LEO / GNSS / INS tightly-combined low-altitude aircraft positioning and speed measuring method and device

Abstract

The embodiment of the invention provides an LEO / GNSS / INS tightly-combined low-altitude aircraft positioning and speed measuring method and device, electronic equipment and a medium, and the method comprises the steps that an INS obtains updated position, speed and attitude state information of a low-altitude aircraft through dead reckoning; constructing an LEO / GNSS / INS tight combination system state and measurement model according to the received GNSS and LEO signals in combination with the updated position, speed and attitude state information of the low-altitude aircraft; and performing filtering estimation on the LEO / GNSS / INS tight combination system state and measurement model by adopting a hypersphere simplex sampling unscented Kalman filtering SS-UKF model, and performing INS error correction to obtain position, speed and attitude information of the finally combined low-altitude aircraft.

Description

Technical Field

[0001] This document relates to the field of low-altitude aircraft positioning or multi-sensor combined positioning, and in particular to a method, device, electronic equipment and medium for positioning and velocity measurement of low-altitude aircraft using a tightly integrated LEO / GNSS / INS system. Background Technology

[0002] Global Navigation Satellite System (GNSS) and Inertial Navigation System (INS) combined navigation and positioning systems typically employ loose and tight combinations to achieve continuous positioning by leveraging their complementary strengths. However, in low-altitude airspace, especially in scenarios involving urban high-rises or suburban canyons, satellite signals are easily obstructed. When fewer than four satellites are visible, GNSS cannot calculate the vehicle's position and velocity. In this case, the loose GNSS / INS combination switches to a single INS dead reckoning method, causing positioning errors to diverge rapidly over time, limiting positioning accuracy. A tight GNSS / INS combination, because it combines data in the range domain, can still maintain short-term positioning accuracy by combining GNSS pseudorange and pseudorange rate with the equivalent pseudorange and pseudorange rate calculated by INS. However, when satellite signal obstruction is severe, and only one or two satellites are visible for an extended period, insufficient observation information and poor satellite spatial geometry lead to divergent positioning errors, rendering the positioning results unusable.

[0003] Compared to GNSS, Low Earth Orbit (LEO) satellites orbit at lower altitudes and with shorter orbital periods, resulting in smaller coverage areas for a single satellite. Therefore, hundreds or even tens of thousands of satellites are needed to form a network to achieve overall high-density coverage. LEO satellites employ enhancement technologies to assist GNSS and INS systems, improving the performance of combined GNSS / INS systems. LEO satellite navigation enhancement technologies are mainly divided into two categories: signal enhancement and information enhancement. Information enhancement assists users in improving positioning accuracy by broadcasting error correction data. Signal enhancement directly transmits navigation signals as an additional observation source, improving positioning accuracy by increasing the number of observed signals.

[0004] Low-Earth orbit (LEO) satellites transmit signals with higher power reaching the Earth's surface than medium- and high-Earth orbit (MEO) satellites, allowing them to penetrate some urban canyons or forest environments and providing strong blocking capabilities. Furthermore, as independent signal sources, they can provide backup positioning capabilities in GNSS-denied environments, enhancing system resilience. As "pseudo-satellites," LEO satellites can transmit independent ranging signals, supplementing the number of satellites in obstructed areas and improving satellite geometry. On the other hand, due to the rapid dynamic changes in LEO satellite orbits and the significant Doppler effect, they improve the velocity measurement accuracy of low-altitude aircraft in obstructed scenarios. Therefore, LEO satellites can be used to fill gaps and enhance GNSS and INS coverage, improving the overall positioning performance of the system.

[0005] Existing technologies disclose a persistent navigation method and device that integrates LEO (Low Orbit) satellite augmentation with GNSS and IMU (Infrastructure Management Unit) fusion. The method employs LEO satellite information augmentation, forwarding GNSS satellite ephemeris data and performing single-satellite positioning using pseudorange, Doppler, and LEO satellite position and velocity information from the LEO augmentation message transmission time. This information is then fused with INS (Infrastructure Management Unit) navigation results to achieve reliable and persistent GNSS positioning in complex electromagnetic environments. While this method utilizes LEO satellite information augmentation to improve the reliability and persistence of the GNSS / INS combined positioning system in open, unobstructed environments, the information augmentation relies on GNSS signals. Therefore, it is ineffective in areas with signal obstruction, such as urban high-rise buildings or suburban canyons, and is not suitable for scenarios with satellite signal blockage.

[0006] Existing technology also discloses a method and apparatus for GNSS / INS integrated navigation under LEO (Low Orbit) navigation enhancement. This method utilizes precise GNSS satellite orbit data, precise GNSS satellite clock bias data, GNSS code deviation correction, Earth rotation parameters, ionospheric correction, and precise LEO satellite orbit and clock bias data from the LEO satellite enhancement message to correct errors in the pseudorange carrier phase observations of GNSS and LEO satellites, obtaining high-precision GNSS navigation and positioning results. These results are then loosely combined with INS in the position and velocity domains to obtain the final high-precision integrated navigation result. While this method uses LEO satellite information enhancement to shorten the convergence time for high-precision positioning, it is only suitable for high-precision real-time positioning in open, unobstructed environments and not for high-precision positioning in environments with obstructed satellite signals.

[0007] Existing technologies also disclose a real-time autonomous integrated navigation and positioning method and apparatus. This method utilizes navigation enhancement information broadcast by low-Earth orbit (LEO) satellites and ephemeris data from medium- and high-Earth orbit (MEO) satellites to obtain precise orbits and clock errors of both LEO and MEO satellites. It then corrects the LEO and MEO satellite observation data, jointly calculates the carrier's positioning information, loosely integrates it with the INS (Instrumentation System) in the position and velocity domains, and employs linear Kalman filtering to obtain the final integrated navigation result. While this method uses LEO satellite information enhancement, which improves the observation quality of MEO satellites and enables stable navigation and positioning services with continuous accuracy, it is still not suitable for high-precision positioning in scenarios with satellite signal obstruction.

