Rotorcraft integrated navigation method and system based on adaptive gradient descent method

Abstract

The application relates to a rotorcraft integrated navigation method and system based on an adaptive gradient descent method. The rotorcraft integrated navigation method comprises the following steps: obtaining accelerometer and gyroscope measurement values of a rotorcraft; adjusting the step length of gradient descent according to the acceleration vector length; determining a first attitude quaternion through the accelerometer based on the gradient descent method with the adjusted step length; determining a second attitude quaternion based on the gyroscope measurement values; and obtaining final rotorcraft attitude information through weight matching of the first attitude quaternion and the second attitude quaternion. The application estimates the rotorcraft motion acceleration to dynamically correct the attitude solution error function and the step length of gradient descent, simultaneously adds a fault detection mechanism, reduces the influence of the motion acceleration on the attitude solution in a strong maneuvering state, and improves the estimation accuracy and fault tolerance performance of the integrated navigation filter.

Description

TECHNICAL FIELD

[0001] Embodiments of the present application relate to the field of aircraft navigation technology, and particularly relate to a rotor aircraft integrated navigation method and system based on an adaptive gradient descent method and a computer readable storage medium. BACKGROUND

[0002] Rotor aircraft has strong environmental adaptability, autonomous take-off and landing capability, flexible maneuvering characteristics and small flight area limitation, etc., which enables it to complete tasks that general fixed-wing aircraft cannot complete, and thus is widely used in various fields such as fire rescue, power line inspection, news reporting, precision delivery, pesticide spraying, military reconnaissance, etc.

[0003] Rotor aircraft has high stability and versatility, and its control and navigation system is more complex than that of fixed-wing aircraft, and the speed and accuracy of attitude calculation directly affect the stability and robustness of the multi-rotor aircraft during flight.

[0004] In related technologies, strapdown inertial navigation (SINS) is a kind of autonomous navigation method, which has the advantages of small size, no regional restriction, full autonomy, low cost, etc., and is widely used in aircraft guidance, underwater navigation, vehicle navigation, etc. It is often combined with global navigation satellite system (GNSS) for information fusion to form complementary advantages and achieve continuous and accurate navigation all day long.

[0005] Regarding the above technical solutions, the inventors have found that at least the following technical problems exist:

[0006] When the multi-rotor aircraft makes strong maneuvering flight in a complex environment, the satellite navigation signal is relatively fragile and is prone to signal interruption or jumping, which pollutes the state estimation value of the integrated navigation filter; at the same time, the strong maneuvering and high-speed flight environment fully exposes the defects of poor dynamic performance of the accelerometer, and the motion acceleration of the aircraft will seriously affect the attitude calculation process of the navigation system, causing its accuracy to decrease or even diverge, which poses a threat and hidden danger to the safety of the aircraft flight.

[0007] Therefore, it is necessary to improve one or more problems existing in the above related technical solutions.

[0008] It should be noted that this section aims to provide background or context for the technical solutions of the present application stated in the claims. The description herein is not admitted to be prior art merely because it is included in this section. SUMMARY

[0009] The application aims to provide a rotorcraft integrated navigation method, system and computer readable storage medium based on adaptive gradient descent method, thereby at least partially solving the problem of inaccurate estimation of integrated navigation system caused by slow convergence of gradient descent algorithm under different flight state switching of the aircraft, and one or more problems caused by limitations and defects of related technologies.

[0010] The application first provides a rotorcraft integrated navigation method based on adaptive gradient descent method, comprising:

[0011] obtaining accelerometer and gyroscope measurement values of the aircraft;

[0012] adjusting the step length of gradient descent according to the acceleration vector module length;

[0013] determining a first attitude quaternion through the accelerometer based on the gradient descent method with the adjusted step length

[0014] determining a second attitude quaternion based on the gyroscope measurement values;

[0015] obtaining final aircraft attitude information through weight proportioning of the first attitude quaternion and the second attitude quaternion.

