Low earth orbit doppler positioning method and device based on epoch dynamic weighting of agent

Abstract

The application discloses a low-orbit Doppler positioning method and device based on epoch dynamic weighting of intelligent agents, and belongs to the technical field of satellite navigation and positioning. The method determines the initial position and initial state covariance matrix of the receiver based on the initial accumulation of the low-orbit non-cooperative signal of the receiver, determines the starting point of the current epoch signal to realize time frame synchronization when a new low-orbit non-cooperative signal is received, determines the state characteristics of the current epoch signal based on at least the starting point, the state characteristics at least including satellite characteristics, epoch state characteristics, signal state characteristics and positioning state characteristics, determines the weight of the current epoch signal based on the state characteristics using a pre-trained intelligent agent, and determines the real-time position of the receiver based on the current epoch signal and the weight of the current epoch signal using a recursive algorithm, thereby realizing adaptive weighting based on the multi-dimensional characteristics of the epoch signal, improving the accuracy of the determined weight of the epoch signal, and improving the positioning accuracy.

Description

Technical Field

[0001] This invention belongs to the field of satellite navigation and positioning technology, specifically relating to a low-orbit Doppler positioning method and device based on dynamic weighting of epochs by an intelligent agent. Background Technology

[0002] Global Navigation Satellite System (GNSS) is currently the most widely used positioning, navigation, and timing (PNT) solution. However, its high orbital altitude (approximately 6300 km) leads to significant signal path loss (-157 dB to -159 dB), causing a sharp decline in performance or even failure in obstructed environments such as urban canyons, dense vegetation, tunnels, and indoor spaces. In recent years, Low Earth Orbit (LEO) satellite signal of opportunity (SOP) has become an important supplement or alternative to GNSS due to its low orbital altitude (only 1 / 20 to 1 / 40 of GNSS), high signal power (more than 20 dB stronger than GNSS), and global coverage.

[0003] However, real-time positioning using LEO SOP typically requires accumulating multiple epoch data points. Traditional methods, such as batch least squares, suffer from epoch redundancy and high computational complexity, while real-time positioning methods like recursive least squares (RLS) do not fully consider the dynamic changes in epoch quality. Existing technologies often handle epoch reliability with fixed weights or simple heuristic rules, optimizing only a single or a few features (such as azimuth, elevation, or GDOP), failing to comprehensively evaluate multi-dimensional epoch features (such as signal-to-noise ratio, ephemeris age, satellite ID, etc.), thus limiting positioning accuracy. For example, the OECW algorithm only corrects orbital errors using azimuth and elevation, the LEO-NNPON architecture only optimizes orbit extrapolation accuracy, and the GDOP-based satellite selection strategy only implements 0-1 weighting, none of which achieve dynamic and flexible weighting of epochs.

[0004] Therefore, there is an urgent need for a localization method that can dynamically evaluate multidimensional features of epochs and adaptively assign weights to improve the localization accuracy and real-time performance of LEOSOP in complex environments. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention provides a low-Earth orbit Doppler positioning method and apparatus based on dynamic weighting of epochs by an intelligent agent. The method processes Iridium satellite signals received from the ground, extracts multi-dimensional feature parameters from satellite epochs, and uses a pre-trained neural network-based intelligent agent to consider these complex, multi-level, and dynamically changing satellite epoch features. The agent then evaluates the credibility of the epochs and transforms the epoch credibility into adaptive weights, which are integrated into RLS for iterative positioning.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0007] In a first aspect, the present invention provides a low-orbit Doppler localization method based on dynamic weighting of epochs by an intelligent agent, the method comprising:

[0008] Step 1: Obtain the initial accumulated LEO noncooperative signal of the receiver, and determine the initial position and initial state covariance matrix of the receiver based on the initial accumulated LEO noncooperative signal.

[0009] Step 2: In response to receiving the low-orbit non-cooperative signal of the current epoch, determine the starting point of the current epoch signal to achieve time frame synchronization;

[0010] Step 3: Determine the state characteristics of the current epoch signal. The state characteristics include at least satellite state characteristics, epoch state characteristics, signal state characteristics, and positioning state characteristics. The satellite state characteristics and epoch state characteristics are determined based at least on the current epoch signal, and the signal state characteristics are determined based at least on the starting point of the current epoch signal. The positioning state characteristics are determined based on the state covariance matrix of the previous epoch. The state covariance matrix of each epoch starts from the initial state covariance matrix and is updated during each positioning.

[0011] Step 4: Use a pre-trained neural network agent to determine the weights of the current epoch signal based on the state characteristics of the current epoch signal;

[0012] Step 5: Using a recursive algorithm, determine the receiver's real-time position in the current epoch based on the current epoch signal and its weight, and update the state covariance matrix of the current epoch.

[0013] In a second aspect, the present invention provides a low-orbit Doppler positioning device based on dynamic weighting of epochs by an intelligent agent, the device comprising:

[0014] The acquisition module is configured to acquire the initial accumulated LEO non-cooperative signal of the receiver and determine the initial position and initial state covariance matrix of the receiver based on the initial accumulated LEO non-cooperative signal.

[0015] The determination module is configured to determine the starting point of the current epoch signal in response to receiving a low-orbit non-cooperative signal at the current epoch to achieve time-frame synchronization.

[0016] The determination module is also configured to determine the state characteristics of the current epoch signal; the state characteristics include at least satellite state characteristics, epoch state characteristics, signal state characteristics, and positioning state characteristics, the satellite state characteristics and epoch state characteristics are determined at least based on the current epoch signal, the signal state characteristics are determined at least based on the starting point of the current epoch signal; the positioning state characteristics are determined based on the state covariance matrix of the previous epoch, and the state covariance matrix of each epoch starts from the initial state covariance matrix and is updated during each positioning.

[0017] The weighting module is configured to use a pre-trained neural network agent to determine the weights of the current epoch signal based on the state features of the current epoch signal.

[0018] The positioning module is configured to use a recursive algorithm to determine the receiver's real-time position in the current epoch based on the current epoch signal and its weights, and to update the state covariance matrix of the current epoch.

