Real-time attitude estimation method and device based on receiver positioning information, equipment and medium

Abstract

The invention relates to a real-time attitude measurement method and device based on receiver positioning information, equipment and a medium, and the method comprises the steps: receiving a real-time navigation signal through a receiver arranged on a rotating carrier, and resolving the position of the receiver through a receiver positioning model according to the real-time navigation signal to obtain positioning data. The method comprises the following steps of: acquiring positioning data of a rotating carrier, processing the positioning data by adopting Kalman filtering and a second-order locked loop, solving the rotating speed of the rotating carrier by utilizing the positioning data of any one of an X axis, a Y axis and a Z axis in the processed positioning data, and solving the roll angle of the rotating carrier according to the processed positioning data by adopting Hilbert transform. Therefore, the real-time attitude of the rotary carrier is obtained. By adopting the method, the accuracy and the efficiency can be effectively improved.

Description

Technical Field

[0001] The present application relates to the field of satellite navigation and positioning technology, and in particular to a real-time attitude estimation method, device, equipment and medium based on receiver positioning information. Background Art

[0002] Satellite navigation services have become a key component of informatization construction in various countries. The use of a single satellite navigation receiver measurement module to achieve positioning and roll attitude measurement functions can play a very important role in roll attitude measurement. Compared with traditional geomagnetic, inertial navigation and other sensor attitude measurement methods, the aircraft attitude measurement technology based on the satellite navigation system can not only improve the accuracy of the navigation system, but also save other sensors for measuring roll, reduce costs and reduce size.

[0003] At present, my country mostly uses inertial navigation and geomagnetic sensors to realize the attitude measurement of aircraft. However, for inertial navigation systems, the calibration time of inertial navigation systems is generally 10-15 minutes, which cannot be used on fast-response aircraft. As time goes by, the error will gradually accumulate, resulting in a decrease in accuracy during long-distance and long-term operation. In addition, the cost of general inertial navigation equipment is high and it is not suitable for large-scale use. For geomagnetic sensors, the earth's magnetic field is distributed globally, but there are differences in magnetic field characteristics in different regions. In some special areas, the geomagnetic changes are complex, which will affect the accuracy of attitude measurement. Its relatively effective attitude measurement range will be subject to certain restrictions. The use of satellite navigation attitude measurement utilizes the global coverage of satellite constellations. No matter where you are, as long as you can receive satellite signals, you can perform attitude measurement. Compared with the existing solution methods that use satellite signal energy information, this method has a wider range of application, and there is no need to worry about the large error in attitude measurement caused by small changes in signal energy. Summary of the invention

[0004] Based on this, it is necessary to provide a real-time attitude estimation method, device, equipment and medium based on receiver positioning information that can improve efficiency and accuracy in response to the above technical problems.

[0005] A real-time attitude estimation method based on receiver positioning information, the method comprising:

[0006] Acquiring real-time navigation signals sent by multiple satellites, wherein the real-time navigation signals are received by a receiver disposed on a rotating carrier;

[0007] Calculating the position of the receiver using a receiver positioning model according to the real-time navigation signal to obtain positioning data of the receiver;

[0008] The positioning data is processed by using a Kalman filter and a second-order locking loop in sequence to obtain processed positioning data;

[0009] Using the positioning data of any one of the X-axis, Y-axis and Z-axis in the processed positioning data, the rotation speed of the rotating carrier is solved to obtain the real-time rotation speed;

[0010] Using Hilbert transform to solve the roll angle of the rotating carrier according to the processed positioning data to obtain a real-time roll angle;

[0011] The real-time posture of the rotating carrier is obtained according to the real-time rotation speed and the real-time roll angle.

[0012] In one embodiment, the receiver is a single patch antenna disposed on the outer side of the cylindrical portion of the rotating carrier.

[0013] In one embodiment, the real-time navigation signals received by the receiver are navigation signals sent by a preset number of satellites sorted according to satellite signal-to-noise ratios.

[0014] In one embodiment, the receiver positioning model is constructed using the pseudo-range between the receiver and the satellite that sends the navigation signal and the least squares method.

