Angle measurement method and device based on maximum likelihood estimation and electronic equipment

Abstract

The invention discloses an angle measurement method based on maximum likelihood estimation, and the method comprises the steps: constructing a correlation expression of an array echo signal model under the condition of employing a uniform linear array for receiving; establishing two hypotheses of GLRT (generalized likelihood ratio test), including an original hypothesis representing only noise and a standby hypothesis representing signal plus noise; respectively constructing probability density functions; constructing an expression of GLRT statistics in a mode of maximizing an angle value after maximum likelihood estimation is carried out on a probability density function under two assumptions on an unknown parameter; unknown parameters are estimated; the estimated value is substituted into the constructed expression of the GLRT statistical magnitude, and the final expression of the GLRT statistical magnitude is obtained; and obtaining a plurality of preset discrete angle values, and determining the discrete angle value which enables the final representation of the GLRT statistical magnitude to obtain a peak value as an estimated angle, thereby solving the problems of poor adaptability and insufficient precision of a traditional method in a low signal-to-noise ratio and complex electromagnetic environment.

Description

Technical Field

[0001] This invention belongs to the field of radar signal processing, and specifically relates to an angle measurement method, device, and electronic device based on maximum likelihood estimation. Background Technology

[0002] Angle of origin (DOA) estimation is a core problem in signal processing, widely used in radar, sonar, wireless communication, electronic reconnaissance, and IoT positioning. However, in real-world scenarios, signals are affected by multipath effects during transmission, have low resolution, and are susceptible to external noise in low signal-to-noise ratio environments, resulting in weak target echoes. These factors lead to decreased angle estimation accuracy, making it difficult to meet the system's requirements for high precision and real-time performance. Traditional angle estimation methods based on array signal processing mainly include the following: (1) Digital beamforming (DBF): This algorithm uses the information of the signal received by the antenna to perform beamforming on the signal through signal processing technology, so that signals from different directions can form a significant signal superposition effect on the array.

[0003] (2) Multivariate signal classification (MUSIC): This algorithm uses the signal received by the array antenna to estimate the spatial spectrum, obtain the spatial spectrum information of the array in different directions, and then obtain the arrival angle of the target in space by extracting and classifying the spatial spectrum.

[0004] (3) Capon Algorithm: Also known as Minimum Variance Distortionless Response (MVDR) beamformer, this algorithm is based on beamforming and optimizes the weight vector by minimizing the output power criterion. Under given constraints, i.e. maintaining a distortionless response in the desired signal direction, the algorithm suppresses interference and noise in undesired directions by minimizing the output power.

[0005] Among them, Cao Zhen proposed in "Research on Multi-Target Angle Estimation Technology of Millimeter Wave Radar [D]. Yantai University, 2023.DOI:10.27437 / d.cnki.gytdu.2023.000509" that the Rife algorithm is used to divide the estimated angle range, limiting the MUSIC algorithm to search within a very small range, which can significantly reduce the computational complexity of the traditional MUSIC algorithm. Tang Jiayu et al. proposed a robust Capon beamforming algorithm with joint correction in "A Robust Capon Beamforming Algorithm with Joint Correction [J]. Radio Communication Technology, 2023, 49(05):971-978". Based on the orthogonality between the steering vector and the noise space, the estimated desired signal steering vector is corrected. Then, based on the elimination of residual noise and the estimation of interference power, a projection matrix is ​​constructed to eliminate the desired signal in the received signal and correct the covariance matrix. Xu Zhenhai et al. proposed a method for determining the angle of motion of a single pulse from the echo model of an array radar and the principle of maximum likelihood estimation. They proposed the difference beam weight vector and the formula for determining the angle of motion of a single pulse. In the paper “Consistency between single pulse and maximum likelihood estimation in array radar [J]. Modern Radar, 2013, 35(10):32-35.DOI:10.16592 / j.cnki.1004-7859.2013.10.012”, Xu Zhenhai et al. proposed a method for determining the angle of motion of a single pulse from the echo model of an array radar and the principle of maximum likelihood estimation.

