139 results about "Position error" patented technology

The invention provides a method and device for determining the collision probability of a low-altitude aircraft to a ground obstacle, and relates to the field of aircraft collision. The method comprises the following steps: determining relative position vectors of a mass center position of a low-altitude aircraft in a three-dimensional Cartesian coordinate system corresponding to a center position of a ground obstacle in a longitude direction, a latitude direction and a gravity reverse direction; determining a relative position error matrix of the low-altitude aircraft and the ground obstacle based on an error value between the nominal flight path data and the actual flight path data of the low-altitude aircraft; processing the relative position vector and the position error matrix corresponding to the reverse direction of the gravity to obtain a first integral parameter and a second integral parameter in the reverse direction of the gravity; and calculating the relative position vectors corresponding to the longitude direction and the latitude direction, the first integral parameter and the second integral parameter to obtain the collision probability of the low-altitude aircraft and the ground obstacle. According to the invention, the complex three-dimensional integral operation is simplified, and the calculation efficiency and precision are improved.

The invention relates to the technical field of unmanned ship trajectory tracking, in particular to an unmanned ship trajectory tracking control method based on self-adaptive dynamic event triggering, and establishes a kinematic model, an expected trajectory and a position error dynamic equation of an unmanned ship. A dynamic event triggering mechanism and self-adaptive dynamic planning are combined, a cost function containing a dynamic event triggering control law and a Hamilton function are given, and resource consumption of an unmanned ship controller is reduced. And an unknown cost function and an optimal control law are approximately obtained by using the good nonlinear approximation capability of the neural network. And applying the calculated optimal control law to a control system of the unmanned ship, and driving the unmanned ship to perform accurate tracking according to a predetermined trajectory. The method provided by the invention can effectively improve the precision of trajectory tracking of the unmanned ship, and saves system calculation and communication resources.

The invention provides an aircraft collision probability acquisition method and device based on non-central chi-square distribution. Relates to the technical field of aircraft collision. The method comprises the following steps of: acquiring a protection box radius of a spherical collision protection box which takes the mass center of any aircraft as a sphere center on the basis of area radiuses of collision areas of two aircrafts; for any aircraft, under a three-dimensional Cartesian coordinate system, the relative position vector of the mass center position of the aircraft relative to the mass center position of the other aircraft is determined, and nominal flight path data and actual flight path data in the cruising stage are obtained; determining a relative position error matrix and a corresponding characteristic value based on an error value between the nominal flight path data and the actual flight path data corresponding to each aircraft; and processing the relative position vector, the relative position error matrix and the corresponding characteristic values by adopting a preset three-degree-of-freedom non-central chi-square distribution algorithm to obtain the collision probability between the two aircrafts. The method improves the efficiency and accuracy of collision probability estimation.

The invention belongs to the technical field of aircraft design, and particularly relates to a static pressure source position error correction method, which comprises the following steps: performing theoretical appearance static pressure source position error correction analysis based on current numerical simulation and flight test state points to obtain a static pressure source pressure coefficient Cp, a flight test static pressure source pressure coefficient Cp0 and an actually measured static pressure source pressure coefficient Cp1; static pressure source position error correction data optimization is carried out, a static pressure source position error correction relation Cp'during real flight of the theoretical appearance is obtained, and static pressure source position error correction is carried out; appearance measurement, CFD simulation and test flight data comprehensive application are realized; correcting the position error of the static pressure source into a function of Mach number, angle of attack and height; and not only can the requirement of the CCAR25 part for the air pressure height be met, but also the requirement of RVSM operation in AC-21-13 for the error of a height measurement system can be met. The method can be used for atmospheric data correction of transportation aircrafts, and atmospheric data correction of other aircrafts can be used for reference.

The invention relates to a polarization / inertial navigation integrated navigation method of an embedded unmanned aerial vehicle kinetic model, which comprises the following steps of: expanding a three-dimensional attitude error angle, a speed error, a position error, an angular speed error and a thrust coefficient error of the unmanned aerial vehicle kinetic model into inertial navigation state parameters as state quantities to form system state quantities; establishing a system state equation; taking the course angle calculated by the polarization sensor and the attitude, the speed and the position of the unmanned aerial vehicle kinetic model as quantity measurement, and establishing a system measurement equation; establishing a credibility discrimination function of polarization course measurement based on course angle innovation, and designing a combined navigation mode switching and thrust coefficient error feedback strategy; a Kalman filtering method is used for estimating system state quantities, namely an inertial navigation attitude misalignment angle, a speed error and a position error, and an attitude error angle, a speed error, a position error and a thrust coefficient error of an unmanned aerial vehicle dynamic model, so that correction of the attitude and the position of the unmanned aerial vehicle is realized.