[0016] In the application, the obtaining of the accelerometer and gyroscope measurement values of the aircraft further comprises:

[0017] obtaining the gyroscope measurement values of the aircraft through strapdown inertial navigation;

[0018] obtaining the accelerometer of the aircraft through global satellite navigation.

[0019] In the application, the adjusting of the step length of gradient descent according to the acceleration vector module length further comprises:

[0020] classifying and determining a motion acceleration inhibition factor according to the acceleration vector module length, and calculating the step length of gradient descent through the inhibition factor.

[0021] In the application, the step length of gradient descent is obtained through the following formula:

[0022]

[0023] wherein, μ is the step length; σ is the motion acceleration inhibition factor; ||ω t is the angular velocity module length; γ is a proportional coefficient determined by experiment.

[0024] In the application, the motion acceleration inhibition factor is obtained through the following formula:

[0025]

[0026] wherein, σ is a motion acceleration inhibition factor; is a acceleration vector module length; c is a constant less than 1 and greater than 0; ε is a constant determined by experiment.

[0027] In the application, the first attitude quaternion and the second attitude quaternion are weighted to obtain final aircraft attitude information, and the application further comprises:

[0028] The weight distribution between the first attitude quaternion and the second attitude quaternion is adjusted based on fault detection.

[0029] In the application, the weight distribution between the first attitude quaternion and the second attitude quaternion is adjusted based on fault detection, and the application further comprises:

[0030] A fault detection value is determined based on residual information of the aircraft attitude information, and it is determined whether a fault exists by comparing the fault detection value with a detection threshold determined by a false alarm probability.

[0031] In the application, the fault detection value is obtained by the following formula:

[0032]

[0033] wherein, Λ k is a non-negative fault detection value; υ k is a residual vector; V k is a residual sequence covariance matrix.

[0034] The application further provides a rotor aircraft integrated navigation system based on an adaptive gradient descent method, comprising:

[0035] A data acquisition module, which acquires accelerometer and gyroscope measurement values of an aircraft;

[0036] A data processing module, which receives the accelerometer and gyroscope measurement values of the aircraft acquired by the data acquisition module and acquires final aircraft attitude information according to the rotor aircraft integrated navigation method in any of the above aspects.

[0037] The application further provides a computer readable storage medium, which stores a computer program that is executed by a processor to implement the steps of the rotor aircraft integrated navigation method in any of the above aspects.

[0038] The technical solution provided by the application can have the following beneficial effects:

[0039] In the present application, by the above method and device, the attitude solution error function and the step length of gradient descent are dynamically corrected by estimating the aircraft motion acceleration, a fault detection mechanism is added, the influence of the motion acceleration on the attitude solution in the strong maneuvering state is reduced, and the estimation accuracy and fault tolerance performance of the integrated navigation filter are improved. BRIEF DESCRIPTION OF DRAWINGS

[0040] The accompanying drawings, which are incorporated herein and form a part of the specification, illustrate embodiments consistent with the present disclosure and, together with the description, further serve to explain the principles of the present disclosure. It is to be expressly understood, however, that the drawings are provided for illustration purposes only and as a part of the specification, and are not intended as a definition of the limits of the disclosure. For the sake of brevity and clarity, the figures described below are merely schematic and are non-limiting exemplary embodiments of the present disclosure.

[0041] Figure 1 A flowchart of a rotorcraft integrated navigation method in an exemplary embodiment of the present application is shown;

[0042] Figure 2 A schematic diagram of a rotorcraft integrated navigation system structure in an exemplary embodiment of the present application is shown;

[0043] Figure 3 A flight trajectory diagram of a simulation experiment in an exemplary embodiment of the present application is shown;

[0044] Figure 4 A schematic diagram of angle error in a simulation experiment in an exemplary embodiment of the present application is shown;

[0045] Figure 5 A schematic diagram of position error in a simulation experiment in an exemplary embodiment of the present application is shown;

[0046] Figure 6 A schematic diagram of velocity error in a simulation experiment in an exemplary embodiment of the present application is shown;