[0006] However, the main drawbacks of existing angle measurement methods are that the model establishment is easily affected by multipath effects and noise distribution, leading to a decline in angle measurement performance. Furthermore, accuracy decreases under low signal-to-noise ratio conditions, they are sensitive to array errors and interference, and have limited resolution, making it difficult to obtain stable and reliable angle estimates. Summary of the Invention

[0007] To address the aforementioned problems in the prior art, this invention provides an angle measurement method, apparatus, and electronic device based on maximum likelihood estimation. The technical problem to be solved by this invention is achieved through the following technical solution: In a first aspect, embodiments of the present invention provide an angle measurement method based on maximum likelihood estimation, the method comprising: S1, Constructing relevant expressions for the array echo signal model when the receiving antenna array adopts a uniform linear array; S2. Based on the relevant expression of the array echo signal model, establish two hypotheses for the generalized likelihood ratio test (GLRT): the null hypothesis characterizing noise only, and the alternative hypothesis characterizing the signal plus noise. S3, construct the probability density functions under the two hypotheses respectively; and construct the expression of the GLRT statistic by maximizing the angle value after making maximum likelihood estimation of the unknown parameters on the probability density functions under the alternative and null hypotheses. S4, Estimate the unknown parameters in the expression of the GLRT statistic; and substitute the estimated values ​​of the unknown parameters into the expression of the constructed GLRT statistic to obtain the final representation of the GLRT statistic; S5, obtain several preset discrete angle values, and determine the discrete angle values ​​that make the final representation of the GLRT statistic obtain the peak value as the estimated angle.

[0008] In one embodiment of the present invention, the relevant expressions of the array echo signal model constructed in S1 include: The expression for the transmitted signal is: ; The transmitted signal is emitted by the signal source; It is a baseband signal; For signal carrier frequency; Indicates time; The imaginary unit; No. The expression for the signal received by each array element is: ; The receiving antenna array uses a uniform linear array, which has There are array elements, and the spacing between the array elements is... ; This represents the angle at which the transmitted signal reaches the receiving antenna array, which is the angle between the signal source and the normal direction; The wavelength is given; the noise received by each array element is independent and follows a zero mean and variance of . Gaussian white noise, denoted as , ; The expression for the array receive vector is: ; in, For continuous signals With sampling frequency The discrete-time signal obtained by sampling. The array receive vector is Vector representation of the discrete-time signal of each array element; yes The resulting discrete-time signal; , is zero-mean complex Gaussian noise, expressed as , yes An identity matrix of order 1; yes The Middle One element; It is the steering vector of the receiving antenna array; The array of received vectors from each sampling point is stacked into a data matrix. The corresponding expression is: ; in, , is the sequence of transmitted signals; It is a noise matrix; The covariance matrix of the received signal, taken as the sample covariance matrix, is expressed as: ; when As the sample covariance approaches infinity, it converges to the true covariance, and the expression for the true covariance matrix is: ; in, It is the signal power.

[0009] In one embodiment of the present invention, in S2, The original hypothesis was based on express; : ; Alternative hypothesis express; : .

[0010] In one embodiment of the present invention, in S3, the probability density functions under the null hypothesis and the alternative hypothesis are respectively: ; ; in, yes Noise variance under given conditions yes Covariance matrix under given conditions; The expression for the constructed GLRT statistic is as follows: ; In the expression for the GLRT statistic The parameter is unknown.

[0011] In one embodiment of the present invention, in S4, the unknown parameter in the expression of the GLRT statistic is... The estimation process includes: Step a1, for Taking the logarithm of the probability density function under the given conditions, we get: ; Step a2: Set the partial derivative of the expression obtained in step a1 to 0 and ignore the relationship with... The irrelevant constant term gives: ; Step a3, solve based on step a2 ,get: .

[0012] In one embodiment of the present invention, in S4, the unknown parameter in the expression of the GLRT statistic is... The estimation process includes: Step b1, for Taking the logarithm of the probability density function under the given conditions, we get: ; Step b2, assuming , ,get: ; ; ; ; set up , ,but ; Step b3, for Taking the partial derivative, we get: ; Step b4, let ,get: ; ; Then we have: ; Step b5, for Taking the partial derivative, we get: ; Step b6, let ,get: ; Step b7, due to the elements of the sample covariance matrix ,get: ; To make the left side equal to the right side, determine... .

