Target signal arrival angle estimation method and device for small sample data

Abstract

The invention provides a target signal arrival angle estimation method and device for small sample data, and relates to the technical field of array signal processing. The method comprises the steps of determining a complete statistic of a received signal sample data set about target angle estimation based on a complete statistic theory; and performing spatial spectrum analysis on the determined complete statistic of the received signal sample data set about the target angle estimation to realize the estimation of the target angle. According to the invention, on the premise that the algorithm complexity is not increased, the target angle estimation accuracy under small sample data, especially under a low signal-to-noise ratio, can be effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of array signal processing, and in particular to a method and device for estimating the arrival angle of a target signal oriented to small sample data. Background Art

[0002] In future advanced networks such as B5G / 6G, Integrated Sensing and Communications (ISAC) will give communication networks full-domain perception capabilities, realize "one network, two uses", and effectively support innovative applications such as smart transportation and digital twins.

[0003] However, there is significant resource competition between communication and perception functions, and both have high requirements for wireless resources such as time, frequency, space, and power. Due to limited wireless resources, communication-centric ISAC systems such as B5G / 6G usually find it difficult to allocate sufficient wireless resources for target perception, resulting in the scale of perception data acquired by the system being significantly smaller than that of traditional radar systems. In the case of insufficient sample data, the accuracy of typical parameter estimation methods represented by Multiple Signal Classification (MUSIC) and Capon algorithm will be significantly reduced.

[0004] The MUSIC algorithm is a classic high-resolution spectrum estimation algorithm. Compared with the Angle of Arrival (AoA) estimation algorithms such as Fast Fourier Transform (FFT) and Maximum Likelihood Estimation (MLE), it can effectively eliminate the influence of the uneven geometric structure of the array. At the same time, it has a sharper spectrum peak and can maintain a high estimation accuracy in the case of multiple targets, weak signals and large noise. Therefore, traditional MUSIC and improved algorithms such as WMUSIC are widely used in AoA estimation in array signal processing.

[0005] The core idea of ​​the MUSIC algorithm is to decompose the eigenspace of the covariance matrix of the received signal into a signal subspace and a noise subspace, and then determine the angle of the signal source by searching for a direction orthogonal to the noise subspace. In 1986, Schmidt R. first proposed a high-resolution spectrum estimation algorithm based on subspace decomposition in his paper, which became the foundational paper of the MUSIC algorithm.

[0006] The following will briefly describe the basic principle of the MUSIC algorithm. For simplicity, it is assumed that the receiving end uses a uniform linear array and the received signal , It can be expressed as:

[0007] (1)

[0008] in, Indicates K independent target echo signals; A vector containing K independent signal arrays The array response matrix, ; It means the mean is 0 and the variance is The additive complex Gaussian white noise vector of ; represents the AoA of K-channel signals; N represents the number of elements in the receiving antenna array, and M represents the number of samples of the received signal. Then, the covariance matrix of the received signal can be expressed as:

[0009] (2)

[0010] in, Indicates the target echo signal The covariance matrix of yes The identity matrix of Indicates expectation.

[0011] On this basis, the covariance matrix is ​​decomposed into eigenvalues ​​to obtain , where the unitary matrix for The eigenvector matrix of yes The eigenvalue matrix of . According to formula (2), It can be expressed as the sum of the signal component and the noise component, so we can further get:

[0012] (3)

[0013] and Represent the signal subspace and noise subspace respectively. The dimension of the signal subspace is theoretically equal to the number of independent signal sources K, where Is The diagonal matrix consisting of the largest K eigenvalues ​​in , Then represents the corresponding K eigenvectors, which correspond to the weight and direction of the signal subspace respectively; similarly, and It consists of the remaining eigenvalues ​​and eigenvectors, corresponding to the weights and directions of the noise subspace, respectively.

[0014] On this basis, the MUSIC space spectrum function is defined by using the orthogonal characteristics of the signal subspace and the noise subspace. :

[0015] (4)

[0016] By calculation And search for the peak position of the spectrum function to obtain the arrival angle .

