CN115585807B - GNSS/INS integrated navigation method based on machine learning - Google Patents

Abstract

The application discloses a GNSS/INS integrated navigation method based on machine learning, which comprises the steps of receiving GNSS signals from a GNSS signal source, generating GNSS data, coupling an INS system to a GNSS receiver, generating IMU signals by using an IMU sensor, generating IMU data, integrating the IMU data with the GNSS data, and generating INS data according to the integrated IMU data and the GNSS data; acquiring geographic location data generated by a GNSS receiver and assistance data other than the geographic location data; training a machine learning model with assistance data to predict positioning errors based on the residuals and satellite direction information; a data representation of a machine learning model representing a trained version of the machine learning model is output. The reliability of application, the reliability of data and the robustness of signal processing are improved, and the GNSS/INS integrated navigation service with full time period, full direction and full space is provided for users.

Description

GNSS/INS integrated navigation method based on machine learning

Technical Field

The application relates to the field of computers, in particular to the field of machine learning, and more particularly relates to a GNSS/INS integrated navigation method based on machine learning.

Background

Global navigation satellite system, GNSS, receivers are widely used to provide autonomous geospatial positioning, and advances in integration have led to GNSS receivers being available as Integrated Circuits (ICs), for example as a single chip system on a chip (SOC). Their low cost and wide availability makes GNSS receivers of high general applicability not only in professional fields like navigation positioning, but also in consumer fields like smartphones, tablet devices, cameras, etc. Examples of global navigation satellite systems include, but are not limited to, GPS, GLONASS and beidou.

However, due to multipath propagation of satellites, GNSS receivers are prone to positioning errors, a phenomenon also known as "multipath reception", which may estimate the distance to the transmitting satellite in an erroneous way when the GNSS receiver tracks multipath signals, for example by reflecting the transmitted radio signals off of a close-range building. This phenomenon is particularly present in urban environments, where line of sight (LOS) to satellites may be obstructed, and several radio signals received by GNSS receivers may be multipath signals.

Several technical solutions have been studied to alleviate the multipath problem, including the design of radio signals that provide better multipath mitigation at the system level, and dedicated signal processing techniques at the receiver side. The main drawbacks of these methods include an increase in the complexity of the GNSS receiver and thus an increase in the cost.

Machine learning is a widely used artificial intelligence technique, and when a machine learning model is generated, different equipment parameter combinations result in different learning effects of the machine learning model. At present, all model parameter combinations within a certain range are generally searched according to a certain step length, machine learning models respectively corresponding to the model parameter combinations are sequentially trained and verified, namely, training and verification are performed in a serial mode, and the optimal model parameter combinations are determined according to verification results.

Disclosure of Invention

The application aims to solve at least one technical problem mentioned in the background, and provides a GNSS/INS integrated navigation method based on machine learning, which can exert the advantages of each positioning system, improve the reliability of application, the reliability of data and the robustness of signal processing, and provide full-time, full-scope and full-space GNSS/INS integrated navigation service for users.

A machine learning based GNSS/INS integrated navigation method, comprising:

a GNSS receiver configured to receive GNSS signals from a GNSS signal source at an antenna coupled to the GNSS receiver, generate GNSS data in response to the GNSS signals, and transmit the GNSS data to the INS system;

an INS system configured to be coupled to the GNSS receiver, generate IMU signals with the IMU sensor, generate IMU data in response to the IMU signals, integrate the IMU data with the GNSS data in a navigation processing unit of the INS system, and generate INS data from the integrated IMU data and GNSS data;

the machine learning step includes: acquiring geographic position data generated by a GNSS receiver and assistance data other than the geographic position data, the assistance data comprising a residual RES associated with the satellite, satellite direction information AZ, EL indicating the direction of the satellite relative to the GNSS receiver; training a machine learning model using the assistance data to predict a positioning error based on the residual and satellite direction information; a data representation of a machine learning model representing a trained version of the machine learning model is output.

In a specific embodiment of the integrated navigation method, reference data TP of the GNSS receiver is obtained, wherein the reference data represents a reference geographical position of the GNSS receiver.

In a specific embodiment of the integrated navigation method, for respective instances of the geographical position data and the reference data, a positioning error is determined as a difference between the calculated geographical position and the reference geographical position.