[0013] In one embodiment of the present invention, step S4 involves substituting the estimated value of the unknown parameter into the expression of the constructed GLRT statistic to obtain the final representation of the GLRT statistic, including: Step c1: Based on the estimated values ​​of the unknown parameters, the probability density functions under the two hypotheses are obtained as follows: ; ; Step c2: Substitute the estimated values ​​of the unknown parameters into the expression for the GLRT statistics to obtain: ; Step c3, Calculate The logarithm of the equation yields: ; Step c4, ignoring the constant term, yields: ; Step c5, will Considered a fixed value, and with Irrelevant, obtained: ; ; Wherein, the sample covariance matrix is ​​expressed as According to prior knowledge, any Hermitian matrix can be decomposed into... ,in It is an eigenvalue. It is the corresponding feature vector, and It is a complete orthogonal basis; Step c6, will It can be represented as a linear combination of eigenvectors. ,in ,yes exist The projection coefficients on the surface are obtained as follows: ; get: ; Step c7, according to as well as Substituting these values, we obtain the final representation of the GLRT statistic: .

[0014] In one embodiment of the present invention, in S5, the range of a number of preset discrete angle values ​​is from -90° to 90°, with an interval of 1°.

[0015] Secondly, embodiments of the present invention provide an angle measuring device based on maximum likelihood estimation, the device comprising: The array echo signal model construction module is used to construct the relevant expressions of the array echo signal model when the receiving antenna array adopts a uniform linear array. The Generalized Likelihood Ratio Test (GLRT) hypothesis building module is used to establish two hypotheses for the GLRT based on the relevant expressions of the array echo signal model, including the null hypothesis characterizing noise only, and the alternative hypothesis characterizing the signal plus noise. The GLRT statistic expression construction module is used to construct the probability density function under two hypotheses respectively; and constructs the expression of the GLRT statistic by maximizing the angle value after performing maximum likelihood estimation on the probability density function under the alternative and null hypotheses for the unknown parameters. The GLRT statistic final representation construction module is used to estimate the unknown parameters in the expression of the GLRT statistic; and substitute the estimated values ​​of the unknown parameters into the expression of the constructed GLRT statistic to obtain the final representation of the GLRT statistic. The angle estimation module is used to obtain several preset discrete angle values, and the discrete angle value that makes the final representation of the GLRT statistic reach the peak value is determined as the estimated angle.

[0016] Thirdly, embodiments of the present invention provide an electronic device, including a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; The memory is used to store computer programs; When the processor executes the program stored in the memory, it implements the steps of the angle measurement method based on maximum likelihood estimation provided in the embodiments of the present invention.

[0017] The beneficial effects of this invention are: The angle measurement method based on maximum likelihood estimation provided in this invention has advantages over traditional angle measurement methods in that it can work stably in complex scenarios by maximizing the likelihood function of the observed signal. Under low signal-to-noise ratio conditions, MLE can effectively fuse the statistical information of weak signals through probability weighting of the likelihood function, achieving accurate angle estimation, which is superior to traditional methods and has no array configuration limitations, making it flexible to adapt to one-dimensional and two-dimensional angle measurement needs. At the same time, it supports multi-parameter joint estimation, further improving the reliability and accuracy of angle measurement, effectively solving the problems of poor adaptability and insufficient accuracy of traditional angle measurement methods in low signal-to-noise ratio and complex electromagnetic environments. Attached Figure Description

[0018] Figure 1This is a flowchart illustrating an angle measurement method based on maximum likelihood estimation provided in an embodiment of the present invention. Figure 2 This is a schematic diagram of a uniform linear array model in an embodiment of the present invention; Figure 3 This is the radiation pattern of a uniform linear array with M=8 in an embodiment of the present invention; Figure 4 These are the radiation patterns of different array elements when the beam points to 0° in an embodiment of the present invention; Figure 5 This is the likelihood spectrum at SNR=10dB in an embodiment of the present invention; Figure 6 This is the likelihood spectrum at SNR=0dB in an embodiment of the present invention; Figure 7 This is the likelihood spectrum at SNR=-10dB in an embodiment of the present invention; Figure 8 This is a schematic diagram of the structure of an angle measuring device based on maximum likelihood estimation provided in an embodiment of the present invention; Figure 9 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0019] The present invention will be further described in detail below with reference to specific embodiments, but the implementation of the present invention is not limited thereto.