[0017] On the basis of the traditional MUSIC algorithm, in order to further enhance the algorithm's anti-noise ability and reduce the algorithm's complexity, improved algorithms such as weighted multiple signal classification (WMUSIC) and root multiple signal classification (ROOT-MUSIC) have been proposed. Among them, the WMUSIC algorithm introduces a weight matrix into the spatial spectrum function to enhance the algorithm's detection ability for the target signal, so that the algorithm can maintain a high estimation accuracy even in a high noise environment. is the weight in different directions of the noise subspace, then the improved WMUSIC spatial spectrum function can be expressed as:

[0018] (5)

[0019] ROOT-MUSIC transforms the spectrum peak search problem of the traditional MUSIC algorithm into the problem of solving polynomial roots to reduce the algorithm complexity and improve the angle estimation accuracy. Its spatial spectrum function is the same as that of the traditional MUSIC algorithm.

[0020] In summary, the traditional MUSIC algorithm and its improved algorithm are based on In actual operation of the algorithm, the estimated value of the covariance matrix is ​​usually used Substituting into the expression in formula (1), we can deduce , Representation and and Covariance matrix When the sample data is large, since the signal and noise are independent of each other, there is ,thus ; However, when the sample size is small, It is difficult to calibrate to 0. The introduced interference cannot be ignored, so when the data sample is small, especially when the signal-to-noise ratio is low, MUSIC and its improved algorithm will be affected. The target angle estimation performance of the algorithm will be significantly degraded due to the interference of the term.

[0021] The Capon algorithm, also known as the Minimum Variance Distortionless Response (MVDR) algorithm, is a multi-target angle estimation algorithm based on beamforming technology. The core idea of ​​the Capon algorithm is to minimize the interference and noise power in other directions while keeping the target signal in a specific direction undistorted by optimizing the weights of each unit in the receiving antenna array. is the beamforming weight vector to be optimized, then the core principle of the Capon algorithm can be expressed as:

[0022] (6)

[0023] in, and The same MUSIC algorithm as above represents the received signal covariance matrix and the corresponding direction angle The closed-form solution of the optimal beamforming vector can be obtained by using the Lagrange multiplier method:

[0024] (7)

[0025] Substituting equation (7) into equation (6), we get the Capon algorithm spectral function:

[0026] (8)

[0027] The directional angle corresponding to the peak of the spectrum function is the estimated target signal AoA.

[0028] Compared with the MUSIC algorithm, the Capon algorithm does not need to explicitly separate the signal and noise subspaces, but directly uses the received signal covariance matrix to perform angle estimation; however, the spectrum peak of the Capon algorithm is not as sharp as that of the MUSIC algorithm, and its angular resolution of multiple adjacent targets is significantly weaker than that of the MUSIC algorithm. In addition, the Capon algorithm, like the MUSIC algorithm, is based on Therefore, the problem of decreased angle estimation accuracy also exists in the case of small samples.

[0029] The existing MUSIC, Capon and related improved algorithms are essentially based on the received signal covariance matrix The estimated value on the sample Perform spatial spectrum analysis to estimate the AoA of the target signal. In practical applications, it is necessary to perform spatial spectrum analysis on the sample data set. Make an estimate and get an estimated value According to the above analysis, we can deduce ,in Representation and Signal and noise Covariance matrix Since the signal and noise are statistically independent, when the sample data set is large, approaches 0; however, when the sample data set is small, Therefore, when the sample data set is small, especially in the case of low signal-to-noise ratio, due to Due to the interference of the term, the target angle estimation performance of the existing algorithm will be significantly reduced.

