Method and device for integrating measurements in a land vehicle navigation system

Abstract

A method and device for integrating measurements in a land vehicle navigation system with an increased frequency of inertial sensor data readings differ from the prior art by virtue of an increase in the frequency of rough accuracy IMU measurements and the subsequent cascade processing thereof. Furthermore, the computational complexity of the algorithms in each cascade increases while the frequency of incoming data to be processed decreases: high-frequency IMU measurements are processed using simple algorithms, the processing results are transmitted to the next cascade with a decrease in data frequency, and in the next cascade, less frequent data are processed using more complex algorithms. As a result of this solution, algorithms for combining navigational measurements can be executed on cheaper and more widely available microprocessors having a lower clock frequency and a lower capacity for representing floating point numbers. By analyzing the mathematical principles of navigation algorithms, the claimed invention makes it possible to achieve a significant increase in the frequency of IMU measurements without impacting the frequency with which a navigation solution is updated and without increasing the required computing power. In addition, the claimed invention makes it possible to reduce the frequency of a navigation solution and the power of the computing device while maintaining the frequency of IMU measurements and a set accuracy of the navigation solution.

Description

[0001] Method and device for integrating measurements in a ground vehicle navigation system

[0002] Field of technology

[0003] The invention relates to the field of satellite navigation of ground-based equipment using global navigation satellite systems (GNS), such as GPS, GLONASS, Beidou, Galileo, etc.

[0004] In particular, the invention relates to navigation systems and devices that include receivers of signals from satellite navigation systems, vehicle speed meters, strapdown inertial measurement modules, and sporadic navigation information sensors.

[0005] State of the art

[0006] The closest to the claimed invention is a strapdown integrated navigation system of medium accuracy for a mobile ground object according to patent RU2.539.131 (hereinafter referred to as the prototype), designed with the possibility of using the measurement of an inertial measurement module (IMM) of a low accuracy class, the measurement of a receiver of signals from satellite navigation systems (SNS) and the measurement of the speed of a vehicle.

[0007] In the prototype, measurements of the IMU performed at discrete moments in time are fed to the inputs of the computing units without preliminary processing, while all navigation calculations are performed at the rate of receipt of measurements from the NMU.

[0008] The indicated method has a disadvantage due to the fact that complex computing blocks are called up with a frequency of receiving measurements, which is the maximum of all the measurement frequencies available in the navigation system.

[0009] As a result, it is necessary to either increase the power of the computing device while maintaining the rate of receipt of measurements, or to decrease the frequency of measurements and reduce the accuracy of the navigation solution due to insufficient power of the computing device.

[0010] Disclosure of invention

[0011] The purpose of the claimed invention is to eliminate the shortcomings of known technical solutions, in particular to reduce the average computational load on the computing device (CD) of the integrated navigation system of a ground vehicle using a coarse-accuracy IMU, while maintaining an acceptable value of errors in calculating the integrated assessment of the navigation parameters of the vehicle.

[0012] The aim of the invention is achieved by increasing the frequency of measurements of the IMU of a coarse accuracy class, and the subsequent cascade processing of these measurements.

[0013] In this case, the computational complexity of the algorithms in each cascade increases as the frequency of receipt of processed data decreases; high-frequency measurements of the IMU are processed by simple algorithms, the results of this processing are transmitted to the next cascade with a decrease in the data frequency, and in the next cascade, data with a reduced frequency are processed by more complex algorithms.

[0014] As a result of this solution, navigation measurement integration algorithms can be implemented on cheaper and more common microprocessors with a reduced clock frequency and reduced bit depth of floating-point number representation.

[0015] To implement the claimed solution, the IMU is rigidly fixed to the vehicle body and the components of the angular velocity vector of the vehicle body and the components of the apparent acceleration vector of the vehicle body are measured at the point of fixation.

[0016] In this case, the SPS signal receiver contains at least one navigation antenna that receives signals from navigation satellites of one or more SPS, the antenna is fixed to the vehicle body, the SPS signal receiver measures the coordinates and components of the vehicle speed vector at the antenna fixing point. The signal receiver can receive signals from two antennas fixed to the vehicle body, in this case, in addition to the coordinates and speed of the fixing point of one antenna, it can measure two angles that specify the direction of the segment connecting the two antennas.