[0019] Thirdly, the present invention provides an electronic device, comprising: a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the above-mentioned low-orbit Doppler positioning method based on dynamic weighting of epochs by an intelligent agent.

[0020] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the aforementioned low-orbit Doppler positioning method based on dynamic weighting of epochs by an intelligent agent.

[0021] The beneficial effects of this invention are as follows:

[0022] The technical solution provided by this invention determines the initial position and initial state covariance matrix of the receiver based on the initial accumulated low-Earth orbit non-cooperative signal. When a new low-Earth orbit non-cooperative signal is received, the starting point of the current epoch signal is determined to achieve time-frame synchronization. At least based on the starting point, the state characteristics of the current epoch signal are determined. These state characteristics include at least satellite characteristics, epoch state characteristics, signal state characteristics, and positioning state characteristics. A pre-trained agent is used to determine the weight of the current epoch signal based on these state characteristics. A recursive algorithm is used to determine the real-time position of the receiver based on the current epoch signal and its weight. This achieves adaptive weighting based on the multi-dimensional features of the epoch signal, improving the accuracy of the determined epoch signal weights and enhancing positioning precision. Attached Figure Description

[0023] Figure 1 This is a flowchart illustrating the low-orbit Doppler positioning method based on dynamic weighting of epochs by an intelligent agent, provided in an embodiment of the present invention.

[0024] Figure 2 This is a flowchart illustrating a method for determining the weight of a current epoch signal based on the state characteristics of the current epoch signal using a pre-trained neural network agent, as provided in an embodiment of the present invention.

[0025] Figure 3 This is a flowchart illustrating the method for determining the real-time position of the receiver in the current epoch and updating the state covariance matrix of the current epoch, as provided in an embodiment of the present invention.

[0026] Figure 4 This is a flowchart illustrating another low-orbit Doppler positioning method based on dynamic weighting of epochs by an intelligent agent, provided in an embodiment of the present invention. Detailed Implementation

[0027] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0028] Figure 1 This is a flowchart illustrating the low-orbit Doppler localization method based on dynamic weighting of epochs by an intelligent agent, provided in an embodiment of the present invention. Figure 1 As shown, the method includes the following steps:

[0029] In step 1, the initial accumulated LEO noncooperative signal of the receiver is acquired, and the initial position and initial state covariance matrix of the receiver are determined based on the initial accumulated LEO noncooperative signal.

[0030] In step 2, in response to receiving the low-orbit non-cooperative signal of the current epoch, the starting point of the current epoch signal is determined to achieve time frame synchronization.

[0031] In step 3, the state characteristics of the current epoch signal are determined.

[0032] The state features include at least satellite state features, epoch state features, signal state features, and positioning state features. The satellite state features and epoch state features are determined at least based on the current epoch signal, and the signal state features are determined at least based on the starting point of the current epoch signal. The positioning state features are determined based on the state covariance matrix of the previous epoch. The state covariance matrix of each epoch starts from the initial state covariance matrix and is updated during each positioning.

[0033] In step 4, a pre-trained neural network agent is used to determine the weights of the current epoch signal based on the state characteristics of the current epoch signal.

[0034] In step 5, a recursive algorithm is used to determine the real-time position of the receiver in the current epoch based on the current epoch signal and its weight, and to update the state covariance matrix of the current epoch.

[0035] In some embodiments of the present invention, the method may be executed by a server or by a terminal device with certain processing capabilities. In one example, the terminal device may be a receiver.

[0036] In some embodiments of the present invention, the initial accumulated low-orbit non-cooperative signal of the receiver can be obtained first, and the initial position and initial state covariance matrix of the receiver can be determined based on the initial accumulated low-orbit non-cooperative signal.

[0037] Furthermore, upon receiving a low-orbit non-cooperative signal at the current epoch, the starting point of the current epoch signal can be determined to achieve time-frame synchronization. In other words, the starting point of the pilot signal can be determined by identifying the starting point of the current epoch signal, thereby demodulating the current epoch signal frame and providing a basis for subsequently extracting the state characteristics of the current epoch signal.

[0038] In some embodiments of the present invention, the state characteristics of the current epoch can be determined, which include at least satellite state characteristics, epoch state characteristics, signal state characteristics, and positioning state characteristics.

[0039] Among them, the satellite state characteristics and epoch state characteristics are determined at least based on the current epoch signal, and the signal state characteristics are determined at least based on the starting point of the current epoch signal; the positioning state characteristics are determined based on the state covariance matrix of the previous epoch, and the state covariance matrix of each epoch starts from the initial state covariance matrix and is updated during each positioning.

[0040] In some embodiments of the present invention, a pre-trained neural network agent can be used to determine the weights of the current epoch signal based on the determined state features of the current epoch. Then, a recursive algorithm can be used to determine the real-time position of the receiver at the current epoch based on the current epoch signal and its weights, and to update the state covariance matrix of the current epoch.

[0041] The technical solution provided in this invention determines the initial position and initial state covariance matrix of the receiver based on the initial accumulated low-Earth orbit non-cooperative signal. Upon receiving a new low-Earth orbit non-cooperative signal, the starting point of the current epoch signal is determined to achieve time-frame synchronization. At least based on this starting point, the state characteristics of the current epoch signal are determined. These state characteristics include at least satellite features, epoch state features, signal state features, and positioning state features. A pre-trained agent determines the weights of the current epoch signal based on these state characteristics. A recursive algorithm determines the real-time position of the receiver based on the current epoch signal and its weights. This achieves adaptive weighting based on the multi-dimensional features of the epoch signal, improving the accuracy of the determined epoch signal weights and enhancing positioning precision.

