Intelligent multi-source autonomous navigation system

Abstract

The invention relates to an intelligent multi-source autonomous navigation system, and belongs to the field of navigation and guidance. The system comprises a multi-source sensing measurement module which is integrated with a plurality of navigation sensors and outputs observed quantity for navigation and positioning; the information processing module is used for receiving observed quantities of multiple navigation sensors and obtaining navigation positioning information of a carrier by adopting corresponding navigation calculation algorithms, and a combination of a single navigation sensor and the corresponding navigation calculation algorithm is recorded as a single navigation information source; according to navigation task requirements, evaluating navigation positioning information output by each single navigation information source to obtain an optimal navigation information source configuration scheme in a current task and a current scene, screening out an optimal information fusion algorithm suitable for optimal navigation information source configuration from a stored navigation algorithm library, and carrying out information fusion according to the optimal information fusion algorithm. And carrying out multi-source fusion on the output of the optimal navigation information source by adopting an optimal information fusion algorithm to obtain an optimized navigation positioning result.

Description

Technical Field

[0001] The invention relates to an intelligent multi-source autonomous navigation system, belonging to the field of navigation and guidance. Background Art

[0002] With the continuous breakthroughs in key technologies such as artificial intelligence, sensor technology, communication technology, and big data processing technology, unmanned system technology has developed rapidly. In some scenarios that require long-term, high-intensity or high-risk operations, the application of unmanned systems can greatly reduce dependence on humans.

[0003] Autonomy is one of the typical characteristics of unmanned systems. In order to realize the autonomous perception, autonomous decision-making, and autonomous execution capabilities of unmanned systems, the system needs to have high-precision positioning and navigation capabilities. At present, the navigation systems and tasks of various unmanned systems are becoming increasingly complex, and the demand for autonomy, safety, and reliability of navigation systems is increasing. Single satellite navigation is not only susceptible to electromagnetic interference, but also has limitations such as obstruction, shielding, and limited transceiver equipment. The intelligent multi-source autonomous navigation system can comprehensively utilize multiple information sources such as Beidou, inertial, atmosphere, geomagnetism, optics, radio signals, images, etc., and determine navigation and positioning information through perception, fusion, decision-making, and evaluation. It can significantly improve navigation accuracy and reliability while ensuring the continuity and stability of navigation and reducing errors and uncertainties caused by a single information source.

[0004] In the existing unmanned system autonomous navigation module technical solutions, laser radar is often used as the main information source for precise autonomous navigation tasks. A robot autonomous navigation method, device, equipment and storage medium (patent number: CN118483996A) uses laser radar to obtain synchronous positioning and cost map, constructs a multimodal semantic map with the cost map and the oral scene, and combines the robot's multi-dimensional cognitive library for autonomous navigation. This method can effectively realize the navigation of unmanned systems, but due to the small number of information sources, the system flexibility is poor and the application scope is limited.

[0005] As the application scenarios of unmanned systems increase and the tasks they undertake become increasingly complex, unmanned systems are constantly developing in the direction of low cost, miniaturization, clustering, and scale expansion. This requires their navigation systems to use high-density integration technology for intelligent multi-source autonomous navigation system integration while ensuring navigation accuracy, so that the navigation system also needs to have the characteristics of low cost and miniaturization.

[0006] At present, my country's unmanned systems such as drones, unmanned vehicles, guided munitions and cruise missiles urgently need a low-cost, multi-functional and cost-effective intelligent multi-source autonomous navigation system. Summary of the invention

[0007] The technical problem solved by the present invention is: to solve the problem of autonomous navigation of unmanned systems under GNSS (Global Navigation Satellite System) denial conditions, an intelligent multi-source autonomous navigation system is provided to achieve hardware integration of multiple information source sensors, multi-source fusion of information and autonomous navigation decision-making, and enhance the system's autonomous navigation capability.

[0008] The technical solution of the present invention is: an intelligent multi-source autonomous navigation system, comprising a multi-source perception measurement module and an information processing module;

[0009] Multi-source perception measurement module, integrating multiple navigation sensors and outputting observation quantities for navigation and positioning;

[0010] The information processing module receives observations from multiple navigation sensors and uses corresponding navigation solution algorithms to obtain navigation and positioning information of the carrier. The combination of a single navigation sensor and its corresponding navigation solution algorithm is recorded as a single navigation information source. According to the navigation task requirements, the navigation and positioning information output by each single navigation information source is evaluated to obtain the optimal navigation information source configuration scheme for the current task and the current scenario, and the optimal information fusion algorithm suitable for the optimal navigation information source configuration is screened out from the stored navigation algorithm library. The optimal information fusion algorithm is used to perform multi-source fusion on the output of the optimal navigation information source to obtain an optimized navigation and positioning result.

[0011] The navigation sensor includes: an IMU unit, a GNSS receiver, a visual camera, and a magnetometer;

[0012] The observations of the IMU unit include the angular velocity information and acceleration information of the carrier;

[0013] The observations of the GNSS receiver include the pseudorange between the carrier and the navigation satellite, the carrier phase and the differential correction, and the differential correction includes the satellite clock error, the receiver clock error, the ionospheric delay and the tropospheric delay;

[0014] The observations of the visual camera include environmental image information;

[0015] The observations of the magnetometer include the magnetic field strength and direction information.

[0016] The information processing module includes a navigation solution module, an information source configuration optimization module, an availability screening module, an optimality screening module, an information fusion module, and a gating switch;

[0017] The navigation solution module uses a mechanical arrangement algorithm to obtain the carrier's speed, position, and attitude information based on the angular velocity information and acceleration information output by the IMU unit; uses a differential positioning algorithm to calculate the carrier's position and velocity information based on the pseudorange, carrier phase, and differential correction output by the GNSS receiver; uses a SLAM algorithm to calculate the carrier's position and attitude information based on the environmental image information; and uses a MAGCOM algorithm to calculate the carrier's position information based on the magnetic field strength and direction information;

[0018] The information source configuration optimization module obtains the navigation positioning information output by each navigation information source, calculates the quality coefficient of each navigation information source and performs normalization processing. Based on the normalized quality coefficient, which includes the accuracy coefficient, real-time coefficient, anti-interference coefficient and fault tolerance coefficient, combined with the externally input navigation source performance index weight, the optimal navigation information source configuration scheme under the current mission scenario is solved through a multi-objective optimization algorithm;

[0019] Availability screening module, which screens out the available information fusion algorithms under the optimal navigation information source configuration;

[0020] The optimality screening module selects the optimal information fusion algorithm from the available information fusion algorithms;

[0021] A strobe switch sends the output of the configured navigation information source to the information fusion module;

[0022] The information fusion module uses the optimal information fusion algorithm to perform multi-source fusion based on the configured navigation information source output to obtain the fused navigation and positioning results of the carrier.

