GNSS (Global Navigation Satellite System) / INS (Inertial Navigation Satellite System) / Vision deep combination system for GNSS carrier phase long coherent integral tracking

Abstract

The invention provides a GNSS (Global Navigation Satellite System) / INS (Inertial Navigation Satellite System) / Vision deep combination system for GNSS carrier phase long coherent integral tracking, which comprises an upper-layer multi-source data fusion subsystem, a bottom-layer GNSS baseband signal processing subsystem and a deep combination auxiliary module, and is characterized in that the upper-layer multi-source data fusion subsystem generates multi-source dynamic auxiliary Doppler information through the deep combination auxiliary module; the signal is input to a bottom layer GNSS baseband signal processing subsystem so as to eliminate dynamic stress generated by receiver-satellite relative motion in a coherent integration process; the bottom GNSS baseband signal processing subsystem provides pseudo-range, Doppler and carrier phase observation for the upper multi-source data fusion subsystem and is used for calculating the navigation state of a receiver. According to the invention, the continuity and availability of the GNSS carrier phase in the complex environment can be improved, continuous centimeter-level positioning is provided in the complex environment, and the precise positioning and navigation requirements of robots and autonomous vehicles in the complex environment are met.

Description

Technical Field

[0001] The present invention belongs to the technical field of GNSS receivers, and in particular relates to improving the continuity of GNSS carrier phase and the availability of high-precision positioning in complex environments. Background Art

[0002] With the rapid development of automation technology, the demand for robots and autonomous vehicles to navigate complex environments is increasing. The Global Navigation Satellite System (GNSS) provides users with all-weather, high-precision position, velocity, and time (PVT) information, making it an indispensable sensor in navigation and positioning solutions. However, in complex environments such as cities and tree-lined roads, GNSS carrier phase accuracy and continuity are severely degraded due to signal attenuation, reflection, and obstruction, significantly limiting the availability of high-precision GNSS positioning.

[0003] Typical high-precision GNSS positioning technologies include real-time kinematic (RTK), precise point positioning (PPP), and precise point positioning - real-time kinematic (PPP-RTK). All of these algorithms require continuous and accurate carrier phase observations. Carrier phase observations are generated by a phase-locked loop (PLL) in the GNSS receiver, which continuously tracks the GNSS carrier signal component. Traditional PLLs are easily lost in complex environments due to minor signal interference, resulting in frequent cycle jumps or even interruption of GNSS carrier phase observations, severely degrading positioning accuracy and continuity. Loose combining and tight combining techniques, which fuse data at the positioning result level and at the observation level (pseudorange, Doppler, carrier phase), respectively, can improve positioning availability and continuity to a certain extent. However, neither approach can improve the continuity of GNSS carrier phase observations, hindering the full potential of GNSS.

[0004] In complex environments, where signals are subject to obstruction, non-line-of-sight (NLOS), and multipath, carrier phase tracking in traditional GNSS receivers suffers from poor robustness. Humphreys studied carrier phase vector tracking, but this method relies heavily on top-level precise positioning and fails when precise positioning alone is unavailable, making it susceptible to failure in complex environments. Li et al. used a multi-channel cooperative tracking loop (Co-Op) to estimate loop clock drift, mitigating the impact of oscillator instability during tracking. O'Driscoll et al. proposed a long coherent integration tracking method, which can mitigate multipath effects and increase signal energy. These methods can all improve carrier phase tracking performance to some extent, with the long coherent integration architecture being compatible with traditional receivers and offering the best performance. However, the trade-off between dynamic stress and thermal noise suppression in GNSS tracking methods without external information assistance remains unresolved, hindering effective extension of coherent integration time. Deep combination techniques that incorporate external sensors to aid baseband signal processing can effectively mitigate the impact of dynamic stress on the loop. Soloviev et al. proposed a GNSS / INS deep integration solution, achieving sub-meter positioning accuracy in dense forests where traditional receiver technology cannot track GNSS signals. Ren et al. used the maximum likelihood method to estimate navigation bits and implemented long coherent integration through deep integration with a vector tracking structure. Bochkati et al. proposed a synthetic aperture method that compresses bandwidth for synthetic antenna aperture processing (SAP), improving signal directivity and reducing multipath interference. However, the rapid accumulation of INS errors over time can severely degrade system performance. Introducing vision and other methods can effectively improve INS recursive capabilities in the event of GNSS failure and significantly enhance dynamic stress assistance. Zuo et al. explored a GNSS / IMU / Vision multi-source deep integration method, but only provided preliminary simulation results. Multi-source deep integration still holds broad research potential, and currently no research has been conducted on multi-source deep integration for carrier phase tracking. The method of multi-sensor fusion-assisted baseband carrier phase tracking can fully suppress the impact of dynamics on the tracking loop, and fundamentally resolve the contradiction between dynamic stress and thermal noise suppression in the loop, thereby fully extending the coherent integration time of the tracking loop and suppressing thermal noise. It is of great significance for stable and continuous tracking of carrier phase and reliable and precise positioning in complex environments. Summary of the Invention

