Method and device for estimating direction of arrival of satellite positioning signal

Abstract

One or more embodiments of the invention provide a method and a device for estimating the direction of arrival of a satellite positioning signal. The method comprises the following steps: converting a received radio frequency signal into a digital baseband signal; wherein the radio frequency signal comprises a satellite positioning signal sent by a satellite; based on a preset time interval, performing correlation operation on the digital baseband signal in each time segment and a reference signal of a satellite to obtain a correlation signal corresponding to each time segment; based on the digital baseband signal in each time segment, calculating to obtain a noise covariance matrix corresponding to each time segment; wherein the noise covariance matrix is used for representing spatial statistical characteristics of interference signals and thermal noise in the digital baseband signals in each time segment; and on the basis of the noise covariance matrix and the related signal, establishing a likelihood function corresponding to each time segment, and deriving the direction of arrival of the satellite positioning signal by adopting a maximum likelihood criterion.

Description

Technical Field

[0001] One or more embodiments of the present disclosure relate to the field of satellite communication technology, and more particularly, to a method and apparatus for estimating the direction of arrival of a satellite positioning signal. Background Art

[0002] The Global Navigation Satellite System (GNSS), a core technology for positioning, navigation, and timing (PNT), has been widely adopted in military and civilian applications. Its core functions (such as positioning, navigation, and timing) rely on receivers accurately processing positioning signals from multiple satellites (such as GNSS signals). However, GNSS signals are weak and susceptible to interference from other signals. This interference can cause unreliable reception of satellite positioning signals or even complete failure.

[0003] In satellite positioning signal processing, Direction of Arrival (DOA) estimation is fundamental to many key applications. Related technologies employ some DOA estimation techniques specifically for static interference scenarios. For example, after suppressing interference through matrix filtering of pre-correlation snapshot signals, DOA estimation is combined with an improved Multiple Signal Classification Algorithm (MUSIC). Alternatively, DOA can be derived using the maximum likelihood criterion by leveraging the statistical properties of pre-correlation and post-correlation snapshot signals.

[0004] However, the above scheme cannot effectively handle scenarios where the spatial characteristics of interference change rapidly, such as flicker interference. Its static interference assumption will severely degrade the accuracy of DOA estimation in such scenarios, rendering DOA estimation completely ineffective. Summary of the Invention

[0005] In view of this, one or more embodiments of this specification provide the following technical solutions:

[0006] According to a first aspect of one or more embodiments of the present specification, a method for estimating the direction of arrival of a satellite positioning signal is proposed, comprising: converting a received radio frequency signal into a digital baseband signal; wherein the radio frequency signal includes a satellite positioning signal transmitted by a satellite; based on a preset time interval, correlating the digital baseband signal in each time segment with a reference signal of the satellite to obtain a correlation signal corresponding to each time segment; based on the digital baseband signal in each time segment, calculating a noise covariance matrix corresponding to each time segment; wherein the noise covariance matrix is used to represent the spatial statistical characteristics of interference signals and thermal noise in the digital baseband signal in each time segment; based on the noise covariance matrix and the correlation signal, constructing a likelihood function corresponding to each time segment, and deriving the direction of arrival of the satellite positioning signal using a maximum likelihood criterion.

[0007] According to a second aspect of one or more embodiments of the present specification, a device for estimating the direction of arrival of a satellite positioning signal is proposed, comprising: a signal receiving module for converting a received radio frequency signal into a digital baseband signal; wherein the radio frequency signal comprises a satellite positioning signal transmitted by a satellite; a first operation module for performing a correlation operation on the digital baseband signal in each time segment with a reference signal from the satellite, based on a preset time interval, to obtain a correlation signal corresponding to each time segment; a second operation module for calculating, based on the digital baseband signal in each time segment, a noise covariance matrix corresponding to each time segment; wherein the noise covariance matrix is used to represent the spatial statistical characteristics of interference signals and thermal noise in the digital baseband signal in each time segment; and a direction of arrival estimation module for constructing a likelihood function corresponding to each time segment based on the noise covariance matrix and the correlation signal, and deriving the direction of arrival of the satellite positioning signal using a maximum likelihood criterion.

[0008] According to a third aspect of one or more embodiments of this specification, an electronic device is proposed, comprising: a processor; a memory for storing processor-executable instructions; wherein the processor implements the steps of the method described in the first aspect by running the executable instructions.

