP-EP-MF rules is applied to the functional modules on the factor graph to estimate the parameters, and the estimated distance, angle, radial velocity and tangential velocity between the moving target and the antenna array are obtained.

2. The method for estimating motion parameters of a moving target according to claim 1, characterized in that, The step of decomposing the MISO system received signal model into multiple factor nodes and constructing a factor graph based on the multiple factor nodes includes: Based on Bayes' theorem, the received signal model of the MISO system is transformed into the observation factor; The observed factors are decomposed into multiple factor nodes, and a factor graph is constructed based on these multiple factor nodes.

3. The method for estimating motion parameters of a moving target according to claim 1, characterized in that, In the factor graph, squares represent function nodes, and circles represent variable nodes.

4. The method for estimating motion parameters of a moving target according to claim 1, characterized in that, The aforementioned functional modules include: The system includes a noise accuracy estimation module, a channel estimation module for propagation paths between different antennas, a propagation path decomposition module for moving targets to antennas, a radial velocity and radial distance decomposition module for moving targets to a single antenna, and a distance and angle decomposition module for moving targets to the origin antenna.

5. The method for estimating motion parameters of a moving target according to claim 4, characterized in that, The parameter estimation is performed by applying a hybrid message-passing algorithm with joint BP-EP-MF rules to the functional modules on the factor graph, including: In the noise accuracy estimation module, the BP rule is used for message passing; in the channel estimation module for propagation paths between different antennas, the EP propagation rule is used for message passing; in the moving target to antenna propagation path decomposition module, the BP-MP rule is used for message passing; in the radial velocity and radial distance decomposition module from the moving target to a single antenna, the BP-MP rule is used for message passing; and in the distance and angle decomposition module from the moving target to the origin antenna, the EP propagation rule is used for message passing. Message passing stops when a preset iteration condition is reached, and the estimated value of the parameter is determined.

6. The method for estimating motion parameters of a moving target according to claim 1, characterized in that, The received signal expression of the MISO system received signal model is as follows: In the formula, The parameter represents the channel gain, t represents the transmission time, and the parameter is... Indicates the first The distance between each base station antenna and the target Indicates the target is in the connection. Velocity components along the straight line between the antenna and the target, noise This represents additive white Gaussian noise, and , ,in and The target distance and angle are measured from the first antenna of the uniform linear array. and These are the radial and lateral velocities of a moving target relative to the same antenna.

7. The method for estimating motion parameters of a moving target according to claim 4, characterized in that, Also includes: Taylor expansion is used to derive the phase variable expression during the message transmission process of the distance and angle decomposition module from the moving target to the origin antenna.

8. A device for estimating motion parameters of a moving target, characterized in that, include: The model building module is used to build a model of the received signal of the MISO system between a moving target and an antenna array. The factor graph construction module is used to decompose the received signal model of the MISO system into multiple factor nodes, construct a factor graph based on the multiple factor nodes, and divide the factor graph into several functional modules. The parameter estimation module is used to perform parameter estimation on the functional modules on the factor graph by applying a hybrid message passing algorithm with joint BP-EP-MF rules, and obtain the estimated distance, angle, radial velocity and tangential velocity between the moving target and the antenna array.

9. An electronic device, characterized in that, Including memory and processor, among which, The memory is used to store programs; The processor, coupled to the memory, is used to execute the program stored in the memory to implement the steps in the method for estimating motion parameters of a moving target as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, Used to store computer-readable programs or instructions, which, when executed by a processor, can implement the steps in the method for estimating motion parameters of a moving target as described in any one of claims 1 to 7.

Images