[0017] The signal receiver generates its own SNS time scale, which is subsequently used to synchronize processes in the integrated navigation system.

[0018] The vehicle speed sensor (odometer) measures the speed at a certain point on the vehicle body.

[0019] A multi-turn encoder mounted on a vehicle wheel can be used as an odometer; in this case, the encoder measures the angle (or angle increment) of the wheel rotation, which is converted into the value of the forward speed of the wheel hub.

[0020] The speed sensor can also be located inside the vehicle, in which case its measurements are read by the system via a main bus laid inside the vehicle body, while such a bus can be, for example, a CAN bus.

[0021] The sporadic navigation information sensor performs measurements of a quantity functionally related to the coordinates, velocity vector and orientation of the vehicle, and the measurements of this sensor are performed at random moments in time.

[0022] For example, a sensor for measuring the coordinates of road signs that are detected and recognized along the vehicle's route can serve as a sporadic navigation information sensor.

[0023] The coordinates, velocity vector and orientation parameters of the vehicle are calculated from the measurements of the IMU by numerically solving a system of ordinary differential equations (ODE) of rigid body kinematics.

[0024] Algorithms for the numerical solution of the ODE system assume the selection of a sequence of discrete non-coinciding points on the time axis - nodes of the computational grid. The use of these methods requires that the moments of time at which the IMU measurements are performed exactly correspond to the nodes of the computational grid, due to which the numerical solution of the system of difference equations must be performed with the frequency of receiving data from the IMU.

[0025] If the IMU measurements are performed at high frequency, the computational power required to numerically solve the ODE system in real time may be unattainable for mass-produced low-cost microprocessors.

[0026] Reducing the frequency of IMU measurements to a level where an inexpensive microprocessor can solve ODE systems in real time leads to an increase in the errors of the calculation results due to an increase in the integration step.

[0027] In addition to numerical methods, there are also approximate analytical methods for solving ODE systems.

[0028] The structure of the ODE system of rigid body kinematics is such that the application of an analytical method to it allows one to write the solution of the ODE system as a function that depends only on the integrals of the IMU measurements and on the initial conditions.

[0029] The IMU measurements obtained at an increased frequency are subjected to numerical integration by some computationally simple method containing a small number of computational operations.

[0030] Integration is performed within each period of the ODE system solution, the duration of which is more than twice the period of the IMU measurement.

[0031] The solution of the ODE for the next period is calculated not from the primary measurements of the IMU, but from the integrals of the measurements of the IMU calculated for this period, but using other, more complex formulas.

[0032] Due to this solution, simple calculations are performed over short time intervals (at a high frequency of IMU measurements), and more complex calculations are performed over longer time intervals (at a moderate frequency of solving the ODE system). IMUs of a coarse accuracy class perform measurements with large errors.

[0033] Large errors in the measurements of the IMU lead to a rapid increase in the errors in the solution of the ODE system, limited only by the limits of the representation of floating-point numbers in the VU.

[0034] For this reason, the ODU solution requires constant correction from additional navigation measuring devices.

[0035] Currently, the Bayesian approach is most often used to correct the ODE solution calculated from IMU measurements, with the most well-known and widely used implementation of the Bayesian approach being the generalized (or extended) Kalman filter.

[0036] List of drawing figures

[0037] Fig. 1 shows a block diagram of a device for integrating navigation measurements.

[0038] Fig.2 shows a block diagram of the synchronization device.

[0039] Fig.3 shows the time diagram of the integration of high-frequency measurements.

[0040] Fig. 4 shows a diagram of the information links between software modules executed within the VU.

[0041] Implementation of the invention

[0042] The claimed invention can be implemented in the format of a method and device performed as follows.

[0043] The block diagram of the device for integrating navigation measurements is shown in Fig. 1.

[0044] The integration device 106 contains a computing device 110 on which the software modules of the integration method are executed.