[0009] According to a fourth aspect of one or more embodiments of this specification, a computer-readable storage medium is provided, on which computer instructions are stored. When the instructions are executed by a processor, the steps of the method described in the first aspect are implemented.

[0010] According to a fifth aspect of one or more embodiments of this specification, a computer program product is proposed, comprising a computer program / instruction, which implements the steps of the method described in the first aspect when executed by a processor.

[0011] It can be seen from the above embodiments that by modeling the received interference signal as segmented static, calculating the relevant signal corresponding to each time segment and the independently estimated interference covariance matrix, and then constructing the likelihood function of each time segment, and using the maximum likelihood criterion to derive the direction of arrival of the satellite positioning signal, the degree of freedom is avoided from being reduced or insufficient due to the interference averaging effect of the relevant DOA estimation technology in the entire time period, thereby improving the accuracy of the satellite positioning signal DOA estimation when facing a suppression interference signal with time-varying spatial characteristics. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 The present invention is a flowchart of a method for estimating the direction of arrival of a satellite positioning signal provided by an exemplary embodiment.

[0013] Figure 2 The present invention is a schematic structural diagram of a satellite positioning signal and an interference signal provided by an exemplary embodiment.

[0014] Figure 3 This is a pulse diagram of an interference signal provided by an exemplary embodiment.

[0015] Figure 4 This is a segmented static schematic diagram of an interference signal provided by an exemplary embodiment.

[0016] Figure 5 It is a structural diagram of a device provided by an exemplary embodiment.

[0017] Figure 6 The present invention is a block diagram of a satellite positioning signal direction of arrival estimation device provided by an exemplary embodiment. DETAILED DESCRIPTION

[0018] The user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this manual are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with the relevant laws, regulations and standards of relevant countries and regions, and corresponding operation entrances are provided for users to choose to authorize or refuse.

[0019] The Global Navigation Satellite System (GNSS), a core technology for positioning, navigation, and timing (PNT), has been widely used in military and civilian applications. However, due to the weak power of GNSS signals, they are susceptible to radio frequency interference (RFI) and other signal interference. These interferences can cause unreliable signal reception or even complete failure, especially in complex environments. In recent years, the increasing variety and complexity of interfering signals have posed significant challenges to GNSS signal processing.

[0020] In GNSS signal processing, direction of arrival (DOA) estimation is fundamental to many key applications. For example, DOA information can be used in scenarios such as attitude determination, anti-interference beamforming, and spoofing signal detection. However, DOA estimation for GNSS signals faces many practical challenges. First, GNSS signals are below thermal noise, resulting in an extremely low signal-to-noise ratio (C / N). Traditional methods rely on processing the statistical properties of large amounts of snapshot data to extract DOA information. Second, interference signals significantly impact GNSS signals during reception, potentially masking the spatial characteristics of GNSS signals and further reducing DOA estimation accuracy. Existing research has proposed several DOA estimation methods for static interference scenarios. For example, matrix filtering of pre-correlation snapshot signals is used to suppress interference, followed by a modified MUSIC algorithm for DOA estimation. Alternatively, the statistical properties of pre-correlation and post-correlation snapshot signals are combined to derive DOA using the maximum likelihood criterion.

[0021] However, almost all current research is limited to the assumption that interference characteristics do not change over time, and cannot effectively handle scenarios where the spatial characteristics of interference change rapidly, such as flicker interference. Flicker interference is a typical form of highly dynamic interference, in which the switching patterns of multiple interference sources change rapidly, which causes the spatial characteristics of the received signal to change significantly over time. The traditional static interference assumption will lead to an "interference averaging effect" in such scenarios, that is, the interference states of multiple time segments are averaged, which increases the number of equivalent interference signals. This effect not only severely reduces the accuracy of DOA estimation, but may also lead to the exhaustion of the degrees of freedom (DoF) of the antenna array, thereby completely rendering the DOA estimation ineffective. Therefore, how to design a DOA estimation algorithm that can dynamically adapt to the time-varying characteristics of interference has become an important problem that needs to be solved in GNSS signal processing.

[0022] In light of this, this specification proposes a method for estimating the direction of arrival of satellite positioning signals. The received interference signal is modeled as a segmented static state. The interference covariance matrix for each time segment is independently estimated. A likelihood function corresponding to each time segment is constructed, and the direction of arrival of the satellite positioning signal is derived using the maximum likelihood criterion.