[0030] Therefore, it is urgent to propose a target parameter estimation method suitable for small sample data scenarios to better meet the needs of future ISAC systems. Summary of the invention

[0031] In order to solve the technical problem of low accuracy of target angle estimation under small sample data in the prior art, an embodiment of the present invention provides a method and device for estimating the arrival angle of a target signal under small sample data. The technical solution is as follows:

[0032] On the one hand, a method for estimating the arrival angle of a target signal based on small sample data is provided. The method is implemented by a target angle estimation device, and the method includes:

[0033] Based on the complete statistics theory, the complete statistics of the received signal sample data set for target angle estimation are determined; where the target angle refers to the arrival angle of the target signal, and the complete statistics of the received signal sample data set for target angle estimation are expressed as:

[0034]

[0035] in, represents the complete statistics of the received signal sample data set about the target angle estimation; M represents the number of samples of the received signal; Indicates the received signal at the receiving end; represents the transmission signal of L transmission array units, represents a real number set, L represents the number of elements of the transmitting antenna array; the superscript H represents the conjugate transpose; Indicates the signal transmission power; and are the sending array response matrix and receiving array response matrix of K independent signals respectively, represents the arrival angle of the K-path signal, and the superscript T represents transposition; It is a diagonal matrix composed of the complex channel gains of K-channel signals; Indicates sending a signal The covariance matrix estimator of ; represents the noise term;

[0036] The target angle is estimated by performing spatial spectrum analysis on the complete statistics of the determined received signal sample data set regarding the target angle estimation.

[0037] Furthermore, the determining of the complete statistics of the received signal sample data set with respect to the target angle estimation based on the complete statistics theory includes:

[0038] Determine the received signal at the receiving end ;

[0039] Confirm the received signal Sending a signal The covariance matrix of ;

[0040] Based on the complete statistics theory and the determined covariance matrix , determine the received signal sample data set Y about the target angle Estimated complete statistics , that is, sending a signal and receive signals The covariance matrix of The estimated amount.

[0041] Furthermore, assuming that the receiving end adopts a uniform linear array, represents the sample count, then the received signal It is expressed as:

[0042]

[0043] in, and Respectively represent the target angle The transmit and receive array response vectors, j represents the imaginary unit; It means the mean is 0 and the variance is The additive complex Gaussian white noise vector of ; N represents the number of elements of the receiving antenna array.

[0044] Furthermore, the received signal Sending a signal The covariance matrix of It is expressed as:

[0045]

[0046] in, represents the transmitted signal covariance matrix, Indicates expectation.

[0047] Furthermore, sending a signal The covariance matrix estimator of , the noise term .

[0048] On the other hand, a device for estimating the angle of arrival of a target signal for small sample data is provided, and the device is applied to a method for estimating the angle of arrival of a target signal for small sample data, and the device comprises:

[0049] The determination module is used to determine the complete statistics of the received signal sample data set about the target angle estimation based on the complete statistics theory; wherein the target angle refers to the arrival angle of the target signal, and the complete statistics of the received signal sample data set about the target angle estimation is expressed as:

[0050]

[0051] in, represents the complete statistics of the received signal sample data set about the target angle estimation; M represents the number of samples of the received signal; Indicates the received signal at the receiving end; represents the transmission signal of L transmission array units, represents a real number set, L represents the number of elements of the transmitting antenna array; the superscript H represents the conjugate transpose; Indicates the signal transmission power; and are the sending array response matrix and receiving array response matrix of K independent signals respectively, represents the arrival angle of the K-path signal, and the superscript T represents transposition; It is a diagonal matrix composed of the complex channel gains of K-channel signals; Indicates sending a signal The covariance matrix estimator of ; represents the noise term;

[0052] The analysis module is used to perform spatial spectrum analysis on the complete statistics of the determined received signal sample data set regarding the target angle estimation to estimate the target angle.

[0053] Furthermore, the determining module is specifically used to determine the received signal of the receiving end ; Determine the received signal Sending a signal The covariance matrix of ; Based on the complete statistics theory and the determined covariance matrix , determine the received signal sample data set Y about the target angle Estimated complete statistics , that is, sending a signal and receive signals The covariance matrix of The estimated amount.

[0054] Furthermore, assuming that the receiving end adopts a uniform linear array, represents the sample count, then the received signal It is expressed as:

[0055]

[0056] in, and Respectively represent the target angle The transmit and receive array response vectors, j represents the imaginary unit; It means the mean is 0 and the variance is The additive complex Gaussian white noise vector of ; N represents the number of elements of the receiving antenna array.

[0057] Furthermore, the received signal Sending a signal The covariance matrix of It is expressed as:

[0058]

[0059] in, represents the transmitted signal covariance matrix, Indicates expectation.