[0020] Traditional direction finding methods have low angle estimation accuracy in low signal-to-noise ratio environments and insufficient resolution when the target angle is close or the signal is correlated, making it impossible to effectively distinguish between real and false targets. Furthermore, traditional angle measurement methods are sensitive to array errors and multipath effects, which can cause angle measurement errors. They are also difficult to maintain accurate estimation in complex electromagnetic environments, thus limiting their application in practical scenarios.

[0021] In order to effectively estimate the direction of arrival of a target in low signal-to-noise ratio (SNR) and complex environments, embodiments of the present invention provide an angle measurement method, apparatus and electronic device based on maximum likelihood estimation.

[0022] It should be noted that the execution entity of the angle measurement method based on maximum likelihood estimation provided in this embodiment of the invention can be an angle measurement device based on maximum likelihood estimation, which can run in an electronic device. This electronic device can be a server or a terminal device, but is not limited to these.

[0023] In a first aspect, embodiments of the present invention provide an angle measurement method based on maximum likelihood estimation, such as... Figure 1 As shown, the method may include the following steps S1 to S5: S1, Constructing relevant expressions for the array echo signal model when the receiving antenna array adopts a uniform linear array; First, a brief explanation of uniform linear arrays will be given.

[0024] A uniform linear array is a collection of sensor elements evenly distributed along a straight line. It consists of identical antenna elements arranged at equal intervals along a straight line, with each element having an equal excitation current amplitude and a phase that increases or decreases along the axis at a fixed ratio. For example... Figure 2 As shown, assuming The array elements are arranged in a straight line with equal spacing, and the spacing between the array elements is [missing information]. Assuming the leftmost antenna element is chosen as the reference point, then the... The time delay of each antenna element relative to the reference point can be expressed as: (1); in, Array element spacing It should typically be less than half the wavelength. To avoid spatial aliasing (grating lobe) caused by insufficient spatial sampling frequency, when the array element spacing... When the wavelength exceeds half the wavelength, a pseudo-main lobe with the same or similar intensity as the main lobe will appear in the radiation pattern, which will lead to ambiguity in the angle estimation. It is the first An incident angle.

[0025] Assuming the propagation distance is much greater than the array size, the signal arrives at the array in the form of a plane wave within the medium. Its radiation pattern is composed of the product of array factors (related to the array set and phase distribution) and element factors (related to the characteristics of the array elements themselves), conforming to the radiation pattern multiplication principle. Its direction vector is: (2); when =8. Normalized element spacing And when the beam direction is [-30°, 0°, 30°], its radiation pattern is as follows: Figure 3 As shown.

[0026] For a uniform linear array, the main lobe width varies with the number of array elements. Increasing the number of array elements can reduce the beamwidth and improve the angular resolution. For example... Figure 4 As shown, when the beam direction is fixed, the number of array elements are respectively =8、 =16 and When the number of array elements is 32, the width of the main lobe is negatively correlated with the number of array elements, meaning that more array elements can further compress the main lobe. Based on this, the relevant expressions for the array echo signal model are constructed, specifically: Suppose there is a... A uniform linear array composed of n elements, with an element spacing of 1. There exists a far-field narrowband signal source whose emitted signal arrives at the array at an angle of . (The angle between the source and the normal direction), then the expression for the transmitted signal is: (3); in, For baseband signals, For signal carrier frequency; Indicates time; It is the imaginary unit.

[0027] No. The expression for the signal received by each array element is: (4); The receiving antenna array uses a uniform linear array, which has There are array elements, and the spacing between the array elements is... ; This represents the angle at which the transmitted signal reaches the receiving antenna array, which is the angle between the signal source and the normal direction; The wavelength is given; the noise received by each array element is independent and follows a zero mean and variance of . Gaussian white noise, denoted as , ; For continuous signals With sampling frequency Sampling yields discrete-time signals. ,Will The discrete signal of each array element is represented as an array receiving vector, and its expression is: (5); Wherein, the array receiving vector is Vector representation of the discrete-time signal of each array element; yes The resulting discrete-time signal; , is zero-mean complex Gaussian noise, expressed as , yes An identity matrix of order 1; yes The Middle One element, outside Represents noise. In Represents a discrete sampling point; It is the steering vector of the receiving antenna array, as shown in formula (2).