[0023] During implementation, the received radio frequency signal is converted into a digital baseband signal; wherein the radio frequency signal includes a satellite positioning signal transmitted by a satellite; based on a preset time interval, a code correlation integral operation is performed on the digital baseband signal in each time segment and the reference signal of the satellite to obtain a correlation signal corresponding to each time segment; a noise covariance matrix corresponding to the digital baseband signal in each time segment is calculated; based on the noise covariance matrix and the correlation signal, a likelihood function corresponding to each time segment is constructed, and the direction of arrival of the satellite positioning signal is derived using a maximum likelihood criterion.

[0024] In the above technical solution, by modeling the received interference signal as segmented static, the relevant signal corresponding to each time segment and the independently estimated interference covariance matrix are calculated, and then the likelihood function of each time segment is constructed, and the maximum likelihood criterion is used to derive the direction of arrival of the satellite positioning signal, thereby avoiding the decrease or lack of degrees of freedom caused by the interference averaging effect of the relevant DOA estimation technology in the entire time period, and improving the accuracy and reliability of the satellite positioning signal DOA estimation when facing the time-varying suppression interference signal with spatial characteristics.

[0025] In order to enable people skilled in the art to better understand the technical solutions in this application, the technical solutions in this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this application.

[0026] See Figure 1 , Figure 1 An exemplary embodiment provides a method for estimating the direction of arrival of a satellite positioning signal. The method can be performed by a device that receives satellite positioning signals, such as a terminal or a navigation device. The method may include the following steps:

[0027] S110 . Convert the received radio frequency signal into a digital baseband signal; wherein the radio frequency signal includes a satellite positioning signal sent by a satellite.

[0028] In one embodiment, the receiving device may receive a radio frequency signal through an antenna or antenna array provided therewith, and the radio frequency signal may include a satellite positioning signal (such as a GNSS signal) and an interference signal transmitted by at least one satellite. The interference signal may include a plurality of interference signals with time-varying spatial characteristics, such as a flickering interference signal. The flickering interference signal may specifically refer to a situation where multiple interference sources are alternately turned on or off at different time intervals, thereby posing a significant dynamic interference threat to the receiver. For example, Figure 2 As shown, when the satellite 201 sends a GNSS signal to the terminal 202, there are two interference sources 211 and 212, and these two interference sources are alternately turned on or off at different time intervals.

[0029] After receiving the RF signal, it needs to be converted into a digital baseband signal through a preprocessing process. For example, down-conversion processing is performed through the RF front end to convert the high-frequency signal into an intermediate frequency or baseband signal suitable for digital processing; then the analog signal is sampled through the analog-to-digital converter (ADC) to generate a discrete digital baseband signal according to the Nyquist sampling theorem, providing a basis for subsequent processing.

[0030] In one embodiment, a baseband signal model can be pre-built to describe the digital baseband signal data. In a flicker interference scenario, a general antenna array model (such as a uniform linear array (ULA), a planar array (Planar Array), etc.) is considered. The antenna array may include M antenna units for receiving K s GNSS signals and K J An intermittent interference signal. Under the assumption of far-field and narrowband signals, the signal incident from the direction of arrival γ can be expressed as the product of the steering vector a(γ) and the signal waveform s. Therefore, the vector model of the digital baseband signal, that is, the pre-correlation signal model, can be expressed as:

[0031]

[0032] Among them, T s represents the sampling interval, represents the DOA of the k-th GNSS signal, is the waveform of the kth GNSS signal, the interference signal n J (iT s ) indicates that all K J The cumulative effect of interference sources on the received signal of the antenna array, n T (iT s ) represents the thermal noise, which is modeled as temporal white noise and obeys a complex symmetric Gaussian distribution.

[0033] In one embodiment, a model of interference signals received by the array antenna can be pre-built. In a flicker interference scenario, the interference signal waveform emitted by the lth interference source in its on state can be expressed as an on-state interference waveform. With the pulse signal p l The product of the pulse signal determines the on or off state of each interference source. Therefore, the interference noise n received by the antenna array is J It can be expressed as:

[0034] in, represents the DOA of the lth interference signal.

[0035] In one embodiment, the interference signal may be of various types such as continuous wave, swept frequency, pulse, and broadband interference.