[0008] Existing technologies also propose a GNSS / IMU tightly coupled positioning method enhanced with pseudorange observation signals from LEO satellites. This method constructs a combined system state model based on the INS error parameter model and the GNSS clock error model. An observation model is constructed by combining the GNSS pseudorange observation model and the LEO satellite pseudorange observation model. The observation equation is a linearized version of the nonlinear pseudorange observation model. Kalman filtering is used for filtering and estimation to obtain the combined positioning result. This method uses LEO satellite signal enhancement, theoretically increasing the observation quantity and improving the system's positioning accuracy by introducing pseudorange from LEO satellites. However, the pseudorange model also introduces significant truncation errors during linearization, affecting positioning accuracy and failing to consistently guarantee positioning accuracy in complex scenarios.

[0009] The main drawback of current LEO satellite information enhancement technology is that it relies on GNSS satellite signal analysis to enhance messages. In scenarios where satellite signals are severely blocked, such as urban high-rise buildings or canyons, GNSS signal interruption causes information enhancement to completely fail. It cannot solve the problem of continuous positioning due to insufficient observations (less than 4 visible satellites) in blocked scenarios, leading to rapid divergence of INS errors.

[0010] The main drawback of LEO satellite signal enhancement technology is its reliance on a single observation model and insufficient utilization of Doppler observations. On one hand, it depends solely on pseudorange observations without incorporating multi-dimensional information such as Doppler frequency shift, resulting in insufficient robustness of the observation equations in obstructed scenarios. On the other hand, it fails to fully utilize the unique high-dynamic Doppler frequency shift of LEO satellites, leading to suboptimal velocity measurement accuracy. Furthermore, the truncation error introduced by nonlinearization in the tightly coupled observation model significantly reduces positioning accuracy, especially with low-elevation satellites or in situations of severe dynamic changes. Summary of the Invention

[0011] The purpose of this invention is to provide a method, device, electronic equipment, and medium for positioning and velocity measurement of LEO / GNSS / INS tightly integrated low-altitude aircraft, aiming to solve the aforementioned problems in the prior art.

[0012] This invention provides a method for locating and measuring the velocity of a LEO / GNSS / INS tightly integrated low-altitude aircraft, comprising:

[0013] INS obtains updated position, velocity, and attitude information of low-altitude aircraft through dead reckoning.

[0014] Based on the received GNSS and LEO signals, and combined with the updated position, velocity and attitude information of the low-altitude aircraft, a state and measurement model of the LEO / GNSS / INS tightly integrated system is constructed.

[0015] The state and measurement models of the LEO / GNSS / INS tightly coupled system are filtered and estimated using the hyperspherical simplex sampling unscented Kalman filter (SS-UKF) model. After INS error correction, the final combined low-altitude aircraft position, velocity, and attitude information are obtained.

[0016] This invention provides a LEO / GNSS / INS tightly integrated low-altitude aircraft positioning and velocity measurement device, comprising:

[0017] The estimation module is used to obtain the updated position, velocity and attitude information of the low-altitude aircraft based on INS dead reckoning.

[0018] The module is used to construct a state and measurement model of the LEO / GNSS / INS tightly coupled system based on the received GNSS and LEO signals and the updated position, velocity and attitude information of the low-altitude aircraft.

[0019] The calculation module is used to perform filtering estimation on the state and measurement model of the LEO / GNSS / INS tightly combined system using the hyperspherical simplex sampling unscented Kalman filter SS-UKF model, and obtain the final combined low-altitude aircraft position, velocity and attitude information through INS error correction.

[0020] This invention also provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, it implements the steps of the above-described GNSS / INS tightly coupled low-altitude aircraft positioning and velocity measurement method with enhanced low-orbit satellite dual observation signals.

[0021] This invention also provides a computer-readable storage medium storing an information transmission implementation program. When the program is executed by a processor, it implements the steps of the above-described GNSS / INS tightly coupled low-altitude aircraft positioning and velocity measurement method with enhanced low-orbit satellite dual observation signals.

[0022] By employing the embodiments of this invention, based on the traditional GNSS / INS tightly combined single pseudorange observation signal, pseudorange and Doppler dual observation signals from low-orbit LEO satellites are introduced. The hyperspherical simplex sampling unscented Kalman filter SS-UKF model is used for filtering estimation, thereby improving the positioning and velocity measurement accuracy of low-altitude aircraft. This meets the autonomous, accurate, and real-time positioning and velocity measurement requirements of low-altitude aircraft in scenarios where satellite signals are obstructed, such as urban high-rise buildings, bridges, and suburban canyons. It is suitable for application scenarios such as low-altitude aircraft inspection and low-altitude logistics. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in one or more embodiments of this specification or in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this specification. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a flowchart of the LEO / GNSS / INS tightly integrated low-altitude aircraft positioning and velocity measurement method according to an embodiment of the present invention;

[0025] Figure 2 This is a block diagram illustrating the principle of LEO / GNSS / INS tightly integrated positioning and velocity measurement according to an embodiment of the present invention.

[0026] Figure 3 This is a schematic diagram of the LEO / GNSS / INS tightly integrated low-altitude aircraft positioning and velocity measurement device according to an embodiment of the present invention;

[0027] Figure 4 This is a schematic diagram of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0028] To enable those skilled in the art to better understand the technical solutions in one or more embodiments of this specification, the technical solutions in one or more embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this specification, and not all of the embodiments. Based on one or more embodiments of this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of this document.