[0005] The purpose of the present invention is to provide a GNSS / INS / Vision deep combination system for GNSS carrier phase long coherent integration tracking, which can improve the availability of GNSS carrier phase in complex environments and meet the navigation needs of robots and autonomous vehicles in complex environments.

[0006] The technical solution of the present invention provides a GNSS / INS / Vision deep integration system for GNSS carrier phase long coherent integration tracking, including an upper-layer multi-source data fusion subsystem, a bottom-layer GNSS baseband signal processing subsystem and a deep integration auxiliary module. The upper-layer multi-source data fusion subsystem generates multi-source dynamic auxiliary Doppler information through a deep combination auxiliary module and inputs it into the underlying GNSS baseband signal processing subsystem to eliminate the dynamic stress generated by the relative motion between the receiver and the satellite during the coherent integration process; The underlying GNSS baseband signal processing subsystem provides pseudorange, Doppler, and carrier phase observations to the upper-layer multi-source data fusion subsystem for calculating the receiver navigation state.

[0007] Moreover, the upper-layer multi-source data fusion subsystem includes a GNSS precision positioning module, an inertial navigation module, a visual navigation module and a multi-source data fusion module.

[0008] Moreover, the multi-source data fusion module uses the GNSS positioning results from the GNSS precision positioning module, the mechanical arrangement results obtained by the inertial navigation module, and the feature point triangulation results obtained by the visual navigation module as input, uses a multi-state constrained Kalman filter to fuse multi-source data, and outputs a high-precision fused positioning result centered on inertial navigation.

[0009] Moreover, the underlying GNSS baseband signal processing subsystem includes a signal preprocessing module, a long coherent integration control module and a carrier phase detection control module.

[0010] Moreover, in the signal preprocessing module, the intermediate frequency signal is stripped of the carrier and pseudo-random code by the mixer and correlator to generate I and Q path correlation results, which are input to the long coherent integration control module for coherent integration of the navigation bit length.

[0011] Furthermore, the long coherent integration control module reads the navigation bits through the external bit auxiliary interface, and after frame synchronization is valid, performs long-term coherent integration of multiple navigation bit lengths based on the navigation bit transmission time.

[0012] Furthermore, the long coherent integration control module adopts a multi-stage convergence method to gradually extend the coherent integration time and compress the loop bandwidth to achieve the target coherent integration time.

[0013] Moreover, the carrier phase detection control module supports switching between two-quadrant and four-quadrant detection modes. The two-quadrant detection mode is adopted in the initial stage. When the loop enters a stable tracking state, the external navigation bit is used to assist in switching to the four-quadrant detection mode.

[0014] Moreover, the deep combination auxiliary module generates multi-source dynamic auxiliary Doppler information based on the receiver dynamics and satellite dynamics. The receiver dynamics are obtained by fusing the positioning results and compensating the antenna arm, and the satellite dynamics are obtained by solving the ephemeris by the GNSS precise positioning module. The multi-source dynamic auxiliary Doppler information is extrapolated through the uniform acceleration model and input into the underlying GNSS baseband signal processing subsystem to eliminate dynamic stress.

[0015] On the other hand, the present invention also provides a positioning method implemented using the above-mentioned GNSS / INS / Vision deep combination system for GNSS carrier phase long coherent integration tracking, which is used for precise positioning and navigation of robots and autonomous vehicles in complex environments.

[0016] The present invention can improve the continuity and availability of GNSS carrier phase in complex environments, provide continuous centimeter-level positioning in complex environments, and meet the precise positioning and navigation needs of robots and autonomous vehicles in complex environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings of the embodiments are briefly introduced below.

[0018] Figure 1 This is an overall block diagram of the multi-source deep combination system structure according to an embodiment of the present invention.

[0019] Figure 2 This is a principle block diagram of the MSCKF fusion positioning solution according to an embodiment of the present invention.

[0020] Figure 3 This is a block diagram of the deep combination mode switching principle of an embodiment of the present invention.