[0036] In one embodiment, the interference signal is broadband interference, It can be a zero-mean Gaussian distribution to represent broadband interference. Broadband interference needs to be suppressed in the spatial domain by array antennas. l Defines the switching pattern of the lth interference source in the flicker interference scenario. For example, Figure 3 The pulse signals p1 and p2 corresponding to the two interference sources 211 and 212 are shown, using a typical synchronous flash interference mode, where the interference period is T g , the two interference sources are activated in sequence, and the duration of each activation is T1 and T2 respectively.

[0037] It should be noted that the proposed signal model is not only applicable to synchronous flicker interference, but can also be extended to the signal model generated by p l A more general interference pattern is defined.

[0038] S120 . Based on a preset time interval, perform a correlation operation on the digital baseband signal in each time segment and a reference signal of the satellite to obtain a correlation signal corresponding to each time segment.

[0039] In one embodiment, because the power of satellite positioning signals is relatively weak and the received digital baseband signal may contain multiple satellite positioning signals, a correlation operation may be performed on the digital baseband signal with a locally stored reference signal of a satellite. This allows the reference signal of the target satellite to enhance the positioning signal of the corresponding target satellite and suppress positioning signals of other satellites. The reference signal may be a pseudorandom noise (PRN) code.

[0040] In one embodiment, the present application proposes a segmented static hypothesis. Although the interference signal has spatial characteristic changes, this change is relatively constant relative to the preset time interval T. c(e.g. 1ms of L1C / A signal) occurs in a longer time. Therefore, it can be assumed that in each time interval T c Within, the interference signal can be regarded as static, that is, there is no change in spatial characteristics, such as Figure 4 As shown, interference signal 1 and interference signal 2 appear alternately, then in each time interval T c The interference signal within can be regarded as segmented static.

[0041] In one embodiment, a correlation operation (also called code correlation integration operation) is performed on the digital baseband signal in each time segment and the reference signal of the satellite at a preset time interval as the correlation time interval (or correlation integration time), thereby obtaining a correlation signal corresponding to each time segment.

[0042] In one embodiment, the preset time interval T c The digital baseband signal in is conjugate-multiplied with the reference signal of the target satellite, and the sum is accumulated to obtain the correlation signal of the satellite positioning signal of the target satellite in each time segment.

[0043] In one embodiment, the satellite positioning signal sent by the kth satellite is in the time interval T with the corresponding reference signal. c The calculation formula for the relevant operation can be expressed as follows:

[0044]

[0045] in, Indicates that the nth time interval T c The relevant signal corresponding to the satellite positioning signal sent by the kth satellite in the divided time segment, T s represents the sampling interval, p is the sampling point index, x(nT c +pT s ) represents the time interval T at the nth preset time interval c The baseband digital signal within, r k represents the reference signal of the kth satellite; N s In a time interval T c The number of sampling points in s =T c / T s For simplicity, the time interval T c The sampling interval T s An integer multiple of , such as 20, 100, 1000, etc.

[0046] In one embodiment, after omitting the subscript k, based on the above correlation operation, the following correlation signal model can be constructed:

[0047]

[0048] in, Indicates that at the nth preset time interval T c The related signal within, P represents the signal power, β represents the phase offset, represents the correlated interference signal, represents correlated white Gaussian noise.

[0049] In one embodiment, assuming that the waveform of the interference signal follows a Gaussian distribution, the interference signal and thermal noise can be combined into a comprehensive spatial noise signal. The simplified digital baseband signal model can be expressed as:

[0050]

[0051] Among them, n(iT s ) represents the integrated spatial noise signal in the digital baseband signal.

[0052] Correspondingly, the simplified correlation signal model can be expressed as:

[0053]

[0054] in, represents the integrated spatial noise signal in the correlated signal.

[0055] S130. Based on the digital baseband signal in each time segment, calculate a noise covariance matrix corresponding to each time segment; wherein the noise covariance matrix is used to represent the spatial statistical characteristics of the interference signal and thermal noise in the digital baseband signal in each time segment.

[0056] In one embodiment, the noise covariance matrix corresponding to each time segment can be accurately estimated by the digital baseband signal in each time segment. represents the noise covariance matrix at the nth time segment.

[0057] In one embodiment, since the power of the satellite signal is lower than that of the white noise, its contribution can be ignored, and the noise covariance matrix can be accurately estimated by the following formula:

[0058]

[0059] in, represents the noise covariance matrix in the nth time segment, represents the noise covariance matrix obtained based on the digital baseband signal, and C can be a normalization constant.

[0060] S140: Based on the noise covariance matrix and the correlation signal, construct a likelihood function corresponding to each time segment, and derive the direction of arrival of the satellite positioning signal using a maximum likelihood criterion.