[0060] Furthermore, sending a signal The covariance matrix estimator of , the noise term .

[0061] On the other hand, a target angle estimation device is provided, which includes: a processor; a memory, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, any one of the above-mentioned target signal arrival angle estimation methods for small sample data is implemented.

[0062] On the other hand, a computer-readable storage medium is provided, wherein at least one instruction is stored in the storage medium, and the at least one instruction is loaded and executed by a processor to implement any one of the above-mentioned target signal arrival angle estimation methods for small sample data.

[0063] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0064] The target signal arrival angle estimation method and device for small sample data provided by the present invention estimate the target angle by determining the complete statistics of the received signal sample data set regarding the target angle estimation; and performing spatial spectrum analysis on the determined complete statistics of the received signal sample data set regarding the target angle estimation; in this way, the target angle estimation accuracy can be effectively improved under small sample data, especially under low signal-to-noise ratio, without increasing the complexity of the algorithm. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0066] Figure 1 This is a flow chart of a method for estimating the angle of arrival of a target signal for small sample data provided by an embodiment of the present invention;

[0067] Figure 2 It is a detailed flow chart of a method for estimating the angle of arrival of a target signal for small sample data provided by an embodiment of the present invention;

[0068] Figure 3 It is a block diagram of a target signal arrival angle estimation device for small sample data provided by an embodiment of the present invention;

[0069] Figure 4 It is a structural schematic diagram of a target angle estimation device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0070] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0071] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "example" in the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of the word "example" is intended to present the concept in a specific way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or it can be either of the two.

[0072] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same. "of", "corresponding, relevant" and "corresponding" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same.

[0073] In the embodiments of the present invention, sometimes the subscripts such as W 1 It may be written in non-subscript form such as W1. When the difference is not emphasized, the meaning is the same.

[0074] In order to make the technical problems, technical solutions and advantages to be solved by the present invention more clear, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0075] The embodiment of the present invention provides a method for estimating the arrival angle of a target signal for small sample data. The method can be implemented by a target angle estimation device, which can be a terminal or a server. Specifically, the embodiment of the present invention introduces the complete statistics theory to improve the existing MUSIC algorithm, and proposes a MUSIC algorithm based on the complete statistics theory, namely SS-MUSIC (Sufficient Statistic-Based MUSIC, SS-MUSIC). Compared with the traditional MUSIC, Capon algorithm and its existing improved algorithms, it can effectively improve the target angle estimation accuracy under small sample data, especially under low signal-to-noise ratio, without increasing the complexity of the algorithm. Figure 1 and Figure 2 As shown, the processing flow of the method may include the following steps:

[0076] S1. Based on the complete statistics theory, determine the complete statistics of the received signal sample data set about the target angle estimation; wherein the target angle refers to the arrival angle of the target signal; specifically, the following steps may be included:

[0077] S11, determine the receiving signal of the receiving end ;

[0078] In this embodiment, in order to briefly describe the basic principle of the SS-MUSIC algorithm, it is assumed that the receiving end uses a uniform linear array and the channel is an additive Gaussian white noise (AGWN) channel. represents the sample count, then the received signal It is expressed as:

[0079] (9)

[0080] in, represents the transmission signal of L transmission array units, and are the sending array response matrix and receiving array response matrix of K independent signals respectively, Indicates the AoA of K-channel signal, is a diagonal matrix composed of the complex channel gains of K signals, is the signal transmission power, It means the mean is 0 and the variance is The additive complex Gaussian white noise vector of , L represents the number of elements of the transmitting antenna array, N represents the number of elements of the receiving antenna array, and M represents the number of samples of the received signal; represents the set of real numbers; the superscript T represents the transpose. Specifically: and Respectively represent the target angle The transmit and receive array response vectors, j represents the imaginary unit.

[0081] S12: Determine the received signal Sending a signal The covariance matrix of ;

[0082] In this embodiment, the received signal Sending a signal The covariance matrix of It can be expressed as:

[0083] (10)

[0084] in, represents the transmitted signal covariance matrix, and the superscript H represents the conjugate transpose. Express expectations.