[0021] Figure 4 1 is a comparison diagram of the loop tracking Doppler and the loop estimation Doppler of the BD13 satellite in the embodiment.

[0022] Figure 5 This is a comparison diagram before and after the multi-source assisted Doppler extrapolation of the BD13 satellite in the embodiment.

[0023] Figure 6 This is a diagram showing the baseband loop tracking principle according to an embodiment of the present invention.

[0024] Figure 7 This is a comparison chart of the traction range of the second and fourth quadrants of the embodiment of the present invention.

[0025] Figure 8 This is a comparison chart of the second and fourth quadrant identification results of the BD13 satellite in the embodiment.

[0026] Figure 9This is a diagram of the discriminator error for different coherent integration time lengths of the BD13 satellite in the embodiment. In the figure, 20ms and 300ms respectively represent schematic diagrams of the discriminator output corresponding to coherent integration time lengths of 20ms and 300ms.

[0027] Figure 10 20ms and 300ms are schematic diagrams of carrier phase errors corresponding to coherent integration times of 20ms and 300ms, respectively.

[0028] Figure 11 Schematic diagram of the comparison results of horizontal positioning errors of a traditional commercial receiver and a multi-source deep combination system with an integration time of 300ms according to an embodiment of the present invention. DETAILED DESCRIPTION

[0029] In order to more clearly illustrate the present invention and / or the technical solutions in the prior art, specific embodiments of the present invention will be described below with reference to the accompanying drawings. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings and other embodiments can be obtained based on these drawings without inventive efforts.

[0030] Example 1 The following takes the carrier phase tracking of the long-term coherent integration of the multi-source deep combination system in the dynamic scenario of the GNSS receiver as an example, and describes the present invention in detail with reference to the accompanying drawings. The accompanying drawings and specific implementation methods are exemplary of the present invention and do not limit the present invention.

[0031] The overall structure of the multi-source deep combination system provided by the embodiment of the present invention is as follows: Figure 1 As shown, it includes the upper-layer multi-source data fusion subsystem, the lower-layer GNSS baseband signal processing subsystem and the deep combination auxiliary module: The upper-layer multi-source data fusion subsystem includes a GNSS precision positioning module, an inertial navigation module, a visual navigation module and a multi-source data fusion module.

[0032] in, The GNSS precision positioning module uses centimeter-level positioning algorithms, fed by observations from each satellite from the GNSS baseband signal processing subsystem. The present invention proposes that high-precision positioning algorithms such as RTK, PPP, and PPP-RTK can be used for this module's positioning, outputting high-precision GNSS positioning results for subsequent integration.

[0033] The inertial navigation module performs inertial navigation mechanical orchestration based on raw data from the IMU's accelerometer and gyroscope. This module propagates navigation states and outputs the vehicle's attitude, velocity, and position. The visual navigation module uses camera data to extract, track, and triangulate feature points, which are then integrated with the inertial navigation mechanical orchestration.

[0034] The multi-source data fusion module uses the GNSS positioning results from the GNSS precision positioning module, the mechanical arrangement results from the inertial navigation module, and the feature point triangulation results from the visual navigation module as input. It then fuses this multi-source data using a multi-state constrained Kalman filter (MSCKF), thereby outputting a high-precision fused positioning result centered on the inertial navigation system, in accordance with the requirements of the present invention. Specifically, the MSCKF fused positioning solution propagates the system error state through the inertial navigation mechanical arrangement. When a camera keyframe arrives, constraints are established to ensure that multiple camera poses observe the same visual feature point, constructing the visual observation equation and completing the Kalman filter update. When a GNSS positioning result arrives, the GNSS position observation equation is constructed and completing the Kalman filter update. The updated Kalman filter error state is fed back to the inertial navigation module to update the IMU error.

[0035] The bottom GNSS baseband signal processing subsystem includes a signal preprocessing module, a long coherent integration control module, and a carrier phase detection control module.

[0036] in, In the signal preprocessing module, the intermediate frequency signal is fed into a mixer and mixed with the carrier signal generated by a local voltage-controlled oscillator. The mixed signal is then fed into a correlator for 1ms correlation, generating I and Q path correlation results. These results are then fed into the long coherent integration control module. After bit synchronization is enabled, the I and Q path correlation results are used to perform coherent integration for the length of one navigation bit, starting from the navigation bit edge.

[0037] The present invention further proposes: The long coherent integration control module reads the navigation bit through the external bit auxiliary interface, and after the frame synchronization is valid, performs long-term coherent integration of multiple navigation bit lengths on its current bit according to the navigation bit transmission time.