[0061] In one embodiment, after calculating the noise covariance matrix and the correlation signal of each time segment, a likelihood function corresponding to each time segment can be constructed, and the direction of arrival of the satellite positioning signal can be derived using a maximum likelihood criterion.

[0062] In one embodiment, the DOA of the satellite positioning signal can be used as the parameter to be estimated and the related signal as the observation data to construct the likelihood function of each time segment. The likelihood function corresponding to each time segment can be summarized, for example, by accumulation or accumulation to determine the objective function, and the direction of arrival of the satellite positioning signal can be derived by maximizing the objective function to obtain an estimated result of the direction of arrival of the satellite positioning signal.

[0063] In the above technical solution, by modeling the received interference signal as segmented static, the relevant signal corresponding to each time segment and the independently estimated interference covariance matrix are calculated, and then the likelihood function of each time segment is constructed, and the maximum likelihood criterion is used to derive the direction of arrival of the satellite positioning signal, thereby avoiding the decrease or lack of degrees of freedom caused by the interference averaging effect of the relevant DOA estimation technology in the entire time period, and improving the accuracy and reliability of the satellite positioning signal DOA estimation when facing the time-varying suppression interference signal with spatial characteristics.

[0064] In one embodiment, a log-likelihood function corresponding to each time segment can be constructed based on the noise covariance matrix and the correlation signal. The parameters to be estimated in the log-likelihood function include the direction of arrival (DOA) of the satellite positioning signal, and the observed data include the correlation signal. A global log-likelihood function is then accumulated for each time segment. The parameters to be estimated in the global data likelihood function include the DOA of the satellite positioning signal, and the observed data includes the correlation signal matrix. An objective function is constructed based on the global log-likelihood function, and an estimate of the direction of arrival (DOA) of the satellite positioning signal is obtained by maximizing the objective function.

[0065] In one embodiment, the global log-likelihood function is expressed as:

[0066]

[0067] Among them, the observed data of the log-likelihood function Represents the correlation signal matrix, which can be represented by N c Column-related signals Composition, N crepresents the number of time segments within the observation time. The estimated parameter γ of the log-likelihood function represents the direction of arrival of the satellite positioning signal. Another estimated parameter represents complex gain, P represents signal power, β represents phase shift, and C1 is a constant.

[0068] In a practical approach, a global log-likelihood function may be used as an objective function to derive the direction of arrival γ of the satellite positioning signal by maximizing the objective function.

[0069] In the above embodiment, the present application proposes a piecewise likelihood accumulation DOA estimation method (Piecewise Likelihood Accumulation Estimation, PLAE), which accumulates signal energy segment by segment and suppresses time-varying interference by accumulating the log-likelihood functions of each time segment to form an objective function. Compared with the traditional method of simply averaging the interference of the entire time period, this method can avoid the confusion of interference characteristics of different segments (such as the difference in on-off state of flicker interference), thereby effectively retaining the time-varying information of the interference, improving the sensitivity of the objective function to the true DOA, and ultimately achieving more accurate wave direction of arrival estimation.

[0070] In one embodiment, in order to simplify the above derivation process, the log-likelihood function can be simplified by spatial whitening transformation to obtain a simplified log-likelihood function. Define the spatial whitening matrix The correlation signal after spatial whitening transformation is recorded as The whitened steering vector is denoted as a n (γ)=W n a(γ), the obtained simplified log-likelihood function.

[0071] Then, the global simplified log-likelihood function can be obtained by accumulating the simplified log-likelihood functions of each time segment, which can be expressed as follows:

[0072]

[0073] in, represents the correlation signal after spatial whitening transformation, Represents the defined spatial whitening matrix, a n (γ)=W n a(γ) represents the steering vector after whitening.

[0074] In a practical approach, a global simplified log-likelihood function may be used as an objective function to derive the direction of arrival γ of the satellite positioning signal by maximizing the objective function.

[0075] In the above embodiment, the log-likelihood function is simplified to a Euclidean distance minimization problem through spatial whitening transformation, which can eliminate the spatial correlation of noise and convert colored noise into white noise, thereby reducing the complexity of the optimization problem.