[0085] S13, based on complete statistics theory and determined covariance matrix , determine the received signal sample data set Y about the target angle Estimated complete statistics , that is, sending a signal and receive signals The covariance matrix of The estimated amount.

[0086] In this embodiment, the received signal sample data set Y is about the target angle Estimated complete statistics It can be expressed as:

[0087] (11)

[0088] in, Indicates sending a signal The covariance matrix estimator of .

[0089] In this embodiment, according to the complete statistics theory, under the AWGN channel, the complete statistics of the received signal sample data set Y for target angle estimation is derived as follows: , that is to say, In terms of statistical concepts, it includes All the information estimated; and then the existing algorithm Replace with , thereby effectively improving the target angle estimation accuracy under small sample data, especially under low signal-to-noise ratio.

[0090] In this embodiment, according to the complete statistics theory, under the AWGN channel, the received signal sample data set Y is about the target angle The estimated complete statistic is , that is, sending a signal and receive signals The covariance matrix of In this embodiment, the following is done by Perform spatial spectrum analysis to achieve target angle estimates, rather than Perform spatial spectrum analysis. , Only one The influence of interference terms and the estimation error caused by insufficient data samples are relatively small. Therefore, the proposed SS-MUSIC algorithm can obtain higher angle estimation accuracy than the existing parameter estimation algorithm under small data samples.

[0091] The analysis of interference terms is as follows: For ease of presentation, the channel matrix is Then formula (9) can be equivalently expressed as , we can further get as well as .in, Same as the noise term in formula (11) . It can be observed that Subject to two The influence of relevant interference terms, Only one In addition, due to the influence of relevant interference terms, the target echo signal ,so , superscript represents conjugation. Therefore, it can also be expressed as: Subject to two The influence of relevant interference terms, Subject to an Influence of related interference items. Since the influence of interference items is more significant under small sample data, especially under low signal-to-noise ratio, it can be concluded that the target signal arrival angle estimation method for small sample data provided in this embodiment has more significant performance advantages than the existing angle estimation algorithm under small sample data, especially under low signal-to-noise ratio.

[0092] S2. Performing spatial spectrum analysis on the complete statistics of the determined received signal sample data set regarding the target angle estimation to estimate the target angle may specifically include the following steps:

[0093] S21, decomposing the feature space of the complete statistics of the determined received signal sample data set with respect to the target angle estimation into a signal subspace and a noise subspace;

[0094] In this embodiment, based on formula (11), Perform eigenvalue decomposition (SVD) and get ; Among them, the unitary matrix and for The left and right eigenvector matrices, diagonal matrices yes The eigenvalue matrix of . According to formula (11), It can be expressed as the sum of the signal component and the noise component, so we can further get:

[0095] (12)

[0096] in, and They represent the signal subspace and the noise subspace respectively. The dimension of the signal subspace is theoretically equal to the number of independent signal sources K; Is The diagonal matrix composed of the largest K eigenvalues ​​in corresponds to the weights of the signal subspace; and Then it represents the corresponding K pairs of left and right eigenvectors, corresponding to the direction of the signal subspace; similarly, , and It consists of the remaining eigenvalues ​​and the left and right eigenvectors. Corresponding to the weights of the noise subspace, and corresponds to the direction of the noise subspace.

[0097] S22. Utilize the orthogonal characteristics of the signal subspace and the noise subspace to construct a spatial spectrum function, and determine the target angle by searching for a direction orthogonal to the noise subspace.

[0098] In this embodiment, the orthogonal characteristics of the signal subspace and the noise subspace are used to construct the spatial spectrum function , where the spatial spectrum function It is expressed as:

[0099] (13)

[0100] in, Indicates the target angle The receiving array response vector of

[0101] By calculation And search for the peak position of the spectrum function to obtain the target angle .

[0102] In summary, the target signal arrival angle estimation method for small sample data provided by an embodiment of the present invention, namely, the SS-MUSIC algorithm, can effectively improve the target angle estimation accuracy under small sample data, especially under low signal-to-noise ratio, without increasing the complexity of the algorithm when the data sample size is small. This can effectively ensure target positioning accuracy and improve system service performance in innovative applications such as Internet of Vehicles and smart parks, and is particularly suitable for radar systems with limited observation time and ISAC systems with tight wireless resources.