[0047] Figure 7 A schematic diagram of angle error in a simulation experiment in an exemplary embodiment of the present application is shown;

[0048] Figure 8 A schematic diagram of position error in a simulation experiment in an exemplary embodiment of the present application is shown;

[0049] Figure 9 A schematic diagram of velocity error in a simulation experiment in an exemplary embodiment of the present application is shown;

[0050] Figure 10 A schematic diagram of a storage medium in an exemplary embodiment of the present application is shown. DETAILED DESCRIPTION

[0051] Example implementations are now described with reference to the drawings. Example implementations can, however, be implemented in many different forms and should not be construed as limited to the examples set forth herein; rather, these example implementations are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the example implementations to those skilled in the art. The described features, structures, or characteristics can be combined in one or more implementations.

[0052] In addition, the accompanying drawings are included to provide a thorough understanding of embodiments of the application and are not intended to be in any way limiting of the application. The drawings can not be to scale, and certain aspects can be exaggerated to illustrate features that can not otherwise be apparent. Like reference numbers in the figures can denote like elements, and repetitive descriptions can be omitted for clarity.

[0053] In the present example implementation, a rotorcraft integrated navigation method based on adaptive gradient descent is first provided, as shown in FIG. 1, the control method comprises the following steps: Figure 1

[0054] Step S101: Obtain the accelerometer and gyroscope measurement values of the aircraft.

[0055] Step S102: Adjust the step size of the gradient descent according to the acceleration vector length.

[0056] Step S103: Determine the first attitude quaternion based on the accelerometer measurement values using the gradient descent method with the adjusted step size.

[0057] Step S104: Determine the second attitude quaternion based on the gyroscope measurement values.

[0058] Step S105: Obtain the final aircraft attitude information by weighting the first attitude quaternion and the second attitude quaternion.

[0059] It should be understood that the gradient refers to the maximum rate of change of a multi-variable function at a certain point, and the direction is the steepest direction in which the function value increases. The gradient descent method is to use the negative gradient direction corresponding to the objective function to update the new direction of each iteration, so that the objective function to be optimized is gradually reduced through each iteration, and is usually used to solve the minimum value of the function.

[0060] It should also be understood that an error function about the carrier attitude is first constructed, when the body motion acceleration is small, the vector a b = [a x , a y , a z ] T is the gravity acceleration g n in the navigation coordinate system multiplied by the attitude transpose matrix ​The vector projected onto the carrier coordinate system, but due to the error in the attitude solution, there is a difference between the two vectors; for the convenience of analysis, a b With g n Both have been normalized, so the target error function can be obtained as follows:

[0061]

[0062] Where, is the attitude quaternion of n system (navigation coordinate system) relative to b system (carrier coordinate system), g b is the standard gravity acceleration g n The projection vector in the b system is calculated by the following formula:

[0063]

[0064] Where, is the conjugate quaternion of The specific expression of equation (1) is as follows:

[0065]

[0066] The derivative of the error function is obtained to obtain the corresponding Jacobian matrix:

[0067]

[0068] Thus the gradient of the error function can be obtained:

[0069]

[0070] The iteration formula for approaching the extreme point of the error function in the negative gradient direction is as follows:

[0071]

[0072] Where, μ represents the step size of gradient descent algorithm, represents the speed of error function tending to the extreme point, too small will lead to slow convergence speed, too large will make the static performance worse, in practical application, the value should be taken according to the specific situation.