[0029] Method Implementation Examples

[0030] According to an embodiment of the present invention, a method for locating and measuring the velocity of a LEO / GNSS / INS tightly integrated low-altitude aircraft is provided. Figure 1This is a flowchart of the LEO / GNSS / INS tightly integrated low-altitude aircraft positioning and velocity measurement method according to an embodiment of the present invention. The LEO / GNSS / INS tightly integrated low-altitude aircraft positioning and velocity measurement method proposed in this invention under satellite signal obstruction scenarios mainly solves the technical problems of LEO / GNSS / INS tightly integrated positioning and velocity measurement when satellite observations are insufficient in obstruction scenarios, and the problem of real-time accurate filtering technology for high-dimensional nonlinear models, thus meeting the positioning and velocity measurement requirements of low-altitude aircraft under satellite obstruction scenarios. Figure 1 As shown, the LEO / GNSS / INS tightly integrated low-altitude aircraft positioning and velocity measurement method according to an embodiment of the present invention specifically includes:

[0031] Step S101: The INS obtains the updated position, velocity, and attitude information of the low-altitude aircraft through dead reckoning; specifically including:

[0032] Based on the initial state of the low-altitude aircraft and the angular rate and acceleration measured by the gyroscope and accelerometer in the INS, the INS calculates the updated position, velocity and attitude information of the low-altitude aircraft through dead reckoning. The initial state of the low-altitude aircraft specifically includes position, velocity and attitude.

[0033] Step S102: Based on the received GNSS and LEO signals, and combined with the updated position, velocity, and attitude information of the low-altitude aircraft, construct a state and measurement model of the LEO / GNSS / INS tightly integrated system; specifically including:

[0034] Receive GNSS and LEO signals to obtain GNSS and LEO pseudorange and pseudorange rate signals;

[0035] Analyze the broadcast ephemeris in the pseudorange and pseudorange rate signals of GNSS and LEO, and calculate the positions of GNSS and LEO based on the broadcast ephemeris;

[0036] Based on the updated position, velocity, and attitude information of the low-altitude aircraft, as well as the positions of the GNSS and LEO, the INS equivalent pseudorange / pseudorange rate is calculated.

[0037] A system state model is constructed based on the INS error state model, the accelerometer and gyroscope offset error model, and the GNSS clock-related error model.

[0038] Based on the pseudorange / pseudorange rate observation models for GNSS and LEO shown in Formulas 1-4, according to the formulas...

[0039] Equation 5 determines the measurement vectors of the LEO / GNSS / INS tightly coupled system and constructs the system measurement model;

[0040]

[0041] In this context, the superscripts GNSS and LEO represent GNSS satellites and LEO satellites, respectively. i Let r be the pseudorange observation value of the i-th satellite. i Let T be the actual distance between the i-th satellite and the airborne receiver. i and I i Let δt represent the tropospheric delay error and the ionospheric delay error, respectively, where c is the speed of light and δt is the velocity of light. r and For receiver clock bias and satellite clock bias, Unknown pseudorange measurement noise;

[0042]

[0043]

[0044] In the formula, "point" represents the pseudorange rate;

[0045]

[0046] Where, ρ INS and For INS equivalent pseudorange vector and pseudorange rate vector;

[0047] The system state model and the system measurement model are discretized over time to obtain the time-discrete LEO / GNSS / INS tightly combined system state and measurement model:

[0048]

[0049] Where x(k) represents the system state vector at time k, y(k) represents the system measurement vector at time k, k represents the current time, h represents the nonlinear measurement model, v(k) represents the measurement noise vector, F(k|k-1) represents the state transition matrix at time k, and ω(k-1) represents the state noise vector.

[0050] Step S103 involves using a hyperspherical simplex sampling unscented Kalman filter (SS-UKF) model to filter and estimate the state and measurement model of the LEO / GNSS / INS tightly coupled system. Through INS error correction, the final combined position, velocity, and attitude information of the low-altitude aircraft are obtained. Specifically, this includes:

[0051] Using Kalman filtering based on Equations 7-8, the state of the LEO / GNSS / INS compact combination system after one-step state update and the corresponding covariance matrix are obtained.

[0052]

[0053] P(k|k-1)=F(k|k-1)P(k-1)F T (k-1)+Q(k-1) Formula 8;

[0054] in, Let F(k|k-1) represent the predicted state at time k, and let F(k|k-1) represent the state transition matrix at time k. Let P(k|k-1) represent the system state at time (k-1), and let P(k|k-1) represent the one-step predicted state. The corresponding covariance matrix, P(k-1), represents the system state at time (k-1). The corresponding covariance matrix, F T (k-1) represents the transpose of the state transition matrix at time k, and Q(k-1) represents the measurement noise covariance matrix;

[0055] Update the system state after obtaining the first-step state. A hypersphere simplex sampling model is used to obtain n+2 sampling points, and the weight corresponding to each sampling point is obtained, where n is a natural number; specifically, this includes: calculating the Sigma sampling point weights according to Formula 15; calculating the initial state iteration vector according to Formula 16; calculating the state iteration vector corresponding to the state dimension expansion according to Formula 17; and calculating the weights according to the formula...

[0056] Equation 18 calculates the Sigma point sampling set;

[0057]

[0058] Among them, w 0 The initial weights are 0 ≤ w 0 ≤1;

[0059]

[0060] Where i represents the extended state dimension.

[0061] Based on Formula 9, for each point in the obtained n+2 sampling point set ξ(k|k-1), calculate the corresponding n+2 measurement prediction values:

[0062]

[0063] in, ξ represents the measurement prediction vector. j (k|k-1) represents the j-th sampling point, and h represents the nonlinear measurement model of the system;

[0064] According to Formula 10, the predicted value of the system is calculated based on n+2 measurement values ​​and their corresponding weights.