[0028] Will The array of received vectors from each sampling point is stacked into a data matrix. The corresponding expression is: (6); in, , is the sequence of transmitted signals; It is a noise matrix; The covariance matrix of the received signal is defined as the sample covariance matrix, and its expression is: (7); when As the sample covariance approaches infinity, it converges to the true covariance, and the expression for the true covariance matrix is: (8); in, It is the signal power.

[0029] It is understood that, in the case of a uniform linear array for receiving antenna array, the relevant expressions of the array echo signal model constructed in the embodiments of the present invention include the above formulas (3) to (8).

[0030] S2. Based on the relevant expression of the array echo signal model, establish two hypotheses for the generalized likelihood ratio test (GLRT): the null hypothesis characterizing noise only, and the alternative hypothesis characterizing the signal plus noise. The core of the generalized likelihood ratio test (GLRT) is to compare the ratio of likelihood functions under the two hypotheses of "signal + noise" and "noise only". In S2, two assumptions are first established: The null hypothesis that characterizes only noise is based on express; : ; The alternative hypothesis for characterizing the addition of noise to the signal is express; : .

[0031] S3, construct the probability density functions under the two hypotheses respectively; and construct the expression of the GLRT statistic by maximizing the angle value after making maximum likelihood estimation of the unknown parameters on the probability density functions under the alternative and null hypotheses. The probability density functions under the null and alternative hypotheses are as follows: (9); (10); in, yes Noise variance under given conditions yes Covariance matrix under given conditions; The expression for the constructed GLRT statistic is as follows: (11); In the expression for the GLRT statistic This is an unknown parameter. It is understandable that, due to... , Parameters under two conditions Unknown, difficult to obtain directly Therefore, the unknown parameter itself needs to be replaced with the maximum likelihood estimate of the unknown parameter.

[0032] S4, Estimate the unknown parameters in the expression of the GLRT statistic; and substitute the estimated values ​​of the unknown parameters into the expression of the constructed GLRT statistic to obtain the final representation of the GLRT statistic; First, regarding the unknown parameters... ,as well as The estimation process will be explained separately.

[0033] (one) Estimating unknown parameters under certain conditions : In S4, the unknown parameters in the expression for the GLRT statistic The estimation process includes: Step a1, for ease of calculation, ... Taking the logarithm of the probability density function under the given conditions, we get: (12); Step a2: Set the partial derivative of the expression obtained in step a1 to 0 and ignore the relationship with... The irrelevant constant term gives: (13); Step a3, solve based on step a2 ,get: (14); Thus, estimate .

[0034] (two) Estimating unknown parameters under certain conditions : In S4, the unknown parameters in the expression for the GLRT statistic The estimation process includes: Step b1, for ease of calculation, ... Taking the logarithm of the probability density function under the given conditions, we get: (15); The derivation , Matrix operations and the definition of matrix trace are required.

[0035] Step b2, assuming , ,get: (16); (17); (18); (19); set up , ,but ; It is a matrix The Middle Line number Column elements, matrix The Middle Line number Column elements; That is ; and They are vectors The element at the corresponding position in the vector yes Dimensional vector.

[0036] Step b3, for Taking the partial derivative, we get: (20); Step b4, let ,get: (twenty one); (twenty two); Then we have: (twenty three); Step b5, for Taking the partial derivative, we get: (twenty four); Step b6, let ,get: (25); Step b7, due to the elements of the sample covariance matrix ,get: (26); To make the left side equal to the right side, determine... .

[0037] Those skilled in the art will understand that the unknown parameters can be estimated through the above process. .

[0038] In S4, the estimated values ​​of the unknown parameters are substituted into the expression of the constructed GLRT statistic to obtain the final representation of the GLRT statistic, including: Step c1: Based on the estimated values ​​of the unknown parameters, the probability density functions under the two hypotheses are obtained as follows: (27); (28); Step c2: Substitute the estimated values ​​of the unknown parameters into the expression for the GLRT statistics to obtain: (29); Step c3, Calculate The logarithm of the equation yields: (30); Step c4, ignoring the constant term, yields: (31); Step c5, and the estimation of unknown parameters precedes angle estimation, therefore After likelihood estimation, it can be regarded as a fixed value, and at Irrelevant, obtained: (32); (33); After obtaining the statistical detection volume, the final step is to establish... and The relationship between them. The right side of equation (33) is the sum of the eigenvalues ​​of a certain matrix, while the sample covariance matrix is ​​expressed as .