[0073] It also needs to be understood that the gradient descent method obtains the quaternion representing the attitude based on the accelerometer, but due to the poor dynamic performance of the accelerometer, the random jump of its measurement value is serious, so in engineering, it is often fused with the attitude value estimated by the gyroscope. The attitude quaternion obtained by the gyroscope is generally realized by the quaternion differential equation, which is shown as follows:

[0074]

[0075]

[0076] where, is the three-axis gyroscope measurement value on the carrier at time k, is the derivative of the attitude quaternion , and Δt is the sampling time interval of the inertial device. According to this, the iterative formula of the attitude quaternion represented by the gyroscope is obtained:

[0077]

[0078] where, q est represents the final complementary filtered attitude output, q ω is the attitude quaternion obtained from the gyroscope measurement value. In this way, the values of the two kinds of attitude calculation are complementary filtered with a certain weight to obtain the final attitude output:

[0079]

[0080] where, w represents the weight proportion of the two algorithms, and it can be proved that the optimal value is obtained only when the convergence speed is equal to the divergence speed, that is, the following relationship is satisfied:

[0081]

[0082] The convergence speed of the gradient descent step size μ is equal to the ratio of the sampling time interval Δt, and β represents the divergence speed of the quaternion differential equation solution, and βΔt refers to the measurement error of the gyroscope. Through the query of the data manual of the related device on the market, it is known that its value is generally very small, usually ≤10 -4 (deg / s); the sampling interval Δt ≈ 0.01 (s). It is found in the actual test process that the step size μ in formula (6) is relatively large, so that the attitude estimation of the last time can be ignored, so formula (6) is approximately:

[0083]

[0084] Substituting formulas (9), (11) and (12) into formula (10) can obtain the final attitude calculation iterative formula:

[0085]

[0086] This is the commonly used gradient descent attitude calculation algorithm in engineering. It needs to be noted that, since the step size and the fusion weight coefficient are set to a constant value, when the aircraft switches between the two states of smooth flight and strong maneuvering flight, the attitude calculation will appear lag phenomenon.

[0087] It also needs to be understood that in the gradient descent algorithm, the length of the vector a b represented by the accelerometer measurement value should be equal to g nThis is when the acceleration vector of motion... The modulus is much smaller than the gravitational acceleration g. n The simplified approach used in model length engineering is highly reliable for UAVs in smooth flight, reducing the complexity of attitude calculation and achieving good accuracy and convergence speed. However, when the aircraft performs strong maneuvers, the instantaneous acceleration can typically reach several times the acceleration due to gravity, rendering the above approximation invalid. Therefore, the error function is optimized:

[0088]

[0089] in, Let α be the acceleration vector in the navigation coordinate system, and α be the scaling factor. The motion acceleration in the navigation coordinate system is obtained by the following method:

[0090]

[0091] in, Let be the attitude transition matrix from the previous time step. and This is the estimate of the vehicle's velocity in the integrated navigation filter. On the other hand, under conditions of high-maneuverability flight, the step size μ needs to be adaptively adjusted according to the vehicle's velocity.

[0092] In the error function, accelerometer measurements typically need to pre-filter out motion acceleration values, which are estimated by the integrated navigation filter. Experiments revealed that when the GNSS received signal deteriorates, velocity information jumps significantly, which is highly detrimental to motion acceleration estimation. To suppress the impact of erroneous information on attitude calculation, in addition to timely isolation of fault information through residual-based outlier detection, motion acceleration suppression processing is also necessary, along with timely adjustment of the gradient descent step size based on changes in the acceleration magnitude.

[0093] By using the above method, the attitude calculation error function and gradient descent step size are dynamically corrected by estimating the aircraft's motion acceleration. At the same time, a fault detection mechanism is added, which reduces the impact of motion acceleration on attitude calculation under strong maneuvering conditions and improves the estimation accuracy and fault tolerance performance of the integrated navigation filter.

[0094] Below, we will refer to Figures 1-2 The method described above in this example implementation will be explained in more detail.

[0095] In some embodiments, step S101 further includes:

[0096] The aircraft's gyroscope measurements are obtained through strapdown inertial navigation.

[0097] The accelerometer of the aircraft is obtained through global satellite navigation.