[0065]

[0066] in, Represents the system measurement prediction vector. ξ represents the j-th sampling point j (k|k-1) is the weight matrix involved in the mean calculation, where m represents the mean.

[0067] After completing the measurement update, the filtered estimated system state error is obtained according to formulas 11-14. And the covariance matrix P(k):

[0068]

[0069] P(k)=P(k|k-1)-K(k)P yy (k|k-1)K(k) T Formula 14;

[0070] Among them, P yy (l|k-1) represents the system measurement prediction vector. The corresponding covariance matrix, R(k) represents the system flow measurement noise covariance matrix, and K(k) represents the gain matrix;

[0071] Combining the updated position, velocity, and attitude information of the low-altitude aircraft, and the filtered data... The position, velocity, and attitude errors in the data are corrected using INS error correction to obtain the final combined absolute position, velocity, and attitude information of the low-altitude aircraft.

[0072] In other words, this invention incorporates both pseudorange and Doppler shift signals from LEO satellites into a tightly coupled GNSS / INS system to construct a tightly coupled LEO / GNSS / INS positioning and velocity measurement system. This improves upon the limitations of existing systems, such as single system observations and limited velocity measurement accuracy, enabling precise positioning and velocity measurement of low-altitude aircraft in satellite signal obstruction scenarios. The hyperspherical simplex sampling strategy is integrated into the UKF nonlinear filtering framework to construct the SS-UKF algorithm. On one hand, since the state model is a linear equation, Kalman filtering is still used, requiring only one linear calculation, reducing the computational resource waste associated with using a nonlinear UKF model for linear models. On the other hand, the measurement model uses a UKF nonlinear model, avoiding truncation errors caused by linearization of the measurement model and ensuring positioning and velocity measurement accuracy. The hyperspherical simplex sampling strategy reduces the number of sampling points from 2n+1 to n+2, requiring only n+2 nonlinear calculations for each measurement, thus improving the filtering rate. Here, n represents the number of system states. Therefore, SS-UKF can balance the filtering accuracy and filtering efficiency of high-dimensional nonlinear models, and meet the positioning and velocity measurement accuracy and real-time performance requirements of LEO / GNSS / INS tightly integrated positioning and velocity measurement systems in satellite signal obstruction scenarios.

[0073] The technical solution of this invention solves the problem of low-altitude aircraft integrated navigation, positioning, and velocity measurement using LOR satellite enhancement in scenarios with satellite signal obstruction (when there are fewer than 4 satellite observation signals). Based on the existing GNSS / INS tightly integrated system, without changing the system state model, it employs LOR satellite signal enhancement technology, introducing LOR satellite pseudorange and Doppler observation signals, and constructs an optimized nonlinear observation model by combining the LOR satellite pseudorange observation model / Doppler frequency shift observation model with the GNSS pseudorange observation model / pseudorange rate model. Simultaneously, it uses a hyperspherical simplex unscented Kalman filter (SS-UKF) model for filtering estimation, with linear Kalman filtering for state updates and an unscented Kalman filtering framework for measurement updates. The use of a hyperspherical simplex sampling model reduces the number of sampling points, improves filtering real-time performance, and solves the problem of accurate real-time positioning and velocity measurement in high-dimensional nonlinear tightly integrated navigation and positioning models.

[0074] The technical solutions described above in the embodiments of the present invention will be explained in detail below.

[0075] An embodiment of the present invention provides a GNSS / INS tightly coupled low-altitude aircraft positioning and velocity measurement method with enhanced dual-observation signals from low-Earth orbit satellites under satellite signal obstruction scenarios. The principle block diagram is shown below. Figure 2 As shown, the main steps are as follows:

[0076] Step 1: The INS combines the initial state of the low-altitude aircraft, including position, velocity, and attitude, with the angular rate and acceleration measured by the gyroscope and accelerometer in the INS, and uses dead reckoning to obtain the updated position, velocity, and attitude information of the low-altitude aircraft.

[0077] Step two: Receive GNSS and LEO signals, and combine them with the updated INS status information from Step one to construct a state and measurement model of the LEO / GNSS / INS tightly coupled system. The steps are as follows:

[0078] Step 201: Receive GNSS and LEO signals and obtain GNSS and LEO pseudorange and pseudorange rate (Doppler frequency shift) signals.

[0079] Step 202: Analyze the broadcast ephemeris in the GNSS and LEO signals, and calculate the positions of the GNSS and LEO signals based on the broadcast ephemeris.

[0080] Step 203: Combine the position and velocity information obtained from the INS dead reckoning in Step 1 with the GNSS position obtained in Step 202, and calculate the INS equivalent pseudorange / pseudorange rate.

[0081] Step 204: Construct a system state model based on the INS error state model, the accelerometer and gyroscope offset error model, and the GNSS clock-related error model.

[0082] Step 205: Construct a system measurement model based on the GNSS pseudorange / pseudorange rate observation model and the LEO pseudorange / pseudorange rate observation model.

[0083] The pseudorange observation model for GNSS and LEO is as follows:

[0084]

[0085] In this context, the superscripts GNSS and LEO represent GNSS satellites and LEO satellites, respectively. i Let r be the pseudorange observation value of the i-th satellite. i Let T be the actual distance between the i-th satellite and the airborne receiver. i and I i Let δt represent the tropospheric delay error and the ionospheric delay error, respectively, where c is the speed of light and δt is the velocity of light. r and For receiver clock bias and satellite clock bias. This is noise from unknown pseudorange measurements.

[0086] The Doppler frequency shift received by the receiver can be converted into the pseudorange rate by using the wavelength.