[0039] Based on prior knowledge, any Hermitian matrix can be decomposed into... ,in It is an eigenvalue. It is the corresponding feature vector, and It is a complete orthogonal basis; It can be represented as a linear combination of eigenvectors.

[0040] Step c6, will It can be represented as a linear combination of eigenvectors. ,in ,yes exist The projection coefficients on the surface are obtained as follows: (34); get: (35); Step c7, according to as well as Substituting these values, we obtain the final representation of the GLRT statistic: (36); S5, obtain several preset discrete angle values, and determine the discrete angle values ​​that make the final representation of the GLRT statistic obtain the peak value as the estimated angle.

[0041] Several preset discrete angle values ​​can be set as needed. In one optional implementation, the preset discrete angle values ​​range from -90° to 90°, with an interval of 1°.

[0042] when When equal to the actual signal angle of arrival, Once the peak value is reached, the angle of the peak location can be estimated by searching for the peak position.

[0043] The purpose of this invention is to provide an angle measurement method based on maximum likelihood estimation (MLE). The main idea is to use signals received by multiple receiving arrays to construct a generalized likelihood ratio statistic, and then estimate the angle of arrival of the signal by searching for the angle value that maximizes the statistic. In other words, by constructing a reasonable likelihood function and optimizing the solution, a high-precision estimation of the direction of arrival (DOA) can be achieved.

[0044] The angle measurement method based on maximum likelihood estimation provided in this invention has advantages over traditional angle measurement methods in that it can work stably in complex scenarios by maximizing the likelihood function of the observed signal. Under low signal-to-noise ratio conditions, MLE can effectively fuse the statistical information of weak signals through probability weighting of the likelihood function, achieving accurate angle estimation, which is superior to traditional methods and has no array configuration limitations, making it flexible to adapt to one-dimensional and two-dimensional angle measurement needs. At the same time, it supports multi-parameter joint estimation, further improving the reliability and accuracy of angle measurement, effectively solving the problems of poor adaptability and insufficient accuracy of traditional angle measurement methods in low signal-to-noise ratio and complex electromagnetic environments.

[0045] The effectiveness of the method of the present invention can be further illustrated by the following experiments.

[0046] To verify the target detection effect of the method of the present invention, detailed experimental parameters are listed in Table 1 and verified.

[0047] Table 1 Angle estimation parameter settings

[0048] Figure 5 , Figure 6 , Figure 7 These are the likelihood spectra of the maximum likelihood estimates for a single signal source with an incident angle of 30° and signal-to-noise ratios of 10dB, 0dB, and -10dB, respectively. In the experiment, the likelihood spectrum estimates have been normalized. Therefore, the estimated angle corresponding to the amplitude of 1 in the likelihood spectrum is the true angle of the signal. It can be seen that the method can still achieve accurate estimation of the signal angle even at low signal-to-noise ratios.

[0049] Secondly, corresponding to the above method embodiments, this invention also provides an angle measuring device based on maximum likelihood estimation, such as... Figure 8 As shown, the device includes: The array echo signal model construction module is used to construct the relevant expressions of the array echo signal model when the receiving antenna array adopts a uniform linear array. The Generalized Likelihood Ratio Test (GLRT) hypothesis building module is used to establish two hypotheses for the GLRT based on the relevant expressions of the array echo signal model, including the null hypothesis characterizing noise only, and the alternative hypothesis characterizing the signal plus noise. The GLRT statistic expression construction module is used to construct the probability density function under two hypotheses respectively; and constructs the expression of the GLRT statistic by maximizing the angle value after performing maximum likelihood estimation on the probability density function under the alternative and null hypotheses for the unknown parameters. The GLRT statistic final representation construction module is used to estimate the unknown parameters in the expression of the GLRT statistic; and substitute the estimated values ​​of the unknown parameters into the expression of the constructed GLRT statistic to obtain the final representation of the GLRT statistic. The angle estimation module is used to obtain several preset discrete angle values, and the discrete angle value that makes the final representation of the GLRT statistic reach the peak value is determined as the estimated angle.

[0050] For details on the specific processing procedures of each module of the device, please refer to the relevant content in the first section, which will not be repeated here.