The invention discloses a self-adaptive control method and system for a two-stage swing type rotor aircraft hoisting system, and the method comprises the steps: building a two-stage swing type rotor aircraft hoisting system dynamics model, taking the rotor aircraft hoisting system to simultaneously drive four rotors to reach an expected position and inhibit the swing of a lifting hook and a load as a control target, and carrying out the control of an outer ring, generalized lifting hook and load position signals are introduced, an energy storage function is constructed, and the energy storage function is dissipated through the generalized lifting hook and load position signals; the outer loop tracking error is converted into a generalized load position error, and a shaped total storage function is obtained; and designing an adaptive law to estimate the load mass online, obtaining a reconstruction energy function of outer ring control, further obtaining an outer ring controller, and realizing control of system displacement and a load swing angle. By adopting a generalized displacement method of the lifting hook and the load and reconstructing an energy storage function, the dynamic coupling characteristic among the four rotors, the lifting hook and the load is remarkably enhanced, and therefore the transient response characteristic of the system is effectively improved.

The invention discloses an air-drop and air-delivery large unmanned aerial vehicle ultra-low-altitude flight control system and method, and belongs to the field of flight control systems, and the system comprises a flight control module, a drop control module, an environment sensing module, a gravity center balance adjustment module and a data storage and interaction module. The flight control module is used for controlling the unmanned aerial vehicle to fly at an ultra-low altitude and ensuring that the flight attitude is stable, so that the position error is less than or equal to 10m, the height error is less than or equal to 2m and the flight path deviation is less than or equal to 10m when the unmanned aerial vehicle flies at the ultra-low altitude; the delivery control module is used for supporting a single delivery mode and a rapid continuous delivery mode, the maximum delivery of 1.2 tons of goods can be achieved, at most four pieces of goods can be delivered in a single flight, the delivery control module is compatible with parachute air delivery and non-parachute air delivery, and the delivery precision of the parachute air delivery at the high altitude of 300 meters is smaller than 100 meters. According to the invention, the unmanned aerial vehicle delivery precision can be improved, the stability is high, and material waste or incapability of accurately delivering to a target area is avoided.

The invention discloses an aircraft performance constraint compliance control method for contact type nondestructive testing, and belongs to the field of aircraft control. The implementation method comprises the following steps: introducing a saturated contact force controller into a position ring, and dynamically adjusting a confidence factor based on a position error to adaptively adjust normal rigidity and tangential flexibility; a zero-order control barrier function ZOCBF restrains the force controller, a three-dimensional dynamic safety boundary is established by using a sampling data mechanism, and the position, speed and contact force of the unmanned aerial vehicle are optimized and limited through quadratic programming; designing a hybrid torque controller in the attitude ring, and dynamically selecting a balance point through a binary logic switching strategy; the controller performs safety limitation on the attitude and angular velocity of the unmanned aerial vehicle in combination with zero-order control obstacle function constraint; monitoring a quaternion scalar component symbol in real time by defining a manifold switching set and a jump set; by fusing position loop and attitude loop control logic, compliance control during contact-type nondestructive testing of the unmanned aerial vehicle is realized.

A system and method for providing airport docking guidance for an aircraft includes supplying, from an airport gate database, airport gate data parking data indicative of gate position points and a gate aircraft parking headings. Aircraft data is supplied from an aircraft data source. Position error data is supplied from a position error receiving system. Airport gate data for one airport gate is retrieved. A processing system processes the aircraft data and the position error data to determine, for the one airport gate, an aircraft position point, an aircraft position deviation, and an aircraft heading deviation. The position error data may be supplied from a ground-based position error transmission system that at least selectively determines, from airport gate data and a ground-based position measurement system, the position error data and transmits the position error data.

The invention relates to a real-time cycle slip detection and restoration method for pseudo-satellite single-frequency observation, and the method comprises the following steps: data collection: obtaining observation data of adjacent epochs based on navigation signals of a receiver and a transmitter, and building a single-frequency carrier observation equation; carrying out time sequence differential processing, and carrying out subtraction on carrier phase observation values of adjacent epochs in combination with correction parameters; constructing a least square adjustment model, and performing parameter estimation by using the difference observed quantity; calculating a residual error after the test; introducing a statistical hypothesis to perform cycle slip detection and repair; and outputting a result. A least square adjustment model is constructed based on a time sequence difference model, and cycle slip detection and repair are realized through chi-square test of a post-test residual error, so that ambiguity initialization and position errors caused by false detection and missing detection are reduced, and the positioning precision and reliability of a pseudo satellite are improved.