[0098] It needs to be understood that the northeast celestial coordinate system is selected as the navigation coordinate system, and the discrete linear state equation of the SINS / GNSS integrated navigation system is:

[0099]

[0100] In the formula, A k,k-1 is the state transition matrix, W k is the state process noise, and Γ k is the noise driving matrix; the state variable X k includes 15 dimensions of errors of the SINS system accelerometers and gyroscopes, and is specifically defined as follows:

[0101]

[0102] Among them: φ E , φ N , φ U are the attitude angle errors in the navigation coordinate system; δv E , δv N , δv U are the eastward, northward and skyward velocity errors of the carrier, respectively; δL, δλ, δh are the latitude, longitude and height errors, respectively; ε x , ε y , ε z are the constant zero biases of the gyroscopes; is the random walk error of the accelerometer. The measurement equation of the SINS / GNSS integrated navigation system is as follows:

[0103] Z k = H k X k + V k Equation (18)

[0104] In the formula, the measurement value Z k selects the position and velocity difference output by the SINS and GNSS, H k is the measurement matrix, V k is the measurement noise, and corresponds to the GNSS position and velocity error. H k is defined as follows:

[0105]

[0106] Among them, R M , R N are the radii of the earth's meridian and the equinoctial circle, respectively. In this way, the conventional Kalman filter recursive algorithm of the SINS / GNSS integrated navigation system can be obtained:

[0107]

[0108] In some embodiments, step S102 further comprises:

[0109] The motion acceleration inhibition factor is classified and determined according to the acceleration vector module length, and the step length of gradient descent is calculated through the inhibition factor.

[0110] It should be understood that when the GNSS received signal is poor, the speed information jump is more serious, which is very unfavorable to the estimation of motion acceleration. The motion acceleration is inhibited, and the gradient descent step length is adjusted in time according to the change of the acceleration module length.

[0111] In some embodiments, the step length of gradient descent is obtained by the following formula:

[0112]

[0113] Wherein, μ is the step length; σ is the motion acceleration inhibition factor; ||ω t || is the angular velocity module length; γ is the proportional coefficient determined by experiment.

[0114] It should be understood that the step length μ needs to be adaptively adjusted according to the carrier motion speed, which generally satisfies the following conditions:

[0115] μ = γ||ω t ||Δt, γ > 1 Equation (22)

[0116] Wherein, ||ω t || is the carrier angular velocity module length, and γ is the proportional coefficient, which is generally determined by experiment. It can be known from equation (13) that the faster the body motion speed is, the longer the sampling interval is, and the step length should be larger; but the step length is too large, which leads to poor static performance. The step length is adjusted comprehensively, and the above-mentioned step length of gradient descent is obtained.

[0117] In some embodiments, the motion acceleration inhibition factor is obtained by the following formula:

[0118]

[0119] Wherein, σ is the motion acceleration inhibition factor; is the acceleration vector module length; c is a constant less than 1 and greater than 0; ε is a constant determined by experiment.

[0120] It should be understood that through the above-mentioned acquisition formula of motion acceleration inhibition factor, the motion acceleration inhibition factor based on the acceleration vector module length can be calculated to obtain the step length of gradient descent, and the motion acceleration is inhibited, and the gradient descent step length is adjusted in time according to the change of the acceleration module length.

[0121] In some embodiments, step S105 further comprises:

[0122] Adjusting the weight ratio between the first attitude quaternion and the second attitude quaternion based on the fault detection.

[0123] It should be understood that when the GNSS received signal is poor, the speed information jumps severely, which is very unfavorable to the estimation of the motion acceleration. In order to suppress the influence of false information on the attitude solution, the fault information is isolated in time through the outlier judgment based on the residual.

[0124] In some embodiments, it further comprises: determining a fault detection value based on the residual information of the aircraft attitude information, and determining whether there is a fault by comparing with a detection threshold determined by the false alarm probability. It should be understood that the false alarm probability can be measured by experiment, and then the detection threshold is set.

[0125] In some embodiments, it further comprises: the fault detection value is obtained by the following formula:

[0126]

[0127] wherein, Λ k is a non-negative fault detection value; υ k is a residual vector; V k is a residual sequence covariance matrix.

