Aircraft flight path optimization method and system based on inertial navigation

Abstract

The invention provides an aircraft flight path optimization method and system based on inertial navigation, and relates to the technical field of flight control, and the method comprises the steps: generating an initial path according to a flight task and a limiting condition; acquiring original acceleration and angular velocity data; the attitude estimation precision is improved based on a quaternion updating algorithm in combination with Kalman filtering and an adaptive noise model; position updating is carried out by combining the acceleration and attitude information; dynamically adjusting and correcting parameters by using a machine learning algorithm; path optimization: defining an objective function which comprehensively considers path length, time, fuel consumption and safety, and adopting a particle swarm optimization algorithm and a simulated annealing algorithm to carry out optimization; evaluating and correcting the path, evaluating the quality of the path in real time, and correcting according to the track deviation degree, the safety and the fuel efficiency. According to the method, through combination of various advanced algorithms and models, the optimization effect of the flight path is remarkably improved, and efficient, safe and economical flight of the aircraft in a complex and dynamic environment is ensured.

Description

Technical Field

[0001] The present invention relates to the field of flight control technology, and in particular to an aircraft flight path optimization method and system based on inertial navigation. Background Art

[0002] In the field of modern aerospace, aircraft navigation and path optimization is one of the key technologies to ensure flight safety and improve flight efficiency. With the increase in the number of aircraft and the complexity of airspace use, traditional navigation and path planning methods have been unable to meet actual needs, especially in flight missions in high-dynamic, high-precision and complex environments. As an autonomous navigation technology, the Inertial Navigation System (INS) can provide continuous attitude, velocity and position estimation without relying on external signals, so it plays an important role in aircraft navigation.

[0003] Inertial navigation systems mainly rely on inertial measurement units (IMUs) to obtain acceleration and angular velocity data, and obtain position and attitude information through integration operations. However, IMUs have cumulative error problems, especially the drift errors of gyroscopes and accelerometers, which cause navigation accuracy to gradually decrease over time. In order to improve the accuracy of inertial navigation, commonly used methods include attitude update algorithms, error compensation techniques, and the integration of auxiliary navigation systems (such as GPS, geomagnetic navigation, etc.). However, in certain specific scenarios, such as environments where signals are blocked or severely interfered, auxiliary navigation systems that rely on external signals may fail, further increasing the technical challenges of inertial navigation.

[0004] In terms of path planning and optimization, traditional methods usually rely on preset waypoints and path points for planning, and lack real-time optimization capabilities. With the development of computational intelligence technology, path optimization methods based on intelligent algorithms such as particle swarm optimization (PSO) and simulated annealing (SA) have gradually emerged. These methods can perform global search and local optimization under complex constraints, significantly improving the efficiency and effectiveness of path planning. However, when facing high-dimensional, multi-objective optimization problems, a single optimization algorithm still has problems with local optimality and slow convergence, which limits its application in actual flight. Summary of the invention

[0005] In order to solve the technical problems in the prior art of cumulative errors, external signal dependence, insufficient ability to implement path planning, algorithm limitations, lack of real-time path evaluation and correction capabilities, incomplete consideration of multi-objective optimization and insufficient utilization of dynamic information of the real-time flight environment, the present invention provides an aircraft flight path optimization method and system based on inertial navigation.

[0006] The technical solution provided by the embodiment of the present invention is as follows:

[0007] First aspect:

[0008] An embodiment of the present invention provides an aircraft flight path optimization method based on inertial navigation, comprising:

[0009] S1. Initial path planning. The initial path is generated by preset waypoints and path points. Let the initial path be P 0 ={p 1 ,p 2 ,...,p n}, where each waypoint p i Contains location r i =(x i ,y i ,z i ) and time information t i ;

[0010] S2, inertial measurement unit (IMU) data processing, obtaining raw acceleration and angular velocity data from the IMU, assuming that the data obtained at time t is a t =[a tx ,a ty ,a tz ] and ω t =[ω tx ,ω ty ,ω tz ];

[0011] S3, improved attitude update algorithm, which calculates the aircraft attitude based on the quaternion update algorithm, and improves the attitude estimation accuracy by combining Kalman filter and adaptive noise model;

[0012] S4. Use the inertial navigation equation to calculate the position, combine the acceleration and attitude information, and set the velocity at the current time t to be v t =[v tx ,v ty ,v tz ], position is p t =[p tx ,p ty ,p tz ], then the position update equation is:

[0013]

[0014] In the above formula, represents the quaternion conjugate, defined as:

[0015]

[0016] The position update equation is:

[0017]

[0018] In the above formula, g=[0,0,g z ] is the gravitational acceleration vector;

[0019] S5, adaptive error correction, using the adaptive error correction model to correct the calculated position and dynamically adjust the correction parameters in combination with the machine learning algorithm;

[0020] S6, path optimization, after defining the objective function, a combined optimization algorithm based on particle swarm optimization (PSO) and simulated annealing (SA) is used to optimize the path;

[0021] S7, path evaluation and correction, combining the flight environment and dynamic information, evaluating the path quality in real time, and correcting it according to the path correction strategy. The path quality is evaluated by path evaluation indicators, and the path evaluation indicators include track deviation, safety and fuel efficiency.