[0087] The pseudorange rate observation model for GNSS and LEO is as follows:

[0088]

[0089] The measurement vectors for the LEO / GNSS / INS tightly coupled system are:

[0090]

[0091] Where, ρ INS and The pseudorange vector and pseudorange rate vector are the equivalents of INS obtained in step 203.

[0092] Step 206: Discretize the system state model and measurement model over time for subsequent filtering estimation.

[0093] The system model after time discretization is as follows:

[0094]

[0095] Step three involves using the Spherical Simplex Unscented Kalman Filter (SS-UKF) model to filter and estimate the state and measurement models of the tightly combined system from step two. After INS error correction, the final combined position, velocity, and attitude information of the low-altitude aircraft are obtained. The specific steps are as follows:

[0096] Step 301: Use Kalman filtering to update the state, and obtain the combined system state and the corresponding covariance matrix after one-step state update.

[0097]

[0098] P(k|k-1)=F(k|k-1)P(k-1)F T (k-1)+Q(k-1) (8)

[0099] Step 302: Update the system state obtained in step 301. A hypersphere simplex sampling model is used to obtain n+2 sampling points, and the weight corresponding to each sampling point is obtained. The specific steps are as follows:

[0100] 1: Calculate the Sigma sampling point weights.

[0101]

[0102] Among them, w 0 The initial weights are 0 ≤ w 0 ≤1.

[0103] 2: Initial state iteration vector acquisition.

[0104]

[0105] 3: Calculate the state iteration vector corresponding to the expansion of the state dimension.

[0106]

[0107] 4: Obtain the Sigma point sampling point set.

[0108]

[0109] Step 303: Based on the set of n+2 sampling points ξ obtained in step 302 j For each point (k|k-1), calculate the corresponding n+2 measurement prediction value.

[0110]

[0111] Step 304: Based on the n+2 measurement values ​​and corresponding weights from step 303, calculate the predicted value of the system.

[0112]

[0113] Step 305: Complete the measurement update and obtain the filtered estimated system state error. The covariance matrix P(k).

[0114]

[0115]

[0116] P(k)=P(k|k-1)-K(k)P yy (k|k-1)K(k) T (18)

[0117] Step 306: Combine the updated position, velocity, and attitude information of the low-altitude aircraft obtained in Step 1, with the filtered data... The position, velocity, and attitude errors in the data are corrected using INS error correction to obtain the final combined absolute position, velocity, and attitude information of the low-altitude aircraft.

[0118] As can be seen from the above processing, the embodiments of the present invention do not require the addition of new sensors or hardware units to the airborne navigation and positioning terminal of low-altitude aircraft. The software part only needs to modify the existing GNSS / INS tightly coupled measurement processing part. The structure of this integrated navigation system is simple and easy to implement. The hyperspherical simplex sampling UKF filtering SS-UKF algorithm of the embodiments of the present invention decomposes the tightly coupled model into a structure. The linear state model adopts Kalman filtering, and the nonlinear measurement adopts unscented Kalman filtering, which solves the problem of suboptimal filtering efficiency of standard UKF when applied to tightly coupled linear / nonlinear models. The embodiments of the present invention introduce a hyperspherical simplex sampling strategy into the unscented Kalman filtering framework, balancing the filtering accuracy and real-time performance of high-dimensional nonlinear models.

[0119] In summary, the technical solution of this invention provides dual signal enhancement for low-Earth orbit (LEO) satellites using pseudorange / Doppler frequency shift in satellite signal obstruction scenarios. When GNSS completely fails, positioning and velocity measurement can continue only with the pseudorange / Doppler frequency shift of a single LEO satellite combined with INS. For the SS-UKF lightweight filtering architecture of high-dimensional nonlinear systems, linear Kalman filtering is used for state updates, and hypersphere sampling UKF is used for measurement updates, requiring only n+2 sampling points, balancing filtering accuracy and real-time performance. The system modification cost is low. This system is a pure software upgrade, modifying only the measurement processing module. Furthermore, the state model can be reused, directly compatible with existing GNSS / INS tightly coupled architectures.

[0120] Device Example 1

[0121] According to an embodiment of the present invention, a LEO / GNSS / INS tightly integrated low-altitude aircraft positioning and velocity measurement device is provided. Figure 3 This is a schematic diagram of a LEO / GNSS / INS tightly integrated low-altitude aircraft positioning and velocity measurement device according to an embodiment of the present invention, as shown below. Figure 3 As shown, the LEO / GNSS / INS tightly integrated low-altitude aircraft positioning and velocity measurement device according to an embodiment of the present invention specifically includes:

[0122] The calculation module 30 is used to obtain the updated position, velocity, and attitude information of the low-altitude aircraft through dead reckoning based on the INS. Specifically, the INS calculates the updated position, velocity, and attitude information of the low-altitude aircraft based on the initial state of the low-altitude aircraft and the angular rate and acceleration measured by the gyroscope and accelerometer in the INS. The initial state of the low-altitude aircraft specifically includes position, velocity, and attitude.

[0123] Module 32 is used to construct a state and measurement model of the LEO / GNSS / INS tightly coupled system based on the received GNSS and LEO signals and combined with the updated position, velocity and attitude information of the low-altitude aircraft; and to receive GNSS and LEO signals to obtain the pseudorange and pseudorange rate signals of GNSS and LEO.

[0124] Analyze the broadcast ephemeris in the pseudorange and pseudorange rate signals of GNSS and LEO, and calculate the positions of GNSS and LEO based on the broadcast ephemeris;

[0125] Based on the updated position, velocity, and attitude information of the low-altitude aircraft, as well as the positions of the GNSS and LEO, the INS equivalent pseudorange / pseudorange rate is calculated.