[0128] It should be understood that in order to detect and exclude fault information from the system level, a fault detection function based on residual estimation is established:

[0129]

[0130]

[0131] wherein, formula (25) (26) are respectively the definition formula of the residual vector and the residual sequence covariance matrix, R k is the covariance matrix of the measurement noise matrix V k , and the above fault detection function is constructed by using the residual, that is, formula (24).

[0132] Obviously, the fault detection function Λ k is non-negative, and Λ k obeys χ 2 distribution with degree of freedom n, n represents the dimension of the observation vector, here the measurement vector Z k is 6-dimensional, so n = 6.

[0133] Let the false alarm probability be θ, then the fault judgment criterion can be obtained as follows:

[0134]

[0135] At the same time, there are:

[0136] P(Λ k > T d ) = θ Equation (28)

[0137] where T d is a detection threshold determined by the false alarm probability θ. A detection factor for the motion acceleration is constructed using the fault detection function:

[0138]

[0139] The purpose is to prevent the disturbance to the attitude estimation when the SINS / GNSS integrated navigation filter fails to estimate the acceleration accurately.

[0140] In the example embodiment, a rotorcraft integrated navigation system based on adaptive gradient descent method is further provided, as shown in Figure 2 , which comprises a data acquisition module, the data acquisition module acquires the accelerometer and gyroscope measurement values of the aircraft; a data processing module, the data processing module receives the accelerometer and gyroscope measurement values of the aircraft acquired by the data acquisition module, and obtains the final aircraft attitude information according to the rotorcraft integrated navigation method in any one of the above embodiments.

[0141] A computer readable storage medium having a computer program stored thereon is also provided, the program can implement the steps of the rotorcraft integrated navigation method in any one of the above embodiments when executed by a processor. In some possible embodiments, various aspects of the present application can also be implemented in the form of a program product, which includes program codes for causing terminal equipment to perform the steps of the control method according to various exemplary embodiments of the present application described in the above control method part of the specification when the program product is run on the terminal equipment.

[0142] Referring to Figure 10 , a program product 500 for implementing the above method according to the embodiments of the present application is described, which can adopt a portable compact disc read-only memory (CD-ROM) and include program codes, and can be run on terminal equipment, such as a personal computer. However, the program product of the present application is not limited to this, and in this document, the readable storage medium can be any tangible medium containing or storing a program, which can be used by or in conjunction with an instruction execution system, device or apparatus.

[0143] The program product can employ any combination of one or more computer-readable media. The computer-readable media can be a computer-readable storage medium or a computer-readable signal medium. The computer-readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium include the following: an electrical connection having one or more wires, a portable disc, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0144] The computer-readable storage medium can include data signals on a carrier wave, propagated over a propagation medium, in which the computer-readable program code is embodied. The computer-readable storage medium can also be any computer-readable medium other than a transitory, propagating signal per se. The computer-readable program code embodied on the computer-readable storage medium can be transmitted by any program medium as desired, including, but not limited to, wireless, wired, optical fiber cable, RF, and the like, or any suitable combination of the foregoing.

[0145] The program code for carrying out operations of the present application can be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C++, and the like, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code can execute entirely on the user's computing device, partly on the user's computing device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device or entirely on the remote computing device or server. In the latter scenario, the remote computing device can be connected to the user's computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computing device, such as through the Internet using an Internet Service Provider (ISP).

[0146] For example, the above-described rotorcraft integrated navigation method is implemented with a more specific embodiment, as shown in the accompanying drawings. Figures 3-9

[0147] ​The MATLAB is used to simulate the process of UAV maneuvering flight and the process of information solution of integrated navigation system. The simulation trajectory includes uniform acceleration, turning, climbing, and flat flying. The constant bias of gyroscope is assumed to be 0, the random walk of accelerometer is assumed to be 100 Hz, the output frequency of GNSS is assumed to be 10 Hz, the position error is assumed to be 2 m, the velocity error is assumed to be 0.2 m / s, the Kalman filter period is assumed to be 0.1 s, the simulation flight time is assumed to be 200 s, and the false alarm probability is assumed to be 0.01. The SINS / GNSS fault detection threshold is shown in FIG. 1, and the flight trajectory is shown in FIG. 2. Figure 3