[0042] In some embodiments of the present invention, acquiring the initial accumulated LEO non-cooperative signal of the receiver and determining the initial position and initial state covariance matrix of the receiver based on the initial accumulated LEO non-cooperative signal may involve: first, acquiring the initial accumulated LEO non-cooperative signal of the receiver, which includes the LEO non-cooperative signal accumulated within a preset time after the receiver is powered on or the positioning function is activated; then, constructing an initial Doppler linear observation equation based on the Doppler observations of the LEO non-cooperative signal; and finally, solving the initial Doppler linear observation equation using a least squares algorithm to obtain the initial position of the receiver, and determining the initial state covariance matrix based on the coefficient matrix of the initial Doppler linear observation equation.

[0043] In other words, the low-Earth orbit Doppler positioning method based on dynamic weighting of epochs provided by the embodiments of the present invention processes low-Earth orbit non-cooperative signals received from the ground, such as Iridium satellite signals, extracts multi-dimensional feature parameters from satellite epochs, uses a pre-trained neural network-based agent to consider these complex, multi-level, and dynamically changing satellite epoch features, evaluates the credibility of epochs, and transforms the epoch credibility into adaptive weights, which are integrated into a recursive algorithm, such as the recursive least squares (RLS) algorithm, for iterative positioning, thereby solving the epoch redundancy problem and improving positioning accuracy.

[0044] Since each step of the RLS iterative localization is recursively derived from the results of the previous step and the newly added observations, it is crucial to construct a reliable initial localization solution during the initial observation phase. As the initial position of the receiver, and to determine the initial state covariance matrix. This serves as the foundation for the first iteration in the subsequent process.

[0045] Assume the receiver starts receiving signals at the time when , can first The complete Iridium satellite signal received internally is cached locally for initial positioning. Then... The received Iridium satellite signal was divided into signal blocks of 4.32 seconds in length, and each signal block was bandpass filtered. Each signal block was further subdivided into several signal segments of equal length, and each signal segment was subjected to a Fast Fourier Transform (FFT), followed by peak detection.

[0046] Since the pilot signal is an unmodulated single carrier, its energy in the frequency domain is concentrated. By comparing the peak values ​​of the FFT for each signal segment, the approximate location of the pilot signal within that signal block can be determined. The FFT estimation result is a coarse estimate of the Doppler frequency corresponding to that epoch. ,in, This is a rough estimate of the Doppler frequency for the current epoch. This is the index of the largest spectral line in this FFT. It is the sampling frequency. It is the number of operation points in the FFT operation.

[0047] This step is only a rough search and lookup of the signal. The number of points in the FFT is small, which limits the estimation accuracy, but the estimate is sufficient for the accuracy requirements of the initial localization solution. Assume... A total of [number] messages were received from [source] within the specified time. The epochs of the i-th satellite, the total number of epochs of the i-th satellite is Then the initial observation equation can be constructed as follows:

[0048] ;

[0049] in This is an approximation of the Taylor expansion of the pseudorange rate of change. , and Let be the partial derivatives of the pseudorange rate of change in the three directions. The covariance matrix is Joint Gaussian white noise.

[0050] For simplicity, the above initial observation equation can be simplified as follows: ,in For the initial observation vector, The initial coefficient matrix, Let be the initial state vector. Based on the solution using the least squares method, The minimum variance unbiased estimate is , This is the initial position solution, i.e., the initial position of the receiver.

[0051] On the other hand, the initial state covariance matrix can be expressed as ,in Let be the initial state covariance matrix.

[0052] In some embodiments of the present invention, after determining the initial position and initial state covariance matrix of the receiver, the starting point of the current epoch signal received each time a new low-orbit non-cooperative signal is received can be determined to achieve time-frame synchronization.

[0053] In one example, the starting point of the current epoch signal can be determined as follows: First, the received current epoch signal is divided into blocks according to a preset period, and each block of signal is preprocessed to obtain the target time frame signal. The target time frame signal includes at least the pilot and unique word of the current epoch signal; wherein the preset period matches the beam polling period of the current epoch signal.

[0054] Then, the target time frame signal is segmented, and each segment of the target time frame signal is subjected to Fast Fourier Transform (FFT) to obtain the frequency domain peak value of each segment signal. The segment signal whose frequency domain peak value meets the preset peak value condition is determined as the target segment signal, and the Doppler frequency of the pilot is roughly estimated based on the frequency domain peak value in the target segment signal.

[0055] Next, the prior information is used to generate a local independent word signal. The peak value of the cross-correlation function between the local independent word signal and the generalized pilot coarse position signal is determined as the pilot start position. The generalized pilot coarse position signal is a signal whose distance from the pilot coarse position is less than a preset distance threshold. The prior information includes at least prior pilot information and prior unique word information.

[0056] Finally, the starting position of the time frame is determined based on the pilot start position.

[0057] In RLS iterative localization, an iteration is performed for each newly captured epoch. However, the status of each input epoch is not equal; instead, weighted processing is required. The larger the weight of an epoch, the greater its impact on the localization result; conversely, the smaller the weight, the smaller the impact. Therefore, it is necessary to determine the weight of each epoch signal. The technical solution provided in this embodiment of the invention can use an agent to determine the weight of each epoch signal. When determining the weight, it is necessary to extract the state features of each epoch signal, and then input the state features into the agent to obtain the weight of each epoch signal.

[0058] When extracting state features, it is necessary to first determine the starting point of the pilot signal, which is also the starting point of the time frame signal. Based on this starting point, the time frame signal can be demodulated, thereby extracting at least some state features.

[0059] In some embodiments of the present invention, the aforementioned coarse estimate of the Doppler frequency can be used, combined with the prior pilot signals of the Iridium satellite frame and the unique word modulation information, to generate a local signal:

[0060] ;

[0061] in, and It is the amplitude of the pilot signal and the unique word. It's the sampling rate. The number of sampling points for each Iridium symbol. For prior Iridium satellite unique character information, This is the floor symbol.

[0062] Then, the cross-correlation function between the generated signal and the signal near the coarse search position is calculated, and its peak value is the starting position of the Iridium frame pilot signal. ,in, It is a generated local signal The number of sampling points, This is the signal near the location coarsely estimated by the piecewise FFT. Once the precise starting position of each pilot signal is determined, subsequent algorithmic operations will be able to precisely segment each part of each Iridium frame, thereby estimating all the characteristic parameters of that epoch.