[0126] A system state model is constructed based on the INS error state model, the accelerometer and gyroscope offset error model, and the GNSS clock-related error model.

[0127] Based on the pseudorange / pseudorange rate observation models for GNSS and LEO shown in Formulas 1-4, according to the formulas...

[0128] Equation 5 determines the measurement vectors of the LEO / GNSS / INS tightly coupled system and constructs the system measurement model;

[0129]

[0130] In this context, the superscripts GNSS and LEO represent GNSS satellites and LEO satellites, respectively. i Let r be the pseudorange observation value of the i-th satellite. i Let T be the actual distance between the i-th satellite and the airborne receiver. i and I i Let δt represent the tropospheric delay error and the ionospheric delay error, respectively, where c is the speed of light and δt is the velocity of light. r and For receiver clock bias and satellite clock bias, Unknown pseudorange measurement noise;

[0131]

[0132] In the formula, "point" represents the pseudorange rate;

[0133]

[0134] Where, ρ INS and For INS equivalent pseudorange vector and pseudorange rate vector;

[0135] The system state model and the system measurement model are discretized over time to obtain the time-discrete LEO / GNSS / INS tightly combined system state and measurement model:

[0136]

[0137] Where x(k) represents the system state vector at time k, y(k) represents the system measurement vector at time k, k represents the current time, h represents the nonlinear measurement model, v(k) represents the measurement noise vector, F(k|k-1) represents the state transition matrix at time k, and ω(k-1) represents the state noise vector.

[0138] Calculation module 34 is used to perform filtering estimation on the state and measurement models of the LEO / GNSS / INS compact system using a hyperspherical simplex sampling unscented Kalman filter (SS-UKF) model. After INS error correction, the final combined position, velocity, and attitude information of the low-altitude aircraft are obtained. According to Equations 7-8, Kalman filtering is used for state updating to obtain the state and corresponding covariance matrix of the LEO / GNSS / INS compact system after one-step state update.

[0139]

[0140] P(k|k-1)=F(k|k-1)P(k-1)F T (k-1)+Q(k-1) Formula 8;

[0141] in, Let F(k|k-1) represent the predicted state at time k, and let F(k|k-1) represent the state transition matrix at time k. Let P(k|k-1) represent the system state at time (k-1), and let P(k|k-1) represent the one-step predicted state. The corresponding covariance matrix, P(k-1), represents the system state at time (k-1). The corresponding covariance matrix, F T (k-1) represents the transpose of the state transition matrix at time k, and Q(k-1) represents the measurement noise covariance matrix;

[0142] Update the system state after obtaining the first-step state. A hyperspherical simplex sampling model is used to obtain n+2 sampling points, and the weight corresponding to each sampling point is obtained, where n is a natural number; specifically used for:

[0143] Calculate the Sigma sampling point weights according to Formula 15:

[0144]

[0145] Among them, w0 The initial weights are 0 ≤ w 0 ≤1;

[0146] Calculate the initial state iteration vector according to Formula 16;

[0147]

[0148] Calculate the state iteration vector corresponding to the state dimension expansion according to Formula 17;

[0149]

[0150] The Sigma point sampling point set is calculated according to Formula 18;

[0151]

[0152] Where i represents the extended state dimension.

[0153] Based on Formula 9, for each point in the obtained n+2 sampling point set ξ(k|k-1), calculate the corresponding n+2 measurement prediction values:

[0154]

[0155] in, ξ represents the measurement prediction vector. j (k|k-1) represents the j-th sampling point, and h represents the nonlinear measurement model of the system;

[0156] According to Formula 10, the predicted value of the system is calculated based on n+2 measurement values ​​and their corresponding weights.

[0157]

[0158] in, Represents the system measurement prediction vector. ξ represents the j-th sampling point j (k|k-1) is the weight matrix involved in the mean calculation, where m represents the mean.

[0159] After completing the measurement update, the filtered estimated system state error is obtained according to formulas 11-14. And the covariance matrix P(k):

[0160]

[0161] P(k)=P(k|k-1)-K(k)P yy (k|k-1)K(k) T Formula 14;

[0162] Among them, Pyy (l|k-1) represents the system measurement prediction vector. The corresponding covariance matrix, R(k) represents the system flow measurement noise covariance matrix, and K(k) represents the gain matrix;

[0163] Combining the updated position, velocity, and attitude information of the low-altitude aircraft, and the filtered data... The position, velocity, and attitude errors in the data are corrected using INS error correction to obtain the final combined absolute position, velocity, and attitude information of the low-altitude aircraft.

[0164] The embodiments of the present invention are device embodiments corresponding to the above method embodiments. The specific operation of each module can be understood with reference to the description of the method embodiments, and will not be repeated here.

[0165] Device Example 2

[0166] This invention provides an electronic device, such as... Figure 4 As shown, it includes: a memory 40, a processor 42, and a computer program stored in the memory 40 and executable on the processor 42, wherein the computer program, when executed by the processor 42, performs the steps as described in the method embodiment.

[0167] Device Example 3

[0168] This invention provides a computer-readable storage medium storing an information transmission implementation program, which, when executed by a processor 42, performs the steps described in the method embodiment.

[0169] The computer-readable storage media described in this embodiment include, but are not limited to, ROM, RAM, disk, or optical disk.

[0170] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for positioning and velocity measurement of a low-altitude aircraft using a tightly integrated LEO / GNSS / INS system, characterized in that, include: INS obtains updated position, velocity, and attitude information of low-altitude aircraft through dead reckoning. Based on the received GNSS and LEO signals, and combined with the updated position, velocity and attitude information of the low-altitude aircraft, a state and measurement model of the LEO / GNSS / INS tightly integrated system is constructed. The state and measurement models of the LEO / GNSS / INS tightly coupled system are filtered and estimated using the hyperspherical simplex sampling unscented Kalman filter (SS-UKF) model. After INS error correction, the final combined low-altitude aircraft position, velocity, and attitude information are obtained.