The embodiment of the invention discloses a ship trajectory tracking event trigger control method and system with a performance game mechanism. The method comprises the following steps: S1, determining a position error and an attitude error corresponding to a ship; s2, designing a first performance index and a first value function; s3, designing a negative gradient estimated value, an optimal virtual control law and an online learning law of the first value function; s4, obtaining a dynamic reference signal and a dynamic control error; s5, defining a second performance index and a corresponding second value function; s6, designing a negative gradient estimated value, an optimal control input and an optimal trigger error corresponding to the second value function; s7, judging whether a triggering rule is met or not; and S8, changing the state of the ship to continuously judge whether the navigation task is completed or not, and if not, re-confirming the current state of the ship and returning to S1. The method solves the problems that in the prior art, it is difficult for an operator to obtain the best triggering effect and control precision, and the robustness of a ship trajectory tracking controller in marine environment interference is poor.

A position error monitoring method and control system of a rotating mechanism, in the position error monitoring method of the rotating mechanism, angular position data of an encoder of a first motor and an encoder of a second motor are acquired as first position data during a process; then, during a non-process, the relative speed between the first motor and the second motor is adjusted while the rotating mechanism remains in a rotating state, so that the relative position of the first rotating part and the second rotating part is dynamically adjusted to a preset calibration phase, and second position data are acquired during operation at the calibration phase; the transition from the process phase to the calibration phase can be completed without stopping the rotating mechanism, time loss and mechanical impact caused by frequent start and stop are avoided, and the operation efficiency and productivity of the rotating mechanism are improved; in addition, whether there is a transmission error is determined by calculating the deviation value between the second position data and the first position data, which helps to find faults in time and improve the consistency and yield of the process.

The invention discloses a position error monitoring method and a control system of a rotating mechanism, and the method comprises the steps: obtaining the angular position data of an encoder of a first motor and an encoder of a second motor as first position data in a process processing period; then, in the non-process treatment period, the relative speed between the first motor and the second motor is adjusted in the rotating state of the rotating mechanism, the relative position of the first rotating component and the relative position of the second rotating component are dynamically adjusted to the preset calibration phase, and second position data are obtained in the calibration phase operation period; transition from a process phase to a calibration phase can be completed without stopping the rotating mechanism, so that time loss and mechanical impact caused by frequent start and stop are avoided, and the operation efficiency and the productivity of the rotating mechanism are improved; in addition, whether transmission errors exist or not is judged by calculating the deviation value between the second position data and the first position data, faults can be found in time, and the consistency and yield of process processing are improved.

The present invention discloses a method for adaptive iterative learning collaborative control of a movable beam and an extrusion rod of an extruder, which relates to the technical field of multi-agent adaptive iterative learning collaborative control and includes the following steps: constructing a dynamic model of the movable beam and the extrusion rod; determining a control target; determining a position error tracking system according to the control target, and designing a controller and a parameter update law based on adaptive iterative learning control theory; and performing theoretical simulation verification. The present invention adopts the above-mentioned method for adaptive iterative learning collaborative control of a movable beam and an extrusion rod of an extruder, regards the movable beam and the extrusion rod of the extruder as intelligent bodies, and combines adaptive iterative learning control theory to effectively realize the collaborative control of the movable beam and the extrusion rod; the method has self-regulating control capabilities, can produce products with higher precision requirements under the influence of nonlinear factors, improve production efficiency and reduce production costs, and is easy to expand and adapt to production needs of different scales.