In a specific embodiment of the integrated navigation method, the geographical position data represents a geographical position calculated by a GNSS receiver.

In a specific embodiment of the integrated navigation method, the residual RES is a pseudo-range residual or an innovative residual obtained from a kalman filter performed by the GNSS receiver.

In a specific embodiment of the integrated navigation method, the satellite direction information includes an azimuth angle AZ and an elevation angle EL of the satellite in the sky at the calculated geographic location.

In a specific embodiment of the integrated navigation method, the method comprises representing the azimuth AZ, the elevation EL and the residual RES as data tuples representing spherical coordinates in a spherical coordinate system.

In a specific embodiment of the integrated navigation method, the method further comprises converting the spherical coordinates into cartesian coordinates in an earth-centered earth-fixed coordinate system, wherein the cartesian coordinates are used for training of the machine learning model.

In a specific embodiment of the integrated navigation method, an integration filter coupled to the GNSS receiver and the INS system and configured to integrate the INS data and the GNSS data is further included.

In a specific embodiment of the integrated navigation method, the INS system is further configured to: transmitting INS data to a GNSS receiver; and integrate the INS data with the GNSS signals to generate GNSS data.

In a specific embodiment of the integrated navigation method, the navigation processing unit is configured to integrate IMU data and GNSS data in the signal domain.

In one particular embodiment of the integrated navigation method, when the GNSS data is in the location domain, the integration in the IMU processor generates a loosely coupled GNSS with INS integration.

In one particular embodiment of the integrated navigation method, when the GNSS data is in the survey domain, the integration in the IMU processor generates a GNSS that is closely coupled to the INS integration.

In one specific embodiment of the integrated navigation method, when the GNSS data is in the signal domain, the integration in the IMU processor generates an ultra-tightly coupled GNSS with INS integration.

In one particular embodiment of the integrated navigation method, wherein the IMU sensor is configurable to enhance IMU sensor performance (accuracy, dynamics, availability, etc.), particularly for low cost and small size memsis.

A machine learning based GNSS/INS integrated navigation system comprising:

a GNSS receiver configured to receive GNSS signals from a GNSS signal source at an antenna coupled to the GNSS receiver, generate GNSS data in response to the GNSS signals, and transmit the GNSS data to the INS system;

an INS system configured to be coupled to the GNSS receiver, generate IMU signals with the IMU sensor, generate IMU data in response to the IMU signals, integrate the IMU data with the GNSS data in a navigation processing unit of the INS system, and generate INS data from the integrated IMU data and GNSS data;

an integration filter configured to be coupled to a GNSS receiver and an INS system and to integrate the INS data and the GNSS data;

the system performs the method described above when running.

The beneficial effects of the application include: the integrated GNSS/INS system can improve the performances of the IMU sensor and the GNSS receiver. Thus, as two complementary positioning techniques, GNSS/INS integration may take advantage of each positioning system, INS bias may be calibrated by GNSS signals, GNSS navigation signal disruptions may be mitigated by INS, meaning including not only improving availability, including but not limited to spanning GNSS navigation signal disruptions, rejecting reliability of data having outliers, and robustness of signal processing. The novel positioning and navigation application service system based on the machine learning model improves the dynamic characteristics and anti-interference performance of the GNSS receiver, improves the capability of tracking satellites in a resource-limited environment, is also beneficial to improving the calibration of an INS system, the air alignment of an inertial navigation system, the stability of a height channel of the inertial navigation system and the like, effectively improves the performance and the precision of the inertial navigation system, and provides full-time, all-dimensional and all-space GNSS/INS combined navigation service for users.

Drawings

The accompanying drawings, which are included to provide a further understanding of the application, are incorporated herein and constitute a part of this specification, and are incorporated into the specification by way of illustration, and not to be construed as limiting the application.

FIG. 1 is a flow chart of training and validation operations of a machine learning model in an embodiment of the application.