[0038] Through multi-stage convergence, the coherent integration time is gradually extended and the loop bandwidth is compressed to achieve the target coherent integration time. The initial coherent integration time is 1 navigation bit length, and the target coherent integration time is N navigation bits length. Multiple loop convergence stages are set between the initial and target coherent integration times. The loop lock indicator (Phase locked indicator, ). Set the PLI threshold , when detected 20 times in a row When the loop converges, it is judged that the loop has converged and switches to the next stage until the target coherent integration time is reached.

[0039] The carrier phase detection control module uses the same external bit-assisted interface as the long coherent integration control module. With the assistance of external bits, it can switch between two- and four-quadrant detection modes. The loop initially uses two-quadrant detection mode. After successfully obtaining a fixed solution and maintaining positioning for more than 6 to 10 epochs, the loop is considered to have entered a stable tracking state and switches to four-quadrant detection mode using external navigation bits.

[0040] The upper-layer multi-source data fusion subsystem provides high-precision receiver and satellite positions, which are fed into the deep integration assistance module to generate multi-source dynamic auxiliary Doppler information. This information is then fed into the underlying GNSS baseband signal processing subsystem to eliminate dynamic stress caused by the relative motion between the receiver and satellite during the coherent integration process. Specifically, the inputs to the deep integration assistance module include receiver dynamics (position, velocity, acceleration) and satellite dynamics (position, velocity), provided by the upper-layer multi-source data fusion subsystem. The high-precision fused positioning results are compensated for by the antenna arm to obtain receiver dynamics. Satellite dynamics are calculated using broadcast ephemeris or precise ephemeris within the GNSS positioning module. The Doppler shift caused by the relative motion between the receiver and satellite is calculated using the dynamic information of the receiver and satellite to generate the multi-source auxiliary Doppler. This invention proposes linearly extrapolating the multi-source auxiliary Doppler information to 1000 Hz using a uniform acceleration model before feeding it into the underlying GNSS signal processing subsystem to eliminate dynamic stress caused by the relative motion between the receiver and satellite during the coherent integration process.

[0041] Preferably, the loop initially operates in 20ms integration mode, sets the comparison threshold to 5Hz-10Hz, uses the clock drift output by the positioning solution module and the multi-source assisted Doppler to form a loop-estimated Doppler, and compares it with the Doppler output by the loop. When the difference between the two is less than the comparison threshold, the multi-source assisted Doppler information is considered valid, and switches to a deep combination mode using multi-source assisted Doppler for compensation.

[0042] The underlying GNSS signal processing subsystem provides pseudorange, Doppler, and carrier phase observations to the upper-layer high-precision GNSS positioning module and calculates the receiver navigation state. The observation extraction frequency is adjustable from 1 to 50 Hz.

[0043] See also Figure 1The overall working process of the system proposed in the present invention is that the intermediate frequency signal generates observation values of each satellite channel through the GNSS baseband signal processing subsystem, and the observation values are used for positioning solution of the GNSS precision module. The visual inertial odometry module performs mechanical arrangement of inertial navigation and feature point extraction, tracking and triangulation of camera data. The multi-source data fusion module fuses the inertial navigation mechanical arrangement results, visual feature measurement results and GNSS high-precision positioning results through MSCKF to obtain high-precision multi-source fusion navigation and positioning results. In the deep combination auxiliary module, the multi-source fusion results and satellite position information are used to calculate the deep combination auxiliary Doppler information, and the Doppler auxiliary information is linearly extrapolated at 1000Hz to assist in different loops of baseband tracking, control the output of the loop NCO, and eliminate the influence of dynamic stress.

[0044] Example 2 Based on the system provided in Example 1, the MSCKF fusion positioning result solution implementation method is further proposed as follows: The multi-state constrained Kalman filter (MSCKF) is used to perform top-level positioning fusion of GNSS, INS and vision. The principle block diagram of MSCKF fusion positioning solution is as follows: Figure 2 As shown, this implementation can refer to existing technologies. The inertial navigation system choreographs navigation state propagation, establishing observation equations for visual updates and GNSS position updates to perform measurement updates. Building on existing MSCKF fusion positioning solution technology, this paper proposes an overall framework with inertial navigation as the central axis. The visual observation model and GNSS position observation model are established using the inertial navigation recursive pose. The updated state after MSCKF is fed back to the inertial navigation recursive position to update the IMU error.

[0045] First, the IMU state vector is augmented by the camera attitude. After feature point extraction and inertial navigation mechanical arrangement recursion, the state is augmented using data at the full second time and the camera sampling time:

[0046] In formula (3): represents the augmented state vector.

