Unmanned aerial vehicle attitude measurement method based on low-orbit satellite communication signals

Abstract

The invention discloses an unmanned aerial vehicle attitude determination method based on a low-orbit satellite communication signal, and the method comprises the steps: converting a vehicle coordinate system of the attitude of an unmanned aerial vehicle into a direction cosine matrix of a local horizontal coordinate system, and obtaining an attitude matrix of the unmanned aerial vehicle; establishing a covariance matrix of single-difference combination observation values of the low earth orbit satellite and the unmanned aerial vehicle, and solving a direction cosine matrix by using the covariance matrix of the observation values; respectively solving an attitude matrix and a covariance matrix of the unmanned aerial vehicle, and constructing an unmanned aerial vehicle attitude determination model by using the solution of the attitude matrix and the solved covariance matrix to obtain a general model for estimating the attitude of the unmanned aerial vehicle by using low-orbit satellite signals; and measuring and calculating the pose of the unmanned aerial vehicle by using the general model for estimating the pose of the unmanned aerial vehicle by using the low-orbit satellite signal. Accurate and high-precision high-frequency dynamic attitude measurement of the unmanned aerial vehicle in a signal shielding or interference environment can be realized, the precision is not reduced in the measurement process, and the problem of accurate attitude determination of the unmanned aerial vehicle in the modern unmanned aerial vehicle application process can be solved.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicles (UAVs), and in particular to a method for measuring the attitude of an UAV based on low-orbit satellite communication signals. Background Art

[0002] With the development of emerging technologies such as the Internet of Things and autonomous driving, the demand for communication signals is no longer limited to the areas where ground base stations are available. It is now moving towards all-weather, all-time, and all-around access. Low-orbit satellites, with their unique orbital and signal characteristics, are poised to become a crucial component of the global communications system. In this context, utilizing low-orbit satellite communication signals to perform drone attitude measurement will significantly increase the number of equivalent navigation satellites. Currently, given the susceptibility of GNSS to interference and spoofing, leveraging the high landing power of low-orbit satellite signals for positioning has attracted widespread attention. Based on this premise, utilizing low-orbit satellite communication signals for high-precision and high-reliability drone attitude measurement has become a theoretically feasible solution.

[0003] Traditional methods for determining the attitude of drones rely primarily on inertial navigation systems (INS) and global navigation satellite systems (GNSS). While INS can provide high-frequency dynamic attitude measurements, its accuracy degrades over time due to drift in the inertial measurement unit. While GNSS offers high accuracy, its performance is severely impacted by signal obstruction or interference in complex environments such as urban canyons and indoor environments. GNSS also has a long convergence time and is susceptible to interference and spoofing. Therefore, achieving accurate attitude determination for drones in these environments has become a pressing issue. Summary of the Invention

[0004] In order to solve some or all of the technical problems existing in the above-mentioned prior art, the present invention provides a UAV attitude measurement method based on low-orbit satellite communication signals, which can realize accurate, high-precision high-frequency dynamic attitude measurement of UAVs in an environment with signal blockage or interference, and the accuracy will not decrease during the measurement process. It can solve the problem of accurate attitude determination of UAVs in the application of modern UAVs.

[0005] The technical solutions of the present invention are as follows:

[0006] A method for measuring the attitude of a UAV based on low-orbit satellite communication signals is provided, comprising:

[0007] Convert the vehicle coordinate system of the UAV attitude into the direction cosine matrix of the local horizontal coordinate system to obtain the attitude matrix of the UAV;

[0008] Establish the covariance matrix of the single-difference combined observations of the low-orbit satellite and the UAV, and use the covariance matrix of the observations to solve the direction cosine matrix;

[0009] Solve the attitude matrix and covariance matrix of the UAV respectively, and use the solution of the attitude matrix and the solved covariance matrix to build a UAV attitude measurement model, and obtain a general model for estimating the attitude of the UAV using low-orbit satellite signals;

[0010] The UAV's position and attitude are calculated using a general model for estimating the UAV's attitude using low-orbit satellite signals.