[0063] In some embodiments of the present invention, satellite status characteristics may include the ephemeris age corresponding to the current epoch, the satellite azimuth angle of the current epoch, the satellite elevation angle of the current epoch, and the satellite-to-ground distance of the current epoch.

[0064] In some embodiments of the present invention, the ephemeris age corresponding to the current epoch can be determined as follows: First, the update time of the two-line orbital data TLE ephemeris used by the current epoch is obtained, along with the signal reception time of the current epoch. Then, the difference between the signal reception time of the current epoch and the update time of the TLE ephemeris used by the current epoch is determined as the ephemeris age corresponding to the current epoch.

[0065] In other words, if we consider the update time of the TLE ephemeris used in the current epoch as... The reception time for the current epoch is Then the ephemeris age is .

[0066] In some embodiments of the present invention, the satellite azimuth angle, the satellite elevation angle, and the satellite-to-ground distance at the current epoch can be determined in the following manner:

[0067] First, determine the first coordinate point of the satellite transmitting the signal at the current epoch in the geocentric-geo-fixed coordinate system based on the TLE ephemeris used at the current epoch, and then obtain the second coordinate point of the receiver's real-time position at the previous epoch in the geocentric-geo-fixed coordinate system.

[0068] Then, the three-dimensional coordinate components of the second coordinate point are converted into latitude, longitude, and altitude, respectively, and a rotation matrix is ​​constructed. ,in, Latitude Longitude. Use the formula. Based on the rotation matrix, the three-dimensional coordinate components of the first coordinate point are converted into east, north, and zenith components in the station-centered coordinate system, where... For the eastern portion, For the northern component, Zenith component , and The first coordinate point represents the three-dimensional coordinate components in the Earth-centered Earth-fixed coordinate system.

[0069] Next, we will use the formula. The satellite azimuth angle for the current epoch is calculated using the formula. The satellite elevation angle at the current epoch is calculated.

[0070] Finally, the Euclidean distance between the first and second coordinate points was determined as the Earth-Star distance for the current epoch.

[0071] In some embodiments of the present invention, the epoch state features may further include the satellite unique identifier of the current epoch and the epoch sequence number of the current epoch.

[0072] In some embodiments of the present invention, if the current epoch signal is a non-cooperative Iridium satellite signal received at the current epoch, the satellite's unique identifier for the current epoch can be determined in the following manner:

[0073] The simplex channel signal of the current epoch is determined based on the starting point of the current epoch. The simplex channel signal is a signal consisting of 163 characters truncated from the starting point. Among them, the first 64 characters are the pilot signal, and the last 99 characters are the independent word and data signal.

[0074] Perform Doppler frequency estimation on the current epoch signal to obtain the estimated Doppler frequency of the current epoch signal;

[0075] First, use the formula The simplex channel signal is down-converted based on Doppler frequency estimation to obtain the down-converted simplex channel signal; whereby... This is the simplex channel signal after down-conversion. It is a simplex channel signal. It is the symbol for imaginary numbers. To estimate the frequency for Doppler, The sampling rate is defined as the segmented Fast Fourier Transform (FFT) of the pilot signal when determining the Doppler estimation frequency. n is the time-domain sampling point index of the pilot signal, where each n value represents a sampling point.

[0076] Then use the formula Determine the phase reference of the pilot signal; whereby As a phase reference, This represents the number of sampling points contained in the pilot signal. The symbol is the conjugate. Further, the phase of the i-th character in the simplex channel signal is determined as... ;in The phase of the i-th character is represented by ms, where ms represents the time slot unit milliseconds, and i is a positive integer greater than or equal to 1 and less than or equal to 163.

[0077] Next, quadrature phase shift keying (QPSK) demodulation is performed on each character to obtain the demodulated result of the i-th character. ;in This represents the demodulation result of the i-th character. Additionally, the characters of the independent words and data signals are converted according to the formula. This yields the bit data of the independent word and data signal, which includes 198 bits of data.

[0078] Next, take adjacent bits from the bit data and exchange, The integer is greater than or equal to 0 and less than 99. Obtain bits 31 to 94 of the swapped bit data, perform standard deinterleaving on the obtained data, and obtain 64 bits of data. Decode the first 31 bits of the 64 bits using standard linear error-correcting code (BCH) to obtain 21 valid data bits and 10 parity bits.

[0079] Finally, the first 7 bits of the 21 valid data bits are converted into decimal numbers to obtain the satellite's unique identifier for the current epoch.

[0080] In some embodiments of the present invention, the epoch number of the current epoch can be determined as follows: First, the epoch signals received by the receiver in each local positioning iteration are classified according to satellite unique identifiers. Then, under each satellite unique identifier category, the epoch signals are numbered and sorted according to the reception time order. Finally, the sorting sequence number corresponding to the current epoch signal under the corresponding satellite unique identifier category is determined as the epoch number of the current epoch.

[0081] In other words, the sequence number of a certain epoch can be defined as which epoch it is among the satellites from which it was received in the current positioning iteration. That is, each epoch is classified according to its satellite ID, and then under each satellite ID, it is numbered according to the order of reception time.

[0082] In some embodiments of the present invention, the signal state characteristics may further include the Doppler estimated frequency of the current epoch signal and the signal-to-noise ratio of the current epoch signal.

[0083] In some embodiments of the present invention, the Doppler estimation frequency of the current epoch signal can be determined in the following manner:

[0084] First, a piecewise Fast Fast Fourier Transform (FFT) is performed on the current epoch signal to obtain a coarse estimate of the Doppler frequency of the current epoch signal. The sampling rate of the piecewise FFT is... .

[0085] Then, the pilot signal is determined based on the starting point of the current epoch. The pilot signal is then subjected to another Fast Fourier Transform (FFT) to obtain the pilot spectrum. Where n is the time-domain sampling point index of the pilot, each n value represents a sampling point, and k is the pilot spectrum line number.