[0047] Indicates that IMU is The state at the epoch.

[0048] ]… ] indicates the first... A camera pose.

[0049] MSCKF uses the feature observation constraints of multiple frames within a window to update the state. When the visual feature tracking is interrupted or the continuous tracking time exceeds the length of the sliding window, the augmented state quantity is used to triangulate the feature, and then the feature point observation equation is constructed and the null space projection is performed, and then the MSCKF update is performed. Frame image The pixel coordinates of the feature point predictions It can be expressed as:

[0050] In formula (4): Indicates the position of the feature point in the navigation coordinate system.

[0051] and Represent the attitude and position of the IMU respectively.

[0052] and They represent the external parameters of camera-IMU respectively.

[0053] K and They represent the intrinsic parameters and distortion parameters of the camera respectively.

[0054] Represents the transformation matrix that transforms feature points in the navigation coordinate system to the camera coordinate system.

[0055] and They represent the projection and distortion processing functions in the image processing process respectively.

[0056] The observation model of visual feature points can be obtained by performing perturbation analysis on the IMU state and feature point positions:

[0057] In formula (3): Represents the pixel observation value.

[0058] Represents the disturbance term of the IMU state.

[0059] Represents the disturbance term of the feature point.

[0060] The Jacobian matrix representing the IMU state disturbance.

[0061] The Jacobian matrix representing the perturbation of feature point positions.

[0062] represents the observation noise.

[0063] In order to reduce the amount of calculation in the algorithm, the left null space projection is used to remove the Item, the measurement model of visual feature points is simplified to:

[0064] in, represents the observation matrix after null space projection, represents the observation noise after null space projection.

[0065] When the GNSS positioning result is updated, the position observation information is calculated using the IMU pose at the corresponding moment to correct the system position. The relationship between the predicted antenna position and the position calculated by the IMU is as follows:

[0066] In formula (5): Represents the predicted antenna position.

[0067] Represents the direction cosine matrix from the b system to the n system.

[0068] Represents the position of the IMU.

[0069] Represents the arm of the pole between the antenna and the center of the IMU.

[0070] The same perturbation analysis is performed to derive the GNSS position observation model:

[0071] In formula (6): Represents the residual of the GNSS position observation.

[0072] Represents the arm of the pole between the antenna and the center of the IMU.

[0073] and represent the disturbance terms of inertial navigation position and attitude respectively.

[0074] is the GNSS observation noise.

[0075] The system status can be updated using the visual observation equation and GNSS position observation equation constructed above, and high-precision fusion navigation results can be output after the update.

[0076] Example 3 Based on the system provided in Example 1, the following implementation method of loop dynamic stress compensation is proposed: The present invention proposes that in the MSCKF fusion positioning module, inertial navigation mechanical arrangement is used as the central axis to provide deeply combined dynamic auxiliary information. When a new key frame or GNSS positioning result arrives, the Kalman filter is updated and the error of the IMU is corrected to ensure continuous high-precision fusion positioning result output.

[0077] In this embodiment, the high-precision fused positioning results output by the MSCKF fusion positioning module provide the inertial navigation posture, velocity and acceleration information, and the dynamic information of the receiver is obtained through antenna arm compensation. Combined with the satellite position motion information from the GNSS positioning solution module, the multi-source auxiliary Doppler is solved and assisted to different baseband satellite channels to eliminate the influence of dynamic stress, thereby compressing the bandwidth and extending the integration time.

[0078] In this embodiment, deep combination multi-source assisted Doppler The Doppler frequency is composed of two parts: the Doppler frequency caused by the receiver motion and the Doppler frequency caused by the satellite motion, as shown in the following formula:

[0079] In formula (7): Indicates that The motion Doppler produced by the satellites.

[0080] Indicates the Doppler caused by the motion of the receiver.

[0081] The Doppler It can be calculated by the following formula:

[0082] In formula (8): is the unit vector in the line-of-sight direction between the object and the satellite in the ECEF coordinate system.

[0083] is the velocity of the carrier in the ECEF coordinate system.

[0084] is the wavelength of the satellite signal.

[0085] in It can be calculated by the following formula:

[0086] In formula (9): It is based on the first The position of the satellite.

[0087] It is the high-precision receiver position information provided by MSCKF.

[0088] Doppler caused by satellite motion The calculation is as follows:

[0089] In formula (10): Represents the speed of the satellite's movement.