[0023] Compared with the prior art, the present invention has the following beneficial effects:

[0024] (1) The present invention provides an intelligent multi-source autonomous navigation system for unmanned systems with a new system architecture, which encapsulates an information processing module, a power management module, a multi-source perception measurement module, a safety monitoring module, and an online storage module in a metal shell, and realizes an integrated architecture design of intelligent multi-source autonomous navigation through a "plug and play" open system architecture;

[0025] (2) The present invention collects multi-source perception sensor data such as angular rate signals, acceleration signals, air pressure altitude signals, magnetic intensity signals, and positioning timing signals, constructs judgment criteria based on observability theory and mathematical statistics theory, and executes multi-objective optimization algorithms in combination with task requirements to improve the credibility of intelligent multi-source autonomous navigation perception signals in complex environments and ensure the fault tolerance of the perception system in the event of sensor failures; and improves the robustness and adaptability of the navigation and positioning system through multi-source fusion of heterogeneous navigation information source solution results;

[0026] (3) The present invention provides a high-density integrated intelligent multi-source autonomous navigation system, which adopts advanced packaging technologies such as TSV, RDL, IPD, SiP, etc. to achieve high-density integration of system modules and three-dimensional integration of the system, reduce the volume, weight and power consumption of the intelligent multi-source autonomous navigation system, and enable the unmanned system to have more effective space and payload;

[0027] (4) Based on the design concept of "software-defined navigation", the present invention fully considers the versatility and extensibility of the hardware level, as well as the modularization and integration of the software level. The overall software system is divided into "navigation information solution module", "sensor configuration optimization module", "availability screening module", "optimality screening module" and "information fusion module" according to its functions, so that the overall software architecture has high readability and is easy to maintain and upgrade, meeting the autonomous and controllable technical requirements of unmanned systems for future military-civilian integration;

[0028] (5) The present invention realizes the hardware integration of various information source sensors such as Beidou, inertial, atmospheric, geomagnetic, optical, radio signal, image, etc., multi-source information fusion and autonomous guidance decision-making, meets the functions of sensor data reception and processing, navigation solution, various interface communications and AD (Analog Digital) acquisition, and solves the urgent needs of various unmanned systems for "software-defined navigation" of navigation, guidance and control systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 A schematic diagram of the composition of an intelligent multi-source autonomous navigation system for unmanned systems according to a preferred embodiment of the present invention;

[0030] Figure 2 A principle block diagram of an intelligent multi-source autonomous navigation system for unmanned systems according to a preferred embodiment of the present invention;

[0031] Figure 3 This is a principle block diagram of an information processing module according to a preferred embodiment of the present invention;

[0032] Figure 4 A flow chart of an intelligent multi-source autonomous navigation system for unmanned systems according to a preferred embodiment of the present invention;

[0033] Figure 5 This is a principle block diagram of “software defined navigation” of an intelligent multi-source autonomous navigation system according to a preferred embodiment of the present invention. DETAILED DESCRIPTION

[0034] In order to make the purpose, technical scheme and advantages of the present invention clearer, the present invention is further described in detail below in conjunction with specific embodiments and with reference to the accompanying drawings. It should be understood that these descriptions are only exemplary and are not intended to limit the scope of the present invention. In addition, in the following description, the description of well-known structures and technologies is omitted to avoid unnecessary confusion of the concept of the present invention.

[0035] like Figure 1 FIG. 1 is a schematic diagram of a composition of an intelligent multi-source autonomous navigation system according to a preferred embodiment of the present invention. Figure 2 This is a principle block diagram of an intelligent multi-source autonomous navigation system for unmanned systems according to a preferred embodiment of the present invention, including an information processing module M1, a power management module M2, a multi-source perception and measurement module M3, a safety monitoring module M4 and an information storage module M5.

[0036] In a preferred specific embodiment of the present invention, the information processing module M1 is composed of a main processor and a crystal oscillator. The power management module M2 is used to receive an external input power supply voltage of 6V to 36V, and convert it into a rated voltage through a DC voltage converter to power each chip inside the intelligent multi-source autonomous navigation system. In addition, the power management module M2 can also receive an external input power supply voltage of 5V to 36V. When the external input voltage is lower than 5V, a charge pump is required to perform a voltage boost and voltage stabilization operation.

[0037] The multi-source perception measurement module M3 integrates multiple navigation sensors and outputs observation quantities for navigation and positioning. It is composed of multi-source perception sensors such as IMU units (including micro-inertial gyroscopes, micro-inertial accelerometers), GNSS receivers, barometers, magnetometers, polarization sensors, data link rangefinders, monocular / binocular vision cameras and expansion modules.

[0038] In a preferred specific embodiment of the present invention, the three-axis micro-gyroscope type adopts a MEMS gyroscope, the three-axis micro-accelerometer type adopts a MEMS accelerometer, and the three-axis magnetometer type adopts a TMR magnetometer.

[0039] The safety monitoring module M4 is composed of a watchdog monitoring chip. The information storage module M5 is composed of a FLASH flash memory and a RAM memory.

[0040] like Figure 3 As shown, the information processing module M1 communicates and transmits data with the power management module M2, the multi-source sensing and measuring module M3, the safety monitoring module M4 and the information storage module M5 in an electrically connected manner.

[0041] The power management module M2 provides instrument power supply signals to the information processing module M1, the multi-source sensing and measuring module M3, the safety monitoring module M4 and the information storage module M5 in an electrically connected manner.