[0086] Next, use the formula Determine the Doppler frequency estimation compensation value for the current epoch signal; where The Doppler frequency estimation compensation value for the current epoch signal. This represents the number of sampling points contained in the pilot signal. denoted as the spectral line number corresponding to the spectral line with the largest mode length in the pilot spectrum, and 'a' as the compensation direction. .

[0087] Finally, a rough estimate of the Doppler frequency was determined. Compensation value for Doppler frequency estimation The sum of these values ​​represents the Doppler estimated frequency of the current epoch signal.

[0088] In some embodiments of the present invention, the signal-to-noise ratio of the current epoch signal can be determined in the following manner:

[0089] Use formula The signal-to-noise ratio of the current epoch signal is calculated; where Let N be the signal-to-noise ratio of the signal at the current epoch. pilot It equals N1.

[0090] In some embodiments of the present invention, the positioning state features include the generalized geometrical dilution precision (GDOP) of the previous epoch.

[0091] In some implementations, the GDOP of the previous epoch can be determined as follows: first, obtain the state covariance matrix of the previous epoch. Then use the formula GDOP is calculated; where tr() is the trace function.

[0092] In other words, although the generalized GDOP is not a feature of a single epoch, it reflects the recursive state at the time of its inclusion and is therefore of significant importance. Since the new epoch has not yet participated in the RLS iteration when calculating the feature parameters, only the covariance matrix obtained from the previous iteration can be used. To obtain the generalized GDOP.

[0093] Using the above method, the precise Doppler frequency, signal-to-noise ratio, satellite ID, elevation angle, azimuth angle, satellite-to-ground distance, ephemeris age, epoch number, and generalized GDOP of each epoch can be extracted, totaling nine feature parameters. Next, the state features of each epoch signal composed of these nine feature parameters can be input into a pre-trained neural network agent, which will then use these feature parameters to determine the weights of each epoch signal.

[0094] Figure 2 This is a flowchart illustrating a method for determining the weights of a current epoch signal using a pre-trained neural network agent based on the state features of the current epoch signal, as provided in an embodiment of the present invention. Figure 2 As shown, the method includes the following steps:

[0095] In step 41, the pre-trained neural network agent is obtained.

[0096] The pre-trained neural network agent is trained based on historical low-orbit non-cooperative signals acquired by the receiver.

[0097] In step 42, the state features of the current epoch signal are input into the pre-trained neural network agent to obtain the weights of the current epoch signal.

[0098] In some embodiments of the present invention, a pre-trained neural network agent trained based on historical low-orbit non-cooperative signals acquired by a receiver can be obtained, and then the state features of the current epoch signal can be input into the pre-trained neural network agent to obtain the weight of the current epoch signal.

[0099] In other words, if the aforementioned 9-dimensional feature parameters are denoted as... Let n represent the nth positioning iteration. The parameters, from left to right, are Doppler frequency, signal-to-noise ratio, satellite-to-ground distance, azimuth angle, elevation angle, ephemeris age, epoch number, and generalized GDOP. The pre-trained neural network-based agent can then determine the weights for the new epoch based on these nine feature parameters. .

[0100] Figure 3 This is a flowchart illustrating the method for determining the real-time position of the receiver in the current epoch and updating the state covariance matrix of the current epoch, provided by an embodiment of the present invention. Figure 3 As shown, the method includes the following steps:

[0101] In step 51, Doppler observations in the current epoch signal are obtained, and the Doppler linear observation equation for the current epoch is constructed based on at least the Doppler observations.

[0102] The Doppler linear observation equation for the current epoch includes the coefficient matrix for the current epoch.

[0103] In step 52, the state covariance matrix of the current epoch is determined based at least on the state covariance matrix of the previous epoch, the coefficient matrix of the current epoch, and the weights of the signal of the current epoch.

[0104] In one example, a formula can be used. Determine the state covariance matrix for the current epoch, where Let be the state covariance matrix of the current epoch. Let be the state covariance matrix of the previous epoch. This is the coefficient matrix for the current epoch. The weight of the signal at the current epoch. It is an identity matrix.

[0105] In step 53, the gain matrix of the current epoch is determined based at least on the state covariance matrix and the coefficient matrix of the current epoch.

[0106] In one example, a formula can be used. Determine the gain matrix for the current epoch, where, This is the gain matrix for the current epoch.

[0107] In step 54, the real-time position of the current epoch is determined at least based on the receiver's real-time position in the previous epoch, the Doppler observations in the current epoch signal, and the current epoch coefficient matrix.

[0108] In one example, a formula can be used. Determine the real-time position of the receiver in the current epoch; where, This represents the receiver's real-time position at the current epoch. This represents the receiver's real-time position in the previous epoch. These are the Doppler observations in the current epoch signal.

[0109] In other words, the receiver performs one RLS iteration for each new epoch it acquires. The input to the iteration is the result of the previous iteration. and Newly added epoch weights Newly added epoch observations and The output is the iteration result of this step. and And will participate in the next RLS iteration.

[0110] Suppose that in the nth iteration, the observations of the new epoch are added as follows: The coefficient matrix is Their relationship with the pseudorange rate of change is as follows:

[0111] ;

[0112] The steps of a single recursive least squares iteration are as follows:

[0113] Step 1: The covariance matrix is ​​updated recursively. When a new observation is added in step n, the new covariance matrix is ​​updated based on the covariance matrix of step (n-1) and the weights obtained by the agent, expressed as:

[0114] .

[0115] Step 2: Determine the gain matrix from step n, used to balance the impact of new observations on the state update, which can be represented as: .

[0116] Step 3: Obtain the weighted recursive update of the solution located in step n, expressed as follows:

[0117] .

[0118] By continuously adding new epochs, determining weights, and iteratively updating the three recursive steps, the location solution after each epoch can be derived until the final location estimate is obtained at the last epoch.