2. The method according to claim 1, characterized in that, The INS obtains updated position, velocity, and attitude information of low-altitude aircraft through dead reckoning, specifically including: Based on the initial state of the low-altitude aircraft and the angular rate and acceleration measured by the gyroscope and accelerometer in the INS, the INS calculates the updated position, velocity and attitude information of the low-altitude aircraft through dead reckoning. The initial state of the low-altitude aircraft specifically includes position, velocity and attitude.

3. The method according to claim 1, characterized in that, Based on the received GNSS and LEO signals, and combined with the updated position, velocity, and attitude information of the low-altitude aircraft, a state and measurement model of the tightly integrated LEO / GNSS / INS system is constructed, specifically including: Receive GNSS and LEO signals to obtain GNSS and LEO pseudorange and pseudorange rate signals; Analyze the broadcast ephemeris in the pseudorange and pseudorange rate signals of GNSS and LEO, and calculate the positions of GNSS and LEO based on the broadcast ephemeris; Based on the updated position, velocity, and attitude information of the low-altitude aircraft, as well as the positions of the GNSS and LEO, the INS equivalent pseudorange / pseudorange rate is calculated. A system state model is constructed based on the INS error state model, the accelerometer and gyroscope offset error model, and the GNSS clock-related error model. Based on the pseudorange / pseudorange rate observation models of GNSS and LEO shown in Formulas 1-4, the measurement vectors of the LEO / GNSS / INS tightly coupled system are determined according to Formula 5, and the system measurement model is constructed. In this context, the superscripts GNSS and LEO represent GNSS satellites and LEO satellites, respectively. i Let r be the pseudorange observation value of the i-th satellite. i Let T be the actual distance between the i-th satellite and the airborne receiver. i and I i Let δt represent the tropospheric delay error and the ionospheric delay error, respectively, where c is the speed of light and δt is the velocity of light. r and For receiver clock bias and satellite clock bias, Unknown pseudorange measurement noise; In the formula, "point" represents the pseudorange rate; Where, ρ INS and These are the pseudorange vector and pseudorange rate vector equivalent to INS. The system state model and the system measurement model are discretized over time to obtain the time-discrete LEO / GNSS / INS tightly combined system state and measurement model: Where x(k) represents the system state vector at time k, y(k) represents the system measurement vector at time k, k represents the current time, h represents the nonlinear measurement model, v(k) represents the measurement noise vector, F(k|k-1) represents the state transition matrix at time k, and ω(k-1) represents the state noise vector.

4. The method according to claim 1, characterized in that, A hyperspherical simplex sampling unscented Kalman filter (SS-UKF) model is used to filter and estimate the state and measurement model of the LEO / GNSS / INS tightly integrated system. Through INS error correction, the final combined low-altitude aircraft position, velocity, and attitude information are obtained, specifically including: Using Kalman filtering based on Equations 7-8, the state of the LEO / GNSS / INS compact combination system after one-step state update and the corresponding covariance matrix are obtained. P(k|k-1)=F(k|k-1)P(k-1)F T (k-1)+Q(k-1) Formula 8; in, Let F(k|k-1) represent the predicted state at time k, and let F(k|k-1) represent the state transition matrix at time k. Let P(k|k-1) represent the system state at time (k-1), and let P(k|k-1) represent the one-step predicted state. The corresponding covariance matrix, P(k-1), represents the system state at time (k-1). The corresponding covariance matrix, F T (k-1) represents the transpose of the state transition matrix at time k, and Q(k-1) represents the measurement noise covariance matrix; Update the system state after obtaining the first-step state. The hypersphere simplex sampling model is used for sampling to obtain n+2 sampling points, and the weight corresponding to each sampling point is obtained, where n represents the system state dimension and is a natural number; Based on Formula 9, for each point in the obtained n+2 sampling point set ξ(k|k-1), calculate the corresponding n+2 measurement prediction values: in, ξ represents the measurement prediction vector. j (k|k-1) represents the j-th sampling point, and h represents the nonlinear measurement model of the system; According to Formula 10, the predicted value of the system is calculated based on n+2 measurement values ​​and their corresponding weights. in, Represents the system measurement prediction vector. ξ represents the j-th sampling point j (k|k-1) is the weight matrix involved in the mean calculation, where m represents the mean. After completing the measurement update, the filtered estimated system state error is obtained according to formulas 11-14. And the covariance matrix P(k): Among them, P yy (k|k-1) represents the system measurement prediction vector. The corresponding covariance matrix, R(k) represents the system flow measurement noise covariance matrix, and K(k) represents the gain matrix; Combining the updated position, velocity, and attitude information of the low-altitude aircraft, and the filtered data... The position, velocity, and attitude errors in the data are corrected using INS error correction to obtain the final combined absolute position, velocity, and attitude information of the low-altitude aircraft.

5. The method according to claim 4, characterized in that, The system state after updating the obtained one-step state A hyperspherical simplex sampling model is used for sampling to obtain n+2 sampling points, and the weight corresponding to each sampling point is obtained, including: Calculate the Sigma sampling point weights according to Formula 15: Among them, w 0 The initial weights are 0 ≤ w 0 ≤1; Calculate the initial state iteration vector according to Formula 16; Calculate the state iteration vector corresponding to the state dimension expansion according to Formula 17; The Sigma point sampling point set is calculated according to Formula 18; Where i represents the extended state dimension.