[0103] It is important to emphasize that:

[0104] (1) The embodiments of the present invention protect the basic design framework of improving the existing MUSIC, Capon and related algorithms by using complete statistical theory;

[0105] (2) The “MUSIC, Capon algorithm and existing improved algorithms thereof” in the embodiments of the present invention are not limited to the MUSIC algorithm and the Capon algorithm, but also involve all related improved algorithms based on the MUSIC and Capon architectures. For example, including but not limited to WMUSIC, ROOT-MUSIC and various two-dimensional MUSIC algorithms;

[0106] (3) The SS-MUSIC algorithm provided in the embodiment of the present invention is not limited to the AWGN channel scenario. In other channel scenarios or interference conditions, the complete statistics theory is used to derive the complete statistics of the received signal sample data set regarding the target angle estimation, and the complete statistics are applied to the existing MUSIC, Capon and related improved algorithms, which are also within the protection scope of the present invention.

[0107] (4) Parameter estimation categories involved in the embodiments of the present invention include but are not limited to target angle, distance, speed, etc.

[0108] Figure 3 The block diagram of a device for estimating the arrival angle of a target signal for small sample data according to an exemplary embodiment is shown. The device is used in a method for estimating the arrival angle of a target signal for small sample data. Figure 3 , the device includes a determination module 310 and an analysis module 320. Wherein:

[0109] The determination module 310 is used to determine the complete statistics of the received signal sample data set with respect to the target angle estimation based on the complete statistics theory; wherein the target angle refers to the arrival angle of the target signal, and the complete statistics of the received signal sample data set with respect to the target angle estimation is expressed as:

[0110]

[0111] in, represents the complete statistics of the received signal sample data set about the target angle estimation; M represents the number of samples of the received signal; Indicates the received signal at the receiving end; represents the transmission signal of L transmission array units, represents a real number set, L represents the number of elements of the transmitting antenna array; the superscript H represents the conjugate transpose; Indicates the signal transmission power; and are the sending array response matrix and receiving array response matrix of K independent signals respectively, represents the arrival angle of the K-path signal, and the superscript T represents transposition; It is a diagonal matrix composed of the complex channel gains of K-channel signals; Indicates sending a signal The covariance matrix estimator of ; represents the noise term;

[0112] The analysis module 320 is used to perform spatial spectrum analysis on the complete statistics of the determined received signal sample data set regarding the target angle estimation to estimate the target angle.

[0113] In this embodiment, the determining module 310 is specifically used to determine the received signal of the receiving end. ; Determine the received signal Sending a signal The covariance matrix of ; Based on the complete statistics theory and the determined covariance matrix , determine the received signal sample data set Y about the target angle Estimated complete statistics , that is, sending a signal and receive signals The covariance matrix of The estimated amount.

[0114] In this embodiment, it is assumed that the receiving end adopts a uniform linear array. represents the sample count, then the received signal It is expressed as:

[0115]

[0116] in, and Respectively represent the target angle The transmit and receive array response vectors, j represents the imaginary unit; It means the mean is 0 and the variance is The additive complex Gaussian white noise vector of ; N represents the number of elements of the receiving antenna array.

[0117] In this embodiment, the received signal Sending a signal The covariance matrix of It is expressed as:

[0118]

[0119] in, represents the transmitted signal covariance matrix, Indicates expectation.

[0120] In this embodiment, the signal is sent The covariance matrix estimator of , the noise term .

[0121] Figure 4 is a schematic diagram of the structure of a target angle estimation device provided by an embodiment of the present invention, such as Figure 4 As shown, the target angle estimation device may include the above Figure 3 The target signal arrival angle estimation device for small sample data is shown. Optionally, the target angle estimation device 410 may include a first processor 2001.

[0122] Optionally, the target angle estimation device 410 may further include a memory 2002 and a transceiver 2003 .