[0045] The inertial measurements are performed by the IMU 114 at an increased frequency and are transmitted to the aggregation device 106 in the form of a serial data stream via a physical wire connection 115. The wire connection 115 is connected to a transceiver 116 of a serial interface included in the aggregation device 106.

[0046] Typical types of serial interface include universal asynchronous receiver / transmitter (UART), multiplexed data channel (MDC), Inter-Integrated Circuit (I2C), Serial Peripheral Interface (SPI), Controlled Area Network (CAN), Universal Serial Bus (USB), Ethernet, and the like.

[0047] The transceiver 116 converts the physical signals received from the wired connection 115 into data packets and transmits them to the VU 110 via the in-circuit physical wire interface 117 for further processing.

[0048] Interface 117 can be either serial (for example, SPI, I2C, U ART, etc.) or parallel (for example, Memory Bus, AMBA ANV, AMBA APV, etc.).

[0049] The integration device 106 comprises a transceiver 124 of a serial CAN interface, to which a physical wire connection 123 is connected, made in accordance with the requirements for the automobile CAN bus.

[0050] Wired connection 123 is connected to the CAN bus via connector 122 installed on the vehicle body.

[0051] The transceiver 124 converts the physical signals received from the wired connection 123 into data packets, which are transmitted for further processing to the VU 110 via the in-circuit physical wire interface 125.

[0052] The VU 110 generates data packets with control information, which are transmitted to the transceiver 124 via the in-circuit interface 125 for conversion into CAN serial interface signals.

[0053] CAN signals are transmitted via physical connection 123 to the vehicle CAN bus via connector 122.

[0054] The integration device 106 comprises a device 127 for reading measurements from a vehicle wheel rotation sensor 129. The sensor 129 is connected to the reading device via a physical wire connection 128.

[0055] If the sensor 129 is a multi-turn encoder, the device 127 counts the number of electrical pulses transmitted by the sensor 129 to the wire connection 128 over a certain period of time.

[0056] If sensor 129 is an angle sensor, device 127 reads its readings at specified points in time.

[0057] Device 127 is connected to VU 110 via physical wired in-circuit interface 126.

[0058] The integration device 106 contains a serial interface transceiver 109, to which, via a physical wire connection 105, a sporadic navigation parameter meter 102 is connected.

[0059] The measuring device 102 performs navigation measurements at random, non-periodic moments in time.

[0060] An example of such a measuring device can be an optical-electronic device that measures the coordinates of recognized road signs that are unpredictably located along the vehicle’s route.

[0061] The transceiver 109 receives a serial data stream from the connection 105, converts it into data packets, which it transmits to the VU 110 for further processing via the wired in-circuit interface 113.

[0062] The VU 102 generates data packets with control information, which are transmitted to the transceiver 109 via the in-circuit interface 113 for serial transmission to the meter 102 via the wired connection 105.

[0063] The integration device 106 contains a serial interface transceiver 107, to which a signal receiver 101 is connected via a physical wire connection 103.

[0064] The transceiver 110 receives a serial data stream from the connection 103, converts it into data packets, which it transmits to the BU 110 for further processing via the physical wired in-circuit interface 111. The BY 110 forms data packets with control information, which are transmitted to the transceiver 107 via the in-circuit interface 111 for serial transmission to the receiver 101 via the wired connection 103.

[0065] Receiver 101, in addition to sequentially transmitting measurements via physical connection 103, generates a periodic pulse signal 1PPS, which is transmitted to the aggregation device 106 via physical wire connection 104.

[0066] The moment of occurrence of the electrical impulse in connection 104 corresponds to a predetermined moment in time in the time scale of receiver 101.

[0067] The digital value of this point in time is transmitted to BY 110 in serial format via wire connection 103.

[0068] The wired connection 104 is connected to the pulse detection device 108, which is part of the integration device 106.

[0069] The device 108 converts electrical pulses received from the wire connection 104 into a control signal 112 of the synchronization device 121, which is part of the VU 110.