[0076] In one embodiment, since the parameters to be estimated of the log-likelihood function include the direction of arrival γ and the complex gain ζ of the satellite positioning signal, the complex gain ζ can be first optimized by Wirtinger derivative to obtain its maximum likelihood estimate:

[0077]

[0078] Will Substituting the global simplified log-likelihood function, maximizing the global simplified log-likelihood function is equivalent to maximizing the following objective function:

[0079]

[0080] By searching for the direction of arrival γ that maximizes the objective function F(γ), the estimated result of the direction of arrival of the satellite positioning signal is obtained.

[0081] In the above embodiment, optimizing the complex gain through the Wirtinger derivative allows for analytical separation of the signal's amplitude and phase parameters, simplifying the original dual-parameter optimization problem (DOA and complex gain) to a single-parameter optimization problem (DOA only). The resulting objective function, based on maximizing the ratio of signal energy to total energy, has a clear physical meaning and is computationally efficient. It can be quickly solved using traversal search or gradient algorithms, significantly reducing computational complexity while maintaining accuracy, making it suitable for real-time dynamic scenarios.

[0082] Figure 5 This is a schematic structural diagram of a device provided by an exemplary embodiment. Figure 5 At the hardware level, the device includes a processor 502, an internal bus 504, a network interface 506, a memory 508, and a non-volatile memory 510. Of course, it may also include hardware required for other functions. One or more embodiments of this specification can be implemented based on software, such as the processor 502 reading the corresponding computer program from the non-volatile memory 510 into the memory 508 and then running it. Of course, in addition to software implementation, one or more embodiments of this specification do not exclude other implementation methods, such as logic devices or a combination of software and hardware, etc., that is, the execution subject of the following processing flow is not limited to each logic unit, but can also be hardware or logic devices.

[0083] Please refer to Figure 6 , the satellite positioning signal direction of arrival estimation device can be applied to Figure 5The device shown in the figure is used to implement the technical solution of this specification. The satellite positioning signal direction of arrival estimation device may include: a signal receiving module 601, a first operation module 602, a second operation module 603 and a direction of arrival estimation module 604. The signal receiving module 601 is used to convert the received radio frequency signal into a digital baseband signal; wherein the radio frequency signal includes a satellite positioning signal transmitted by a satellite; the first operation module 602 is used to perform a correlation operation on the digital baseband signal in each time segment with the satellite reference signal based on a preset time interval to obtain a correlation signal corresponding to each time segment; the second operation module 603 is used to calculate the noise covariance matrix corresponding to each time segment based on the digital baseband signal in each time segment; wherein the noise covariance matrix is used to represent the spatial statistical characteristics of the interference signal and thermal noise in the digital baseband signal in each time segment; the direction of arrival estimation module 604 is used to construct a likelihood function corresponding to each time segment based on the noise covariance matrix and the correlation signal, and derive the direction of arrival of the satellite positioning signal using the maximum likelihood criterion.

[0084] In one embodiment, the direction of arrival estimation module 604 is used to construct a log-likelihood function corresponding to each time segment based on the noise covariance matrix and the related signal; obtain an objective function by accumulating the log-likelihood functions of each time segment, and obtain an estimated result of the direction of arrival of the satellite positioning signal by maximizing the objective function.

[0085] In one embodiment, the direction of arrival estimation module 604 is configured to simplify the log-likelihood function by spatial whitening transformation to obtain a simplified log-likelihood function; and obtain a target function by accumulating the simplified log-likelihood functions of each time segment.

[0086] In one embodiment, the direction of arrival estimation module 604 is configured to optimize the complex gain ζ by using Wirtinger derivatives to obtain a maximum likelihood estimate of the complex gain; and determine an objective function based on the maximum likelihood estimate of the complex gain.

[0087] Based on the same concept as the above method, this specification also provides an electronic device, including: a processor; a memory for storing processor-executable instructions; wherein the processor implements the steps of the method described in any of the above embodiments by running the executable instructions.

[0088] Based on the same concept as the above method, this specification also provides a computer-readable storage medium on which computer instructions are stored. When the instructions are executed by a processor, the steps of the method described in any of the above embodiments are implemented.

[0089] Based on the same concept as the above method, this specification also provides a computer program product, including a computer program / instruction, which implements the steps of the method described in any of the above embodiments when executed by a processor.