[0022] Second aspect:

[0023] An embodiment of the present invention provides an aircraft flight path optimization system based on inertial navigation, comprising:

[0024] processor;

[0025] A memory having computer-readable instructions stored therein, wherein when the computer-readable instructions are executed by the processor, the aircraft flight path optimization method based on inertial navigation as described in the first aspect is implemented.

[0026] The third aspect:

[0027] An embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, the method for optimizing an aircraft flight path based on inertial navigation as described in the first aspect is implemented.

[0028] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0029] (1) In the present invention, the accumulated error in the inertial navigation system is effectively reduced through an improved attitude update algorithm and an adaptive error correction model, which significantly improves the navigation accuracy and attitude stability of the aircraft, especially in long-term flight or complex environments;

[0030] (2) In the present invention, the combined optimization strategy of particle swarm optimization and simulated annealing algorithm is combined to achieve global search and local optimization of the flight path, effectively balancing multiple objectives such as path length, flight time, fuel consumption and safety, and significantly improving the optimization efficiency and economy of the flight path;

[0031] (3) In the present invention, a real-time path evaluation and correction mechanism is introduced, and the path is adjusted in combination with the dynamic information of the flight environment, so that the aircraft can respond to emergencies and environmental changes in a timely manner, thereby improving the safety and flexibility of the flight mission and providing reliable navigation guarantee for flight missions in complex flight environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0033] Figure 1 A schematic diagram of a flow chart of an aircraft flight path optimization method based on inertial navigation provided by an embodiment of the present invention;

[0034] Figure 2 A schematic structural diagram of an aircraft flight path optimization system based on inertial navigation provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0035] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0036] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "example" in the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of the word "example" is intended to present the concept in a specific way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or it can be either of the two.

[0037] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same. "of", "corresponding, relevant" and "corresponding" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same.

[0038] In the embodiments of the present invention, sometimes the subscripts such as W 1 It may be mistakenly written as a non-subscript form such as W1. When the difference is not emphasized, the meanings they express are the same.

[0039] In order to make the technical problems, technical solutions and advantages to be solved by the present invention more clear, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0040] Reference Manual Attached Figure 1 , showing a flow chart of an aircraft flight path optimization method based on inertial navigation provided in an embodiment of the present invention.

[0041] The embodiment of the present invention provides an aircraft flight path optimization method based on inertial navigation, which can be implemented by an aircraft flight path optimization device based on inertial navigation, and the aircraft flight path optimization device based on inertial navigation can be a terminal or a server. The processing flow of the aircraft flight path optimization method based on inertial navigation can include the following steps:

[0042] S1. Initial path planning. The initial path is generated by preset waypoints and path points. Let the initial path be P 0 ={p 1 ,p 2 ,...,p n}, where each waypoint p i Contains location r i =(x i ,y i ,z i ) and time information t i .

[0043] In the present invention, in order to ensure the safety and efficiency of the aircraft from the take-off point to the target point, it is necessary to first plan the initial path. It plans the flight route in advance by considering factors such as air traffic management, obstacle avoidance requirements, and fuel saving, laying the foundation for the smooth execution of the flight mission.

[0044] S2, Inertial Measurement Unit (IMU) data processing, obtain the original acceleration and angular velocity data from the IMU, assuming that the data obtained at time t is a t =[a tx ,aty ,q tz ] and ω t =[ω tx ,ω ty ,ω tz ].

[0045] In the present invention, it is necessary to obtain acceleration and angular velocity from the IMU as basic data for subsequent attitude and position estimation. By processing and analyzing them, the current state and motion of the aircraft can be inferred.

[0046] S3, improved attitude update algorithm, calculates aircraft attitude based on quaternion update algorithm, combines Kalman filter (KalmanFilter) and adaptive noise model to improve attitude estimation accuracy.

[0047] In the present invention, quaternions are used to describe the attitude of the aircraft. By fusing data from the IMU and data from other sensors (such as magnetometers), Kalman filtering is used for attitude estimation and noise compensation, thereby reducing error accumulation and improving the stability and accuracy of the navigation system. The adaptive noise model can dynamically adjust the noise parameters and optimize the attitude estimation process according to the characteristics of real-time measurement data, ensuring that good attitude control and navigation accuracy can be maintained under different flight conditions.