[0148] It is assumed that the SINS system is fault-free during the entire flight process, and the GNSS receiver has a position error accuracy of 10 m and a velocity error accuracy of 2 m / s during the time period of 100 s to 150 s due to signal obstruction. In order to verify the effectiveness of the algorithm, a comparative simulation experiment is conducted, and the simulation experiment results are shown in FIG. 3. Figures 4-9

[0149] As shown in FIG. 4, Figures 4-6 when the aircraft performs rapid pitching, rolling, and turning movements, the introduced motion acceleration will inevitably affect the accuracy of attitude estimation, resulting in a deviation of about 0.1 rad in the calculation of large-amplitude attitude angles. The estimation deviation of the attitude angle causes a deviation in the calculation of the attitude transition matrix, which in turn causes the position and velocity estimation errors of the entire SINS / GNSS integrated navigation system to become larger. The absolute value of the position error is about 1.5 m, and the absolute value of the velocity error is about 0.25 m / s. During the time period of 100 s to 150 s, the absolute value of the position error reaches about 3 m due to the deterioration of the satellite signal, and the absolute value of the velocity error reaches about 0.5 m / s.

[0150] As shown in FIG. 5, Figures 6-9 the motion acceleration suppression algorithm for integrated navigation can effectively reduce the deviation in the estimation of large attitude angles. The error of the pitch angle and the roll angle is less than 0.1 rad, the error of the yaw angle is less than 0.1 rad after the attitude calculation stabilizes, and the overall error during the fault occurrence stage does not exceed 0.1 rad. The position and velocity errors output by the SINS / GNSS integrated navigation filter are also improved. The absolute value of the velocity error is about 0.2 m / s on average, and it is basically stable at about 0.1 m / s during the fault-free period. The absolute value of the position error is about 0.5 m on average, and the error during the fault stage also remains within 1.5 m.

[0151] From the above simulation experiment comparison, it can be seen that the motion acceleration suppression algorithm can effectively improve the estimation accuracy of the attitude angle, thereby improving the speed and position estimation accuracy of the entire SINS / GNSS integrated navigation system. At the same time, the robustness and anti-interference performance of the integrated navigation filter are also enhanced, and the fault tolerance performance of the navigation system is correspondingly improved.

[0152] ​​The simulation results show that, compared with the traditional integrated navigation system, the method can suppress the influence of motion acceleration on attitude solution, improve the estimation accuracy of the integrated navigation filter for attitude, velocity and position, and has certain reference significance for improving the autonomous fault-tolerant capability of the unmanned aerial vehicle integrated navigation system in complex environment and ensuring the flight safety of the unmanned aerial vehicle.

[0153] It should be understood that the terms "center", "longitudinal", "transverse", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise" and the like in the above description indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the embodiments of the present application and simplifying the description, and therefore cannot be understood as indicating or implying that the devices or elements referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as limiting the embodiments of the present application.

[0154] In addition, the terms "first", "second" are only for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the technical features indicated. Therefore, the features defined with "first", "second" can explicitly or implicitly include one or more of the features. In the description of the embodiments of the present application, the meaning of "multiple" is two or more, unless otherwise explicitly specified and limited.

[0155] In the embodiments of the present application, unless otherwise explicitly specified and limited, the terms "mounting", "connection", "connection", "fixing" and the like should be understood in a broad sense, for example, it can be fixed connection, or detachable connection, or integral; it can be mechanical connection, or electrical connection; it can be directly connected, or indirectly connected through an intermediate medium, or it can be the internal communication of two elements or the interaction relationship between two elements. For those skilled in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances.