[0011] Furthermore, in the above-mentioned UAV attitude measurement method based on low-orbit satellite communication signals, the attitude matrix of the UAV is:

[0012] B i =R i {r}{p}{y}L i ;

[0013] Among them, B i Represents the baseline matrix of the carrier coordinate system that can be measured in advance, R i Represents the direction cosine matrix rotated in sequence according to the three-axis attitude, r represents the roll angle, p represents the pitch angle, y represents the heading angle, L i represents the baseline matrix of the local horizontal coordinate system, and {·} represents the operator containing the Euler angles.

[0014] Furthermore, in the above-mentioned UAV attitude measurement method based on low-orbit satellite communication signals, the covariance matrix of the single-difference combined observation values of the low-orbit satellite and the UAV includes:

[0015]

[0016] Among them, Q ΔY represents the covariance matrix of the single-differenced observations of the low-orbit satellite and the UAV, P n represents the correlation matrix of the master and slave antennas of the low-orbit satellite, Q s represents the covariance matrix of the single-difference observations of each antenna of the low-orbit satellite, represents the Kronecker product, represents P n and Q s Operations between two matrices of arbitrary size.

[0017] Furthermore, in the above-mentioned UAV attitude measurement method based on low-orbit satellite communication signals, the correlation matrix of the low-orbit satellite master and slave antennas includes:

[0018]

[0019] Here, n represents the order of the matrix.

[0020] Furthermore, in the above-mentioned UAV attitude measurement method based on low-orbit satellite communication signals, the attitude matrix and covariance matrix of the UAV are obtained by solving the least squares method.

[0021] Furthermore, in the above-mentioned UAV attitude measurement method based on low-orbit satellite communication signals, the UAV attitude measurement model is constructed using the solution of the attitude matrix and the solved covariance matrix. The general model for estimating the UAV attitude using low-orbit satellite signals includes:

[0022]

[0023] in, It represents the accuracy of the general model for estimating the attitude of UAV using low-orbit satellite signals, expressed as the covariance of the attitude matrix after straightening operation. Represents the coordinate matrix of the master and slave antenna baselines of each low-orbit satellite in the carrier coordinate system, represents the inverse operation of the correlation matrix between baselines caused by inter-station differences, F n represents the coordinate matrix of the master and slave antenna baselines of each low-orbit satellite in the carrier coordinate system, σ represents the ranging error of the low-orbit earth satellite signal, G represents the LOS direction vector matrix of the low-orbit earth satellite signal, G T represents the transpose operation of the LOS direction vector matrix of the low earth orbit satellite signal, and I represents the identity matrix.

[0024] Furthermore, in the above-mentioned UAV attitude measurement method based on low-orbit satellite communication signals, Calculated by the following formula:

[0025]

[0026] in, represents the attitude rotation matrix estimated by the least squares algorithm, represents the inverse operation of the covariance matrix of the single-difference observation value of each antenna of the low-orbit satellite, and ΔY represents the double-difference observation value matrix.

[0027] Furthermore, in the above-mentioned UAV attitude measurement method based on low-orbit satellite communication signals, the low-orbit earth satellite signal ranging error σ is calculated by the following formula:

[0028]

[0029] Where c represents the speed of light, β e Denotes the equivalent bandwidth of low-orbit satellite, B n represents the bandwidth of the low-orbit satellite, T represents the duration of the low-orbit satellite signal, and SNR represents the signal-to-noise ratio of the low-orbit satellite signal;

[0030] The signal-to-noise ratio of the low-orbit satellite signal is calculated using the following formula:

[0031]

[0032] Among them, E s Indicates the cumulative energy of low-orbit satellite signals.

[0033] Furthermore, in the above-mentioned UAV attitude measurement method based on low-orbit satellite communication signals, constructing a UAV attitude measurement model using the solution of the attitude matrix and the solved covariance matrix includes:

[0034] A general model for determining the attitude of a UAV using signals from low-Earth orbit satellites;

[0035] Establish and solve the information matrix to obtain the time delay error of the communication signal, and use the obtained time delay error of the communication signal as the communication signal ranging error;

[0036] According to the attitude determination model based on the communication signal and the signal delay derivative, and through the carrier measurement baseline factor and the satellite distribution factor, the single epoch attitude determination error of the signal and the single epoch attitude solution are obtained;

[0037] The UAV attitude measurement model is constructed based on the obtained general model and the established information matrix.