Claims

1. A method for estimating the direction of arrival of a satellite positioning signal, characterized in that: include: Converting a received radio frequency signal into a digital baseband signal; wherein the radio frequency signal includes a satellite positioning signal transmitted by a satellite; Based on a preset time interval, the digital baseband signal in each time segment is correlated with the reference signal of the satellite to obtain a correlation signal corresponding to each time segment; Based on the digital baseband signal in each time segment, a noise covariance matrix corresponding to each time segment is calculated; wherein the noise covariance matrix is used to represent the spatial statistical characteristics of the interference signal and thermal noise in the digital baseband signal in each time segment; Based on the noise covariance matrix and the correlation signal, a likelihood function corresponding to each time segment is constructed, and the direction of arrival of the satellite positioning signal is derived using a maximization likelihood criterion.

2. The method according to claim 1, characterized in that The method of constructing a likelihood function corresponding to each time segment based on the noise covariance matrix and the correlation signal, and deriving the direction of arrival of the satellite positioning signal by using a maximum likelihood criterion, includes: Constructing a log-likelihood function corresponding to each time segment based on the noise covariance matrix and the correlation signal; The objective function is obtained by accumulating the log-likelihood functions of each time segment, and the estimation result of the direction of arrival of the satellite positioning signal is obtained by maximizing the objective function.

3. The method according to claim 1, characterized in that The calculation formula of the related operation is expressed as follows: in, Indicates that at the nth preset time interval T c The correlation signal corresponding to the satellite positioning signal sent by the kth satellite, T s represents the sampling interval, p is the sampling point index, x(nT c +pT s ) represents the time interval T at the nth preset time interval c The baseband digital signal within, r k represents the reference signal of the kth satellite; N s In a time interval T c The number of sampling points within .

4. The method according to claim 1, wherein The noise covariance matrix is calculated by the following formula: in, represents the noise covariance matrix in the nth time segment, represents the noise covariance matrix estimated based on the digital baseband signal, and C is a constant.

5. The method according to claim 2, characterized in that The cumulative result of the log-likelihood function is expressed as follows: Among them, the observed data of the log-likelihood function Represents the correlation signal matrix, which can be represented by N c Column-related signals Composition, N c represents the number of time segments within the observation duration, and the estimated parameter γ of the log-likelihood function represents the direction of arrival of the satellite positioning signal. represents complex gain, P represents signal power, β represents phase shift, and C1 is a constant.

6. The method according to claim 5, characterized in that The objective function is obtained by accumulating the log-likelihood functions of each time segment, including: Simplifying the log-likelihood function by spatial whitening transformation to obtain a simplified log-likelihood function; The objective function is obtained by accumulating the simplified log-likelihood functions of each time segment.

7. The method according to claim 6, characterized in that The cumulative result of the simplified log-likelihood function is expressed as follows: in, represents the correlation signal after spatial whitening transformation, Represents the defined spatial whitening matrix, a n (γ)=W n a(γ) represents the steering vector after whitening.

8. The method according to claim 7, characterized in that The method further comprises: The complex gain ζ is optimized by the Wirtinger derivative to obtain the maximum likelihood estimate of the complex gain; Based on the maximum likelihood estimation of the complex gain, the determined objective function is expressed as follows: Among them, a n (γ) represents the whitened steering vector, z n Represents the correlation signal after spatial whitening transformation.

9. A satellite positioning signal direction of arrival estimation device, characterized in that: include: A signal receiving module, configured to convert a received radio frequency signal into a digital baseband signal; wherein the radio frequency signal includes a satellite positioning signal transmitted by a satellite; A first operation module is configured to perform a correlation operation on the digital baseband signal in each time segment and the reference signal of the satellite based on a preset time interval to obtain a correlation signal corresponding to each time segment; A second operation module is used to calculate a noise covariance matrix corresponding to each time segment based on the digital baseband signal in each time segment; wherein the noise covariance matrix is used to represent the spatial statistical characteristics of the interference signal and thermal noise in the digital baseband signal in each time segment; The direction of arrival estimation module is used to construct a likelihood function corresponding to each time segment based on the noise covariance matrix and the correlation signal, and derive the direction of arrival of the satellite positioning signal by adopting a maximization likelihood criterion.

10. An electronic device, characterized in that: include: processor; A memory for storing processor-executable instructions; wherein the processor implements the steps of the method according to any one of claims 1 to 8 by executing the executable instructions.

11. A computer-readable storage medium, characterized in that Computer instructions are stored thereon, and when the instructions are executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

12. A computer program product, characterized in that The method comprises a computer program / instruction, which implements the steps of the method according to any one of claims 1 to 8 when the computer program / instruction is executed by a processor.

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