Method for determining vehicle odometry, comprising: Receiving initial sensor data by a controller (34) of a vehicle (10); Receiving second sensor data by the controller (34) of the vehicle (10); Determining an initial longitudinal position, an initial lateral position and an initial heading of the vehicle (10) by the controller (34) of the vehicle (10) using the first sensor data, wherein the first sensor data are generated by an inertial measurement unit (IMU) of the vehicle (10), a wheel speed sensor (WSS) and a steering angle sensor (SAS) of the vehicle (10); Determining a longitudinal position error, a lateral position error and a course error of the vehicle (10) by the controller (34) of the vehicle (10) using the second sensor data, wherein the second sensor data are generated by a radar device of the vehicle (10); Determining a longitudinal speed of the vehicle (10) using the first sensor data and the second sensor data; Determining a lateral speed of the vehicle (10) using the first sensor data and the second sensor data; Determining a yaw rate of the vehicle (10) using the first sensor data and the second sensor data; Correcting the initial longitudinal position, initial lateral position, and initial heading of the vehicle (10) using the longitudinal position error, lateral position error, and heading error, respectively, thereby generating a corrected longitudinal position, a corrected lateral position, and a corrected heading of the vehicle (10), wherein the corrected longitudinal position, the corrected lateral position, and the corrected heading of the vehicle (10) are determined using an adaptive filter, wherein the corrected longitudinal position is based on the enhancement of a longitudinal position, the corrected lateral position is based on the enhancement of a lateral position, and the corrected heading is based on the enhancement of a heading. where the adaptive filter is implemented using a multitude of equations, and the multitude of equations includes: Δxk + 1 = Δxk + gk, xex, k Δ yk + 1 = Δ yk + gk , yey , k Δ ψ k + 1 = Δ ψ k + gk , ψ e ψ , k where: k is a time step; Δx k+1 is a filtered term for the initial longitudinal position of the vehicle (10); Δy k+1 is a filtered term for the initial lateral position of the vehicle (10); Δψ k+1 is a filtered term for the initial course of the vehicle (10); Δx k is a change in the longitudinal position of the vehicle (10) at time k; Δy k is a change in the lateral position of the vehicle (10) at time k; Δψ k is a change in the course of the vehicle (10) at time k; G k,x is the strengthening of the longitudinal position at time k; G k,y is the strengthening of the transverse position at time k; G k,ψ is the strengthening of the price at time k; ex,k is the error of the longitudinal position at time k; e y,k is the error of the transverse position at time k; and e ψ,k is the error of the course at time k; where the longitudinal position error, the transverse position error, and the course error are calculated using the following equations: ex = vs ( 1 ) . dt − Δ x ˜ ey = vs ( 2 ) . dt − Δ y ˜ e ψ = atan2 (vs (2) vs (1)) − Δ ψ ˜ where: v s is the scene speed of the vehicle as detected by the radar (10); e x is the error term for the x-position (i.e., the longitudinal position) of the vehicle (10); e y is the error term for the y-position (i.e., for the lateral position) of the vehicle (10); e ψ is the error term for the course of the vehicle (10); Δ x ˜ Delta x-position with uncertainty; Δ y ˜ Delta y-position with uncertainty; Δ ψ ˜ Delta price with uncertainty; and t is time; and Steering the vehicle (10) using the corrected longitudinal position, the corrected lateral position and the corrected course.

The invention discloses an aircraft control method and device, electronic equipment, an aircraft and a storage medium. The method comprises the following steps: acquiring a target position corresponding to the multi-rotor aircraft, wherein rotors of the multi-rotor aircraft are provided with mounting angles; determining a position error corresponding to the current moment based on the target position of the multi-rotor aircraft and an actually measured position corresponding to the current moment fed back by the multi-rotor aircraft; determining a target acceleration corresponding to the current moment based on the position error corresponding to the current moment and an actually measured speed corresponding to the current moment fed back by the multi-rotor aircraft; and determining a target rotating speed corresponding to the current moment based on the target acceleration corresponding to the current moment, and controlling the multi-rotor aircraft to work based on the target rotating speed corresponding to the current moment. The method can improve the response speed and control bandwidth of the aircraft controlled in the horizontal direction.

The invention relates to a polarization / inertial navigation combined navigation method with an embedded unmanned aerial vehicle (UAV) dynamics model. The method comprises the following steps: taking the three-dimensional attitude error angle, velocity error, position error, angular velocity error and thrust coefficient error of the UAV dynamics model as state quantities and expanding them into inertial navigation state parameters to form system state quantities, and establishing a system state equation; taking the heading angle solved by a polarization sensor and the attitude, velocity and position of the UAV dynamics model as quantity measurements to establish a system measurement equation; establishing a credibility discriminant function of the polarization heading vector measurement based on heading angle innovation information, and designing a combined navigation mode switching and thrust coefficient error feedback strategy; and using a Kalman filtering method to estimate the system state quantities, namely the inertial navigation attitude misalignment angle, velocity error and position error, and the attitude error angle, velocity error, position error and thrust coefficient error of the UAV dynamics model, to achieve correction of the UAV attitude and position.