[0038] Furthermore, in the above-mentioned UAV attitude measurement method based on low-orbit satellite communication signals, the general model of the UAV attitude includes:

[0039]

[0040] Among them, B_DOP represents the baseline structure error factor, and PDOP represents the satellite structure error factor.

[0041] The main advantages of the technical solution of the present invention are as follows:

[0042] The present invention's method for determining the attitude of a drone based on low-orbit satellite communication signals not only effectively utilizes low-orbit satellites due to their low orbits and high signal strength, but also maintains high signal quality and measurement accuracy. Therefore, in complex environments such as urban canyons and indoor environments where traditional GNSS signals are limited, drones can achieve stable and accurate attitude determination, thereby ensuring the reliability of their navigation and control. Furthermore, the present invention's algorithm for determining the attitude of a drone based on low-orbit satellite communication signals exhibits good adaptability and flexibility. It is not limited by the coding and modulation methods of low-orbit satellite signals and can adapt to different communication signal types, making the present invention's method potentially applicable in a wide range of applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The drawings described herein are used to provide a further understanding of the embodiments of the present invention and constitute a part of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0044] Figure 1 A schematic flow chart of a method for measuring the attitude of an unmanned aerial vehicle based on low-orbit satellite communication signals provided by one embodiment of the present invention;

[0045] Figure 2 A schematic diagram of the geometric relationship between the baseline and satellite signals in a method for measuring the attitude of an unmanned aerial vehicle based on low-orbit satellite communication signals provided by one embodiment of the present invention;

[0046] Figure 3 This is a diagram showing the relationship between coefficients, baseline lengths, and angles when multiple antennas form a multi-antenna system in a method for measuring the attitude of an unmanned aerial vehicle based on low-orbit satellite communication signals provided by one embodiment of the present invention;

[0047] Figure 4 A flowchart illustrating a method for determining the attitude of a UAV based on low-orbit satellite communication signals, which uses the solution of the attitude matrix and the solved covariance matrix to construct a UAV attitude measurement model, is provided in accordance with an embodiment of the present invention;

[0048] Figure 5 This is a CRLB effect diagram of using OFDM signals for attitude measurement in a UAV attitude measurement method based on low-orbit satellite communication signals provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0049] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with specific embodiments of the present invention and corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0050] In order to make the technical solution of this application more complete and facilitate the understanding and implementation of this solution, the technical features involved in the present invention are first briefly described and explained.

[0051] GNSS attitude measurement technology was first proposed by Spinney (1976). This technology utilizes multiple GNSS antennas mounted on a vehicle to accurately measure the vehicle's attitude based on carrier phase differential technology. GNSS attitude measurement avoids the drift issues associated with inertial navigation and offers advantages such as low cost, compact size, light weight, and stable accuracy. Research based on GNSS attitude measurement has achieved considerable success. In 1978, MIT and NASA proposed using three non-collinear GPS antennas to form a baseline vector for attitude measurement and using GPS carrier phase differential technology to obtain the vehicle's attitude information. Ashtech developed a three-dimensional attitude measurement system based on GPS interferometry, laying the foundation for the development of GPS attitude measurement technology. Cohen (1992) and Lu (1995) established a mathematical model for multi-antenna GNSS attitude measurement. Jiunhan (2000) first achieved high-precision attitude measurement based on clock synchronization using two receivers connected to a common crystal oscillator. This research promoted the research and development of GPS single-device, multi-antenna attitude measurement technology.