[0015] In one embodiment, the receiver positioning model is expressed as:

[0016]

[0017] In the above formula, x, y, and z represent the receiver position coordinates to be solved. n ,y n 、z n Indicates the position coordinates of the nth satellite, δt u represents the receiver clock error, p d represents the pseudorange after adding the error.

[0018] In one embodiment, when solving the rotation speed of the rotating carrier using the positioning data, the positioning data corresponding to the X-axis is selected.

[0019] In one embodiment, the radius of the cylindrical portion of the rotating carrier is 0.3 to 0.5 meters.

[0020] The present application also provides a real-time attitude estimation device based on receiver positioning information, the device comprising:

[0021] A navigation signal receiving module, used to obtain real-time navigation signals sent by multiple satellites, wherein the real-time navigation signals are received by a receiver arranged on a rotating carrier;

[0022] A positioning data acquisition module, used to calculate the position of the receiver according to the real-time navigation signal using a receiver positioning model to obtain positioning data of the receiver;

[0023] A data processing module, used to process the positioning data using Kalman filtering and a second-order locking loop in sequence to obtain processed positioning data;

[0024] A real-time rotation speed solving module is used to solve the rotation speed of the rotating carrier by using the positioning data of any one of the X-axis, Y-axis and Z-axis in the processed positioning data to obtain the real-time rotation speed;

[0025] A real-time roll angle solving module, used for solving the roll angle of the rotating carrier according to the processed positioning data by using Hilbert transform to obtain a real-time roll angle;

[0026] The real-time attitude estimation module is used to obtain the real-time attitude of the rotating carrier according to the real-time rotation speed and the real-time roll angle.

[0027] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0028] Acquiring real-time navigation signals sent by multiple satellites, wherein the real-time navigation signals are received by a receiver disposed on a rotating carrier;

[0029] Calculating the position of the receiver using a receiver positioning model according to the real-time navigation signal to obtain positioning data of the receiver;

[0030] The positioning data is processed by using a Kalman filter and a second-order locking loop in sequence to obtain processed positioning data;

[0031] Using the positioning data of any one of the X-axis, Y-axis and Z-axis in the processed positioning data, the rotation speed of the rotating carrier is solved to obtain the real-time rotation speed;

[0032] Using Hilbert transform to solve the roll angle of the rotating carrier according to the processed positioning data to obtain a real-time roll angle;

[0033] The real-time posture of the rotating carrier is obtained according to the real-time rotation speed and the real-time roll angle.

[0034] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the following steps:

[0035] Acquiring real-time navigation signals sent by multiple satellites, wherein the real-time navigation signals are received by a receiver disposed on a rotating carrier;

[0036] Calculating the position of the receiver using a receiver positioning model according to the real-time navigation signal to obtain positioning data of the receiver;

[0037] The positioning data is processed by using a Kalman filter and a second-order locking loop in sequence to obtain processed positioning data;

[0038] Using the positioning data of any one of the X-axis, Y-axis and Z-axis in the processed positioning data, the rotation speed of the rotating carrier is solved to obtain the real-time rotation speed;

[0039] Using Hilbert transform to solve the roll angle of the rotating carrier according to the processed positioning data to obtain a real-time roll angle;

[0040] The real-time posture of the rotating carrier is obtained according to the real-time rotation speed and the real-time roll angle.

[0041] The above-mentioned real-time attitude measurement method, device, equipment and medium based on receiver positioning information obtains real-time navigation signals by receiving a receiver arranged on a rotating carrier, obtains positioning data by solving the position of the receiver according to the real-time navigation signals using a receiver positioning model, processes the positioning data using a Kalman filter and a second-order lock loop, solves the rotation speed of the rotating carrier using the positioning data of any one of the X-axis, Y-axis and Z-axis in the processed positioning data, and simultaneously solves the roll angle of the rotating carrier according to the processed positioning data using a Hilbert transform to obtain the real-time attitude of the rotating carrier. The use of this method can effectively provide accuracy and efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 A schematic diagram of a flow chart of a real-time attitude measurement method based on receiver positioning information in one embodiment;

[0043] Figure 2 A schematic diagram of a rotating carrier single antenna receiving satellite signals in one embodiment;

[0044] Figure 3 A schematic diagram of modeling the receiver motion in a simulation experiment that simulates real-life conditions;

[0045] Figure 4 This is a schematic diagram of the FFT solution of the receiver in the x-axis direction in a simulation experiment;