[0119] Figure 4 This is a flowchart illustrating another low-orbit Doppler localization method based on dynamic weighting of epochs by an intelligent agent, provided in an embodiment of the present invention. Figure 4 As shown, the initial position can be calculated first using batch least squares, followed by acquiring the observation features and the latest GDOP of the new epoch. Next, the agent evaluates the reliability of this epoch, mapping the evaluated reliability to epoch weights. Using these weights, a recursive least squares iteration is performed to calculate the receiver position in real time. Then, it is determined whether to continue acquiring epoch signals. If so, the steps are executed sequentially starting from acquiring the observation features and the latest GDOP of the new epoch to obtain the new real-time receiver position. Conversely, if acquiring epoch signals is discontinued, the localization process ends.

[0120] The technical solution provided in this invention proposes a real-time weighted recursive least squares low-Earth orbit satellite opportunistic signal positioning framework. This framework allows the agent to evaluate the reliability of each epoch in real time based on its multi-dimensional feature parameters and convert these evaluations into weights for iteration. This framework strengthens the contribution of high-quality epochs to positioning accuracy and weakens the adverse effects of poor epochs, thus significantly improving positioning accuracy during the iteration process.

[0121] Meanwhile, a fine processing flow for low-Earth orbit non-cooperative signals such as Iridium satellites was designed. This flow can extract multi-dimensional characteristic parameters such as Doppler frequency, signal-to-noise ratio, satellite ID, elevation angle, azimuth angle, satellite-to-ground distance, ephemeris age, and epoch number from Iridium satellite signals for each epoch.

[0122] The technical solution provided by the embodiments of the present invention has the following advantages:

[0123] Improving positioning accuracy: By dynamically evaluating multi-dimensional feature parameters of epochs (including Doppler frequency, signal-to-noise ratio, satellite ID, elevation angle, azimuth angle, satellite-to-ground distance, ephemeris age, epoch number, and generalized GDOP) through an intelligent agent and adaptively assigning weights, this method can more accurately select high-quality epochs and suppress interference from low-quality epochs. Compared to traditional fixed-weight or single-feature optimization methods (such as the OECW algorithm which only relies on azimuth and elevation angles), this invention comprehensively considers multiple levels of error sources, significantly improving the stability and accuracy of positioning results.

[0124] Enhanced real-time performance: By employing a recursive least squares (RLS) framework combined with real-time weighting of the agent, the high computational complexity of batch least squares is avoided, achieving efficient iterative solution. Simultaneously, the agent's dynamic decision-making optimizes epoch utilization, reduces redundant computation, and enables faster convergence of the localization process, making it suitable for real-time navigation requirements.

[0125] Adaptability to Complex Environments: This invention utilizes multi-dimensional feature fusion to dynamically address complex scenarios such as signal obstruction, multipath effects, and ephemeris errors. For example, epochs with high signal-to-noise ratios and new ephemeris are assigned higher weights, while epochs of interfered or aged ephemeris are deweighted, thereby improving the system's robustness in harsh environments.

[0126] Flexibility and scalability: The agent is trained on a neural network and can continuously optimize its weight strategy through data learning to adapt to different satellite constellations (such as Iridium and Starlink) or signal characteristics. Furthermore, the dimensions of the feature parameters can be further expanded (e.g., by adding carrier phase, Doppler rate of change, etc.), reserving space for future technological upgrades.

[0127] On the other hand, a low-orbit Doppler positioning device based on dynamic weighting of epochs by an intelligent agent includes:

[0128] The acquisition module is configured to acquire the initial accumulated LEO non-cooperative signal of the receiver and determine the initial position and initial state covariance matrix of the receiver based on the initial accumulated LEO non-cooperative signal.

[0129] The determination module is configured to determine the starting point of the current epoch signal in response to receiving a low-orbit non-cooperative signal at the current epoch to achieve time-frame synchronization.

[0130] The determination module is also configured to determine the state characteristics of the current epoch signal; the state characteristics include at least satellite state characteristics, epoch state characteristics, signal state characteristics, and positioning state characteristics, the satellite state characteristics and epoch state characteristics are determined at least based on the current epoch signal, the signal state characteristics are determined at least based on the starting point of the current epoch signal; the positioning state characteristics are determined based on the state covariance matrix of the previous epoch, and the state covariance matrix of each epoch starts from the initial state covariance matrix and is updated during each positioning.

[0131] The weighting module is configured to use a pre-trained neural network agent to determine the weights of the current epoch signal based on the state features of the current epoch signal.

[0132] The positioning module is configured to use a recursive algorithm to determine the receiver's real-time position in the current epoch based on the current epoch signal and its weights, and to update the state covariance matrix of the current epoch.

[0133] Thirdly, the present invention provides an electronic device, comprising: a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the above-mentioned low-orbit Doppler positioning method based on dynamic weighting of epochs by an intelligent agent.

[0134] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the aforementioned low-orbit Doppler positioning method based on dynamic weighting of epochs by an intelligent agent.

[0135] The above specific embodiments further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above are merely specific embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A low-orbit Doppler positioning method based on dynamic weighting of epochs by an intelligent agent, characterized in that, The method includes: Step 1: Obtain the initial accumulated LEO noncooperative signal of the receiver, and determine the initial position and initial state covariance matrix of the receiver based on the initial accumulated LEO noncooperative signal; Step 2: In response to receiving the low-orbit non-cooperative signal of the current epoch, determine the starting point of the current epoch signal to achieve time frame synchronization; Step 3: Determine the state characteristics of the current epoch signal; the state characteristics include at least satellite state characteristics, epoch state characteristics, signal state characteristics, and positioning state characteristics. The satellite state characteristics and the epoch state characteristics are determined at least based on the current epoch signal, and the signal state characteristics are determined at least based on the starting point of the current epoch signal; the positioning state characteristics are determined based on the state covariance matrix of the previous epoch, and the state covariance matrix of each epoch starts from the initial state covariance matrix and is updated during each positioning; the satellite state characteristics include the ephemeris age corresponding to the current epoch, the satellite azimuth angle of the current epoch, the satellite elevation angle of the current epoch, and the satellite-to-ground distance of the current epoch; the epoch state characteristics include the satellite unique identifier and the epoch number of the current epoch; the signal state characteristics include the Doppler estimated frequency and the signal-to-noise ratio of the current epoch signal; the positioning state characteristics include the generalized geometrical precision factor (GDOP) of the previous epoch; Step 4: Using a pre-trained neural network agent, determine the weights of the current epoch signal based on the state characteristics of the current epoch signal; Step 5: Using a recursive algorithm, determine the real-time position of the receiver in the current epoch based on the current epoch signal and its weight, and update the state covariance matrix of the current epoch.