[0048] Specifically, the quaternion update algorithm in S3 includes:

[0049] Quaternion initialization:

[0050] q 0 =[q 0w ,q 0x ,q 0y ,q 0z ]=[1,0,0,0]

[0051] In the above formula, q 0w ,q 0x ,q 0y and q 0z They represent the scalar part and three vector parts of the quaternion respectively, and the initial attitude is the unit quaternion;

[0052] It can be understood that, in the present invention, quaternion initialization refers to determining the initial quaternion state before the start of flight or at a certain stage during the flight, and this initial state is usually set based on initial conditions and prior knowledge.

[0053] Quaternion Update:

[0054]

[0055] In the above formula, Δt represents the time interval, |ω t| represents the modulus of angular velocity, defined as:

[0056]

[0057] It can be understood that in the present invention, the quaternion update algorithm uses the angular velocity data from the IMU, obtains the increment of attitude change through integration, and then applies this increment to the current quaternion state to obtain the updated quaternion. This process is one of the key steps in attitude update in the present invention, and the attitude changes of the aircraft are tracked by continuously updating the attitude.

[0058] The update formula for quaternion multiplication is:

[0059]

[0060] In the above formula, represents quaternion multiplication and is defined as:

[0061]

[0062] q t =[q tx ,q ty ,q tz ]

[0063] Δq=[Δq x ,Δq y ,Δq z ].

[0064] It can be understood that quaternion multiplication is used to combine two quaternions and calculate their conformal rotation. In the present invention, this operation is used to apply the rotation increment to the current quaternion during the posture update process to obtain a new posture estimate. Through appropriate quaternion multiplication operations, it can be ensured that the posture update is continuous and accurate.

[0065] It should be noted that the quaternion update algorithm, as the core technology of attitude estimation in the method, significantly improves the accuracy and stability of the navigation system through continuous and accurate updates of attitude. This is particularly important for navigation in long-term flights or complex environments, and can effectively reduce error accumulation and improve the navigation safety of aircraft. The quaternion update algorithm has the ability to dynamically adjust the attitude and can quickly update the attitude state of the aircraft based on the real-time measured angular velocity data and environmental changes. This real-time and responsiveness enables the aircraft to maintain stable and accurate attitude control under complex aerial conditions. The quaternion update algorithm is combined with Kalman filtering and adaptive noise models to further optimize the accuracy of attitude estimation and system stability. This comprehensive method is not only applicable to single environmental conditions, but can also cope with various complex flight scenarios, improving the performance and reliability of the overall navigation system.

[0066] Specifically, the Kalman filter in S3 includes:

[0067] Define the state vector x t =[q t ,b t ] T , where b t is the gyroscope bias, and the Kalman filter equation is as follows:

[0068]

[0069] In the above formula, represents the prior state estimate, represents the posterior state estimate, F represents the state transfer matrix, B represents the control matrix, u t represents control input;

[0070] It is understood that in the present invention, a state vector is defined to describe the attitude of the aircraft and other related states. The state vector may include parameters such as gyroscope bias, which can be dynamically estimated and updated by Kalman filtering. The state transfer matrix describes the evolution of the state vector over time in the Kalman filter. It is related to the dynamic changes in the attitude of the aircraft and helps predict the state at the next moment.

[0071] Prediction update of the covariance matrix:

[0072] P t|t-1 =FP t-1|t-1 F T +Q

[0073] In the above formula, P t|t-1 represents the prior covariance matrix, P t-1|t-1 represents the posterior covariance matrix, Q represents the process noise covariance matrix;

[0074] It can be understood that the prior covariance matrix and the posterior covariance matrix in the Kalman filter represent the prediction and update of the uncertainty of the state estimation. By predicting and updating the covariance matrix, the system model and sensor errors can be taken into account in the state update process, thereby improving the accuracy and stability of the attitude estimation.

[0075] Calculation of Kalman gain:

[0076] K t =P t|t-1 H T (HP t|t-1 H T +R) -1

[0077] In the above formula, K trepresents the Kalman gain, H represents the observation matrix, and R represents the observation noise covariance matrix;

[0078] It is understood that the Kalman gain is a key adjustment parameter in the Kalman filter, which is used to balance the weights of the predicted value and the observed value. It is calculated by observing the relationship between the noise covariance matrix and the state prediction error. The calculation of the Kalman gain helps adjust the state estimate according to the actual observation value measured by the sensor, thereby reducing the error of the attitude estimation and improving the stability and accuracy of the navigation system.