[0046] Figure 5 A schematic diagram showing the comparison between simulated data and measured data in a simulation experiment;

[0047] Figure 6 This is a schematic diagram of the rotating carrier speed obtained by using this method in a simulation experiment;

[0048] Figure 7 A schematic diagram of the rolling angle of a rotating carrier obtained by using this method in a simulation experiment;

[0049] Figure 8 is a structural block diagram of a real-time attitude measurement device based on receiver positioning information in one embodiment;

[0050] Fig. 9 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0051] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0052] In view of the problems existing in the prior art when estimating the posture of a rotating carrier, such as Figure 1 As shown, a real-time attitude estimation method based on receiver positioning information is provided, which specifically includes the following steps:

[0053] Step S100, acquiring real-time navigation signals transmitted by a plurality of satellites, wherein the real-time navigation signals are received by a receiver disposed on a rotating carrier.

[0054] Step S110, using the receiver positioning model to solve the position of the receiver according to the real-time navigation signal to obtain the positioning data of the receiver.

[0055] Step S120, using Kalman filtering and a second-order item-locked loop in sequence to process the positioning data to obtain processed positioning data.

[0056] Step S130, using the positioning data of any one of the X-axis, Y-axis and Z-axis in the processed positioning data, the rotation speed of the rotating carrier is solved to obtain the real-time rotation speed.

[0057] Step S140, using Hilbert transform to solve the roll angle of the rotating carrier according to the processed positioning data to obtain a real-time roll angle.

[0058] Step S150, obtaining the real-time posture of the rotating carrier according to the real-time rotation speed and the real-time roll angle.

[0059] In the present application, when performing attitude estimation for a rotating carrier with a low rotation speed, its positioning information is used to solve the roll angle of the rotating carrier. At the same time, Kalman filtering and a second-order phase-locked loop are used to filter the positioning information to improve the accuracy of subsequent attitude parameter solutions and enhance stability.

[0060] In step S100, the rotating carrier refers to a rotating carrier having a cylindrical structure. Further, the rotating carrier is a low-speed rotating aircraft represented by a rocket. Then, the receiver is a single patch antenna arranged on the cylindrical portion of the rotating carrier, i.e., the outer side of the cylindrical structure, such as Figure 2 shown.

[0061] like Figure 2 As shown, in the carrier coordinate system, the y-axis points to the antenna, the carrier rotates around the x-axis, and the z-axis is perpendicular to the other two axes.

[0062] Furthermore, since satellite positioning requires at least four satellites, the satellite selection rule used to measure the roll angle here is different from the selection of the navigation satellite constellation, but is sorted according to the satellite signal-to-noise ratio, which can more accurately determine the position of the rotating carrier. Therefore, in this embodiment, the real-time navigation signal received by the receiver is the navigation signal sent by the preset number of satellites sorted according to the satellite signal-to-noise ratio.

[0063] In this embodiment, after caching the navigation information sent by multiple satellites received by the receiver, subsequent attitude estimation is performed on at least one period of navigation information to obtain the roll angle at any time in the period. Wherein, one period is at least the navigation information received when the rotating carrier rotates one circle.

[0064] In step 110, the receiver positioning model is constructed using the pseudo-range between the receiver and the satellite that sends the navigation signal and the least square method. The essence of its pseudo-range positioning is to solve the nonlinear equations shown in the following formula:

[0065]

[0066] In formula (1), (x n ,y n 、z n ) represents the position coordinates of the nth satellite, p m It represents the geometric distance from the receiver to the satellite n, and uses the cosine theorem to calculate that the receiver's motion radius is tangent to the earth's surface, proving that the receiver is moving on the earth's surface.

[0067] However, due to the clock error between the receiver clock and the satellite clock and the delay of the radio signal through the atmosphere, there is a certain deviation between the distance actually measured by the receiver and the true geometric distance from the satellite to the receiver. Therefore, the receiver positioning model is expressed as:

[0068]

[0069] In formula (2), x, y, and z represent the receiver position coordinates to be solved. n ,y n 、z nIndicates the position coordinates of the nth satellite, δt u represents the receiver clock error, p d represents the pseudorange after adding the error.