2. The low-orbit Doppler positioning method based on dynamic weighting of epochs by an intelligent agent according to claim 1, characterized in that, Step 1 includes: Acquire the initial accumulated LEO non-cooperative signal of the receiver, which includes the LEO non-cooperative signal accumulated within a preset time after the receiver is powered on or the positioning function is enabled. An initial Doppler linear observation equation is constructed based on the Doppler observations of the aforementioned low-orbit non-cooperative signal. The initial Doppler linear observation equation is solved using the least squares algorithm to obtain the initial position of the receiver, and the initial state covariance matrix is ​​determined based on the coefficient matrix of the initial Doppler linear observation equation.

3. The low-orbit Doppler positioning method based on dynamic weighting of epochs by an intelligent agent according to claim 2, characterized in that, Step 2 includes: The received current epoch signal is divided into blocks according to a preset period, and each block of signal is preprocessed to obtain a target time frame signal. The target time frame signal includes at least the pilot and unique word of the current epoch signal. The preset period is matched with the beam polling period of the current epoch signal. The target time frame signal is segmented, and each segment of the target time frame signal is subjected to Fast Fourier Transform (FFT) to obtain the frequency domain peak value of each segment signal. The segment signal whose frequency domain peak value meets the preset peak value condition is determined as the target segment signal. The Doppler frequency of the pilot is roughly estimated based on the frequency domain peak value in the target segment signal. A local independent word signal is generated using prior information. The peak value of the cross-correlation function between the local independent word signal and the generalized pilot coarse position signal is determined as the pilot start position. The generalized pilot coarse position signal is a signal whose distance from the pilot coarse position is less than a preset distance threshold. The prior information includes at least prior pilot information and prior unique word information. The starting position of the time frame is determined based on the pilot start position.

4. The low-orbit Doppler positioning method based on dynamic weighting of epochs by an intelligent agent according to claim 1, characterized in that, The ephemeris age corresponding to the current epoch is determined in the following way: Get the update time of the two lines of orbital data used in the current epoch (TLE ephemeris) and the signal reception time of the current epoch; The difference between the signal reception time of the current epoch and the update time of the TLE ephemeris used by the current epoch is determined as the ephemeris age corresponding to the current epoch.

5. The low-orbit Doppler positioning method based on dynamic weighting of epochs by an intelligent agent according to claim 1, characterized in that, The satellite azimuth, elevation, and distance from Earth at the current epoch are determined as follows: Determine the first coordinate point of the satellite transmitting the signal at the current epoch in the geocentric-geo-fixed coordinate system based on the TLE ephemeris used for the current epoch. Obtain the second coordinate point of the receiver's real-time position in the previous epoch in the geocentric-geostatic coordinate system; The three-dimensional coordinate components of the second coordinate point are converted into latitude, longitude, and altitude, respectively, and a rotation matrix is ​​constructed. ,in, For the latitude, The longitude is mentioned. Use formula Based on the rotation matrix, the three-dimensional coordinate components of the first coordinate point are converted into east, north, and zenith components in the station-centered coordinate system, where... For the eastern portion, For the northern component, Zenith component , and These are the three-dimensional coordinate components of the first coordinate point in the geocentric-solid coordinate system. Use formula The satellite azimuth angle for the current epoch is calculated; Use formula The satellite elevation angle for the current epoch is calculated; The Euclidean distance between the first and second coordinate points is determined as the star-to-ground distance for the current epoch.

6. The low-orbit Doppler positioning method based on dynamic weighting of epochs by an intelligent agent according to claim 1, characterized in that, In response to determining that the current epoch signal is an Iridium non-cooperative signal received at the current epoch, the satellite's unique identifier for the current epoch is determined as follows: The simplex channel signal of the current epoch signal is determined based on the starting point of the current epoch. The simplex channel signal is a signal consisting of 163 characters truncated from the starting point. Among them, the signal consisting of the first 64 characters is the pilot signal, and the signal consisting of the last 99 characters is the independent word and data signal. The Doppler frequency of the current epoch signal is estimated by performing Doppler frequency estimation on the current epoch signal; Using formula The simplex channel signal is down-converted based on the Doppler estimated frequency to obtain the down-converted simplex channel signal; wherein This is the simplex channel signal after down-conversion. It is a simplex channel signal. It is the symbol for imaginary numbers. Estimate the frequency for the Doppler. The sampling rate is the piecewise fast Fourier transform (FFT) of the pilot signal when determining the Doppler estimation frequency, where n is the time-domain sampling point index of the pilot, and each n value represents a sampling point. Use formula Determine the phase reference of the pilot signal; wherein As a phase reference, This represents the number of sampling points contained in the pilot signal. The conjugate symbol; The phase of the i-th character of the simplex channel signal is determined as follows: ;in The phase of the i-th character, ms represents the time slot unit millisecond, and i is a positive integer greater than or equal to 1 and less than or equal to 163; Perform quadrature phase shift keying (QPSK) demodulation on each character to obtain the demodulated result of the i-th character. ;in This represents the demodulation result of the i-th character; The characters of the independent words and data signals are converted according to the formula. This yields bit data of independent words and data signals, wherein the bit data includes 198 bits of data; adjacent bits in the bit data and exchange, An integer greater than or equal to 0 and less than 99; Obtain bits 31 to 94 from the swapped bit data, and perform standard deinterleaving on the obtained data to obtain 64 bits of data; The first 31 bits of the 64-bit data are decoded using the standard linear error-correcting code BCH to obtain 21 valid data bits and 10 parity bits. The first 7 bits of the 21 valid data bits are converted into decimal numbers to obtain the satellite's unique identifier for the current epoch.