[0070] The synchronization device 121 synchronizes the internal time scale of the VU 110 with the time scale of the SPS transmitted to the aggregation device 106 from the SPS signal receiver 101 via a wire connection 104 in the form of a periodic pulse signal 1PPS.

[0071] The integration device 106 contains a serial interface transceiver 119, which transmits the integrated navigation solution from the VU 110 to external navigation information consumers via a physical wire connection 120.

[0072] The transceiver 119 is connected to the VU 110 via a physical wired in-circuit interface 118.

[0073] In addition to transmitting navigation information, the VU 110 performs a two-way exchange of control information with external sources and consumers via a wired connection 120, a transceiver 119 and an in-circuit interface 118. The block diagram of the synchronization device 121 is shown in Fig. 2.

[0074] The synchronization device 121 receives a clock signal 202 at the input from the clock signal generator 201, which is part of the VU 110.

[0075] The clock signal 202 is fed to the counter device 203, which calculates the moment in time 204 of the occurrence of each pulse of the clock signal on the time scale of the counter 110.

[0076] For the calculation, the current estimate 206 of the rate of drift of the time scale of the VU 110 relative to the time scale of the SNS and the current estimate 207 of the shift of the time scale of the VU 110 relative to the time scale of the SNS, calculated in the estimation block 208, are used.

[0077] The calculated value 204 of the moment of time of occurrence of the clock signal pulse on the time scale of the CU 110 is written into register 205, accessible for reading in the address space of the data memory of the CU 110.

[0078] At the moment of occurrence of control signal 112, the time value 204 is written to register 211.

[0079] The value written to register 211 marks the moment of occurrence of the next 1PPS pulse on the time scale of VU 110.

[0080] After receiving from the in-circuit interface 111 the digital value of the moment of occurrence of the same pulse on the SNS time scale, in the discriminator 210 the difference 209 is calculated between the moments of time of occurrence of the pulse in the 1PPS signal, measured on the SNS time scale and on the time scale of the VU 110.

[0081] The difference 209 is transmitted to the input of the evaluation block 208, which calculates the updated values ​​of the estimate of the time scale drift rate 206 and the time scale shift 207.

[0082] In Fig. 3. on the time axis 303, a time diagram of the integration of high-frequency measurements of the NIM, obtained with a constant period 302 equal to T, is shown. иим .

[0083] Also shown in this figure are the moments of obtaining various correction measurements on the time axis 303. The next measurement 301 of the IMU contains the measured value of the angular velocity vector, designated as ω n and the measured value of the apparent acceleration vector, denoted as nп .

[0084] The measurement is performed at time 304, equal to t n =t0+n T иим . where n is the serial number of the IMU measurement, t0 is the moment the IMU operation starts.

[0085] Integration is performed on successive time intervals 305 with the same duration T Инт = T иим N инт , where N инт > 2 - an integer, by some computationally simple method, for example, the rectangle method or the trapezoid method.

[0086] The result of integration 306 is given at a discrete moment of time 308, equal to t k =t Q +k T ИHT =t0+kN инт T иим, where k is the ordinal number of the integral from the IIM measurement.

[0087] Under conditions of good reception of satellite navigation signals, the measurements of the SNS receiver are received by the VU with a constant period of 311, which is designated T снс , and T инс " T инт .

[0088] Odometer measurements are sent to the VU with a constant period of 312, which is designated T Одм in this case, as a rule, G одм > G cнс .

[0089] Sporadic navigation measurements occur at random times 309 and do not have a periodic structure.

[0090] Fig. 4 shows a diagram of the information links between the software modules executed inside the VU 110.

[0091] The VU 110 has its own time scale, generated from the clock generator 201, synchronized with the SNS time scale using the synchronization device 121.

[0092] The values ​​of time moments synchronized with the SNS time scale are available to software modules through register 205 of synchronization device 121, located in the address space of data memory of VU 110.

[0093] The high-frequency measurements of the IMU 114, transmitted to the VU 110 via the in-circuit interface 115, contain high-frequency measurements of 408 components of the angular velocity vector ω n And high-frequency measurements of 401 components of the apparent acceleration vector n n with a period of 302 equal to T иим . High-frequency measurements of the IMU can be either synchronized with the time scale of the VU 110 or performed asynchronously, under the control of the IMU's own clock generator.