[0042] Each component is packaged in a metal shell through a high-density integration method, and the metal shell is installed and fixed on the unmanned system through mounting holes. The high-density integration method is achieved through high-density module integration and high-density system integration. The high-density module integration refers to the use of TSV adapter boards to achieve electrical signal interconnection between chips in the module in the information processing module M1, the power management module M2 and the information storage module M5, and the horizontal electrical signal interconnection between chips in the module is achieved through multi-layer RDL, passive device integration is achieved through IPD, and flip-chip solder balls are used to achieve electrical signal interconnection from chips in the module to the TSV adapter board, and SiP packaging technology is used to achieve complete module functions through interconnection between functional layers. The high-density system integration refers to the use of rigid-flex boards to achieve electrical signal interconnection between chips and modules, the use of thermal conductive glue to achieve structural interconnection between rigid-flex boards and metal tube shells, the use of thermal conductive silicone grease to achieve chip heat dissipation, and the complete intelligent multi-source autonomous navigation system functions for unmanned systems through interconnection between functional layers.

[0043] In a preferred specific embodiment of the present invention, the processor in the information processing module M1 receives various types of perception and feedback information input by the multi-source perception measurement module M3, the safety monitoring module M4 and the information storage module M5, and performs complex navigation attitude solution. In the information processing module M1, the processor main frequency is better than 250MHz, the computing power is better than 300DMIPS, supports floating-point operations and multi-level interrupt management, and supports complex navigation attitude solution. In the information processing module M1, the crystal oscillator frequency is better than 24MHz. The information processing module M1 is configured with a navigation interface. The navigation interface is configured as a power supply 6V~36V input, a 3.3V reference output, a power ground, 3 UART serial communication, 2 CAN buses, 1 RS232 serial communication, 1 RS422 serial communication, 1 SPI serial communication, 1 I2C communication, 1 USB communication, 3 GPIO general interfaces, and 8 ADCs. The multi-source sensing and measuring module M3 includes a micro-inertial gyroscope, a micro-inertial accelerometer, a GNSS receiver, a barometer, a magnetometer, a polarization sensor, a data link rangefinder, a monocular / binocular vision camera and an expansion module; and is electrically connected to the information processing module M1, while the power management module M2 provides power to the multi-source sensing and measuring module M3, and acts as a filter circuit to reduce ripple and surge interference. The micro-inertial gyroscope adopts a three-axis (X / Y / Z) MEMS gyroscope, the micro-inertial accelerometer adopts a three-axis (X / Y / Z) MEMS accelerometer, and the magnetometer adopts a three-axis (X / Y / Z) TMR magnetometer. The X / Y / Z three axes follow the "right-hand rule" distribution, and the three axes are orthogonal to each other. The three-axis MEMS gyroscope outputs angular rate signals; the three-axis micro-accelerometer outputs acceleration signals; the GNSS receiver outputs navigation positioning and timing signals; the barometer outputs air pressure altitude signals; the three-axis magnetometer outputs magnetic induction intensity signals; the polarization sensor outputs navigation positioning signals; the data link rangefinder outputs relative distance signals; the monocular / binocular vision camera outputs image signals; the expansion module can expand other sensors through a plug-and-play interface protocol; the above-mentioned multi-source perception signals are transmitted to the processor of the information processing module M1 through an SPI interface, an I2C interface, a CAN bus or a serial port.

[0044] In a preferred specific embodiment of the present invention, the GNSS module can use one or any combination of BDS, GPS, Galileo, GLONASS to perform positioning and timing calculation. The safety monitoring module M4 monitors the status information and alarm information of each module in real time through a watchdog chip, and outputs it to the processor of the information processing module M1 through a serial port.

[0045] The information processing module receives observations from multiple navigation sensors, adopts corresponding navigation solution algorithms, obtains navigation and positioning information of the carrier, and the combination of a single navigation sensor and its corresponding navigation solution algorithm is recorded as a single navigation information source; according to the navigation task requirements, the navigation and positioning information output by each single navigation information source is evaluated to obtain the optimal navigation information source configuration scheme under the current task and the current scenario, and the optimal information fusion algorithm suitable for the optimal navigation information source configuration is screened out from the stored navigation algorithm library, and the optimal information fusion algorithm is used to perform multi-source fusion on the output of the optimal navigation information source to obtain an optimized navigation and positioning result.

[0046] The information processing module includes a navigation solution module, an information source configuration optimization module, an availability screening module, an optimality screening module, an information fusion module, and a gating switch;

[0047] The navigation solution module uses a mechanical arrangement algorithm to obtain the carrier's speed, position, and attitude information based on the angular velocity information and acceleration information output by the IMU unit; uses a differential positioning algorithm to calculate the carrier's position and velocity information based on the pseudorange, carrier phase, and differential correction output by the GNSS receiver; uses a SLAM algorithm to calculate the carrier's position and attitude information based on the environmental image information; and uses a MAGCOM algorithm to calculate the carrier's position information based on the magnetic field strength and direction information;

[0048] The information source configuration optimization module obtains the navigation positioning information output by each navigation information source, calculates the quality coefficient of each navigation information source and performs normalization processing. Based on the normalized quality coefficient, which includes the accuracy coefficient, real-time coefficient, anti-interference coefficient and fault tolerance coefficient, combined with the externally input navigation source performance index weight, the optimal navigation information source configuration scheme under the current mission scenario is solved through a multi-objective optimization algorithm;

[0049] Availability screening module, which screens out the available information fusion algorithms under the optimal navigation information source configuration;

[0050] The optimality screening module selects the optimal information fusion algorithm from the available information fusion algorithms;

[0051] A strobe switch sends the output of the configured navigation information source to the information fusion module;

[0052] The information fusion module uses the optimal information fusion algorithm to perform multi-source fusion based on the configured navigation information source output to obtain the fused navigation and positioning results of the carrier.

[0053] The quality coefficient normalization is calculated using the min-max method:

[0054]

[0055] in, is the kth quality coefficient of the jth navigation source; C kmin is the minimum value of the kth quality coefficient of all navigation sources; C kmax is the maximum value of the kth quality coefficient of all navigation sources.

[0056] The navigation source performance indicators include accuracy, anti-interference, real-time and fault tolerance.