[0079] Status Update:

[0080]

[0081] In the above formula, represents the posterior state estimate, z t represents the observed value;

[0082] Covariance matrix update:

[0083] P t|t =(IK t H)P t|t-1

[0084] In the above formula, P t|t represents the posterior covariance matrix and I represents the identity matrix.

[0085] It can be understood that the state update equation and the covariance matrix update equation describe how to use the Kalman gain to update the prior state estimate to the posterior state estimate and how to update the covariance matrix. These update processes combine IMU data and other sensor data to achieve accurate tracking and dynamic adjustment of the aircraft's attitude, thereby optimizing the planning and control of the flight path.

[0086] Specifically, the adaptive error correction model in S5 includes:

[0087] Error estimate:

[0088] Define the error estimation function h(·), input the current state and historical data, and output the error estimate e t =[e tx ,e ty ,e tz ];

[0089] It is understood that error estimation is a step in real-time calculation and evaluation of system errors during navigation. These errors include sensor measurement errors, system model errors, and external interference. In inertial navigation, error estimation is based on the difference between actual observed values ​​and predicted values. By continuously measuring the state of the aircraft (such as attitude, speed, and position) and comparing it with the state predicted by the Kalman filter, the error can be estimated. The error estimation process in the present invention is a key step in improving the accuracy of attitude and path estimation. Through accurate error estimation, the navigation system can identify and quantify various errors in the system, thereby providing a basis for subsequent adaptive correction. Error estimation helps detect and quantify various errors that occur during flight, providing accurate data support for the adaptive correction process.

[0090] Adaptive correction:

[0091] Let the correction factor be k t =[k tx ,k ty ,k tz ], the corrected position is:

[0092] p' t+1 =p t+1 +K t e t

[0093] In the above formula, K t represents the dynamic correction coefficient matrix, which is defined as:

[0094]

[0095] It can be understood that adaptive correction is the process of dynamically adjusting system parameters and states according to the real-time estimated error. It can adaptively optimize the performance of the navigation system according to the changes in the flight environment and system state. Adaptive correction uses the results of error estimation to reduce the impact of errors on system performance by adjusting system model parameters (such as filter gain, noise covariance, etc.). The adaptive correction model can adjust the parameters of the Kalman filter in real time so that it can more accurately estimate the attitude and path of the aircraft. The adaptive correction model in the present invention can significantly improve the robustness and accuracy of the navigation system by adjusting the system parameters in real time, so that the aircraft can still maintain accurate attitude and path control in a complex and dynamic flight environment. Adaptive correction improves the system's adaptability to various errors and environmental changes through dynamic optimization, ensuring that the aircraft can maintain the best navigation performance under various flight conditions. The combination of the adaptive correction model and the Kalman filter enables attitude update and path optimization to respond to environmental changes and error correction in real time, thereby achieving higher navigation accuracy and stability.

[0096] S4. Use the inertial navigation equation to calculate the position, combine the acceleration and attitude information, and set the velocity at the current time t to be v t =[v tx ,v ty ,v tz ], position is p t =[p tx ,p tx ,p tz ], then the position update equation is:

[0097]

[0098] In the above formula, represents the quaternion conjugate, defined as:

[0099]

[0100] The position update equation is:

[0101]

[0102] In the above formula, g=[0,0,g z ] is the gravitational acceleration vector.

[0103] It is understandable that the position of the aircraft is calculated in real time by the inertial navigation equation to ensure that the navigation system can track the motion trajectory of the aircraft. This is crucial for the planning and optimization of the flight path. By combining acceleration and attitude information, the position update equation can accurately calculate the position change. The attitude information is represented by quaternions, and the coordinate transformation is performed by quaternion conjugation so that the acceleration data can be correctly reflected in the navigation coordinate system. The position update equation takes into account the influence of the gravity acceleration vector, corrects the influence of acceleration, and further improves the accuracy of position estimation. This is consistent with the idea of ​​improving accuracy through Kalman filtering and adaptive error correction in the method of the present invention. The real-time calculated position data provides basic data support for path optimization. Combined with the particle swarm optimization and simulated annealing algorithm mentioned in the present invention, the flight path can be dynamically optimized to improve the efficiency and accuracy of the navigation system.

[0104] S5, adaptive error correction, uses the adaptive error correction model to correct the calculated position, and combines the machine learning algorithm to dynamically adjust the correction parameters.

[0105] It is understood that adaptive error correction uses an adaptive error correction model to make real-time corrections to the calculated position. The model combines a machine learning algorithm to dynamically adjust the correction parameters and automatically optimize the correction strategy based on the current aircraft state and environmental changes. Through error estimation and historical data analysis, the machine learning algorithm can identify and predict different types of error sources and adjust the correction parameters accordingly, thereby reducing error accumulation and improving the accuracy and stability of position estimation. This process ensures that the aircraft can still maintain accurate navigation and path control in complex and dynamic environments, significantly improving the overall performance of the navigation system.