The invention provides a radar-to-air detection function level simulation model-based test evaluation method and system, and relates to the technical field of aerospace, and the method comprises the steps: editing a combat scene and combat platform attributes through a simulation control module, completing the registration of a simulation model, and generating a simulation scene deployment file; the performance evaluation test module evaluates a beam detection function and a power detection function according to a radar detection result; the evaluation of the beam detection function is realized by judging whether a target azimuth angle is within a radar beam irradiation range or not; the evaluation of the power detection function is realized by judging whether the target signal-to-noise ratio is greater than the minimum detectable signal-to-noise ratio; the evaluation of the speed detection function is realized by judging whether the radial speed of the target is located in a radar zero-crossing detection blind area and a maximum relative speed range or not; the evaluation of the air-to-air target detection precision performance is realized by judging whether a radar detection target position error and a target speed error are within a radar detection precision range or not.

The application discloses a satellite ranging auxiliary aircraft trajectory end positioning method, which comprises the following steps: S1, positioning the current position of the aircraft by using an inertial navigation system, and calculating the interval distance from the destination; then, calculating the estimation error of the aircraft positioning based on a Monte Carlo position error estimation method; S2, judging whether the aircraft enters the destination neighborhood range according to the interval distance, if yes, entering step S3, otherwise, entering step S4; S3, judging whether the estimation error is greater than the end threshold, if yes, entering step S5, otherwise, returning to step S1; S4, judging whether the estimation error is greater than the process threshold, if yes, entering step S5, otherwise, returning to step S1; S5, positioning the aircraft by using a single satellite fusion positioning algorithm based on an unscented Kalman filter, and returning to step S1 after a preset time length is executed.

The invention discloses a method for calibrating the system error of a rotating strapdown inertial navigation system. On the basis that the rotating inertial navigation system has a rotating mechanism, a 30-position rotation calibration strategy is set, and the position flipping of the inertial navigation system in the rotation strategy can fully stimulate various errors of the inertial measurement unit; secondly, a Kalman filter model is established, and the speed error and position error are used as measurement quantities to estimate the inertial navigation error; finally, through simulation experiments, it is verified that the method for calibrating the system error of the 30-position rotation strapdown inertial navigation system designed by the invention can calibrate the constant error, scale coefficient error and installation error of the gyroscope and accelerometer at one time with high precision.

The application provides an aerial docking method and device based on a contact force-position neural network model. The method provided by the application comprises the following steps: building a ground data acquisition device, collecting training data of contact force and corresponding position parameters in three dimensions of horizontal direction, vertical direction and relative angle through the ground data acquisition device; constructing a contact force-position neural network model, training the contact force-position neural network model by using the training data; acquiring relative position and attitude information between a work unmanned aerial vehicle and a target, and predicting contact force based on the contact force-position neural network model; combining the predicted contact force, position error of the work unmanned aerial vehicle and the target, and preset expected docking force to generate a preliminary reference trajectory; introducing a control allocation method to optimize and adjust the preliminary reference trajectory, and performing aerial docking between the work unmanned aerial vehicle and the target based on the optimized trajectory.

The embodiment of the invention provides a control method of an airship hanging load, electronic equipment, a storage medium and a program product, and relates to the technical field of nonlinear control, the control method of the airship hanging load provided by the embodiment of the invention designs a component of a virtual control force along a cable direction in load trajectory tracking control and position error control, so that the control precision of the airship hanging load is improved; and driving the load to approach the expected trajectory. A cable direction error is introduced in cable swing angle suppression, a component perpendicular to a cable is designed, swing is suppressed, stability is guaranteed, in airship driving control implementation, according to the load control requirement, analog control force needed by the airship is solved, dynamic balance is achieved by adjusting a propelling system of the airship, the dynamic coupling effect of an airship speed ring is considered, and the airship swing angle is suppressed. The self-adaptive control of the attitude is realized, the problems of large deviation of load tracking expected trajectory, insufficient cable swing suppression and unstable airship attitude are solved, and the hoisting control precision of the airship hanging load is improved.