[0057] The anti-interference coefficient is calculated by the following formula:

[0058] D j =a 1 η j +a 2 ε j ,

[0059] Where D j is the anti-interference coefficient of the jth navigation information source; a 1 ,a 2 is the weight parameter, which can be adjusted according to engineering experience; η j ,ε j are the system observability and system disturbance of the j-th navigation information source, respectively, and are calculated by the following formula:

[0060]

[0061] In the formula, is the state observability of the i-th output of the j-th navigation information source; is the state disturbance degree of the ith output of the jth navigation information source; r is the number of outputs of the jth navigation information source, i=1~r.

[0062] State Observability Obtained by the following method:

[0063] For a single navigation information source, a linear steady-state system is constructed:

[0064] X k =FX k-1 +w k-1 ,

[0065] Z k =HX k +v k ,

[0066] In the formula, H k is the state variable of the navigation information source, including the estimated error of the carrier's motion state and the navigation information source error-related variables (such as the random walk and zero bias of the inertial navigation gyroscope); F is the state transfer matrix; Z kis the observed variable, i.e., the output of the navigation information source; H is the measurement matrix; w k-1 ,v k are process noise and measurement noise respectively;

[0067] Construct the observability matrix Q and perform SVD singular value decomposition on it:

[0068]

[0069] In the formula, Its diagonal elements are the singular values ​​of Q; U is an orthogonal matrix, and its column vectors are QQ T The eigenvector of ; V is an orthogonal matrix, whose column vector is Q T The eigenvector of Q;

[0070] Calculate the state observability of the i-th output of the j-th navigation information source:

[0071]

[0072] In the formula, σ i is the i-th singular value of the observability matrix of the system corresponding to the j-th navigation information source; max(σ i ) represents the maximum value among all its singular values; this indicator mainly reflects the characteristics of the navigation information source itself, which is determined by its measurement principle (state transfer matrix F) and output (measurement matrix H).

[0073] The state disturbance Obtained by the following method:

[0074] For the i-th output of navigation information source j Construct an extended state observer (ESO) and transform the output As the input of the observer, the ESO expansion state observation signal of the i-th output is obtained, the variance of the ESO expansion state observation signal of the i-th output is obtained, and the state disturbance degree is calculated:

[0075]

[0076] In the formula, is the initial observation noise variance of the i-th output of the j-th navigation source, is the variance of the ESO expansion state observation signal of the ith output.

[0077] For the i-th output of navigation information source j Can be ordered Build the following system:

[0078]

[0079] In the formula, f(x1 ,x 2 ,t) is usually an unknown term, including internal disturbances caused by modeling inaccuracies and external disturbances of the system; y is the system output; u is the system control input; b is the system control coefficient;

[0080] Add expansion state x to the system 3 =f(x 1 ,x 2 ,t), Construct an extended state observer (ESO) to observe the system state x 1 ,x 2 ,x 3 ; The ESO observer takes the system control input u and the system output y as input and outputs Three state observations; let Then output for navigation information source The ESO observer can be constructed as follows:

[0081]

[0082] In the formula, is the observer output; β 01 ,β 02 ,β 03 is the ESO observer parameter, and its setting depends on engineering experience; is a nonlinear feedback function, and α can be taken 1 =0.5,α 2 =0.25 (general experience value), δ is a constant that affects the filtering effect. To balance the tracking speed of the observer and the filtering effect, the values ​​of α and δ should be considered in combination with engineering experience; u is the system control input; b is the system control coefficient;

[0083] In summary, the state disturbance degree is defined as:

[0084]

[0085] In the formula, is the i-th output of the j-th navigation information source The initial observation noise variance is, Output for navigation information source The ESO expansion state observation signal, that is, the output of the observer The variance of .

[0086] The navigation information source fault tolerance coefficient is calculated by the following method:

[0087] In order to characterize the possibility of sensor failure, statistics can be constructed based on the redundant information of the common output of multiple navigation sources. For the j-th navigation source, the following fault tolerance coefficient can be constructed:

[0088]

[0089] Where r′ is the total number of common outputs (such as carrier positions) of each navigation information source; is the ith common output of the jth navigation information source; μ i is the common output state z of all navigation sources i The average value of .

[0090] The optimization problem of finding the optimal navigation sensor configuration solution is stated as follows:

[0091]

[0092] f 1 (V) = V·[e 1 ,e 2 …e M ] T ,

[0093] f 2 (V) = V · [T 1 ,T 2 …T M ] T ,

[0094] f 3 (V) = V · [D 1 ,D 2 …D M ] T ,

[0095] f 4 (V) = V · [Θ 1 ,Θ 2 …Θ M ] T ,

[0096] Where J is the objective function to be optimized; V is a 1×M navigation source configuration vector, M is the number of navigation sources, the i-th column element of V is 1, indicating that the i-th navigation source is used, and 0 indicates that the navigation source is not used; Ω is the optimized decision space, that is, all possible navigation source configuration vectors; ω 1 ,ω 2 ,ω 3 ,ω 4 are the weights of the navigation source performance indicators, representing the priorities of navigation source accuracy, real-time performance, anti-interference and fault tolerance in the mission requirements; is the navigation source performance function normalized by the min-max method, corresponding to the accuracy, real-time, anti-interference and fault tolerance coefficients respectively; e jis the precision coefficient of the jth navigation source, that is, the average deviation of its total output relative to the mean of the total output of all navigation sources; T j is the real-time coefficient of the jth navigation source, that is, the time consumed by its information solution; Θ j is the fault tolerance coefficient of the jth navigation source.