[0090] In this embodiment, to ensure that the provided dynamic auxiliary information can timely and effectively eliminate the effects of dynamic stress, the auxiliary information output rate must be sufficiently high. Therefore, it is necessary to perform linear extrapolation on the calculated motion Doppler. In this embodiment, a uniform acceleration extrapolation model is used for both the satellite motion Doppler and the receiver motion Doppler.

[0091] For the Doppler caused by carrier motion, the extrapolation method is as follows:

[0092] In formula (11): and They represent the Doppler frequency shift caused by the carrier motion at two consecutive update moments.

[0093] Indicates the acceleration of the receiver's motion.

[0094] Indicates the update period of the receiver's position and velocity information.

[0095] Indicates the update period of the baseband loop.

[0096] The present invention proposes that the acceleration of the receiver motion in the above formula is obtained by projecting the acceleration of the high-precision fusion positioning result:

[0097] In formula (12): and represent the antenna and inertial navigation acceleration in the navigation coordinate system respectively.

[0098] Represents the direction cosine matrix from the vehicle coordinate system to the navigation coordinate system.

[0099] Indicates the inertial angular velocity in the carrier coordinate system.

[0100] Represents the differential of the inertial angular velocity.

[0101] Indicates the antenna mast arm.

[0102] Then project the acceleration in the navigation coordinate system:

[0103] In formula (13): It represents the acceleration of the receiver motion in the Earth-centered Earth-fixed coordinate system (ECEF).

[0104] Represents the direction cosine matrix from the navigation coordinate system to the Earth-centered Earth-fixed coordinate system (ECEF).

[0105] Similarly, for the Doppler caused by satellite motion, the extrapolation method is as follows:

[0106] In formula (14): and Indicates the Doppler shift caused by satellite motion between two consecutive update moments.

[0107] Indicates the difference in satellite Doppler between two update times.

[0108] Indicates the positioning cycle.

[0109] Figure 3 The block diagram of the system's deep combination mode switching principle is shown. In the GNSS baseband signal processing subsystem, the input intermediate frequency signal is pre-processed for different satellite channels, and the pre-processed signal is loop filtered and input to the NCO for control. Multi-source auxiliary information can replace the Doppler information of the local loop tracking. Control the NCO. Because the present invention notes that the Doppler information tracked by the PLL loop It includes two parts: dynamic stress and receiver clock drift. Therefore, when using multi-source auxiliary Doppler information to control the loop, it is necessary to use the receiver clock drift output from the positioning solution module to convert it into Doppler information. , the Doppler estimation value of the multi-source auxiliary Doppler loop Compare this to the Doppler output of the actual tracking loop. By comparison, when the difference between the two is less than the comparison threshold of 5-10 Hz, the multi-source assisted Doppler information is considered valid, and the system switches to the deep combination mode using multi-source assisted Doppler for auxiliary tracking. Figure 4The comparison of the loop-estimated Doppler and the loop-tracked Doppler of a strong signal is shown. It can be seen that the trends of the two are highly consistent. Multi-source assisted Doppler can eliminate most of the dynamic stress in the loop, and the loop tracks the residual Doppler after assistance.

[0110] In this embodiment, the multi-source auxiliary Doppler is linearly extrapolated to 1000Hz to meet the high update requirements of the baseband tracking loop. The auxiliary Doppler is then input to the numerically controlled oscillator (NCO) to control the loop, eliminating the influence of dynamic stress, thereby compressing the bandwidth and achieving longer integration time. Figure 5 As shown in the figure, the Doppler information before extrapolation is consistent with the frequency of the inertial navigation update. After extrapolation, the acceleration information is used to update the Doppler information in each inertial navigation update cycle. The multi-source assisted Doppler information output is more timely and smoother, which can better compensate for the dynamic stress in the baseband loop.

[0111] Example 3 Based on the system provided in Example 1, it is proposed to use a four-quadrant discriminator to perform phase discrimination on the loop:

[0112] In formula (15): Represents the output structure of the phase detector; () indicates the calculation of the inverse tangent function; and are the coherent integration results respectively.