[0106] S6. Path optimization. After defining the objective function, a combined optimization algorithm based on particle swarm optimization (PSO) and simulated annealing (SA) is used to optimize the path.

[0107] It can be understood that the path optimization part optimizes the flight path by defining the objective function and using a combined optimization algorithm based on the particle swarm optimization algorithm (PSO) and the simulated annealing algorithm (SA). The objective function comprehensively considers flight time, fuel consumption, safety and environmental factors. PSO uses swarm intelligence to search for the global optimal solution and quickly find the initial optimal path. SA conducts local search on this basis, avoids falling into the local optimum through the simulated annealing process, and improves the optimization effect. Combining the advantages of the two algorithms, the combined optimization algorithm can find the optimal flight path that better meets actual needs in a shorter time, thereby improving flight efficiency and safety.

[0108] Optionally, the objective function defined in S6 includes:

[0109] The objective function f(p) takes into account path length, time, fuel consumption and safety factors:

[0110] f(p)=w 1 L(p)+w 2 T(p)+w 3 F(p)+w 4 S(p)

[0111] In the above formula, L(p) represents the path length, which is defined as:

[0112]

[0113] T(p) represents time and is defined as:

[0114]

[0115] F(p) represents fuel consumption and is defined as:

[0116]

[0117] In the above formula, C f represents the fuel consumption coefficient, S(p) represents safety, and is defined as:

[0118]

[0119] In the above formula, σ(p i ) represents waypoint p i The safety index, weight coefficient w 1 、w 2 、w 3 and w 4 Set according to your needs.

[0120] It is understandable that path length is a basic indicator for optimizing flight paths, which directly affects flight time and fuel consumption. By calculating the total path length, the actual navigation route of the aircraft in the air can be evaluated and optimized to ensure the shortest flight path, thereby improving efficiency. Flight time is an important factor in optimizing flight paths. The time calculation takes into account the speed of each path segment, ensuring that path optimization not only focuses on distance, but also takes into account flight speed and time costs. This is closely related to the real-time dynamic path adjustment and optimization process. Fuel consumption directly affects flight costs and environmental impacts. The introduction of the fuel consumption coefficient enables the optimization algorithm to weigh the economy and environmental protection of the flight path, ensuring that the flight path is not only efficient, but also reduces fuel consumption and emissions. Safety is a key factor in path optimization. The safety of the flight path is evaluated through the safety indicators of the waypoints to ensure that the optimized path can ensure flight safety while meeting flight efficiency and economy. This part is related to the improvement of the stability and robustness of the navigation system.

[0121] Optionally, the particle swarm optimization algorithm in S6 includes:

[0122] Initialize the particle swarm and set the particle position and speed

[0123] Calculate the fitness value of each particle

[0124] Update particle velocity and position:

[0125]

[0126] In the above formula, ω represents the inertia weight, which is defined as:

[0127]

[0128] In the above formula, ω 0represents the initial inertia weight, ω f represents the final inertia weight, k max represents the maximum number of iterations, k represents the current number of iterations, and the learning factor c 1 and c 2 , defined as:

[0129]

[0130] In the above formula, and represents the initial learning factor, and Represents the final learning factor, random number r 1 and r 2 Following the uniform distribution, update the example position:

[0131]

[0132] It is understandable that the initialization of the particle swarm provides a variety of initial path solutions, ensuring that the algorithm has a strong exploration capability. The calculated fitness value evaluates the quality of each path solution and provides a basis for subsequent speed and position updates. The updated speed and position guide the particles to gradually converge to the optimal path through the dynamic adjustment of the inertia weight and learning factor, improving the efficiency and effect of path optimization.

[0133] Optionally, the simulated annealing algorithm in S6 includes:

[0134] Initialization temperature T 0 ;

[0135] Stepwise decrease of temperature T is defined as:

[0136] T=T 0 α k

[0137] In the above formula, α represents the cooling rate, 0<α<1, and k represents the number of iterations;

[0138] Perform a local search at the current temperature and receive probability:

[0139]

[0140] In the above formula, ΔE represents the energy difference, which is defined as:

[0141] ΔE=f(x new )-f(x old ).

[0142] It can be understood that the simulated annealing algorithm balances global search and local optimization during flight path optimization through initial high temperature, gradual cooling, and temperature-dependent acceptance probability mechanism. The high temperature in the initial stage allows the algorithm to explore a wide range of path solutions and prevent early convergence to the local optimum. As the temperature decreases, the algorithm gradually focuses on searching for better path solutions, improving the accuracy and stability of path optimization. The simulated annealing algorithm is combined with the particle swarm optimization algorithm to ensure the optimization effect of the flight path in multiple dimensions and improve the overall performance of the navigation system.