[0097] The optimization of navigation source configuration is a multi-objective optimization problem, which can be described as:

[0098] minF(V)=[f 1 (V),f 2 (V)…f n (V)] T

[0099] stV∈Ω

[0100] Ω={V=[v 1 ,v 2 …v M ]∈R M |v 1 ,v 2 …v M ∈{0,1}},

[0101] Where F(V) is the objective function to be optimized; f 1 (V),f 2 (V)…f n (V) is the navigation source performance index, n is the number of performance indexes; Ω is the optimized decision space, that is, all possible navigation source configuration combinations; V is a 1×M navigation source configuration vector, M is the number of navigation sources, and the i-th column element of V is 1, indicating that the i-th navigation source is used, and 0, indicating that the navigation source is not used;

[0102] When solving the optimal navigation source configuration, the weighted method can be used to transform the multi-objective optimization problem into a single-objective optimization problem. The specific form is:

[0103]

[0104] f 1 (V) = V·[e 1 ,e 2 …e M ] T ,

[0105] f 2 (V) = V · [T 1 ,T 2 …T M ] T ,

[0106] f 3 (V) = V · [D1 ,D 2 …D M ] T ,

[0107] f 4 (V) = V · [Θ 1 ,Θ 2 …Θ M ] T ,

[0108] Where J is the single objective function to be optimized; ω 1 ,ω 2 ,ω 3 ,ω 4 are the weights of the navigation source performance indicators, representing the priorities of navigation source accuracy, real-time performance, anti-interference and fault tolerance in the mission requirements; are the navigation source performance functions normalized by the min-max method, corresponding to the accuracy, real-time, anti-interference and fault tolerance coefficients respectively; e j is the precision coefficient of the jth navigation source, that is, the average deviation of its total output relative to the mean of the total output of all navigation sources; T j is the real-time coefficient of the jth navigation source, that is, the time consumed by its information solution; Θ j is the fault tolerance coefficient of the jth navigation source.

[0109] The optimal navigation source configuration scheme V * It is a 1×M vector, where M is the number of navigation sources.

[0110] The specific steps of the availability screening module are as follows:

[0111] Construct an availability matching matrix A between navigation solution algorithms and navigation sources. The availability matching matrix is ​​an M×N matrix, where M is the number of navigation sources and N is the number of navigation solution algorithms. The rows of the availability matching matrix represent navigation sources, and the columns represent navigation solution algorithms. The element of the j-th row and the i'th column of the availability matching matrix is ​​1, indicating that the i'th navigation solution algorithm is applicable to the j-th navigation source. The element of the j-th row and the j-th column is 0, indicating that the i'th navigation solution algorithm is not applicable to the j-th navigation source. j=1~M ， i'=1~N;

[0112] Receive the optimal navigation source configuration vector V * The input is a 1×M vector, where the jth element is 1, indicating that the jth navigation source is used, and 0, indicating that the i'th navigation source is abandoned;

[0113] Construct an available algorithm vector B, which is a 1×N vector, where the i'th element is 1, indicating that the i'th information fusion algorithm is available under the current navigation source configuration, and 0, indicating that it is unavailable;

[0114] Perform AND and XOR operations on the navigation source configuration vector and the availability matching matrix element by element to obtain the available algorithm vector. The specific calculation method is as follows:

[0115] B=XNOR(V * ,A&V * ),

[0116] In the formula, XNOR is the element-wise XNOR operation; & is the element-wise AND operation;

[0117] The output of the availability screening module is a series of information fusion algorithm vectors decomposed from the available algorithm vector B, and the relationship between them conforms to the following formula:

[0118] B=P 1 +P 2 +…P i′ …+P q ,

[0119] Where P i′ is a 1×N vector, with only one element being 1 and the rest being 0, P i′ 1 indicates that it represents the i'th information fusion algorithm; q is the total number of available information fusion algorithms.

[0120] The optimality screening module uses a multi-objective optimization algorithm to obtain the optimal information fusion algorithm:

[0121] The weighted method is used to transform the problem of finding the optimal information fusion algorithm into a single-objective optimization problem. The specific form of the optimization problem is:

[0122]

[0123] f 3 (P) = P·[ζ 1 ,ζ 2 …ζ N ] T ,

[0124] Where J′ is the objective function to be optimized; N is the number of available information fusion algorithms; P is a 1×N vector representing a specific information fusion algorithm, with only one element being 1 and the rest being 0; p i′ Indicates that the i'th information fusion algorithm is available; Ω' is the decision space, that is, all available information fusion algorithms; ω' 1 ,ω′ 2 ,ω′ 3 are the weights of the information fusion algorithm performance indicators, representing the priorities of information fusion algorithm accuracy, real-time performance, and anti-interference in task requirements; They are the quality coefficients of the information fusion algorithm after normalization by the min-max method; is the precision coefficient of the jth algorithm, that is, the average deviation of its fusion result relative to the fusion results of all algorithms; is the real-time coefficient of the jth information fusion algorithm, that is, the time consumed by its operation; ζ j is the filter observability of the jth information fusion algorithm, i.e., the anti-interference coefficient;

[0125] The covariance matrix Y maintained by the information fusion algorithm can be k To define the filtered observability:

[0126]

[0127] Where Y 0(j′j′) Represents the j'th row and j'th column element of the given initial covariance matrix, that is, the standard deviation of the j'th filter state error, Y k(jj) Represents the standard deviation of the filtering state error of the state at time k;

[0128] The optimal solution P * The column number i' of the only non-zero element indicates that the optimal information fusion algorithm is the i'th available information fusion algorithm.

[0129] The navigation solution algorithm includes extended Kalman filter, unscented Kalman filter, cubature Kalman filter, federated Kalman filter, particle filter, and factor graph optimization.

[0130] The extended Kalman filter uses a first-order Taylor expansion to approximate nonlinear systems; the particle filter is based on the Bayesian filter and is a method for obtaining Bayesian numerical solutions through Monte Carlo simulation. It uses a set of weighted sampling to approximate the statistical distribution of the state of the nonlinear system. Compared with the extended Kalman filter, it can be applied to non-Gaussian systems. Factor graph optimization uses a Bayesian network generation model to describe the state estimation problem, decomposes the conditional probability product of the observed quantity and the state quantity in the form of factors, and the factors are generally in the form of exponential functions, thereby converting the factor graph optimization problem of probability into a least squares problem. Various types of navigation algorithms have different computational efficiency and accuracy, and are therefore suitable for different mission scenarios. The present invention uses a multi-objective optimization algorithm to select the optimal navigation algorithm for the current mission scenario from the algorithm library.