[0123] The first processor 2001, the memory 2002 and the transceiver 2003 may be connected via a communication bus.

[0124] Combine the following Figure 4 The components of the target angle estimation device 410 are specifically introduced as follows:

[0125] The first processor 2001 is the control center of the target angle estimation device 410, and may be a processor or a general term for multiple processing elements. For example, the first processor 2001 is one or more central processing units (CPUs), or may be application specific integrated circuits (ASICs), or may be configured to implement one or more integrated circuits of the embodiments of the present invention, such as one or more microprocessors (digital signal processors, DSPs), or one or more field programmable gate arrays (field programmable gate arrays, FPGAs).

[0126] Optionally, the first processor 2001 may perform various functions of the target angle estimation device 410 by running or executing a software program stored in the memory 2002 and calling data stored in the memory 2002 .

[0127] In a specific implementation, as an embodiment, the first processor 2001 may include one or more CPUs, such as Figure 4 CPU0 and CPU1 are shown in FIG.

[0128] In a specific implementation, as an embodiment, the target angle estimation device 410 may also include multiple processors, such as Figure 4 The first processor 2001 and the second processor 2004 are shown in FIG. Each of these processors may be a single-core processor (single-CPU) or a multi-core processor (multi-CPU). The processor here may refer to one or more devices, circuits, and / or processing cores for processing data (e.g., computer program instructions).

[0129] The memory 2002 is used to store the software program for executing the solution of the present invention, and is controlled to be executed by the first processor 2001. The specific implementation method can refer to the above method embodiment, which will not be repeated here.

[0130] Optionally, the memory 2002 may be a read-only memory (ROM) or other types of static storage devices that can store static information and instructions, a random access memory (RAM) or other types of dynamic storage devices that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical disc, laser disc, optical disc, digital versatile disc, Blu-ray disc, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory 2002 may be integrated with the first processor 2001, or may exist independently, and access the target angle estimation device 410 through the interface circuit ( Figure 4 (not shown) is coupled to the first processor 2001, which is not specifically limited in this embodiment of the present invention.

[0131] The transceiver 2003 is used to communicate with a network device or a terminal device.

[0132] Optionally, the transceiver 2003 may include a receiver and a transmitter ( Figure 4 The receiver is used to implement a receiving function, and the transmitter is used to implement a sending function.

[0133] Optionally, the transceiver 2003 may be integrated with the first processor 2001, or may exist independently, and may be connected to the target angle estimation device 410 through an interface circuit ( Figure 4 (not shown) is coupled to the first processor 2001, which is not specifically limited in this embodiment of the present invention.

[0134] It should be noted that Figure 4 The structure of the target angle estimation device 410 shown in the figure does not constitute a limitation on the router, and the actual knowledge structure recognition device may include more or less components than those shown in the figure, or combine certain components, or arrange the components differently.

[0135] In addition, the technical effects of the target angle estimation device 410 can refer to the technical effects of the target signal arrival angle estimation method for small sample data described in the above method embodiment, which will not be repeated here.

[0136] It should be understood that the first processor 2001 in the embodiment of the present invention may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0137] It should also be understood that the memory in the embodiments of the present invention may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0138] The above embodiments can be implemented in whole or in part by software, hardware (such as circuits), firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0139] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0140] In the present invention, "at least one" means one or more, and "plurality" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b, or c can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0141] It should be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0142] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0143] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described equipment, devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0144] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0145] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0146] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0147] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program codes.

[0148] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for estimating the angle of arrival of a target signal for small sample data, characterized in that: The method comprises: Based on the complete statistics theory, the complete statistics of the received signal sample data set for target angle estimation are determined; where the target angle refers to the arrival angle of the target signal, and the complete statistics of the received signal sample data set for target angle estimation are expressed as: ； in, represents the complete statistics of the received signal sample data set about the target angle estimation; M represents the number of samples of the received signal; Indicates the received signal at the receiving end; represents the transmission signal of L transmission array units, represents a real number set, L represents the number of elements of the transmitting antenna array; the superscript H represents the conjugate transpose; Indicates the signal transmission power; and are the sending array response matrix and receiving array response matrix of K independent signals respectively, represents the arrival angle of the K-path signal, and the superscript T represents transposition; It is a diagonal matrix composed of the complex channel gains of K-channel signals; Indicates sending a signal The covariance matrix estimator of ; represents the noise term; The target angle is estimated by performing spatial spectrum analysis on the complete statistics of the determined received signal sample data set regarding the target angle estimation.