[0051] The angle measurement device based on maximum likelihood estimation provided by this invention is a probabilistic model based on statistical detection. Its purpose is to offer an angle measurement method based on maximum likelihood estimation (MLE). By constructing a likelihood function for the direction of arrival and optimizing it, it gains greater applicability to the statistical characteristics of noise. By establishing the corresponding likelihood function, complex signal and noise characteristics can be accurately incorporated into the estimation model, avoiding the failure of subspace eigenvalue decomposition methods. This invention overcomes the shortcomings of existing technologies, such as insufficient angle measurement accuracy, poor stability, and inability to balance computational efficiency in low signal-to-noise ratio, array error, and multi-target scenarios.

[0052] Thirdly, embodiments of the present invention also provide an electronic device, such as... Figure 9 As shown, it includes a processor 001, a communication interface 002, a memory 003, and a communication bus 004, wherein the processor 001, the communication interface 002, and the memory 003 communicate with each other through the communication bus 004. The memory is used to store computer programs; When the processor executes the program stored in the memory, it implements the steps of any of the angle measurement methods based on maximum likelihood estimation provided in the first aspect of the present invention.

[0053] The communication bus mentioned in the above electronic devices can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus can be divided into address bus, data bus, control bus, etc. For ease of illustration, only one thick line is used to represent it in the diagram, but this does not mean that there is only one bus or one type of bus.

[0054] The communication interface is used for communication between the aforementioned electronic devices and other devices.

[0055] The memory may include random access memory (RAM) or non-volatile memory (NVM), such as at least one disk storage device. Optionally, the memory may also be at least one storage device located remotely from the aforementioned processor.

[0056] The processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0057] The method provided in this invention can be applied to electronic devices. Specifically, the electronic device can be a desktop computer, a portable computer, a smart mobile terminal, a server, etc. No limitation is made herein; any electronic device that can implement this invention falls within the protection scope of this invention.

[0058] It should be noted that the device and electronic device in the embodiments of the present invention are respectively devices and electronic devices that apply the above-mentioned angle measurement method based on maximum likelihood estimation. Therefore, all embodiments of the above-mentioned angle measurement method based on maximum likelihood estimation are applicable to the device and electronic device, and can achieve the same or similar beneficial effects.

[0059] It should be noted that, in the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. In addition, those skilled in the art can combine and integrate the different embodiments or examples described in this specification.

[0060] The above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention are included within the scope of protection of the present invention.

Claims

1. An angle measurement method based on maximum likelihood estimation, characterized in that, include: S1, Constructing relevant expressions for the array echo signal model when the receiving antenna array adopts a uniform linear array; S2. Based on the relevant expression of the array echo signal model, establish two hypotheses for the generalized likelihood ratio test (GLRT): the null hypothesis characterizing noise only, and the alternative hypothesis characterizing the signal plus noise. S3, construct the probability density functions under the two hypotheses respectively; and construct the expression of the GLRT statistic by maximizing the angle value after making maximum likelihood estimation of the unknown parameters on the probability density functions under the alternative and null hypotheses. S4, Estimate the unknown parameters in the expression of the GLRT statistic; and substitute the estimated values ​​of the unknown parameters into the expression of the constructed GLRT statistic to obtain the final representation of the GLRT statistic; S5, obtain several preset discrete angle values, and determine the discrete angle values ​​that make the final representation of the GLRT statistic obtain the peak value as the estimated angle.

2. The method according to claim 1, characterized in that, The relevant expressions for the array echo signal model constructed in S1 include: The expression for the transmitted signal is: ； The transmitted signal is emitted by the signal source; It is a baseband signal; For signal carrier frequency; Indicates time; The imaginary unit; No. The expression for the signal received by each array element is: ； The receiving antenna array uses a uniform linear array, which has There are array elements, and the spacing between the array elements is... ; This represents the angle at which the transmitted signal reaches the receiving antenna array, which is the angle between the signal source and the normal direction; The wavelength is given; the noise received by each array element is independent and follows a zero mean and variance of . Gaussian white noise, denoted as , ; The expression for the array receive vector is: ； in, For continuous signals With sampling frequency The discrete-time signal obtained by sampling. The array receive vector is Vector representation of the discrete-time signal of each array element; yes The resulting discrete-time signal; , is zero-mean complex Gaussian noise, expressed as , yes An identity matrix of order 1; yes The Middle One element; It is the steering vector of the receiving antenna array; The array of received vectors from each sampling point is stacked into a data matrix. The corresponding expression is: ； in, , is the sequence of transmitted signals; It is a noise matrix; The covariance matrix of the received signal, taken as the sample covariance matrix, is expressed as: ； when As the sample covariance approaches infinity, it converges to the true covariance, and the expression for the true covariance matrix is: ； in, It is the signal power.