[0131] Figure 4 This is a flow chart of an intelligent multi-source autonomous navigation system for unmanned systems according to a preferred embodiment of the present invention. The method comprises the following steps:

[0132] Step S1: internal self-check and initialization of the intelligent multi-source autonomous navigation system;

[0133] Step S2: real-time monitoring of intelligent multi-source autonomous navigation status parameters and local storage of data;

[0134] Step S3: Online switching and autonomous reconstruction of intelligent multi-source autonomous navigation system scenes;

[0135] Step S4: intelligent multi-source autonomous navigation algorithm library and intelligent pattern recognition;

[0136] Step S5: Intelligent multi-source autonomous navigation and guidance task processing.

[0137] In a preferred specific embodiment of the present invention, in step S1, the internal self-test of the intelligent multi-source autonomous navigation system includes the bottom-level drive test self-test of the information processing module M1, the power management module M2, the multi-source perception measurement module M3, the safety monitoring module M4 and the information storage module M5, the power-on timing logic self-test and the initialization of interrupts, clocks, GPIO, I2C, ADC, SPI, UART, serial ports, etc.

[0138] In a preferred specific embodiment of the present invention, in the step S2, the real-time monitoring of the intelligent multi-source autonomous navigation state parameters is performed in real time by the safety monitoring module M4; and local data is stored by the information storage module M5 carried by the drone.

[0139] In a preferred specific embodiment of the present invention, in step S3, the intelligent multi-source autonomous navigation system scenarios include reconnaissance and detection scenarios, disaster relief and firefighting scenarios, urban building scenarios, outdoor jungle scenarios, narrow street fighting scenarios, intensive artillery fire scenarios, and typical expansion scenarios; the unmanned system-oriented intelligent multi-source autonomous navigation system performs online switching of scenes based on the information sources collected by the multi-source perception and measurement module M3 sensor, and autonomously reconstructs the working scenes based on the increase or decrease of sensor information sources.

[0140] In a preferred specific embodiment of the present invention, the intelligent mode recognition function in step S4 is to intelligently identify the complexity of the navigation task according to the current scene information, so as to adaptively select the optimal navigation mode and realize the upgrading and downgrading of the navigation strategy based on the task scene; for example, under normal road conditions, the zero-speed correction algorithm can be used to improve the navigation accuracy of the land-based carrier, while in the case of high-speed movement of the carrier, the carrier dynamics-assisted navigation method is more applicable, and when the complexity of the motion environment further increases, it is necessary to use the physical information neural network-assisted navigation. These three algorithms constitute a set of navigation strategies that can be upgraded;

[0141] In a preferred specific embodiment of the present invention, in step S5, the intelligent multi-source autonomous navigation and guidance task processing is mainly performed by presetting a reference time calculation unit in the processor inside the information processing module M1, and allocating different internal task processing function time slots through interrupt operations, thereby realizing multi-source autonomous navigation multi-task parallel processing, including but not limited to 1ms tasks, 5ms tasks and 10ms tasks. The 1ms task includes but is not limited to the MEMS gyroscope raw angular velocity output and online error calibration tasks; the MEMS accelerometer raw specific force output and online error calibration tasks; the 5ms task includes but is not limited to the navigation positioning timing signal output by the GNSS receiver, the magnetic induction intensity signal output by the three-axis magnetometer, the air pressure altitude signal output by the barometer, and the navigation positioning signal output by the polarization sensor; the relative distance signal output by the data link rangefinder; the output image signal of the monocular / binocular vision camera, wherein the monocular vision camera image output has a maximum resolution of 752×480 and a frame rate of 20FPS, and the binocular vision camera image output has a maximum resolution of 4416×1242 and a frame rate of 15FPS; the 10ms task includes but is not limited to the intelligent multi-source autonomous navigation extended Kalman filter operation.

[0142] In a specific embodiment of the present invention, an intelligent multi-source autonomous navigation module for unmanned systems is proposed, and the module includes an information processing module unit, a power management unit, a multi-source perception measurement unit, a safety monitoring unit, an online storage unit, and an expansion module unit. The information processing module unit is composed of a main processor and a crystal oscillator. The power management unit is used to receive an external 6V to 36V input power supply voltage, and convert it into a rated voltage through a DC voltage converter to power each chip inside the intelligent multi-source autonomous navigation system. The multi-source perception measurement unit is composed of multi-source perception sensors such as a micro-inertial gyroscope, a micro-inertial accelerometer, a GNSS receiver, a barometer, a magnetometer, a polarization sensor, a data link rangefinder, a single / binocular vision camera, and an expansion module. The safety monitoring unit is composed of a watchdog monitoring chip. The online storage unit is composed of a FLASH flash memory and a RAM memory. The expansion module unit reserves a universal transmission device protocol plug-and-play interface for expanding other perception sensors, processing sensors, and actuator sensors. The information processing module unit communicates and transmits data with the power management unit, the multi-source sensing and measuring unit, the safety monitoring unit, the online storage unit and the expansion module unit in an electrically connected manner. The power management unit provides an instrument power supply signal to the information processing module unit, the multi-source sensing and measuring unit, the safety monitoring unit, the online storage unit and the expansion module unit in an electrically connected manner.

[0143] like Figure 5 This is a principle block diagram of “software defined navigation” of an intelligent multi-source autonomous navigation system according to a preferred embodiment of the present invention.

[0144] In the preferred specific embodiment of the present invention, the idea of ​​"software-defined navigation" of the intelligent multi-source autonomous navigation system for unmanned systems is based on micro-nano manufacturing technology, integrating micro-electromechanical, micro-optoelectronics, micro-sensing, micro-RF, micro-integration and other technologies, and based on an open system architecture. Multi-functional components such as high-precision inertial / GNSS / air pressure multi-source perception, processors, power conversion and management are integrated at the system level on a microscale basis, and scenario applications are used to drive the online switching and autonomous reconstruction of the navigation algorithm library. Other multi-source perception devices are expanded through plug-and-play interfaces to form a miniaturized and intelligent multi-source autonomous navigation system suitable for unmanned systems.

[0145] In the preferred specific embodiment of the present invention, the intelligent multi-source autonomous navigation system "software defined navigation" mainly includes an autonomous navigation physical layer, an autonomous navigation communication protocol layer, an autonomous navigation system management layer, an autonomous navigation algorithm library layer and an autonomous navigation scenario application layer. The entire multi-source autonomous navigation system adopts an open architecture system with hierarchical management to achieve multi-source detection, information coordination, overall planning and parallel development.