[0052] GNSS attitude measurement technology has achieved remarkable results after over three decades of development. However, it also has inherent limitations. Compared to inertial navigation, GNSS has a lower sampling rate, making it susceptible to loss of high-frequency dynamic information when operating in highly dynamic UAV motion. GNSS short-term noise levels are also high, and the distance between multiple antennas is limited when used in UAVs. Since GNSS attitude measurement accuracy is approximately proportional to baseline length, GNSS attitude measurement cannot meet the accuracy requirements of UAVs. Furthermore, GNSS signals still have many significant flaws, and their secure application faces challenges in complex electromagnetic environments. For example, spoofing jammers transmit spoofing signals to induce user receivers to produce erroneous position, velocity, or time information, impacting receiver operation and enabling control of the target receiver. If the system uses this erroneous information, serious consequences can result. Of all interference types, spoofing jammers are the most harmful. They can, unnoticed by the terminal, induce receivers to produce erroneous timing and positioning results, compromising user safety and severely impacting the robustness of UAV attitude measurement.

[0053] The following is combined with Figure 1-5 , describes in detail the technical solution provided by the embodiments of the present invention.

[0054] Specifically, if Figure 1 As shown, an embodiment of the present invention provides a method for measuring the attitude of a UAV based on a low-orbit satellite communication signal, comprising the following steps S1 to S4:

[0055] Step S1: Convert the vehicle coordinate system of the UAV attitude into the direction cosine matrix of the local horizontal coordinate system to obtain the attitude matrix of the UAV;

[0056] The attitude determination of the UAV can be expressed as the direction cosine matrix of the vehicle coordinate system converted to the local horizontal coordinate system, and the vehicle coordinate system of the UAV attitude is converted to the direction cosine matrix of the local horizontal coordinate system. The flight attitude of the UAV is determined by the following formula:

[0057] B i =R i {r}{p}{y}L i ;

[0058] Among them, B i Represents the baseline matrix of the carrier coordinate system that can be measured in advance, R i Represents the direction cosine matrix rotated in sequence according to the three-axis attitude, r represents the roll angle, p represents the pitch angle, y represents the heading angle, L i represents the baseline matrix of the local horizontal coordinate system, and {·} represents the operator containing the Euler angles.

[0059] In the case of a short baseline, the communication signal from the low-orbit satellite can be regarded as a plane wave, so the unit line of sight vector from the low-orbit satellite j to the antennas at both ends of the baseline (master and slave antennas) can be regarded as the same line of sight vector. The geometric relationship between the baseline and the satellite signal is as follows: Figure 2 shown.

[0060] The geometric relationship formula between satellite signals and baselines can be expressed as:

[0061] Δρ j =s j b;

[0062] Combine Figure 2 As shown, in Figure 2 In the equation, satllite j represents a low-orbit satellite, Main antenna represents the main antenna, Slave antenna represents the slave antenna, and Δρ j , s j denotes the difference matrix between the distance from satellite j to the master and slave antennas and the line-of-sight vector, respectively, and b represents the baseline vector matrix of the master and slave antennas in the local horizontal coordinate system. Traditional GNSS attitude determination typically requires first determining the position of each antenna and then performing inter-station differencing to obtain baseline information. This paper utilizes broadband signals from low-orbit satellites, and the TDOA method naturally allows for inter-station single-differences.

[0063] As an example, in the case of multiple baselines, assume that n+1 antennas simultaneously track the satellite signals of m+1 low earth orbit (LEO) satellites. The multi-baseline observation equation model is as follows: i =R2{r}R1{p}R3{y}L i and the formula Δρ j =s j The b combination yields the following results:

[0064]

[0065] Among them, G represents the LOS direction vector matrix of the LEO signal, R represents the attitude rotation matrix, The coordinate matrix representing each master and slave antenna baseline in the carrier coordinate system is defined as f1,f2,…,f n They are respectively represented by V, which represents the non-model observation noise and Y, which is a matrix expressed as the difference in the observation distance from satellite j to baseline i, defined as

[0066] Therefore, the above formula The observation value equation represented by can be rewritten into the following format after double difference operation:

[0067]

[0068] In order to eliminate the ionosphere and troposphere errors in low-orbit satellite observations, inter-satellite single difference is usually used. The satellite with the highest elevation angle is selected and the observations of other satellites are subtracted from it.