[0070] In this embodiment, the receiver positioning model shown in formula (2) is used to solve the real-time position information of the receiver in the rotating carrier coordinate system, that is, positioning data, according to the position information of each satellite. It should be noted here that the positioning data includes multiple position coordinates sorted in chronological order.

[0071] Since, when solving the position coordinates of the receiver, there is still a distance error in formula (2) caused by the difference in related models such as the receiver clock and tropospheric delay, which will lead to inaccuracy in the subsequent solution process, so in step S120, the positioning data is processed in combination with the Kalman filter and the second-order phase-locked loop. The prediction and update mechanism of the Kalman filter can provide a more accurate initial value and state estimation for the second-order phase-locked loop, thereby accelerating the convergence speed of the phase-locked loop, enabling it to achieve phase lock faster, and improving the response speed of the system. In a relatively complex noise environment, the Kalman filter can adjust the estimation strategy in real time according to the dynamic model and observation data of the system, helping the second-order phase-locked loop to better resist the influence of noise and interference, so as to improve the accuracy of the data, enhance the stability of the data, improve the data processing efficiency, and at the same time improve the anti-interference ability of the entire system.

[0072] Specifically, in terms of improving data accuracy, the Kalman filter can give the optimal estimate of the current state through a recursive algorithm based on the system's state equation and observation equation, combined with the estimated value at the previous moment and the current observation value. It can effectively process the noise in the positioning data, remove the random noise introduced by factors such as satellite signal transmission and receiver errors, and make the positioning data closer to the true value. Based on the preliminary processing of the Kalman filter, the second-order phase-locked loop can further track the phase changes of the positioning data, accurately adjust the data with phase jitter or deviation, make the positioning data more accurate in phase, and thus improve the accuracy of the overall data.

[0073] Specifically, in terms of enhancing data stability, the Kalman filter can effectively suppress data fluctuations by continuously updating and predicting system states, making positioning data more stable in time series. Even when the satellite signal is temporarily interrupted or interfered with, its prediction function can be relied on to maintain the relative stability of positioning data. The second-order phase-locked loop has good tracking performance and locking characteristics, and can quickly track and lock the frequency and phase changes in positioning data. It can stabilize the frequency and phase of positioning data within a smaller fluctuation range, further enhancing the stability of the data and providing a stable and reliable data basis for the subsequent rotation speed and roll angle solution.

[0074] Specifically, in terms of improving data processing efficiency, the recursive algorithm structure of the Kalman filter is simple, the amount of calculation is relatively small, and it is easy to implement real-time calculations on hardware devices. It can quickly process a large amount of positioning data, and can give a relatively accurate estimate in a short time, thereby increasing the speed of data processing. The second-order phase-locked loop adopts the feedback control principle, which can quickly track the phase changes of positioning data and realize real-time adjustment and optimization of data. Combined with the Kalman filter, the two can work in parallel or serially when processing positioning data, making full use of their respective advantages, improving the overall data processing efficiency, and meeting real-time requirements.

[0075] Specifically, in terms of improving the anti-interference ability of the entire system, the Kalman filter can model and estimate the noise and interference in the system by establishing the state model and observation model of the system, and suppress it during the filtering process. When the rotating carrier is affected by external electromagnetic interference or other factors, the Kalman filter can reduce the impact of interference on the positioning data by adjusting the estimation parameters. The second-order phase-locked loop has a certain anti-interference ability, especially it has a good inhibitory effect on phase interference. It can enhance the resistance to interference signals by adjusting the internal loop parameters, ensure that the phase of the positioning data can still be accurately tracked in an interference environment, and work together with the Kalman filter to further improve the anti-interference ability of the system.

[0076] In this embodiment, the Kalman filter algorithm can effectively reduce the influence of noise on state estimation in the presence of noise, thereby providing relatively accurate state estimation. Therefore, in order to further increase the convergence probability of the second-order phase-locked loop, the Kalman filter algorithm can be used before the second-order phase-locked loop. From the previous analysis, it can be seen that the accuracy of the receiver positioning information is an important factor in whether the rotating carrier posture can be correctly solved. Therefore, the positioning data of the receiver is used as the state equation of the system, and the prediction equation of the system is:

[0077]

[0078] Formula (3) represent The state equation of the system is the optimal estimate of the state at time k. A is the state transfer matrix. The receiver positioning data is a one-dimensional signal, so A is set to 1.