[0113] In this embodiment, the principle of baseband tracking loop data processing is shown in FIG. Figure 6 In the pre-processing module, the intermediate frequency signal passes through the mixer and correlator to remove the carrier and pseudo-random code, and then inputs into the long coherent integration control module for long coherent integration. The long coherent integration output and The two signals are sent to the carrier phase detection control module. In the carrier phase detection control module, a phase discriminator is used to perform phase discrimination on the I and Q signals, and the phase discrimination error is filtered and sent to the NCO for loop control. At the same time, the carrier NCO is also combined with the multi-source auxiliary Doppler information from the outside for joint control. Among them, this embodiment preferably proposes to use external navigation bit assistance to eliminate the influence of navigation bit jumps and switch to the four-quadrant phase detection mode after successfully obtaining a fixed solution and continuously positioning for more than 6 to 10 epochs. Figure 7 As shown in the figure, it can be seen that the linear working range of the four-quadrant discriminator is extended to ±180°, which is twice the discrimination range of the traditional two-quadrant phase detector (±90°). Figure 8The comparison of the two-quadrant and four-quadrant identification results of the BD13 satellite in a complex environment is shown. It can be seen that the loop will frequently lose lock when using the two-quadrant discriminator, while the four-quadrant discriminator can maintain stable tracking of the signal.

[0114] According to the results of the four-quadrant discriminator, the present invention proposes a signal anomaly indicator To control the extraction of carrier phase observations:

[0115] In formula (16): and Represents epochs and The corresponding moment.

[0116] and They are the phase comparison results of the current moment and the previous moment respectively.

[0117] When the indicator result exceeds the given 180° threshold, the signal anomaly flag is determined to be true, indicating that a cycle slip may have occurred. The carrier phase observations of this epoch will be considered as errors and will not be used for GNSS positioning.

[0118] Example 4 Based on the system provided in the above embodiment, a positioning method is implemented for robots and autonomous vehicles to accurately locate and navigate in complex environments.

[0119] In order to understand the technical effect of the present invention, experimental tests are carried out to verify: A wheeled robot was used to conduct tests in complex experimental environments, including tree-lined avenues and obstructed buildings, to verify the feasibility and advancement of the present invention. The Leador A15 high-precision positioning system (POS) was used as a true reference. GNSS signals were recorded by a Spirent GSS6450 GNSS satellite signal recorder, which downconverted the satellite signals to an intermediate frequency. Only the BD and GPS L1 frequency signals were processed in the experiment. Grayscale images were acquired using an AVT G192 monocular camera, and all data was synchronized to GPS time using a self-developed integrated navigation module. To verify the effectiveness of the present invention's multi-source deep combination system for carrier tracking and positioning output, evaluations were conducted at three levels: baseband tracking, observations, and final positioning results.

[0120] Figure 9 The BD13 satellite's loop phase detector output was compared for different coherent integration times (20ms and 300ms). It can be seen that as the integration time increases, the loop phase detector error decreases significantly, significantly improving tracking performance.

[0121] The carrier phase error is calculated using a high-precision reference system. The carrier phase error result only includes cycle slips and white noise, allowing analysis at the observation level. Figure 10 A comparison of the carrier phase observation errors output by the BD-13 satellite at 20ms and 300ms integration times shows that the 20ms integration time is more prone to inducing a greater number of half-cycle and full-cycle slips than the 300ms integration time. Furthermore, the carrier phase error decreases significantly with increasing integration time. Long coherent integration tracking can fundamentally improve carrier phase accuracy and continuity. Based on a deep multi-sensor integration approach, the coherent integration time can be fully extended, ensuring accurate and continuous carrier phase observation output even in extremely harsh environments.

[0122] Figure 11 The horizontal positioning results of a traditional commercial receiver and a multi-source deep combination system with 300ms coherent integration were compared. The RTK positioning ambiguity fixation rates for the two systems were 7.6% and 100.0%, respectively, with positioning errors at the meter and centimeter levels, respectively. In complex environments, traditional commercial receivers were unable to fix ambiguities due to frequent signal loss, resulting in large positioning errors. However, the 300ms coherent integration of the multi-source deep combination system was able to maintain stable tracking of the signal carrier phase, providing centimeter-level positioning in complex environments. The positioning results demonstrate that the multi-source deep combination system has high performance in resisting signal fading and multipath mitigation, resolving the issue of poor availability of high-precision GNSS in complex environments.

[0123] The test results show that the GNSS / INS / Vision deep combination system based on GNSS carrier phase long coherent integration tracking of the present invention improves the availability of GNSS carrier phase in extremely challenging environments and meets the navigation needs of robots and autonomous vehicles in complex environments. It is worth noting that the 300ms long coherent integration architecture used by the multi-source deep combination system in this test example is set for the data in the complex environment of the test, and does not represent the coherent integration limit time and carrier phase optimal sensitivity of the method. When testing other data sets, the integration period can be reasonably extended according to actual conditions to achieve better signal tracking and positioning effects.