[0069] Then After matrix straightening operation, it is converted into:

[0070]

[0071] Among them, ΔY represents the double difference observation matrix, vecV represents the sum of errors without model error correction, and F n represents the coordinate matrix of each master-slave antenna baseline in the carrier coordinate system, vecR represents the attitude rotation matrix after matrix straightening operation, and vecΔY represents the single-difference combined observation value of the low-orbit satellite and the UAV.

[0072] Step S2: Establish the covariance matrix of the single-difference combined observations of the low-orbit satellite and the UAV, and use the covariance matrix of the observations to solve the direction cosine matrix;

[0073] In order to correctly estimate the direction matrix R, the covariance matrix of the observations must also be established.

[0074] The observation value vecΔY comes from the single difference combination between satellites. Assuming that the covariance of the single difference observation value of each antenna is the same, it is set to Q s , which can also represent the ranging accuracy of low earth orbit communication signals, defined as Q s =σ 2 (G T IG) -1 ;

[0075] Therefore, the covariance matrix of the single-difference combination observations of the low-orbit satellite and the UAV includes:

[0076]

[0077] Among them, Q ΔY represents the covariance matrix of the observations, P n represents the correlation matrix of the master and slave antennas of the low-orbit satellite, Q s represents the covariance matrix of the single-difference observations of each antenna of the low-orbit satellite, represents the Kronecker product, represents P n and Q s Operations between two matrices of arbitrary size.

[0078] In this embodiment, the correlation matrix of the master and slave antennas of the low-orbit satellite includes:

[0079]

[0080] Here, n represents the order of the matrix.

[0081] From this, the covariance of ΔY can be formulated as:

[0082]

[0083] Among them, Q ΔY Represents the covariance matrix of the observations, e represents a column vector of all 1s, I n represents the identity matrix of dimension n×n, e n represents a vector of length n that is all 1, Indicates e n The transpose of the same dimension as the master-slave baseline, P n represents the correlation matrix of the master and slave antennas.

[0084] In some optional implementations of this embodiment, the solution of the attitude matrix and the covariance matrix of the drone are obtained by solving the least squares method.

[0085] Step S3: Solve the attitude matrix and covariance matrix of the UAV respectively, and use the solution of the attitude matrix and the solved covariance matrix to build a UAV attitude measurement model, and obtain a general model for estimating the attitude of the UAV using low-orbit satellite signals;

[0086] Specifically, the attitude matrix solution and the solved covariance matrix are used to construct a UAV attitude measurement model. The general model for estimating the attitude of a UAV using low-orbit satellite signals includes:

[0087]

[0088] in, represents a general model for estimating the attitude of a UAV using low-orbit satellite signals, represents the inverse operation of the correlation matrix between baselines caused by inter-station differences, F n Represents the coordinate matrix of the master and slave antenna baselines of each low-orbit satellite in the carrier coordinate system, represents the coordinate matrix of the master and slave antenna baselines of each low-orbit satellite in the carrier coordinate system, σ represents the ranging error of the low-orbit earth satellite signal, G represents the LOS direction vector matrix of the low-orbit earth satellite signal, G T represents the transpose operation of the LOS direction vector matrix of the low earth orbit satellite signal, and I represents the identity matrix. Specifically, the attitude matrix Calculated by the following formula:

[0089]

[0090] in, Represents the posture rotation matrix after matrix straightening operation, represents the inverse operation of the covariance matrix of the single-difference observation value of each antenna of the low-orbit satellite, and ΔY represents the double-difference observation value matrix.

[0091] thus,

[0092] Specifically, in this embodiment, the low-orbit satellite signal ranging error σ is calculated using the following formula:

[0093]

[0094] Where c represents the speed of light, β e Denotes the equivalent bandwidth of low-orbit satellite, B n represents the bandwidth of the low-orbit satellite, T represents the duration of the low-orbit satellite signal, and SNR represents the signal-to-noise ratio of the low-orbit satellite signal. The signal-to-noise ratio of the low-orbit satellite signal is calculated using the following formula:

[0095]

[0096] Among them, E s Indicates the cumulative energy of low-orbit satellite signals.