[0143] S7, path evaluation and correction, combines the flight environment and dynamic information to evaluate the path quality in real time and make corrections based on the path correction strategy. The path quality is evaluated through path evaluation indicators, which include track deviation, safety and fuel efficiency.

[0144] It is understandable that path evaluation and correction ensures that the aircraft always maintains the best path during flight by monitoring the flight path deviation, safety and fuel efficiency in real time. By dynamically adjusting and optimizing the path, the system can adapt to the changing flight environment and conditions to ensure efficient, safe and economical flight. This process improves the real-time and accuracy of the navigation system and ensures the smooth completion of the flight mission.

[0145] Optionally, the path evaluation indicators in S7 include:

[0146] Track deviation, the degree to which the path actually deviates from the planned path, is defined as:

[0147]

[0148] In the above formula, r i Indicates the actual location, Indicates the planned location;

[0149] Safety assessment, whether the path avoids dangerous areas, is evaluated using the risk function:

[0150]

[0151] In the above formula, ρ(r i ) represents waypoint r i The risk value of

[0152] Fuel efficiency, the fuel consumption of a route, is defined as:

[0153]

[0154] In the above formula, d i =||r i -r i-1 || indicates the distance between adjacent waypoints.

[0155] Optionally, the path correction strategy in S7 includes:

[0156] Dynamically adjust path nodes to avoid obstacles and dangerous areas, using obstacle avoidance functions:

[0157]

[0158] In the above formula, o(r i ) represents waypoint r i The obstacle impact value of

[0159] Adjust the route according to real-time weather information to improve safety and efficiency, using the weather correction function:

[0160]

[0161] In the above formula, w(r i ,t i ) represents waypoint r i At time t i The weather impact value.

[0162] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0163] (1) In the present invention, the accumulated error in the inertial navigation system is effectively reduced through an improved attitude update algorithm and an adaptive error correction model, which significantly improves the navigation accuracy and attitude stability of the aircraft, especially in long-term flight or complex environments;

[0164] (2) In the present invention, the combined optimization strategy of particle swarm optimization and simulated annealing algorithm is combined to achieve global search and local optimization of the flight path, effectively balancing multiple objectives such as path length, flight time, fuel consumption and safety, and significantly improving the optimization efficiency and economy of the flight path;

[0165] (3) In the present invention, a real-time path evaluation and correction mechanism is introduced, and the path is adjusted in combination with the dynamic information of the flight environment, so that the aircraft can respond to emergencies and environmental changes in a timely manner, thereby improving the safety and flexibility of the flight mission and providing reliable navigation guarantee for flight missions in complex flight environments.

[0166] Reference Manual Attached Figure 2 , showing a schematic structural diagram of an aircraft flight path optimization system based on inertial navigation provided in an embodiment of the present invention.

[0167] The present invention also provides an aircraft flight path optimization system based on inertial navigation, which is applied to an aircraft flight path optimization method based on inertial navigation, comprising:

[0168] processor;

[0169] A memory having computer-readable instructions stored therein, wherein when the computer-readable instructions are executed by the processor, an aircraft flight path optimization method based on inertial navigation as in the method embodiment is implemented.

[0170] The aircraft flight path optimization system based on inertial navigation provided by the present invention can execute the above-mentioned aircraft flight path optimization method based on inertial navigation and achieve the same or similar technical effects. To avoid repetition, the present invention will not go into details.

[0171] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0172] (1) In the present invention, the accumulated error in the inertial navigation system is effectively reduced through an improved attitude update algorithm and an adaptive error correction model, which significantly improves the navigation accuracy and attitude stability of the aircraft, especially in long-term flight or complex environments;

[0173] (2) In the present invention, the combined optimization strategy of particle swarm optimization and simulated annealing algorithm is combined to achieve global search and local optimization of the flight path, effectively balancing multiple objectives such as path length, flight time, fuel consumption and safety, and significantly improving the optimization efficiency and economy of the flight path;

[0174] (3) In the present invention, a real-time path evaluation and correction mechanism is introduced, and the path is adjusted in combination with the dynamic information of the flight environment, so that the aircraft can respond to emergencies and environmental changes in a timely manner, thereby improving the safety and flexibility of the flight mission and providing reliable navigation guarantee for flight missions in complex flight environments.

[0175] It should be understood that the processor in the embodiment of the present invention may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0176] It should also be understood that the memory in the embodiments of the present invention may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link DRAM (SLDRAM), and direct RAM bus random access memory (DR RAM).