6. A LEO / GNSS / INS tightly integrated low-altitude aircraft positioning and velocity measurement device, characterized in that, Specifically, it includes: The estimation module is used to obtain the updated position, velocity and attitude information of the low-altitude aircraft based on INS dead reckoning. The module is used to construct a state and measurement model of the LEO / GNSS / INS tightly coupled system based on the received GNSS and LEO signals and the updated position, velocity and attitude information of the low-altitude aircraft. The calculation module is used to perform filtering estimation on the state and measurement model of the LEO / GNSS / INS tightly combined system using the hyperspherical simplex sampling unscented Kalman filter SS-UKF model, and obtain the final combined low-altitude aircraft position, velocity and attitude information through INS error correction.

7. The apparatus according to claim 6, characterized in that, The calculation module is specifically used for: the INS to calculate the updated position, velocity and attitude information of the low-altitude aircraft based on the initial state of the low-altitude aircraft and the angular rate and acceleration measured by the gyroscope and accelerometer in the INS through dead reckoning. The initial state of the low-altitude aircraft specifically includes position, velocity and attitude. The construction module is specifically used to: receive GNSS and LEO signals, and obtain GNSS and LEO pseudorange and pseudorange rate signals; Analyze the broadcast ephemeris in the pseudorange and pseudorange rate signals of GNSS and LEO, and calculate the positions of GNSS and LEO based on the broadcast ephemeris; Based on the updated position, velocity, and attitude information of the low-altitude aircraft, as well as the positions of the GNSS and LEO, the INS equivalent pseudorange / pseudorange rate is calculated. A system state model is constructed based on the INS error state model, the accelerometer and gyroscope offset error model, and the GNSS clock-related error model. Based on the pseudorange / pseudorange rate observation models of GNSS and LEO shown in Formulas 1-4, the measurement vectors of the LEO / GNSS / INS tightly coupled system are determined according to Formula 5, and the system measurement model is constructed. In this context, the superscripts GNSS and LEO represent GNSS satellites and LEO satellites, respectively. i Let r be the pseudorange observation value of the i-th satellite. i Let T be the actual distance between the i-th satellite and the airborne receiver. i and I i Let δt represent the tropospheric delay error and the ionospheric delay error, respectively, where c is the speed of light and δt is the velocity of light. r and For receiver clock bias and satellite clock bias, Unknown pseudorange measurement noise; In the formula, "point" represents the pseudorange rate; Where, ρ INS and These are the pseudorange vector and pseudorange rate vector equivalent to INS. The system state model and the system measurement model are discretized over time to obtain the time-discrete LEO / GNSS / INS tightly combined system state and measurement model: Where x(k) represents the system state vector at time k, y(k) represents the system measurement vector at time k, k represents the current time, h represents the nonlinear measurement model, v(k) represents the measurement noise vector, F(k|k-1) represents the state transition matrix at time k, and ω(k-1) represents the state noise vector. The calculation module is specifically used to: perform state updates using Kalman filtering according to Formulas 7-8, and obtain the state and corresponding covariance matrix of the LEO / GNSS / INS compact combination system after one-step state update. P(k|k-1)=F(k|k-1)P(k-1)F T (k-1)+Q(k-1) Formula 8; in, Let F(k|k-1) represent the predicted state at time k, and let F(k|k-1) represent the state transition matrix at time k. Let P(k|k-1) represent the system state at time (k-1), and let P(k|k-1) represent the one-step predicted state. The corresponding covariance matrix, P(k-1), represents the system state at time (k-1). The corresponding covariance matrix, Let Q(k-1) denote the transpose of the state transition matrix at time k, and let Q(k-1) denote the measurement noise covariance matrix. Update the system state after obtaining the first-step state. A hypersphere simplex sampling model is used for sampling to obtain n+2 sampling points, and the weight corresponding to each sampling point is obtained, where n represents the state dimension and is a natural number; Based on Formula 9, for each point in the obtained n+2 sampling point set ξ(k|k-1), calculate the corresponding n+2 measurement prediction values: in, ξ represents the measurement prediction vector. j (k|k-1) represents the j-th sampling point, and h represents the nonlinear measurement model of the system; According to Formula 10, the predicted value of the system is calculated based on n+2 measurement values ​​and their corresponding weights. in, Represents the system measurement prediction vector. ξ represents the j-th sampling point j (k|k-1) is the weight matrix involved in the mean calculation, where m represents the mean. After completing the measurement update, the filtered estimated system state error is obtained according to formulas 11-14. And the covariance matrix P(k): P(k) = P(k|k - 1) - K(k)P yy (k|k - 1)K(k) T Equation 14; Among them, P yy (k|k-1) represents the system measurement prediction vector. The corresponding covariance matrix, R(k) represents the system flow measurement noise covariance matrix, and K(k) represents the gain matrix; Combining the updated position, velocity, and attitude information of the low-altitude aircraft, and the filtered data... The position, velocity, and attitude errors in the data are corrected using INS error correction to obtain the final combined absolute position, velocity, and attitude information of the low-altitude aircraft.

8. The apparatus according to claim 6, characterized in that, The calculation module is specifically used for: Calculate the Sigma sampling point weights according to Formula 15: Among them, w 0 The initial weights are 0 ≤ w 0 ≤1; Calculate the initial state iteration vector according to Formula 16; Calculate the state iteration vector corresponding to the state dimension expansion according to Formula 17; The Sigma point sampling point set is calculated according to Formula 18; Where i represents the extended state dimension.

9. An electronic device, characterized in that, include: The memory, the processor, and the computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the steps of the LEO / GNSS / INS tightly coupled low-altitude vehicle positioning and velocity measurement method as described in any one of claims 1 to 5.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores an implementation program for information transmission, which, when executed by a processor, implements the steps of the LEO / GNSS / INS tightly coupled low-altitude aircraft positioning and velocity measurement method as described in any one of claims 1 to 5.

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