[0097] Thus, a general model for estimating the attitude of UAV using low-orbit satellite signals can be obtained. G represents the sight vector matrix, that is, the LOS direction vector matrix of the low-orbit satellite signal. Therefore, the above (G T IG) -1 It can also be expressed as PDOP·σ 2 , PDOP·σ 2 The dilution of precision of the satellite's spatial position is related only to the satellite's geometric configuration. represents the baseline distribution, P n represents the correlation matrix between baselines caused by inter-station differences. Thus, the baseline factor B_DOP is defined as:

[0098]

[0099] From this, we can conclude that B_DOP is only related to the baseline. After decomposing B_DOP, the first term is related to the baseline geometry, and the second term is related to the number of baselines. This shows that the larger the plane area spanned by the baselines and the greater the number of baselines, the higher the pose estimation accuracy.

[0100] As an example, using three antennas to form two main baselines, the precision coefficients corresponding to different baseline configurations are calculated. The baseline lengths range from 1 to 3 meters and the angles range from 20° to 160°. The relationship between the coefficients, baseline lengths, and angles is shown in Figure 2. Figure 3 As shown, according to the attached Figure 3 It can be observed that the accuracy is highest when the two main baselines are orthogonal, and the baseline DOP decreases continuously with the increase of baseline length.

[0101] Therefore, if Figure 4 As shown, constructing a UAV attitude measurement model using the solution of the attitude matrix and the solved covariance matrix includes steps S31 to S34:

[0102] Step S31: Determine a general model of the UAV's attitude using low earth orbit satellite signals;

[0103] Specifically, the general model of the drone's attitude includes:

[0104]

[0105] Among them, B_DOP represents the baseline structure error factor obtained by measuring the position of the carrier antenna, and PDOP represents the satellite structure error factor, so that the attitude matrix can be solved and the covariance of the pose matrix

[0106] Step S32: Establish and solve the information matrix to obtain the time delay error of the communication signal, and use the obtained time delay error of the communication signal as the communication signal ranging error σ;

[0107] Step S33: According to the attitude determination model based on the communication signal and the signal delay derivative, and by measuring the baseline factor B_DOP and the satellite distribution factor PDOP, the single epoch attitude determination error of the signal is obtained. and single epoch attitude solution

[0108] Step S34: Construct a UAV attitude measurement model based on the obtained general model and the established information matrix.

[0109] Specifically, the general model for determining the attitude of a drone using LEO signals can be summarized as follows:

[0110]

[0111] The estimation of the time delay can be equivalent to the joint estimation of the fixed delay value and the Doppler frequency shift. Assume that σ 2 is the lower bound of the CRLB of the communication signal delay, and when the navigation packet waveform of the communication signal is given, σ can be estimated 2 .

[0112] Assume that the received communication signal expression is:

[0113]

[0114] Where t represents the duration of the drone receiving the low-orbit satellite, s(t) represents the baseband signal with a duration of t, t∈[-T / 2,T / 2], T represents the duration of the low-orbit satellite signal, τ represents the signal delay, j represents the complex sign, π represents pi, f represents the signal frequency, Δt represents the time when the low-orbit satellite signal is located, represents the phase of the signal, ω(t) represents the mean value is 0 and the variance is Gaussian white noise, where represents the noise variance;

[0115] Among them, the probability density function of x(t) can be expressed as follows:

[0116]

[0117] The unknown parameters are θ = (τ, fΔ), where τ and fΔ represent the time delay and frequency offset of the LEO communication signal, respectively. ∝ indicates that p(t; θ) is proportional to the following parameters, exp{·} represents an exponential function operation, N0 represents the signal power spectral density, s represents the signal, and τ0 represents the signal delay. According to information geometry theory, the lower bound of the unknown parameter θ is given by the following formula:

[0118]

[0119] Among them, var{·} represents the variance operator, represents the parameter to be estimated, I -1 represents the inverse operation of the identity matrix, [·] ii Represents the information matrix, where ii represents the position of each item in the matrix.