Detailed Description

Example 1:

provided is a GNSS/INS integrated navigation system based on machine learning, which is characterized by comprising:

a GNSS receiver configured to receive GNSS signals from a GNSS signal source at an antenna coupled to the GNSS receiver, generate GNSS data in response to the GNSS signals, and transmit the GNSS data to the INS system;

an INS system configured to be coupled to the GNSS receiver, generate IMU signals with the IMU sensor, generate IMU data in response to the IMU signals, integrate the IMU data with the GNSS data in a signal domain, and generate INS data from the integrated IMU data and GNSS data; is also configured to transmit the INS data to a GNSS receiver and integrate the INS data with GNSS signals to generate GNSS data

An integration filter configured to be coupled to a GNSS receiver and an INS system and to integrate the INS data and the GNSS data.

When the GNSS data is in the location domain, the integration generates a loosely coupled GNSS with INS integration in the IMU processor.

In the integrated navigation system, when GNSS data is in the survey domain, the integrated GNSS is generated in the IMU processor and is integrated with the INS tightly coupled.

In the integrated navigation system, when GNSS data is in a signal domain, ultra-tightly coupled GNSS with INS integration is generated in an IMU processor.

In the integrated navigation system, in which the IMU sensor may be configured to enhance IMU sensor performance (accuracy, dynamics, availability, etc.), particularly for low cost and small size memsis.

Example 2:

on the basis of the foregoing embodiments, a machine learning-based GNSS/INS integrated navigation method is provided, which specifically includes the following steps.

Reference data TP of the GNSS receiver is obtained, wherein the reference data represents a reference geographical position of the GNSS receiver.

The positioning error is determined as the difference between the calculated geographic location and the reference geographic location.

The geographic position data represents a geographic position calculated by the GNSS receiver.

The residual RES is a pseudo-range residual or an innovative residual obtained from the kalman filtering performed by the GNSS receiver.

The satellite direction information includes azimuth AZ and elevation EL of the satellite in the sky at the calculated geographic location.

The method comprises representing azimuth AZ, elevation EL and residual RES as data tuples representing spherical coordinates in a spherical coordinate system. The training data may comprise data tuples, each consisting of azimuth AZ, elevation EL and residual RES, which in turn may be considered to represent coordinates or vectors in a spherical coordinate system. That is, elevation and azimuth may indicate a direction to a satellite in units of degrees, while the residual may indicate an error in the direction to the satellite in units of meters, indicating an amplitude toward or away from the satellite.

Further comprising converting the spherical coordinates to Cartesian coordinates in an earth-centered earth fixed coordinate system, wherein the Cartesian coordinates are used for training of the machine learning model. For training, the acquisition of azimuth AZ, elevation EL and residual RES is beneficial, wherein the represented data elements have the same physical meaning. Such a representation may be obtained by converting azimuth, elevation and residual errors into cartesian coordinates centered on earth, whereas if represented in an earth-fixed (ECEF) coordinate system, e.g. XYZ coordinate system, all elements may in this case have the same physical meaning, e.g. meters, kilometres etc.

Acquiring geographic position data generated by a GNSS receiver and assistance data other than the geographic position data, the assistance data comprising a residual RES associated with the satellite, satellite direction information AZ, EL indicating the direction of the satellite relative to the GNSS receiver; training a machine learning model using the assistance data to predict a positioning error based on the residual and satellite direction information; a data representation of a machine learning model representing a trained version of the machine learning model is output.

The machine learning model may be used to correct the output of the GNSS receiver during operation, more specifically, to correct the calculated geographic position by applying the machine learning model to assistance data to obtain a positioning error of the calculated geographic position, which may be used as a correction term for the calculated geographic position and may be applied to the calculated geographic position to produce a corrected geographic position, which may be more accurate than the originally calculated geographic position, in the sense of a geographic position that is less deviated from the actual geographic position, i.e. has a smaller positioning error relative to the actual geographic position.

As shown in fig. 1, the further optimization scheme further comprises the following steps.

The machine learning model is generated by: a combination of best model parameters corresponding to the machine learning model to be generated is determined based on the validation score, and a machine learning model corresponding to the combination of best model parameters is generated.

Some optional modes of the machine learning model further include: training and validation operations are performed using Map tasks in a Map-Reduce model of the Hadoop distributed computing framework, and model generation operations are performed using Reduce tasks in a MapReduce model of the distributed computing framework.