[0094] The measurements of the IMU 408 and 401 are fed to the inputs of the software module for integrating the angular velocity vector 409 and the software module for integrating the apparent acceleration vector 402.

[0095] At the outputs of these blocks with a period of 305 T инт the value of the integral Ѳ is formed 410 к from the angular velocity vector and the value of the integral π 403 к from the apparent acceleration vector.

[0096] The values ​​of integrals 410 and 403 are transmitted to software module 406, in which the correction of the zero offsets of the inertial sensors and the correction of the scale factor of the course gyroscope are performed.

[0097] At the output of module 406, a corrected value 411 of the integral from the gyroscope measurements and a corrected value 404 of the integral from the accelerometer measurements are formed.

[0098] The adjusted values ​​are formed with a period of 305.

[0099] Values ​​411 and 404 are transmitted to the input of module 407, in which the ODE system of rigid body kinematics is solved.

[0100] The solution of the system, calculated for each moment of time t k with a period of 305, consists of three coordinates of the IMU 114 relative to the ECEF, the velocity vector of the IMU 114 relative to the ECEF, and the orientation of the IMU 144 relative to the ECEF.

[0101] These elements form an a priori integrated assessment of 405 navigation parameters of the vehicle at time t k .

[0102] The orientation of the IMU relative to the ECEF can be calculated in the form of a quaternion, denoted as

[0103] Together with the a priori estimate 405, its covariance matrix 412 is calculated, which is called the a priori covariance matrix.

[0104] The prior complex estimate 405 and the prior covariance matrix 412 are calculated for each time point t k with a period of 305.

[0105] At the moment of time 310 of the occurrence of the next measurement of the receiver of signals of the SNS 101, in the software module 413 an observation matrix 416 is constructed for the a priori complexed assessment 405 using the measurements of the receiver of signals of the SNS 101.

[0106] At the moment of time 313 of the occurrence of the next odometer measurement, in the software module 414 an observation matrix 417 is constructed for the a priori complex assessment 405 using the odometer measurements.

[0107] At a random moment in time 309 of the occurrence of a sporadic measurement, the software module 415 constructs an observation matrix 418 for the a priori complexed estimate 405 using this sporadic measurement.

[0108] Each observation matrix, after its construction, is transferred to the software module 419, in which this matrix is ​​used to correct the a priori complex estimate 405 based on the results of the corresponding measurement.

[0109] If several different measurements are obtained at one point in time, their observation matrices are combined in module 419 and the correction of the a priori estimate 405 is performed using the combined matrix.

[0110] The operations performed in module 419 are called a posteriori correction of the integrated assessment of the navigation parameters of the vehicle.

[0111] The complex estimate 422 obtained after correction is called the a posteriori complex estimate.

[0112] A posteriori correction of the a priori quaternion is performed in a multiplicative manner in the form — a multiplicative correction to the a priori quaternion, calculated from the observation matrix and the correction observation itself; ° is the symbol for quaternion multiplication.

[0113] The multiplicative correction is best consistent with the mathematical nature of the quaternion as a representation of a group of three-dimensional rotations.

[0114] Together with the a posteriori complexed estimate 422 of the navigation parameters of the vehicle, its covariance matrix 423 is calculated, which is called the a posteriori covariance matrix.

[0115] A posteriori correction is the most computationally complex stage of integration of navigation measurements; it is performed with periods of occurrence of correction measurements that are many times greater than the period 302 of the IMU measurement and the period 305 of updating the a priori estimate.

[0116] After performing a posteriori correction for any correction measurement, or set of simultaneous correction measurements, the posteriori 422 and covariance 423 matrices are transferred to the a priori estimate calculation module 407.

[0117] In this module, vector 422 is used as the initial condition when solving the system of ODEs of rigid body kinematics to obtain the next a priori state vector 405.

[0118] Matrix 423 is used as the initial condition in solving the Riccati equation to obtain the next a priori covariance matrix 412.