[0079] Furthermore, the state prediction error at time K can be obtained as Substituting into the system state equation, the prediction covariance matrix is:

[0080] P K / K-1 =AP K-1 A T +Q (4)

[0081] In formula (4), P K / K-1 represent The state equation of the system is the state X at time k K The covariance generated when making predictions, P K-1 is the system state covariance at time k-1, Q is the system process noise. In this application, Q is set to 0.01. After completing the system prediction, the Kalman gain can be calculated:

[0082] P K / K-1 =AP K-1 A T +Q (5)

[0083] K K =P K / K-1 H T (HP K-1 H T +R) -1 (5)

[0084] In formula (5), K K represents the Kalman gain, H represents the system observation matrix, in this embodiment, H=1, R represents the observation noise equation, and its value is 0.1, so the optimal state estimation representation of the system is expressed as:

[0085]

[0086] Finally, after obtaining the estimated value of the system, the covariance of the system is updated, which is expressed as:

[0087] P P =(IK K H)P K / K-1 (7)

[0088] After Kalman filtering, the convergence probability of the second-order phase-locked loop can be further improved.

[0089] Furthermore, since phase-locked tracking can obtain the period and phase of the signal very accurately and can better track the signal submerged in the noise, a second-order phase-locked loop is used to track the noise signal. The second-order phase-locked loop mainly includes a phase error detector, a loop filter and a numerical control oscillator. The positioning data after Kalman filtering is used as the input data of the second-order phase-locked loop. Before the positioning data is output, it is normalized in cycles and then used as the input signal of the second-order phase-locked loop.

[0090] Specifically, the input data is expressed as:

[0091] u i (t) = u m sin[ω i t+θ i (t)] (8)

[0092] The signal output by the numerically controlled oscillator is expressed as:

[0093] u o (t) = u om sin[ω o t+θ o (t)] (9)

[0094] In formula (9), ω o Represents the angular frequency.

[0095] The signal output by the analog multiplier is expressed as:

[0096]

[0097] Next, the sum frequency component in the above formula is filtered out by a low-pass filter LF, and the remaining difference frequency component is used as the input signal of the numerical control oscillator, which is expressed as:

[0098]

[0099] In formula (11), θ o (t) and θ i (t) represent the instantaneous phase of the input signal and the output signal respectively. According to the relationship between the phasors, the relationship between the instantaneous frequency and the instantaneous phase is:

[0100]

[0101] In formula (12), the instantaneous phase difference θ(t) is expressed as:

[0102] θ d =(ω i -ω o )t+θ i (t)-θ o (t) (13)

[0103] Differentiating both sides of formula (13), the relationship between the frequency difference can be expressed as:

[0104]

[0105] When formula (14) is equal to zero, it means that the phase-locked loop enters the phase-locked state. At this time, the mean error between the output satellite signal and the input satellite signal with white noise approaches zero. Since the frequency can be obtained by taking the derivative of the phase, the frequency error is naturally eliminated by locking the phase, so the tracking can be completed.

[0106] In step S130, when the rotation speed of the rotating carrier is solved by using the positioning data, by analyzing any axis in the motion image of the receiver, it can be clearly observed that the satellite signal is almost masked by the environmental noise. In order to accurately obtain the current rotation speed of the receiver, the frequency spectrum of the motion state of the receiver is obtained by using fast Fourier transform.

[0107] Preferably, the positioning data corresponding to the X-axis is selected to solve the rotation speed of the rotating carrier, using the following formula:

[0108]

[0109] In formula (15), x m represents the positioning data in the time domain, X k It is a signal sequence in the frequency domain, k represents the frequency number, N represents the signal length, and the frequency of the receiver's motion image is found out by performing spectrum analysis on the motion image of the receiver. When the rotation speed unit is r / s, the rotation speed of the receiver and its frequency are numerically equal, so the rotation speed of the receiver can be analyzed.

[0110] In step 140, in order to further obtain the roll angle of the rotating carrier, the measured data is transformed from a one-dimensional signal to a two-dimensional complex plane by Hilbert transform. Assuming that the original receiver signal is x(t), its Hilbert transform is recorded as The analysis signal is:

[0111]

[0112] Then further, the phase is expressed as The current receiver phase value range is Transform its range to (0,π).