[0120] Considering that I(θ) is the Fisher information matrix of the complex signal, the items in the information matrix can be expressed as:

[0121]

[0122] Therefore, the time delay error of the communication signal is obtained, which also represents the communication signal ranging error:

[0123]

[0124] Among them, σ τ Indicates the delay error of low-orbit satellite communication signals or the ranging error of communication signals. Defined as the signal-to-noise ratio, E s is the cumulative energy of the signal B n and β e are bandwidth and equivalent bandwidth, respectively, defined as:

[0125]

[0126]

[0127] in, represents a signal of duration t.

[0128] According to the attitude determination model based on the communication signal and the signal delay derivative, the single epoch attitude determination error of the signal can be obtained, which is expressed as follows:

[0129]

[0130] Here, c represents the speed of light.

[0131] Step S4: Calculate the posture of the UAV using a general model for estimating the posture of the UAV using low-orbit satellite signals.

[0132] Therefore, in the drone attitude measurement method based on low-orbit satellite communication signals of the present invention, since the orbit of low-orbit satellites is low and the signal strength is high, not only can low-orbit satellites be effectively utilized, but also high signal quality and measurement accuracy can be maintained. Therefore, in complex environments such as urban canyons and indoors where traditional GNSS signals are limited, drones can achieve stable and accurate attitude determination, thereby ensuring the reliability of their navigation and control. At the same time, the drone attitude measurement algorithm based on low-orbit satellite communication signals of the present invention has good adaptability and flexibility, is not limited by the coding and modulation methods of low-orbit satellite (LEO) signals, and can adapt to different types of communication signals, so that the method of the present invention has a wide range of application potential. At the same time, the method of the present invention can flexibly adjust the antenna layout and signal processing algorithm according to actual needs to adapt to different flight missions and environmental conditions, providing flexible technical support for autonomous navigation and control of drones.

[0133] As an example, Figure 5 As shown, Figure 5 The CRLB for attitude measurement using OFDM signals is shown. Two orthogonal main baselines are formed by three antennas on the ground, each 1 meter long, with a PDOP of 1. The signal-to-noise ratio (SNR) of the communication signal is set to a range of -20 to 10 dB, and the coherent integration time is set to 0.1 ms. The signal bandwidth range is set to 10-80 MHz. It can be observed that when the signal bandwidth is 20 MHz, a signal with a -20 dB SNR can achieve an attitude determination accuracy better than 1°. As the SNR and bandwidth increase, the attitude determination accuracy can even be better than 0.1°. This demonstrates the significant practical value of using LEO satellite communication signals for attitude determination.

[0134] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device that includes a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In addition, "front", "back", "left", "right", "upper" and "lower" in this document are all referenced to the placement states shown in the accompanying drawings.

[0135] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A method for measuring the attitude of an unmanned aerial vehicle based on low-orbit satellite communication signals, characterized in that: include: Convert the vehicle coordinate system of the UAV attitude into the direction cosine matrix of the local horizontal coordinate system to obtain the attitude matrix of the UAV; Establish the covariance matrix of the single-difference combined observations of the low-orbit satellite and the UAV, and use the covariance matrix of the observations to solve the direction cosine matrix; Solve the attitude matrix and covariance matrix of the UAV respectively, and use the solution of the attitude matrix and the solved covariance matrix to build a UAV attitude measurement model, and obtain a general model for estimating the attitude of the UAV using low-orbit satellite signals; The UAV's position and attitude are calculated using a general model for estimating the UAV's attitude using low-orbit satellite signals.

2. The method for measuring the attitude of an unmanned aerial vehicle based on low-orbit satellite communication signals according to claim 1, wherein: The attitude matrix of the drone includes: B i =R i {r}{p}{y}L i ; Among them, B i Represents the baseline matrix of the carrier coordinate system that can be measured in advance, R i Represents the direction cosine matrix rotated in sequence according to the three-axis attitude, r represents the roll angle, p represents the pitch angle, y represents the heading angle, L i represents the baseline matrix of the local horizontal coordinate system, and {·} represents the operator containing the Euler angles.