[0119] The results of the integration of navigation measurements are output from the a posteriori correction module 419 in the form of a state vector 420 and its covariance matrix 421.

[0120] The period of output of results coincides with the period 305 of updating the a priori state vector.

[0121] If at the current moment in time an a posteriori estimate of the state vector is obtained, then vector 422 is output as the current value of vector 420, and matrix 423 is output as the current value of covariance matrix 421.

[0122] Otherwise, if the posterior estimate is not available at the current time, the prior vector 405 is output as the vector 420, and the prior matrix 412 is output as the current value of the matrix 421.

[0123] To reduce the error in calculating the a priori estimate 405, an additional software module 415 can be used, in which the current value of the scaling factor of the course gyroscope is estimated.

[0124] The integrals 411 from the gyroscope measurements, in which the zero offsets and the last obtained a posteriori estimate 422 are corrected, are fed to the module input. The software module 415 is called immediately after receiving the next a posteriori estimate 422 and the current value of k is updated. Ytscaling factor of the course gyro.

[0125] Additionally, the claimed device can be structurally and functionally combined with the SNS signal receiver into one device, or can be structurally implemented using standard units and hardware resources of the SNS signal receiver.

[0126] Additionally, the claimed device can be structurally and functionally combined with the vehicle multimedia system into one device, or can be structurally implemented using standard units and hardware resources of the vehicle multimedia system.

[0127] Additionally, the claimed device can be structurally and functionally combined with a communication device into one device, or can be structurally implemented using standard units and hardware resources of the communication device.

[0128] In the claimed invention, due to the analysis of the mathematical foundations of navigation algorithms, without changing the frequency of updating the navigation solution, and without increasing the power of the computing device, the frequency of measurements of the IMU is significantly increased.

[0129] Additionally, the claimed invention makes it possible to reduce the frequency of the navigation solution and the power of the computing device while maintaining the frequency of IMU measurements and the specified accuracy of the navigation solution.

Claims

Formula 1. A method for combining navigation measurements obtained from an inertial measurement unit (IMU) and from physical quantity meters functionally linked to the navigation parameters of a vehicle (V) to obtain integrated estimates of the V navigation parameters (hereinafter referred to as integrated estimates) at discrete points in time with a constant period, using discrete measurements of the IMU obtained with a constant period, the duration of which is two or more times shorter than the constant period for obtaining integrated estimates, characterized in that: a. to determine the discrete values ​​of the moments in time of obtaining integrated estimates, hereinafter designated as epochs, the time scale of the computing device is used, for each epoch its integrated estimate is calculated, and for each integrated estimate a time stamp is assigned in the form of a digital value of the epoch of obtaining this estimate; b.for a period of time equal to the period of calculation of the integrated estimate, the definite integrals of the discrete measurements of the IMU are calculated, with the previous epoch being used as the lower limit of integration, and the current epoch being used as the upper limit of integration; c. for numerical integration of a system of ordinary differential equations of rigid body kinematics for a period of time equal to the period of calculation of the integrated estimates, the calculated definite integrals of the measurements of the IMU are used, with the previous epoch being used as the lower limit of integration, the current epoch being the final moment of integration, and the integrated estimate calculated for the previous epoch being used as the initial conditions of the numerical integration, with the result of this integration being an estimate of the increments of the navigation parameters of the vehicle for the current epoch; d. to calculate the integrated estimate for the current epoch, hereinafter referred to as the a priori estimate, and its covariance matrix, hereinafter referred to as the a priori covariance matrix, the calculated estimates of the increments of the vehicle navigation parameters and the selected initial conditions are used; e. if measurements of the vehicle navigation parameters are available for the current epoch, then these measurements are used to correct the a priori estimate and to correct the a priori covariance matrix, while a new integrated estimate, hereinafter referred to as the a posteriori estimate, is calculated for the current epoch, and a new covariance matrix, hereinafter referred to as the a posteriori covariance matrix, is calculated; f.for the current epoch, a complex estimate is selected that will be used as the initial conditions for solving the system of ordinary differential equations of rigid body kinematics for calculating the complex estimate at the time immediately following the current epoch, hereinafter referred to as the next epoch, with the selection being made as follows: if the a posteriori estimate and its covariance matrix have already been calculated for the current epoch, then they are used as the initial conditions, otherwise the a priori estimate and its covariance matrix are used as the initial conditions; g. as the output complex estimate of the vehicle navigation parameters and its covariance matrix for the current epoch, the complex estimate and its covariance matrix selected as the initial conditions for calculating the complex estimate for the next epoch are issued to external data consumers.