The present application relates to a kind of system level calibration method for correcting the zero offset of acceleration channel of inertial measuring device, belongs to the field of inertial measurement.The zero position error of acceleration channel is compensated, the method does not need to disassemble inertial measuring device, by the vertical direction of three axes of aircraft respectively, the zero position error of acceleration channel of inertial measuring device can be calculated.The present application proposes the system level calibration method for quickly correcting the zero offset of acceleration channel of inertial measuring device, by simple calibration path setting, the zero offset error of acceleration channel can be effectively calculated.When the change of accelerometer itself zero offset error occurs, it is no longer necessary to disassemble inertial measuring device from aircraft and re-performs discrete calibration, and the influence of installation error caused by repeated installation of inertial measuring device is avoided.

The present invention proposes an aircraft trajectory planning method considering positioning error correction under multiple constraints. The method first solves a general solution that satisfies the constraints through a greedy algorithm, then traverses the search space, and uses the general solution of the greedy algorithm as a benchmark to accurately prune unnecessary search space using the precise pruning-enumeration method, thereby quickly finding an optimal solution. After analysis, the algorithm can ensure that the track length reaches the theoretical optimum, the number of corrections is also small, and the solution speed is high.

The present invention discloses an inertial navigation modulation axis control method based on an LQR regulator, which relates to the technical field of inertial navigation, and includes: sampling the current angular velocity and the angular position of the motor and respectively taking the differences from the expected values of the inertial navigation system, and taking the two differences as the angular velocity error and the angular position error respectively and inputting them into the LQR regulator; taking the state equation of the motor as the objective function in the LQR regulator, generating and solving the Riccati equation, inputting the expected acceleration of the motor into the PID controller and tracking the expected acceleration, taking the difference between the expected acceleration and the currently sampled acceleration of the motor, and calculating the control quantity as the output of the PID controller, and the control quantity is the Q-axis control voltage of the motor in the rotating coordinate system; the inertial navigation system calculates and processes the control quantity through the SVPWM algorithm to control the modulation axis of the inertial navigation device, and the modulation axis issues an instruction to the motor to correct the operating state of the motor.

This invention belongs to the field of aircraft control simulation technology and relates to a method for correcting the position error of a static pressure source during the landing phase of an aircraft simulator. It aims to address the problem that existing static pressure source position error correction technologies lack targeted calibration for ground effects during the landing phase. The invention includes: acquiring real flight data and simulated flight data during the landing phase; calculating the difference in flight parameters to obtain the expected residual value; fitting error parameters to obtain the pressure coefficient caused by the static pressure source position error to determine the position error; superimposing the position error onto the flight parameters of the simulated flight data; recalculating the difference until the expected residual value is less than a threshold, thus completing the correction; the error parameters include the pressure coefficient difference at zero angle of attack, the slope of the pressure coefficient difference as a function of the angle of attack, and the influence factor of ground effects on the position error. This invention performs real-time correction of static pressure sensor measurements, effectively eliminating the static pressure source position error caused by ground effects during the landing phase.

This application belongs to the field of aircraft design technology, and particularly relates to a static pressure source position error correction method. Based on current numerical simulation and flight test state points, a static pressure source position error correction analysis is performed on the theoretical configuration, obtaining the static pressure source pressure coefficient Cp, the flight test static pressure source pressure coefficient Cp0, and the measured static pressure source pressure coefficient Cp1. The static pressure source position error correction data is optimized to obtain the static pressure source position error correction relationship Cp′ during actual flight of the theoretical configuration, and then the static pressure source position error is corrected. The application of configuration measurement, CFD simulation, and flight test data is achieved. The static pressure source position error is corrected as a function of Mach number, angle of attack, and altitude. This method not only meets the pressure altitude requirements of CCAR Part 25, but also meets the altitude measurement system error requirements of RVSM operations in AC‑21‑13. This method can be used for atmospheric data correction on transport aircraft and can be used as a reference for atmospheric data correction on other types of aircraft.

The present invention discloses a gravity matching method based on the characteristics of gravity measurement data. The present invention takes into account the variation characteristics of gravity measurement data, analyzes the extreme value measurement points, and combines with the gravity background map to obtain a relatively accurate position estimation point that is not affected by the initial position error of the inertial navigation. Then, taking this position estimation point as the initial position point of the filtering gravity matching algorithm for gravity matching, it can effectively reduce the influence of the matching initial error caused by the inertial navigation system and the real-time gravity measurement data error, and improve the gravity matching positioning accuracy. In addition, the present invention also introduces the concept of direction measurement according to the relationship between the underwater vehicle's heading angle and the navigation displacement direction within the matching period in the filtering gravity matching algorithm, which can effectively solve the problem of slow convergence speed in the filtering gravity matching algorithm. At the same time, the algorithm also has a certain ability to resist outliers.