3. The method for measuring the attitude of an unmanned aerial vehicle based on low-orbit satellite communication signals according to claim 1, wherein: The covariance matrix of the single-differenced observations of the low-orbit satellite and the UAV includes: Among them, Q ΔY represents the covariance matrix of the single-differenced observations of the low-orbit satellite and the UAV, P n represents the correlation matrix of the master and slave antennas of the low-orbit satellite, Q s represents the covariance matrix of the single-difference observations of each antenna of the low-orbit satellite, represents the Kronecker product, represents P n and Q s Operations between two matrices of arbitrary size.

4. The method for measuring the attitude of an unmanned aerial vehicle based on low-orbit satellite communication signals according to claim 3, characterized in that: The correlation matrix of the master and slave antennas of low-orbit satellites includes: Here, n represents the order of the matrix.

5. The method for measuring the attitude of an unmanned aerial vehicle based on low-orbit satellite communication signals according to claim 1, characterized in that: The attitude matrix and covariance matrix of the UAV are obtained by solving the least squares method.

6. The method for measuring the attitude of an unmanned aerial vehicle based on low-orbit satellite communication signals according to claim 1, characterized in that: The UAV attitude measurement model is constructed using the solution of the attitude matrix and the solved covariance matrix. The general model for estimating the attitude of the UAV using low-orbit satellite signals includes: in, It represents the accuracy of the general model for estimating the attitude of the UAV using low-orbit satellite signals, and it represents the covariance of each attitude after the attitude matrix is straightened. represents the inverse operation of the correlation matrix between baselines caused by inter-station differences, F n Represents the coordinate matrix of the master and slave antenna baselines of each low-orbit satellite in the carrier coordinate system, represents the transpose operation of the coordinate matrix of each low-orbit satellite master and slave antenna baseline in the carrier coordinate system, σ represents the ranging error of the low-orbit earth satellite signal, G represents the LOS direction vector matrix of the low-orbit earth satellite signal, G T represents the transpose operation of the LOS direction vector matrix of the low earth orbit satellite signal, and I represents the identity matrix.

7. The method for measuring the attitude of an unmanned aerial vehicle based on low-orbit satellite communication signals according to claim 6, characterized in that: Calculated by the following formula: in, represents the attitude rotation matrix estimated by the least squares algorithm, represents the inverse operation of the covariance matrix of the single-difference observation value of each antenna of the low-orbit satellite, and ΔY represents the double-difference observation value matrix.

8. The method for measuring the attitude of an unmanned aerial vehicle based on low-orbit satellite communication signals according to claim 6, characterized in that: The ranging error σ of the low-orbit satellite signal is calculated using the following formula: Where c represents the speed of light, β e Denotes the equivalent bandwidth of low-orbit satellite, B n represents the bandwidth of the low-orbit satellite, T represents the duration of the low-orbit satellite signal, and SNR represents the signal-to-noise ratio of the low-orbit satellite signal; The signal-to-noise ratio of the low-orbit satellite signal is calculated using the following formula: Among them, E s Indicates the cumulative energy of low-orbit satellite signals.

9. The method for measuring the attitude of an unmanned aerial vehicle based on low-orbit satellite communication signals according to claim 1, characterized in that: The UAV attitude measurement model is constructed using the solution of the attitude matrix and the solved covariance matrix, including: A general model for determining the attitude of a UAV using signals from low-Earth orbit satellites; Establish and solve the information matrix to obtain the time delay error of the communication signal, and use the obtained time delay error of the communication signal as the communication signal ranging error; According to the attitude determination model based on the communication signal and the signal delay derivative, and through the carrier measurement baseline factor and the satellite distribution factor, the single epoch attitude determination error of the signal and the single epoch attitude solution are obtained; The UAV attitude measurement model is constructed based on the obtained general model and the established information matrix.

10. The method for measuring the attitude of an unmanned aerial vehicle based on low-orbit satellite communication signals according to claim 9, characterized in that: The general model of drone attitude includes: Among them, B_DOP represents the baseline structure error factor, and PDOP represents the satellite structure error factor.

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