[0124] In specific implementations, the methods proposed in the technical solutions of the present invention can be automated by those skilled in the art using computer software technologies. For example, corresponding positioning software can be provided for user operation, and the software can be integrated into robots or autonomous vehicles. System devices implementing the methods, such as computer-readable storage media storing the computer programs corresponding to the technical solutions of the present invention and computer devices running the corresponding computer programs, are also within the scope of protection of the present invention.

[0125] In a possible embodiment, the present invention also provides a computer program product, which includes a computer program. The computer program can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the software processing part of the above-mentioned methods.

[0126] In a possible embodiment, the present invention further provides a non-transitory computer-readable storage medium having a computer program stored thereon, which is implemented to execute the software processing portion of the above methods when executed by a processor.

[0127] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0128] Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented using software plus a necessary general-purpose hardware platform, or of course, hardware. Based on this understanding, the essence of the above technical solution, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, or an optical disk, and includes a number of instructions for causing a computer device (such as a personal computer, server, or network device) to execute the methods described in each embodiment or certain portions of the embodiments.

[0129] The specific embodiments described herein are merely illustrative of the spirit of the present invention. Persons skilled in the art may make various modifications, additions, or substitutions to the described specific embodiments without departing from the spirit of the present invention or exceeding the scope of the appended claims.

Claims

1. A GNSS / INS / Vision deep integration system for GNSS carrier phase long coherent integration tracking, characterized by: It includes upper-layer multi-source data fusion subsystem, lower-layer GNSS baseband signal processing subsystem and deep combination auxiliary module. The upper-layer multi-source data fusion subsystem generates multi-source dynamic auxiliary Doppler information through a deep combination auxiliary module and inputs it into the underlying GNSS baseband signal processing subsystem to eliminate the dynamic stress generated by the relative motion between the receiver and the satellite during the coherent integration process; The underlying GNSS baseband signal processing subsystem provides pseudorange, Doppler, and carrier phase observations to the upper-layer multi-source data fusion subsystem for calculating the receiver navigation state.

2. The deep combination system according to claim 1, characterized in that The upper-layer multi-source data fusion subsystem includes a GNSS precision positioning module, an inertial navigation module, a visual navigation module and a multi-source data fusion module.

3. The deep combination system according to claim 2, characterized in that The multi-source data fusion module uses the GNSS positioning results from the GNSS precision positioning module, the mechanical arrangement results obtained from the inertial navigation module, and the feature point triangulation results obtained from the visual navigation module as input, uses a multi-state constrained Kalman filter to fuse the multi-source data, and outputs a high-precision fused positioning result centered on the inertial navigation.

4. The deep combination system according to claim 1, characterized in that The underlying GNSS baseband signal processing subsystem includes a signal preprocessing module, a long coherent integration control module, and a carrier phase detection control module.

5. The deep combination system according to claim 4, characterized in that In the signal preprocessing module, the intermediate frequency signal is stripped of the carrier and pseudo-random code by the mixer and correlator to generate I and Q path correlation results, which are input to the long coherent integration control module for coherent integration of the navigation bit length.

6. The deep combination system according to claim 4, characterized in that The long coherent integration control module reads the navigation bit through the external bit auxiliary interface, and after the frame synchronization is valid, performs long-term coherent integration of multiple navigation bit lengths based on the navigation bit transmission time.

7. The deep combination system according to claim 4, characterized in that The long coherent integration control module adopts a multi-stage convergence method to gradually extend the coherent integration time and compress the loop bandwidth to achieve the target coherent integration time.

8. The deep combination system according to claim 4, characterized in that The carrier phase detection control module supports switching between two-quadrant and four-quadrant detection modes. The two-quadrant detection mode is adopted in the initial stage. When the loop enters a stable tracking state, the external navigation bit is used to assist in switching to the four-quadrant detection mode.

9. The deep combination system according to claim 1, characterized in that The deep combination assistance module generates multi-source dynamic auxiliary Doppler information based on receiver dynamics and satellite dynamics. The receiver dynamics are obtained by fusing positioning results and compensating for the antenna arm. The satellite dynamics are obtained by solving the ephemeris of the GNSS precise positioning module. The multi-source dynamic auxiliary Doppler information is extrapolated through a uniform acceleration model and input into the underlying GNSS baseband signal processing subsystem to eliminate dynamic stress.

10. A positioning method implemented using the GNSS / INS / Vision deep combined system for GNSS carrier phase long coherent integration tracking according to any one of claims 1 to 9, characterized in that: Used for accurate positioning and navigation of robots and autonomous vehicles in complex environments.

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