2. The method according to paragraph 1, characterized in that a single-antenna receiver of signals from satellite navigation systems is used to measure the navigation parameters of the vehicle, the antenna of which is installed on the body of the vehicle, while periodically measuring the coordinates and components of the antenna velocity vector.

3. The method according to paragraph 1, characterized in that for measuring the navigation parameters of the vehicle, a two-antenna receiver of signals from satellite navigation systems is used, the two antennas of which are installed on the body of the vehicle, while periodically measuring the coordinates and components of the velocity vector of one of the antennas, and the values ​​of two angles that define the direction of the segment connecting the two antennas.

4. The method according to paragraph 1, characterized in that, for measuring the navigation parameters of the vehicle, data from a sporadic navigation measuring device designed with the ability to detect objects surrounding the vehicle are used.

5. The method according to paragraph 1, characterized in that the value of the vehicle speed is obtained from the vehicle’s on-board information network.

6. The method according to claim 1, characterized in that the angle of rotation of the vehicle wheel is measured by a multi-turn encoder sensor.

7. The method according to claim 1, characterized in that the integrated assessment includes data corresponding to: a. three coordinates of the IMU relative to the ECEF coordinate system; b. three projections of the IMU velocity vector onto the axes of the ECEF coordinate system; c. four elements of the IMU orientation quaternion relative to the ECEF coordinate system; d. three components of the zero offsets for three IMU gyroscopes; e. three components of the zero offsets for three IMU accelerometers.

8. The method according to paragraph 7, characterized in that an additional evaluation is made of the scale factor of the gyroscope measuring the projection of the angular velocity vector of the vehicle onto the axis of its course rotation.

9. The method according to paragraph 7, characterized in that the updating of the orientation quaternion in the a priori estimate and the correction of the quaternion in the a posteriori estimate are performed in a multiplicative manner.

10. A device for integrating navigation measurements, comprising: a. a computing device (hereinafter referred to as the CDM); b. a serial interface transceiver for connecting an inertial measurement unit; c. a serial interface transceiver for connecting a receiver of signals from satellite navigation systems (hereinafter referred to as the SNS); d. a pulse signal receiver for connecting a pulse synchronization signal generated by the SNS signal receiver; e. a serial interface transceiver for connecting a sporadic navigation parameter meter; f. a CAN interface transceiver for connection to the on-board CAN network of the vehicle; g. a device for reading measurements from the wheel angle sensor of the vehicle; h.a serial interface transceiver for transmitting the results of aggregating navigation measurements to external consumers and for exchanging data between the aggregation device and external consumers of control commands and status check requests, characterized in that it contains a time scale generation unit VU, configured to ensure synchronization of the VU time scale with the SNS time scale using synchronization pulses generated by the SNS signal receiver, so that the digital value of the moment of time of occurrence of the next synchronization pulse, measured on the VU time scale, is maintained equal to the digital value of the moment of time of occurrence of this same pulse, received from the SNS signal receiver, which measured this value relative to the SNS time scale.

11. The device according to item 10, characterized in that it is structurally and functionally combined with the SNS signal receiver into one device, or structurally implemented using standard units and hardware resources of the SNS signal receiver.

12. The device according to item 10, characterized in that it is structurally and functionally combined with the vehicle multimedia system into one device, or structurally implemented using standard units and hardware resources of the vehicle multimedia system.

13. The device according to item 10, characterized in that it is structurally and functionally combined with the communication device into one device, or structurally implemented using standard units and hardware resources of the communication device.

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