Training and validation of the machine learning model may be performed using the Map-Reduce model of the Hadoop distributed computing framework. The training and validation operations may be performed using Map tasks in Hadoop, and the model generation operations may be performed using Reduce tasks in Hadoop.

Determining the optimal model parameter combination corresponding to the machine learning model to be generated includes: after training and verifying the machine model corresponding to the model parameters, respectively, a plurality of times, calculating average parameter values corresponding to a plurality of verification scores of the machine learning model, respectively; using the average parameter value as a reference model parameter value; and determining an optimal model parameter combination corresponding to the machine learning model to be generated based on the reference model parameter values. For example, when a user sends a request for generating a machine learning model to a server through a terminal, and then trains and validates the machine learning model in parallel, the server returns reference model parameter values, that is, average values of validation scores corresponding to the model parameter combinations, to the user terminal, and the user determines an optimal model parameter combination corresponding to the machine learning model to be generated from the reference model parameter values.

The novel positioning and navigation application service system based on the machine learning model improves the dynamic characteristics and anti-interference performance of the GNSS receiver, improves the satellite tracking capacity of the GNSS receiver in a resource-limited environment, is also beneficial to improving the calibration of an INS system, the air alignment of the inertial navigation system, the stability of a height channel of the inertial navigation system and the like, effectively improves the performance and the precision of the inertial navigation system, and provides full-period, all-dimensional and all-space GNSS/INS combined navigation service for users.

The above description is only of the preferred embodiments of the present application and is not intended to limit the present application, but various modifications and variations can be made to the present application by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the protection scope of the present application.

The application is a well-known technique.

Claims (2)

1. The GNSS/INS integrated navigation method based on machine learning is characterized by comprising the following steps:

a GNSS receiver receives GNSS signals from a GNSS signal source at an antenna coupled to the GNSS receiver, generates GNSS data in response to the GNSS signals, and transmits the GNSS data to the INS system;

the INS system is coupled to the GNSS receiver, generates IMU signals by using the IMU sensor, generates IMU data in response to the IMU signals, integrates the IMU data with the GNSS data in a signal domain by using a navigation processing unit of the INS system, and generates INS data according to the integrated IMU data and the GNSS data; the INS system also transmits INS data to the GNSS receiver and integrates the INS data with GNSS signals to generate GNSS data;

an integration filter coupled to the GNSS receiver and the INS system, integrating the INS data and the GNSS data;

the integrated navigation method performs the following machine learning steps: acquiring geographic position data generated by a GNSS receiver and assistance data other than the geographic position data, wherein the geographic position data represents a geographic position calculated by the GNSS receiver, the assistance data comprising a residual RES associated with satellites, the residual RES being an innovative residual obtained from a kalman filter performed by the GNSS receiver, the satellite direction information comprising an azimuth AZ and an elevation EL of the satellite in the sky at the calculated geographic position, satellite direction information indicating the direction of the satellite relative to the GNSS receiver; training a machine learning model using assistance data to predict a positioning error based on residual and satellite direction information, wherein azimuth AZ, elevation EL and residual RES are represented as data tuples representing spherical coordinates in a spherical coordinate system, the spherical coordinates are converted into cartesian coordinates in an earth-centered earth-fixed coordinate system for training of the machine learning model, the positioning error being a difference between a geographic position calculated by a GNSS receiver and a reference geographic position of the GNSS receiver, the positioning error being used as a correction term for the geographic position calculated by the GNSS receiver; a data representation of a machine learning model representing a trained version of the machine learning model is output.

2. A machine learning based GNSS/INS integrated navigation system characterized by comprising:

a GNSS receiver configured to receive GNSS signals from a GNSS signal source at an antenna coupled to the GNSS receiver, generate GNSS data in response to the GNSS signals, and transmit the GNSS data to the INS system;

an INS system configured to be coupled to the GNSS receiver, generate IMU signals with the IMU sensor, generate IMU data in response to the IMU signals, integrate the IMU data with the GNSS data in a navigation processing unit of the INS system, and generate INS data from the integrated IMU data and GNSS data;

an integration filter configured to be coupled to a GNSS receiver and an INS system, integrating the INS data and the GNSS data;

the system performs the method of claim 1 when run.