[0177] The above embodiments can be implemented in whole or in part by software, hardware (such as circuits), firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0178] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0179] In the present invention, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can be represented by: a, b, c, ab, ac, bc, or abc, where a, b, c can be single or multiple.

[0180] It should be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0181] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0182] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described equipment, devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0183] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0184] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0185] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0186] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0187] An embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein when the program is executed by a processor, the method for optimizing an aircraft flight path based on inertial navigation as described in the method embodiment is implemented.

[0188] A computer-readable storage medium provided by the present invention can implement the steps and effects of the aircraft flight path optimization method based on inertial navigation of the above method embodiment. To avoid repetition, the present invention will not go into details.

[0189] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0190] (1) In the present invention, the accumulated error in the inertial navigation system is effectively reduced through an improved attitude update algorithm and an adaptive error correction model, which significantly improves the navigation accuracy and attitude stability of the aircraft, especially in long-term flight or complex environments;

[0191] (2) In the present invention, the combined optimization strategy of particle swarm optimization and simulated annealing algorithm is combined to achieve global search and local optimization of the flight path, effectively balancing multiple objectives such as path length, flight time, fuel consumption and safety, and significantly improving the optimization efficiency and economy of the flight path;

[0192] (3) In the present invention, a real-time path evaluation and correction mechanism is introduced, and the path is adjusted in combination with the dynamic information of the flight environment, so that the aircraft can respond to emergencies and environmental changes in a timely manner, thereby improving the safety and flexibility of the flight mission and providing reliable navigation guarantee for flight missions in complex flight environments.

[0193] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art who is familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

[0194] There are a few points to note:

[0195] (1) The drawings of the embodiments of the present invention only relate to the structures related to the embodiments of the present invention, and other structures may refer to the general design.

[0196] (2) For the sake of clarity, in the drawings used to describe the embodiments of the present invention, the thickness of the layers or regions is exaggerated or reduced, that is, these drawings are not drawn according to the actual scale. It is understood that when an element such as a layer, film, region or substrate is referred to as being "on" or "under" another element, the element may be "directly" "on" or "under" the other element or there may be intermediate elements.

[0197] (3) In the absence of conflict, the embodiments of the present invention and the features therein may be combined with each other to obtain new embodiments.

[0198] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. The protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A method for optimizing an aircraft flight path based on inertial navigation, characterized in that: include: S1. Initial path planning. The initial path is generated by preset waypoints and path points. Suppose the initial path is P0 = {p1, p2, ..., p n }, where each waypoint p i Contains location r i =(x i ,y i , z i ) and time information t i ; S2, inertial measurement unit (IMU) data processing, obtaining raw acceleration and angular velocity data from the IMU, assuming that the data obtained at time t is a t =[a tx , a ty , a tz ] and ω t =[ω tx ,ω ty ,ω tz ]; S3, improved attitude update algorithm, which calculates the aircraft attitude based on the quaternion update algorithm, and improves the attitude estimation accuracy by combining Kalman filter and adaptive noise model; S4. Use the inertial navigation equation to calculate the position, combine the acceleration and attitude information, and set the velocity at the current time t to be v t =[v tx , v ty , v tz ], position is p t =[p tx , p ty , p tz ], then the position update equation is: In the above formula, represents the quaternion conjugate, defined as: The position update equation is: In the above formula, g = [0, 0, g z ] is the gravitational acceleration vector; S5, adaptive error correction, using the adaptive error correction model to correct the calculated position and dynamically adjust the correction parameters in combination with the machine learning algorithm; S6, path optimization, after defining the objective function, a combined optimization algorithm based on particle swarm optimization (PSO) and simulated annealing (SA) is used to optimize the path; S7, path evaluation and correction, combining the flight environment and dynamic information, evaluating the path quality in real time, and correcting it according to the path correction strategy. The path quality is evaluated by path evaluation indicators, and the path evaluation indicators include track deviation, safety and fuel efficiency.