[0146] In the preferred specific embodiment of the present invention, the autonomous navigation physical layer serves as the foundation and key of the intelligent multi-source autonomous navigation system, and develops chip-level hardware such as sensors and processors for the multi-source navigation system, wherein the sensors include MEMS gyroscopes, MEMS accelerometers, GNSS receivers, TMR magnetometers, barometers, etc. In terms of software, it includes the modularization of hardware driver layer software for multi-source perception, multi-source fusion, autonomous decision-making, etc., and the standardized processing of output transmission protocols, etc.

[0147] In the preferred specific embodiment of the present invention, the autonomous navigation communication protocol layer serves as an interactive "bridge" between the processor and other functional elements and components, and realizes the communication, scheduling and management of the autonomous navigation physical layer through the intelligent multi-source autonomous navigation system standard protocol. According to the interface type and communication requirements of each functional unit, the research and formulation of the data bus and communication protocol are completed, especially the unified "standard communication protocol" is implemented to realize the communication between the processor and each functional component and the scalable "plug and play".

[0148] In a preferred specific embodiment of the present invention, the management layer of the autonomous navigation system is at the bottom layer of the system processor, and completes tasks such as task management, task decomposition, resource management, and clock management, wherein task management refers to dynamic management and priority management of various concurrent tasks; task allocation is the module decomposition for computing and processing a certain task, so as to realize high-speed parallel computing; resource management is the dynamic scheduling of the system's storage resources, computing resources, etc. according to the characteristics of the task; clock management realizes the division and multiplication of the main frequency signal, as well as the real-time correction of the time signal; driver management mainly completes the unified management and dynamic configuration of the bottom-level drivers of different interfaces; time slot allocation is the internal time slot allocation of embedded execution functions for different tasks and processing cycles.

[0149] In the preferred specific embodiment of the present invention, the autonomous navigation algorithm library layer mainly completes the research and development of general algorithms and models, including model library, navigation solution algorithm library, information fusion algorithm library, basic code module library, etc. Among them, the model library is mainly responsible for the modeling of interference factors in various complex environments, the error mechanism and propagation model modeling of various sensors, etc.; the navigation solution algorithm library is mainly responsible for the processing and solution of various sensor information and the output of navigation and positioning results, including the strapdown solution algorithm of inertial navigation, the PPP or RTK algorithm of GNSS, the MAGCOM algorithm of magnetometer, the image processing algorithm and SLAM algorithm of visual camera, etc.; the information fusion algorithm library is mainly responsible for the fusion filtering of multi-source information, and the estimation and correction of multi-source heterogeneous errors, including a series of Kalman filtering, particle filtering and factor graph optimization algorithms; the basic code library is mainly responsible for completing general data reading, data analysis, standard operations and other operations.

[0150] In the preferred specific embodiment of the present invention, the autonomous navigation scene application layer is an application development platform that can be flexibly configured by users of unmanned systems, specifically including modules such as user-defined tasks, user extension models, user databases, user configuration management, health status monitoring, and task execution evaluation. The user-defined task module is intended to provide users with a rapid development platform for task description and planning; the user extension model provides users with an expandable model basic type and basic configuration interface for rapid expansion; the user database provides historical data management, expert knowledge management, etc.; the user configuration management user's conventional parameter configuration and management; health status monitoring is mainly a rapid development platform and tool for health status monitoring of specific tasks; task evaluation mainly provides standards and criteria for various types of task evaluation, as well as evaluation algorithms, etc. In the specific embodiment of the present invention, the intelligent multi-source autonomous navigation system for unmanned systems performs online switching of scenes based on the information sources collected by the multi-source perception measurement module M3 sensor, and autonomously reconstructs the working scene based on the increase or decrease of sensor information sources.

[0151] The present invention provides an intelligent multi-source autonomous navigation system for unmanned systems with a brand-new system architecture, which encapsulates an information processing module, a power management module, a multi-source perception measurement module, a safety monitoring module, and an online storage module in a metal shell, and realizes an integrated architecture design of intelligent multi-source autonomous navigation through a "plug and play" open system architecture; the present invention adopts multi-source perception sensor data collection such as angular rate signals, acceleration signals, air pressure altitude signals, magnetic intensity signals, positioning timing signals, etc., and improves the credibility and autonomous navigation capability of the perception signals of the intelligent multi-source autonomous navigation system in complex environments through fusion processing of multi-source information; the present invention provides a high-density integrated intelligent multi-source autonomous navigation system, which adopts advanced packaging technologies such as TSV, RDL, IPD, SiP, etc., to achieve high-density integration of system modules and three-dimensional integration of the system, and reduce the volume, weight and power consumption of the intelligent multi-source autonomous navigation system; based on the design concept of "software-defined navigation", the present invention fully considers the versatility and extensibility at the hardware level, as well as the modularization and integration at the software level, to meet the autonomous and controllable technical requirements of unmanned systems in future military-civilian integration.

[0152] It should be understood that the above specific embodiments of the present invention are only used to illustrate or explain the principles of the present invention, and do not constitute a limitation of the present invention. Therefore, any modifications made without departing from the spirit and scope of the present invention should be included in the protection scope of the present invention. The claims attached to the present invention are intended to cover all changes and modifications that fall within the scope and boundaries of the attached claims, or the equivalent forms of such scope and boundaries.

Claims

1. An intelligent multi-source autonomous navigation system, characterized in that It includes a multi-source perception measurement module and an information processing module; Multi-source perception measurement module, integrating multiple navigation sensors and outputting observation quantities for navigation and positioning; The information processing module receives observations from multiple navigation sensors and uses corresponding navigation solution algorithms to obtain navigation and positioning information of the carrier. The combination of a single navigation sensor and its corresponding navigation solution algorithm is recorded as a single navigation information source. According to the navigation task requirements, the navigation and positioning information output by each single navigation information source is evaluated to obtain the optimal navigation information source configuration scheme for the current task and the current scenario, and the optimal information fusion algorithm suitable for the optimal navigation information source configuration is screened out from the stored navigation algorithm library. The optimal information fusion algorithm is used to perform multi-source fusion on the output of the optimal navigation information source to obtain an optimized navigation and positioning result.