[0113] In this embodiment, the above method can be applied to a rotating carrier having a cylindrical portion with a radius of 0.3 to 0.5 meters.

[0114] In this paper, simulation experiments are also used to prove the effectiveness of this method.

[0115] During the simulation stage, the antenna can be used to obtain the real position information of 4 or more satellites to further increase the reliability and authenticity of the simulation stage. The positioning information of the receiver is modeled by assuming the initial position, rotation speed and motion radius of the receiver. The stability of the system is improved as much as possible by applying the Kalman filter algorithm and the second-order phase-locked loop algorithm mentioned above to the model in turn, and then the position signal is subjected to fast Fourier transform and Hilbert transform to solve the rotation speed and roll angle of the receiver.

[0116] like Figure 3As shown, it can be seen that in actual situations, due to the clock difference between the receiver clock and the satellite clock and the delay of the radio signal passing through the atmosphere, errors will occur in the receiver positioning.

[0117] like Figure 4 As shown, when the receiver rotation speed is set to 1r / s, the movement radius is 0.5m, and the white noise added is 0.5m, the receiver movement speed is calculated to be 0.98r / s, with an error of 0.02hz and an amplitude of 0.39m, which is 0.11m error compared with the set receiver movement radius of 0.5m.

[0118] like Figure 5 As shown, by comparison, it is found that when the simulated receiver motion radius is 0.5m, the white noise added with 0.2m is close to the measured data.

[0119] like Figure 6 As shown in the figure, by comparing the rotation speed output by the Hall sensor and the rotation speed calculated using the position information, it can be seen that at the beginning of the experiment, the amount of data was too small, resulting in a low number of sampling points, that is, the accuracy of FFT was not very high. However, with the accumulation of time and data, the receiver roll speed output by this method gradually stabilized. After about 1500s, the receiver speed output by this algorithm was 0.1097r / s, which had an error of 0.0097r / s compared with the 0.1r / s output by the Hall sensor.

[0120] like Figure 7 As shown, by comparing the roll angle output by the Hall sensor and the roll angle output using position information, it can be seen that the maximum error between the two is within 30°, and the average error is 3.48°.

[0121] By comparing the data of this method with that of Hall calibration, it is found that this method can effectively solve the problem of serious error accumulation of inertial components (gyroscope zero drift, etc.) and a substantial decrease in the accuracy of attitude measurement accumulated by the aircraft during long-term flight. In this method, the rotation speed and roll angle calculated after 1500s are not much different from the calibration data, which reflects the stability guarantee of the rotation speed and roll angle calculation under long-term operation. In addition, this method can calculate the rotation attitude of the receiver only based on the position information of the receiver, that is, the method can be used for calculation after the rotating carrier completes one rotation, which can avoid the time of inertial sensor calibration and save other sensors for measuring roll attitude, thereby reducing costs and size.

[0122] Furthermore, in the simulation experiment, the relationship between the radius, ie, the volume, of the rotating carrier and the successful locking using the second-order phase-locked loop is experimentally demonstrated, as shown in Table 1 as the probability of successful convergence.

[0123] Table 1 Successful convergence probability

[0124]

[0125]

[0126] It can be seen from Table 1 that the larger the radius, the greater the impact of white noise on the second-order phase-locked loop. When the radius is less than 0.15m, the second-order phase-locked loop is in a unlocked state when the white noise is superimposed from 1m to 5.5m. When the receiver movement radius is 0.5m and the white noise is superimposed to 1m, this method can solve the roll attitude of the receiver with a probability of 93.59%, and the overall convergence probability of the second-order phase-locked loop meets the previous expected effect. In order to further improve the convergence probability of the second-order phase-locked loop, the Kalman filter algorithm can be used before the second-order phase-locked loop tracks the motion state of the receiver. The results are shown in Table 2.

[0127] Table 2 Successful convergence probability after Kalman filtering

[0128]

[0129] It can be seen from Table 2 that after Kalman filtering, the improvement ability of the second-order phase-locked loop with a convergence probability of more than 70% is weak, but for poor data, the use of Kalman filtering can further improve the convergence probability of the second-order phase-locked loop.