2. The method for optimizing an aircraft flight path based on inertial navigation according to claim 1, characterized in that: The quaternion update algorithm in S3 specifically includes: Quaternion initialization: q0=[q0w, q0x, q0y, q0z]=[1,0,0,0] In the above formula, q 0w ,q 0x ,q 0y and q 0z They represent the scalar part and three vector parts of the quaternion respectively, and the initial attitude is the unit quaternion; Quaternion Update: In the above formula, Δt represents the time interval, |ω t | represents the modulus of angular velocity, defined as: The update formula for quaternion multiplication is: In the above formula, represents quaternion multiplication and is defined as: q t =[q tx ,q ty ,q tz ] Δq=[Δq x ,Δq y ,Δq z ]。 3. The method for optimizing an aircraft flight path based on inertial navigation according to claim 1, characterized in that: The Kalman filter in S3 specifically includes: Define the state vector x t =[q t , b t ] T , where b t For the gyroscope bias, the Kalman filter equation is as follows: In the above formula, represents the prior state estimate, represents the posterior state estimate, F represents the state transfer matrix, B represents the control matrix, u t represents control input; Prediction update of the covariance matrix: P t|t-1 =FP t-1|t-1 F T +Q In the above formula, P t|t-1 represents the prior covariance matrix, P t-1|t-1 represents the posterior covariance matrix, Q represents the process noise covariance matrix; Calculation of Kalman gain: K t =P t|t-1 H T (HP t|t-1 H T +R) -1 In the above formula, K t represents the Kalman gain, H represents the observation matrix, and R represents the observation noise covariance matrix; Status Update: In the above formula, represents the posterior state estimate, z t represents the observed value; Covariance matrix update: P t|t =(I-K t H)P t|t-1 In the above formula, P t|t represents the posterior covariance matrix and I represents the identity matrix.

4. The method for optimizing an aircraft flight path based on inertial navigation according to claim 1, characterized in that: The adaptive error correction model in S5 specifically includes: Error estimate: Define the error estimation function h(·), input the current state and historical data, and output the error estimate e t =[e tx , e ty , e tz ]; Adaptive correction: Let the correction factor be k t =[k tx , k ty , k tz ], the corrected position is: p′ t+1 =p t+1 +K t e t In the above formula, K t represents the dynamic correction coefficient matrix, which is defined as:

5. The method for optimizing an aircraft flight path based on inertial navigation according to claim 1, characterized in that: The objective function defined in S6 specifically includes: The objective function f(p) takes into account path length, time, fuel consumption and safety factors: f(p)=w1L(p)+w2T(p)+w3F(p)+w4S(p) In the above formula, L(p) represents the path length, which is defined as: T(p) represents time and is defined as: F(p) represents fuel consumption and is defined as: In the above formula, C f represents the fuel consumption coefficient, S(p) represents safety, and is defined as: In the above formula, σ(p i ) represents waypoint p i The safety index, weight coefficients w1, w2, w3 and w4 are set according to requirements.

6. The method for optimizing an aircraft flight path based on inertial navigation according to claim 1, characterized in that: The particle swarm optimization algorithm in S6 specifically includes: Initialize the particle swarm and set the particle position and speed Calculate the fitness value of each particle Update particle velocity and position: In the above formula, ω represents the inertia weight, which is defined as: In the above formula, ω0 represents the initial inertia weight, ω f represents the final inertia weight, k max represents the maximum number of iterations, k represents the current number of iterations, and the learning factors c1 and c2 are defined as: In the above formula, and represents the initial learning factor, and Represents the final learning factor, random numbers r1 and r2 follow a uniform distribution, and update the example position:

7. The method for optimizing an aircraft flight path based on inertial navigation according to claim 1, characterized in that: The simulated annealing algorithm in S6 specifically includes: Initialization temperature T0; Stepwise decrease of temperature T is defined as: T=T0·α k In the above formula, α represents the cooling rate, 0<α<1, and k represents the number of iterations; Perform a local search at the current temperature and receive probability: In the above formula, ΔE represents the energy difference, which is defined as: ΔE=f(x new )-f(x old )。 8. The method for optimizing an aircraft flight path based on inertial navigation according to claim 1, characterized in that: The path evaluation indicators in S7 specifically include: Track deviation, the degree to which the path actually deviates from the planned path, is defined as: In the above formula, ri represents the actual position, Indicates the planned location; Safety assessment, whether the path avoids dangerous areas, is evaluated using the risk function: In the above formula, ρ(r i ) represents waypoint r i The risk value of Fuel efficiency, the fuel consumption of a route, is defined as: In the above formula, d i =||r i -r i-1 || indicates the distance between adjacent waypoints.

9. The method for optimizing an aircraft flight path based on inertial navigation according to claim 1, characterized in that: The path correction strategy in S7 specifically includes: Dynamically adjust path nodes to avoid obstacles and dangerous areas, using obstacle avoidance functions: In the above formula, o(r i ) represents waypoint r i The obstacle impact value of Adjust the route according to real-time weather information to improve safety and efficiency, using the weather correction function: In the above formula, w(r i , t i ) represents waypoint r i At time t i The weather impact value.

10. An aircraft flight path optimization system based on inertial navigation, characterized in that: include: processor; A memory having computer-readable instructions stored thereon, wherein when the computer-readable instructions are executed by the processor, the aircraft flight path optimization method based on inertial navigation as claimed in any one of claims 1 to 9 is implemented.

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