3. The method according to claim 2, characterized in that, In S2, The original hypothesis was based on express; : ; Alternative hypothesis express; : .

4. The method according to claim 3, characterized in that, In S3, the probability density functions under the null hypothesis and the alternative hypothesis are as follows: ； ； in, yes Noise variance under given conditions yes Covariance matrix under given conditions; The expression for the constructed GLRT statistic is as follows: ； In the expression for the GLRT statistic The parameter is unknown.

5. The method according to claim 4, characterized in that, In S4, the unknown parameters in the expression for the GLRT statistic The estimation process includes: Step a1, for Taking the logarithm of the probability density function under the given conditions, we get: ； Step a2: Set the partial derivative of the expression obtained in step a1 to 0 and ignore the relationship with... The irrelevant constant term gives: ； Step a3, solve based on step a2 ,get: 。 6. The method according to claim 4 or 5, characterized in that, In S4, the unknown parameters in the expression for the GLRT statistic The estimation process includes: Step b1, for Taking the logarithm of the probability density function under the given conditions, we get: ； Step b2, assuming , ,get: ； ； ； ； set up , ,but ; Step b3, for Taking the partial derivative, we get: ； Step b4, let ,get: ； ； Then we have: ; Step b5, for Taking the partial derivative, we get: ； Step b6, let ,get: ； Step b7, due to the elements of the sample covariance matrix ,get: ； To make the left side equal to the right side, determine... .

7. The method according to claim 6, characterized in that, In S4, the estimated values ​​of the unknown parameters are substituted into the expression of the constructed GLRT statistics to obtain the final representation of the GLRT statistics, including: Step c1: Based on the estimated values ​​of the unknown parameters, the probability density functions under the two hypotheses are obtained as follows: ； ； Step c2: Substitute the estimated values ​​of the unknown parameters into the expression for the GLRT statistics to obtain: ； Step c3, Calculate The logarithm of the equation yields: ； Step c4, ignoring the constant term, yields: ； Step c5, will Considered a fixed value, and with Irrelevant, obtained: ； ； Wherein, the sample covariance matrix is ​​expressed as According to prior knowledge, any Hermitian matrix can be decomposed into... ,in It is an eigenvalue. It is the corresponding feature vector, and It is a complete orthogonal basis; Step c6, will It can be represented as a linear combination of eigenvectors. ,in ,yes exist The projection coefficients on the surface are obtained as follows: ； get: ； Step c7, according to as well as Substituting these values, we obtain the final representation of the GLRT statistic: 。 8. The method according to claim 1, characterized in that, In S5, the preset discrete angle values ​​range from -90° to 90°, with an interval of 1°.

9. An angle measuring device based on maximum likelihood estimation, characterized in that, include: The array echo signal model construction module is used to construct the relevant expressions of the array echo signal model when the receiving antenna array adopts a uniform linear array. The Generalized Likelihood Ratio Test (GLRT) hypothesis building module is used to establish two hypotheses for the GLRT based on the relevant expressions of the array echo signal model, including the null hypothesis characterizing noise only, and the alternative hypothesis characterizing the signal plus noise. The GLRT statistic expression construction module is used to construct the probability density function under two hypotheses respectively; and constructs the expression of the GLRT statistic by maximizing the angle value after performing maximum likelihood estimation on the probability density function under the alternative and null hypotheses for the unknown parameters. The GLRT statistics final representation construction module is used to estimate the unknown parameters in the expression of the GLRT statistics; Then, the estimated values ​​of the unknown parameters are substituted into the expression of the constructed GLRT statistics to obtain the final representation of the GLRT statistics; The angle estimation module is used to obtain several preset discrete angle values, and the discrete angle value that makes the final representation of the GLRT statistic reach the peak value is determined as the estimated angle.

10. An electronic device, characterized in that, It includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; The memory is used to store computer programs; When the processor executes the program stored in the memory, it implements the steps of the method according to any one of claims 1-8.

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