[0156] In the embodiments of the present application, unless otherwise explicitly specified and limited, the "upper" or "lower" of the first feature to the second feature can include that the first and second features are in direct contact, or that the first and second features are not in direct contact but are in contact through another feature between them. Moreover, the "upper", "above" and "on" of the first feature to the second feature include that the first feature is directly above and obliquely above the second feature, or only indicates that the horizontal height of the first feature is higher than that of the second feature. The "below", "under" and "under" of the first feature to the second feature include that the first feature is directly below and obliquely below the second feature, or only indicates that the horizontal height of the first feature is less than that of the second feature.

[0157] In the description of the application, reference can be made to terms such as "one embodiment", "some embodiments", "an example", "a specific example" or "some examples" etc. It is to be understood that such terms are merely used to describe a particular feature, structure, material or characteristic under discussion. Therefore, in no way these terms restrict the scope of the application. Further, when used in the description, the terms "a" or "an" are used in the sense that they mean "one or more" unless the context clearly dictates otherwise. In addition, the term "based on" is used to describe one or more feature, structure, material or characteristic that is / are used for an action or a step which is / are performed one or more times. Also, the term "based on" is used to describe one or more feature, structure, material or characteristic that is / are used for performing an action or a step which is / are performed one or more times.

[0158] Other embodiments of the application will be apparent to those skilled in the art from consideration of the specification and practice of the application disclosed herein. It is intended that the specification and examples be considered as exemplary only, with the true scope and spirit of the application being indicated by the following claims.

Claims

1. A combined navigation method for rotorcraft based on adaptive gradient descent, characterized in that, include: Acquire the accelerometer and gyroscope measurements of the aircraft; The step size of gradient descent is adjusted according to the magnitude of the acceleration vector; wherein, the motion acceleration suppression factor is classified and determined according to the magnitude of the acceleration vector, and the step size of gradient descent is calculated through the suppression factor; The first attitude quaternion is determined by the accelerometer using a gradient descent method with an adjusted step size. The second attitude quaternion is determined based on the gyroscope measurements. The first attitude quaternion and the second attitude quaternion are weighted to obtain the final aircraft attitude information; Specifically, the weight ratio between the first attitude quaternion and the second attitude quaternion is adjusted based on fault detection; the fault detection value is determined based on the residual information of the aircraft attitude information, and the presence of a fault is determined by comparing it with the detection threshold determined by the false alarm probability, and the fault information is isolated by the determination result. The motion acceleration inhibition factor is obtained by the following formula: in, σ It is a motion acceleration inhibitor; The magnitude of the acceleration vector; c It is a constant that is less than 1 and greater than 0; ε It is a constant determined experimentally; k 0 is the lower limit of the set acceleration vector magnitude; k 1 represents the upper limit of the set acceleration vector magnitude; The fault detection value is obtained using the following formula: Among them, Λ k The fault detection value is non-negative; υ k It is the residual vector; P υk Let be the covariance matrix of the residual sequence.

2. The rotary-wing aircraft integrated navigation method according to claim 1, characterized in that, Obtaining accelerometer and gyroscope measurements from an aircraft also includes: The aircraft's gyroscope measurements are obtained through strapdown inertial navigation. The accelerometer of the aircraft is obtained through global satellite navigation.

3. The rotary-wing aircraft integrated navigation method according to claim 1, characterized in that, Also includes: The step size for gradient descent is obtained using the following formula: in, μ Step size; μ 0 is the initial step size; σ It is a motion acceleration inhibitor; The angular velocity modulus; γ The proportionality constant is determined experimentally; △ t This represents the time interval for data sampling.

4. A rotary-wing aircraft integrated navigation system based on adaptive gradient descent, characterized in that, include: The data acquisition module acquires the accelerometer and gyroscope measurements of the aircraft. The data processing module receives the accelerometer and gyroscope measurements of the aircraft obtained by the data acquisition module, and obtains the final aircraft attitude information according to the rotorcraft integrated navigation method according to any one of claims 1-3.

5. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the steps of the rotorcraft integrated navigation method according to any one of claims 1-3.

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