7. The low-orbit Doppler positioning method based on dynamic weighting of epochs by an intelligent agent according to claim 1, characterized in that, The epoch number of the current epoch is determined in the following way: The epoch signals received by the receiver in each local positioning iteration are classified according to the satellite's unique identifier; Under each satellite's unique identifier category, the signals of each epoch are numbered and sorted according to the order of reception time. The sorting sequence number corresponding to the current epoch signal under the corresponding satellite unique identifier classification is determined as the epoch sequence number of the current epoch.

8. The low-orbit Doppler positioning method based on dynamic weighting of epochs by an intelligent agent according to claim 1, characterized in that, The Doppler estimation frequency of the current epoch signal is determined in the following manner: Perform a piecewise Fast Fast Fourier Transform (FFT) on the current epoch signal to obtain a coarse estimate of the Doppler frequency of the current epoch signal. The sampling rate of the piecewise FFT is... ; Determine the pilot signal based on the starting point of the current epoch. The pilot signal is then subjected to a Fast Fourier Transform (FFT) again to obtain the pilot spectrum. Where n is the time-domain sampling point index of the pilot, each n value represents a sampling point, and k is the pilot spectrum line number; Use formula Determine the Doppler frequency estimation compensation value of the current epoch signal; wherein The Doppler frequency estimation compensation value for the current epoch signal. This represents the number of sampling points contained in the pilot signal. Here, 'a' represents the spectral line number corresponding to the spectral line with the largest mode length in the pilot spectrum, and 'a' represents the compensation direction. ; Determine the coarse estimate of the Doppler frequency. and the Doppler frequency estimation compensation value The sum of these values ​​represents the Doppler estimated frequency of the current epoch signal.

9. The low-orbit Doppler positioning method based on dynamic weighting of epochs by an intelligent agent according to claim 8, characterized in that, The signal-to-noise ratio of the current epoch signal is determined in the following way: Use formula The signal-to-noise ratio of the current epoch signal is calculated; where N represents the signal-to-noise ratio of the current epoch signal. pilot It equals N1.

10. The low-orbit Doppler positioning method based on dynamic weighting of epochs by an intelligent agent according to claim 1, characterized in that, The GDOP is determined in the following manner: Obtain the state covariance matrix of the previous epoch. ; Use formula The GDOP is calculated; where tr() is the trace function.

11. The low-orbit Doppler positioning method based on dynamic weighting of epochs by an intelligent agent according to claim 1, characterized in that, Step 4 includes: A pre-trained neural network agent is obtained, which is trained based on historical low-orbit non-cooperative signals acquired by the receiver. The state features of the current epoch signal are input into the pre-trained neural network agent to obtain the weights of the current epoch signal.

12. The low-orbit Doppler positioning method based on dynamic weighting of epochs by an intelligent agent according to claim 1, characterized in that, Step 5 includes: Obtain the Doppler observations in the current epoch signal, and construct the Doppler linear observation equation for the current epoch based at least on the Doppler observations. The Doppler linear observation equation for the current epoch includes the current epoch coefficient matrix. Use formula The state covariance matrix of the current epoch is determined at least based on the state covariance matrix of the previous epoch, the coefficient matrix of the current epoch, and the weights of the signal of the current epoch; wherein Let be the state covariance matrix of the current epoch. Let be the state covariance matrix of the previous epoch. This is the coefficient matrix for the current epoch. The weight of the signal at the current epoch. It is the identity matrix; Use formula Determine the gain matrix for the current epoch; where, This is the gain matrix for the current epoch; Use formula Determine the real-time position of the receiver in the current epoch; wherein, This represents the receiver's real-time position at the current epoch. This represents the receiver's real-time position in the previous epoch. These are the Doppler observations in the current epoch signal.

13. A low-orbit Doppler positioning device based on dynamic weighting of epochs by an intelligent agent, characterized in that, The device includes: The acquisition module is configured to acquire the initial accumulated LEO non-cooperative signal of the receiver, and determine the initial position and initial state covariance matrix of the receiver based on the initial accumulated LEO non-cooperative signal. The determination module is configured to determine the starting point of the current epoch signal in response to receiving a low-orbit non-cooperative signal at the current epoch to achieve time-frame synchronization. The determining module is further configured to determine the state characteristics of the current epoch signal; the state characteristics include at least satellite state characteristics, epoch state characteristics, signal state characteristics, and positioning state characteristics, wherein the satellite state characteristics and the epoch state characteristics are determined at least based on the current epoch signal, and the signal state characteristics are determined at least based on the starting point of the current epoch signal; the positioning state characteristics are determined based on the state covariance matrix of the previous epoch, and the state covariance matrix of each epoch starts from the initial state covariance matrix and is updated during each positioning; the satellite state characteristics include the ephemeris age corresponding to the current epoch, the satellite azimuth angle of the current epoch, the satellite elevation angle of the current epoch, and the satellite-to-ground distance of the current epoch; the epoch state characteristics include the satellite unique identifier of the current epoch and the epoch number of the current epoch; the signal state characteristics include the Doppler estimated frequency of the current epoch signal and the signal-to-noise ratio of the current epoch signal; the positioning state characteristics include the generalized geometrical precision factor (GDOP) of the previous epoch; The weighting module is configured to use a pre-trained neural network agent to determine the weights of the current epoch signal based on the state features of the current epoch signal; The positioning module is configured to use a recursive algorithm to determine the receiver's real-time position in the current epoch based on the current epoch signal and the weight of the current epoch signal, and to update the state covariance matrix of the current epoch.

14. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes a computer program, it implements the low-orbit Doppler positioning method based on the dynamic weighting of epochs by an intelligent agent as described in any one of claims 1 to 12.

15. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the low-orbit Doppler positioning method based on the dynamic weighting of epochs by an intelligent agent as described in any one of claims 1 to 12.

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