CN-116007622-B - Unmanned ship INS and GPS integrated navigation optimization method

CN116007622BCN 116007622 BCN116007622 BCN 116007622BCN-116007622-B

Abstract

The invention discloses an optimization method for integrated navigation of an unmanned aerial vehicle INS and a GPS, which utilizes a double-propeller unmanned aerial vehicle power model obtained by the relation between an unmanned aerial vehicle power signal and thrust to improve the navigation precision of an inertial navigation system, thereby improving the integrated navigation precision of the unmanned aerial vehicle INS/GPS. When global positioning system signals are available, the particle filter algorithm is used as a data fusion algorithm for the INS/GPS integrated navigation system. Under the condition that the GPS signal is disconnected or fragile, the dynamic model of the double-propeller unmanned ship is used as an event trigger switch to perform effective acceleration integration, so that the inertial navigation system of the unmanned ship can be positioned with high precision. The method effectively solves the problems that the positioning accuracy of the INS navigation system is low and the effective time is short due to the fact that the acceleration error measured by the economic inertial navigation system is large under the influence of external environment when the unmanned ship sails.

Inventors

YANG XIAOFEI
YAN XIN
Feng Beizhen
SHE HONGWEI
HU XIANGBING
YE HUI
LIU WEI
LI JIANZHEN

Assignees

江苏科技大学

Dates

Publication Date: 20260508
Application Date: 20230105

Claims (5)

1. The optimization method for the unmanned ship INS and GPS integrated navigation is characterized by comprising the following steps of: S1, acquiring starting point position information of an unmanned ship, and carrying out attitude calculation and attitude coordinate conversion on inertial navigation data under a carrier coordinate system b to acquire the attitude of the unmanned ship under a navigation coordinate system n; S2, judging whether the unmanned ship needs to perform effective acceleration integration or not by introducing a power model and triggering by using an event: If the integration of effective acceleration is needed, the integration of inertial navigation accelerometer data corresponding to the y b axis and the z b axis of the carrier coordinate system b is ignored, and the integration of inertial navigation accelerometer data corresponding to the x b axis is only performed to obtain the actual course speed of the unmanned ship Obtaining corresponding speed under a navigation coordinate system n through coordinate rotation ; If the integration of the effective acceleration is not needed, the speed under the n system is the corresponding speed at the last moment; s3, integrating the north-oriented speed, the east-oriented speed and the sky-oriented speed of the n system respectively to obtain the position information of the unmanned ship INS; s4, checking whether the GPS signal is interrupted or fragile: if the GPS signal is interrupted or fragile, the position information obtained in the step S3 is directly output; If the GPS signal is normal, carrying out particle filtering processing on the position information obtained in the step S3 and the position information of the GPS; s5, acquiring longitude and latitude position information of integrated navigation; in step S3, the integral equation of the longitude, latitude, and altitude of the INS is: ; ; ; And h is longitude, latitude and altitude respectively, And h 0 is the initial longitude, latitude and altitude respectively, R M is the earth meridian radius, and R N is the earth meridian radius; in step S4, the particle filter processing is as follows: (1) Establishing a state transfer equation of unmanned ship motion: ; Wherein the method comprises the steps of And The north speed and the east speed at the current moment under the n series are respectively, and the rand and the GPS speed Estimated speed of system from last moment In relation, the number of sampling particles is N, Evenly distributed over the interval, wherein the parameter k 1 ,k 2 >0; (2) Calculation of weight of each particle (i) to determine the individual particle And The distance between the longitude and the latitude of the reference position is dis, and the weight (i) is expressed as: ; weight normalization: ; for the normalized weight, sum_weight is the accumulated value of the weight, and the longitude and latitude of the reference position L S is: ; Wherein k 3 is a weight parameter, and ; (3) Estimating longitude and latitude values: ; ; (4) Setting a weight threshold weight_min when at least When the weight of the particle is smaller than the threshold value, the parameters k 1 and k 2 in the value interval are adjusted to resample so as to be smaller than The weight of the particle is less than weight_min. Wherein the method comprises the steps of If the part of particles with the weight less than weight_min is distributed at the left side of the value interval, k 1 is properly adjusted to be large, otherwise, k 2 is properly adjusted to be small.
2. The method of claim 1 wherein in step S1, the position information of the starting point of the unmanned ship is the real-time measurement point of the existing known point or the measuring instrument, the INS uses the position information as the integral starting point of the position information, the navigation coordinate system n is the northeast-oriented coordinate system, and the posture under the unmanned ship n is , wherein, For a real-time heading angle, For a real-time pitch angle, The real-time roll angle is given by inertial navigation measurement in real time.
3. The method for optimizing integrated navigation of an unmanned aerial vehicle INS and a GPS according to claim 1, wherein in step S2, the power model of the unmanned aerial vehicle is a mathematical relationship between a power control signal of the unmanned aerial vehicle and a thrust force generated by a propeller, and for a double-propeller unmanned aerial vehicle, the power model is: ; Wherein S L and S R are control signals of left and right propellers respectively, F is a relation between thrust force F of a single propeller and the control signals, Is the sum of the thrust of the left propeller and the right propeller.
4. The method for optimizing integrated navigation of unmanned aerial vehicle INS and GPS of claim 3, wherein the method is characterized by comparing the previous time And the current time If the value of (1) Within a threshold range In which the acceleration integration is stopped, whereas the effective acceleration integration is started, in which ;
5. The method for optimizing integrated navigation of unmanned aerial vehicle INS and GPS according to claim 2, wherein in step S2, when effective acceleration integration is required, the b direction of the carrier coordinate system is right-front-up, and the b-to-n-system posture conversion matrix is: ; Thus, V n under n is: ; Three velocity components of the effective velocity V b are converted into the effective velocity V n under the n system through the gesture conversion matrix, and the three velocity components are respectively effective velocities in the east direction, the north direction and the sky direction under the n system.