[0130] This method shows that when the simulated receiver motion radius is 0.5m and white noise is added to 0.2m, the simulated signal can be close to the measured signal. And by analyzing Table 1 and Table 2, it is shown that under the joint action of Kalman filtering and the second-order phase-locked loop, even if the interference to the rotating carrier is greater than normal, the effectiveness and accuracy of the solution can still be guaranteed, and it has good anti-noise characteristics.

[0131] In the above-mentioned real-time attitude estimation method based on receiver positioning information, first, the real-time navigation signal is received by the receiver set on the rotating carrier, and the positioning data is obtained by solving the positioning model to provide a basis for subsequent processing. Then, Kalman filtering and second-order phase-locked loop processing are used to effectively remove noise, suppress interference, improve the accuracy and stability of positioning data, and enhance data reliability. The rotation speed is solved by using the single-axis data in the processed positioning data, and the roll angle is solved by using the Hilbert transform. These two unique solution methods are not only novel, but also can accurately obtain key parameters, and finally comprehensively obtain the real-time attitude of the rotating carrier. Compared with traditional methods, it has higher accuracy and stronger real-time performance. It can also more stably achieve accurate measurement of the rotating carrier attitude in complex environments, and may also save other sensors for measuring roll, reduce costs, and reduce volume to provide reliable support for related applications.

[0132] It should be understood that although Figure 1The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, Figure 1 At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0133] In one embodiment, Figure 8 As shown, a real-time attitude estimation device based on receiver positioning information is provided, comprising: a navigation signal receiving module 200, a positioning data acquisition module 210, a data processing module 220, a real-time rotation speed solving module 230, a real-time roll angle solving module 240 and a real-time attitude estimation module 250, wherein:

[0134] A navigation signal receiving module 200 is used to obtain real-time navigation signals sent by multiple satellites, wherein the real-time navigation signals are received by a receiver arranged on a rotating carrier;

[0135] The positioning data acquisition module 210 is used to calculate the position of the receiver according to the real-time navigation signal using the receiver positioning model to obtain the positioning data of the receiver;

[0136] A data processing module 220 is used to process the positioning data using a Kalman filter and a second-order locking loop in sequence to obtain processed positioning data;

[0137] The real-time rotation speed solving module 230 is used to solve the rotation speed of the rotating carrier by using the positioning data of any one axis among the X-axis, the Y-axis and the Z-axis in the processed positioning data to obtain the real-time rotation speed;

[0138] A real-time roll angle solving module 240, configured to solve the roll angle of the rotating carrier according to the processed positioning data using Hilbert transform to obtain a real-time roll angle;

[0139] The real-time attitude estimation module 250 is used to obtain the real-time attitude of the rotating carrier according to the real-time rotation speed and the real-time roll angle.

[0140] For the specific definition of the real-time attitude estimation device based on the receiver positioning information, please refer to the definition of the real-time attitude estimation method based on the receiver positioning information above, which will not be repeated here. Each module in the above-mentioned real-time attitude estimation device based on the receiver positioning information can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0141] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as follows: Fig. 9 As shown. The computer device includes a processor, a memory, a network interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a real-time attitude estimation method based on the positioning information of the receiver is implemented. The display screen of the computer device can be a liquid crystal display or an electronic ink display, and the input device of the computer device can be a touch layer covered on the display screen, or it can be a computer device. The buttons, trackball or touchpad set on the shell can also be an external keyboard, touchpad or mouse, etc.

[0142] Those skilled in the art will understand that Fig. 9 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0143] In one embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the following steps are implemented:

[0144] Acquiring real-time navigation signals sent by multiple satellites, wherein the real-time navigation signals are received by a receiver disposed on a rotating carrier;

[0145] Calculating the position of the receiver using a receiver positioning model according to the real-time navigation signal to obtain positioning data of the receiver;

[0146] The positioning data is processed by using a Kalman filter and a second-order locking loop in sequence to obtain processed positioning data;

[0147] Using the positioning data of any one of the X-axis, Y-axis and Z-axis in the processed positioning data, the rotation speed of the rotating carrier is solved to obtain the real-time rotation speed;

[0148] Using Hilbert transform to solve the roll angle of the rotating carrier according to the processed positioning data to obtain a real-time roll angle;

[0149] The real-time posture of the rotating carrier is obtained according to the real-time rotation speed and the real-time roll angle.