2. The method for estimating the angle of arrival of a target signal based on small sample data according to claim 1, characterized in that: The method of determining the complete statistics of the received signal sample data set about the target angle estimation based on the complete statistics theory includes: Determine the received signal at the receiving end ; Confirm the received signal Sending a signal The covariance matrix of ; Based on the complete statistics theory and the determined covariance matrix , determine the received signal sample data set Y about the target angle Estimated complete statistics , that is, sending a signal and receive signals The covariance matrix of The estimated amount.

3. The method for estimating the arrival angle of a target signal based on small sample data according to claim 1, characterized in that: Assume that the receiving end adopts a uniform linear array, represents the sample count, then the received signal It is expressed as: ； in, and Respectively represent the target angle The transmit and receive array response vectors, j represents the imaginary unit; It means the mean is 0 and the variance is The additive complex Gaussian white noise vector of ; N represents the number of elements of the receiving antenna array.

4. The method for estimating the angle of arrival of a target signal based on small sample data according to claim 1, characterized in that: Receiving Signals Sending a signal The covariance matrix of It is expressed as: ； in, represents the transmitted signal covariance matrix, Indicates expectation.

5. The method for estimating the arrival angle of a target signal based on small sample data according to claim 3, characterized in that: Sending a signal The covariance matrix estimator of , the noise term .

6. A target signal arrival angle estimation device for small sample data, the target signal arrival angle estimation device for small sample data is used to implement the target signal arrival angle estimation method for small sample data as claimed in any one of claims 1 to 5, characterized in that: The device comprises: The determination module is used to determine the complete statistics of the received signal sample data set about the target angle estimation based on the complete statistics theory; wherein the target angle refers to the arrival angle of the target signal, and the complete statistics of the received signal sample data set about the target angle estimation is expressed as: ； in, represents the complete statistics of the received signal sample data set about the target angle estimation; M represents the number of samples of the received signal; Indicates the received signal at the receiving end; represents the transmission signal of L transmission array units, represents a real number set, L represents the number of elements of the transmitting antenna array; the superscript H represents the conjugate transpose; Indicates the signal transmission power; and are the sending array response matrix and receiving array response matrix of K independent signals respectively, represents the arrival angle of the K-path signal, and the superscript T represents transposition; It is a diagonal matrix composed of the complex channel gains of K-channel signals; Indicates sending a signal The covariance matrix estimator of ; represents the noise term; The analysis module is used to perform spatial spectrum analysis on the complete statistics of the determined received signal sample data set regarding the target angle estimation to estimate the target angle.

7. The target signal arrival angle estimation device for small sample data according to claim 6, characterized in that: The determining module is specifically used to determine the received signal of the receiving end ; Determine the received signal Sending a signal The covariance matrix of ; Based on the complete statistics theory and the determined covariance matrix , determine the received signal sample data set Y about the target angle Estimated complete statistics , that is, sending a signal and receive signals The covariance matrix of The estimated amount.

8. The target signal arrival angle estimation device for small sample data according to claim 6, characterized in that: Assume that the receiving end adopts a uniform linear array, represents the sample count, then the received signal It is expressed as: ； in, and Respectively represent the target angle The transmit and receive array response vectors, j represents the imaginary unit; It means the mean is 0 and the variance is The additive complex Gaussian white noise vector of ; N represents the number of elements of the receiving antenna array.

9. The target signal arrival angle estimation device for small sample data according to claim 6, characterized in that: Receiving Signals Sending a signal The covariance matrix of It is expressed as: ； in, represents the transmitted signal covariance matrix, Indicates expectation.

10. The target signal arrival angle estimation device for small sample data according to claim 8, characterized in that: Sending a signal The covariance matrix estimator of , the noise term .

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