Description

Unmanned ship INS and GPS integrated navigation optimization method Technical Field The invention relates to the technical field of unmanned ship integrated navigation, in particular to an optimization method for unmanned ship INS and GPS integrated navigation. Background Navigation systems are one of the most important components of unmanned boat systems, and generally determine the accuracy and effectiveness of unmanned boat operations. The integrated navigation system composed of the global positioning system (Global Positioning System, GPS) and the inertial navigation system (Inertial Navigation System, INS) not only can overcome the defects existing in each system independently, but also can fully exert the advantages of each system and improve the precision and reliability of the system, so that the integrated navigation system is more commonly used. However, the influence of external factors such as wind, waves and ocean currents and internal factors such as IMU performance in the actual sailing process is considered, the INS navigation accuracy is low, the effective time is short, and when the GPS signal is interrupted or fragile, the accuracy of unmanned ship INS/GPS integrated navigation is low, and the navigation error is large. Disclosure of Invention Aiming at the problems, the invention aims to provide an optimization method for integrated navigation of an unmanned ship INS and a GPS, which solves the problems of low positioning accuracy and short effective time of an unmanned ship INS navigation system and improves the positioning accuracy of the INS and the GPS integrated navigation. The technical scheme is that the optimization method for the unmanned ship INS and GPS integrated navigation comprises the following steps: S1, acquiring starting point position information of an unmanned ship, and carrying out attitude calculation and attitude coordinate conversion on inertial navigation data under a carrier coordinate system b to acquire the attitude of the unmanned ship under a navigation coordinate system n; S2, judging whether the unmanned ship needs to perform effective acceleration integration or not by introducing a power model and triggering by using an event: If the integration of effective acceleration is needed, the integration of inertial navigation accelerometer data corresponding to the y b axis and the z b axis of the carrier coordinate system b is ignored, and the integration of inertial navigation accelerometer data corresponding to the x b axis is only performed to obtain the actual course speed of the unmanned ship Obtaining corresponding speed under the navigation coordinate system n through coordinate rotation If the integration of the effective acceleration is not needed, the speed under the n system is the corresponding speed at the last moment; s3, integrating the north-oriented speed, the east-oriented speed and the sky-oriented speed of the n system respectively to obtain the position information of the unmanned ship INS; s4, checking whether the GPS signal is interrupted or fragile: if the GPS signal is interrupted or fragile, the position information obtained in the step S3 is directly output; If the GPS signal is normal, carrying out particle filtering processing on the position information obtained in the step S3 and the position information of the GPS; s5, acquiring longitude and latitude position information of the integrated navigation. Further, in step S1, the position information of the starting point of the unmanned ship is the current known point or the real-time measurement point of the measuring instrument, the INS uses this as the integration starting point of the position information, the navigation coordinate system n is the east-north-sky-direction coordinate system, the attitude under the unmanned ship n is att= [ ψθphi ] T, where ψ is the real-time heading angle, θ is the real-time pitch angle, and Φ is the real-time roll angle, which are given in real time by inertial navigation measurement. Further, in step S2, the power model of the unmanned ship is a mathematical relationship between the power control signal of the unmanned ship and the thrust generated by the propeller, and for a double-propeller unmanned ship, the power model is: τu＝f(sL)+f(sR); wherein, S L and S R are control signals of the left propeller and the right propeller respectively, F is a relation between the thrust force F of the single propeller and the control signals, and τ u is the sum of the thrust forces of the left propeller and the right propeller. The power model of the unmanned ship is obtained through experimental data, only real-time control signal data and real-time thrust data are needed to be obtained, and the power model of the unmanned ship is obtained through a data fitting mode. In unmanned ship experiments, inertial navigation accelerometers are greatly affected by the environment, so that acceleration data errors are large, and speed information obtained throu