[0150] In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0151] Acquiring real-time navigation signals sent by multiple satellites, wherein the real-time navigation signals are received by a receiver disposed on a rotating carrier;

[0152] Calculating the position of the receiver using a receiver positioning model according to the real-time navigation signal to obtain positioning data of the receiver;

[0153] The positioning data is processed by using a Kalman filter and a second-order locking loop in sequence to obtain processed positioning data;

[0154] Using the positioning data of any one of the X-axis, Y-axis and Z-axis in the processed positioning data, the rotation speed of the rotating carrier is solved to obtain the real-time rotation speed;

[0155] Using Hilbert transform to solve the roll angle of the rotating carrier according to the processed positioning data to obtain a real-time roll angle;

[0156] The real-time posture of the rotating carrier is obtained according to the real-time rotation speed and the real-time roll angle.

[0157] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0158] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0159] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. A real-time attitude estimation method based on receiver positioning information, characterized in that: The method comprises: Acquiring real-time navigation signals sent by multiple satellites, wherein the real-time navigation signals are received by a receiver disposed on a rotating carrier; Calculating the position of the receiver using a receiver positioning model according to the real-time navigation signal to obtain positioning data of the receiver; The positioning data is processed by using a Kalman filter and a second-order locking loop in sequence to obtain processed positioning data; Using the positioning data of any one of the X-axis, Y-axis and Z-axis in the processed positioning data, the rotation speed of the rotating carrier is solved to obtain the real-time rotation speed; Using Hilbert transform to solve the roll angle of the rotating carrier according to the processed positioning data to obtain a real-time roll angle; The real-time posture of the rotating carrier is obtained according to the real-time rotation speed and the real-time roll angle.

2. The real-time attitude estimation method based on receiver positioning information according to claim 1, characterized in that: The receiver is a single patch antenna arranged on the outer side of the cylindrical portion of the rotating carrier.

3. The real-time attitude estimation method based on receiver positioning information according to claim 2, characterized in that: The real-time navigation signals received by the receiver are navigation signals sent by a preset number of satellites sorted according to satellite signal-to-noise ratios.

4. The real-time attitude estimation method based on receiver positioning information according to claim 3 is characterized in that: The receiver positioning model is constructed by using the pseudo-range between the receiver and the satellite that sends the navigation signal and the least square method.

5. The real-time attitude estimation method based on receiver positioning information according to claim 4 is characterized in that: The receiver positioning model is expressed as: In the above formula, x, y, and z represent the receiver position coordinates to be solved. n ,y n 、z n Indicates the position coordinates of the nth satellite, δt u represents the receiver clock error, p d represents the pseudorange after adding the error.

6. The real-time attitude estimation method based on receiver positioning information according to claim 5, characterized in that: When solving the rotation speed of the rotating carrier using the positioning data, the positioning data corresponding to the X-axis is selected.

7. The real-time attitude estimation method based on receiver positioning information according to any one of claims 1 to 6, characterized in that: The radius of the cylindrical part of the rotating carrier is 0.3 to 0.5 meters.

8. A real-time attitude estimation device based on receiver positioning information, characterized in that: The device comprises: A navigation signal receiving module, used to obtain real-time navigation signals sent by multiple satellites, wherein the real-time navigation signals are received by a receiver arranged on a rotating carrier; A positioning data acquisition module, used to calculate the position of the receiver according to the real-time navigation signal using a receiver positioning model to obtain positioning data of the receiver; A data processing module, used to process the positioning data using Kalman filtering and a second-order locking loop in sequence to obtain processed positioning data; A real-time rotation speed solving module is used to solve the rotation speed of the rotating carrier by using the positioning data of any one of the X-axis, Y-axis and Z-axis in the processed positioning data to obtain the real-time rotation speed; A real-time roll angle solving module, used for solving the roll angle of the rotating carrier according to the processed positioning data by using Hilbert transform to obtain a real-time roll angle; The real-time attitude estimation module is used to obtain the real-time attitude of the rotating carrier according to the real-time rotation speed and the real-time roll angle.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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