2. The intelligent multi-source autonomous navigation system according to claim 1, characterized in that: The navigation sensor includes: an IMU unit, a GNSS receiver, a visual camera, and a magnetometer; The observations of the IMU unit include the angular velocity information and acceleration information of the carrier; The observations of the GNSS receiver include the pseudorange between the carrier and the navigation satellite, the carrier phase and the differential correction, and the differential correction includes the satellite clock error, the receiver clock error, the ionospheric delay and the tropospheric delay; The observations of the visual camera include environmental image information; The observations of the magnetometer include the magnetic field strength and direction information.

3. The intelligent multi-source autonomous navigation system according to claim 2, characterized in that: The information processing module includes a navigation solution module, an information source configuration optimization module, an availability screening module, an optimality screening module, an information fusion module, and a gating switch; The navigation solution module uses a mechanical arrangement algorithm to obtain the carrier's speed, position, and attitude information based on the angular velocity information and acceleration information output by the IMU unit; uses a differential positioning algorithm to calculate the carrier's position and velocity information based on the pseudorange, carrier phase, and differential correction output by the GNSS receiver; uses a SLAM algorithm to calculate the carrier's position and attitude information based on the environmental image information; and uses a MAGCOM algorithm to calculate the carrier's position information based on the magnetic field strength and direction information; The information source configuration optimization module obtains the navigation positioning information output by each navigation information source, calculates the quality coefficient of each navigation information source and performs normalization processing. Based on the normalized quality coefficient, which includes the accuracy coefficient, real-time coefficient, anti-interference coefficient and fault tolerance coefficient, combined with the externally input navigation source performance index weight, the optimal navigation information source configuration scheme under the current mission scenario is solved through a multi-objective optimization algorithm; Availability screening module, which screens out the available information fusion algorithms under the optimal navigation information source configuration; The optimality screening module selects the optimal information fusion algorithm from the available information fusion algorithms; A strobe switch sends the output of the configured navigation information source to the information fusion module; The information fusion module uses the optimal information fusion algorithm to perform multi-source fusion based on the configured navigation information source output to obtain the fused navigation and positioning results of the carrier.

4. The intelligent multi-source autonomous navigation system according to claim 1, characterized in that: The navigation source performance indicators include accuracy, anti-interference, real-time and fault tolerance.

5. The intelligent multi-source autonomous navigation system according to claim 1, characterized in that: The anti-interference coefficient is calculated by the following formula: D j =a1η j +a2ε j , Where D j is the anti-interference coefficient of the jth navigation information source; a1, a2 are weight parameters, which can be adjusted according to engineering experience; η j ,ε j are the system observability and system disturbance of the j-th navigation information source, respectively, and are calculated by the following formula: In the formula, is the state observability of the i-th output of the j-th navigation information source; is the state disturbance degree of the ith output of the jth navigation information source; r is the number of outputs of the jth navigation information source, i=1~r.

6. The intelligent multi-source autonomous navigation system according to claim 5, characterized in that: State Observability Obtained by the following method: For a single navigation information source, a linear steady-state system is constructed: X k =FX k-1 +w k-1 , Z k =HX k +v k , In the formula, H k is the state variable of the navigation information source, including the estimated error of the carrier's motion state and the error-related variables of the navigation information source; F is the state transfer matrix; Z k is the observed variable, i.e., the output of the navigation information source; H is the measurement matrix; w k-1 ,v k are process noise and measurement noise respectively; Construct the observability matrix Q and perform SVD singular value decomposition on it: In the formula, Its diagonal elements are the singular values ​​of Q; U is an orthogonal matrix, and its column vectors are QQ T The eigenvector of ; V is an orthogonal matrix, whose column vector is Q T The eigenvector of Q; Calculate the state observability of the i-th output of the j-th navigation information source: In the formula, σ i is the i-th singular value of the observability matrix of the system corresponding to the j-th navigation information source; max(σ i ) represents the maximum value among all its singular values.

7. The intelligent multi-source autonomous navigation system according to claim 5, characterized in that: The state disturbance Obtained by the following method: For the i-th output of navigation information source j Construct an extended state observer (ESO) and transform the output As the input of the observer, the ESO expansion state observation signal of the i-th output is obtained, the variance of the ESO expansion state observation signal of the i-th output is obtained, and the state disturbance degree is calculated: In the formula, is the initial observation noise variance of the i-th output of the j-th navigation source, is the variance of the ESO expansion state observation signal of the ith output.

8. The intelligent multi-source autonomous navigation system according to claim 1, characterized in that: For the j-th navigation source, the following fault tolerance coefficient is constructed: Where r′ is the total number of outputs of each navigation information source; is the ith common output of the jth navigation information source; μ i is the common output state z of all navigation sources i The average value of .

9. The intelligent multi-source autonomous navigation system for unmanned systems according to claim 1, characterized in that: The optimization problem of finding the optimal navigation sensor configuration solution is stated as follows: f1(V)=V·[e 1 ,e 2 ...e M ] T , f2(V)=V·[T 1 ,T 2 …T M ] T , f3(V)=V·[D 1 ,D 2 …D M ] T , f4(V)=V·[Θ 1 ,Θ 2 …Θ M ] T , Where J is the objective function to be optimized; V is a 1×M navigation source configuration vector, M is the number of navigation sources, the i-th column element of V is 1, indicating that the i-th navigation source is used, and 0 indicates that the navigation source is not used; Ω is the optimized decision space, that is, all possible navigation source configuration vectors; ω1, ω2, ω3, ω4 are the weights of the navigation source performance indicators, which respectively represent the priority of navigation source accuracy, real-time performance, anti-interference and fault tolerance in the mission requirements; is the navigation source performance function normalized by the min-max method, corresponding to the accuracy, real-time, anti-interference and fault tolerance coefficients respectively; e j is the precision coefficient of the jth navigation source, that is, the average deviation of its total output relative to the mean of the total output of all navigation sources; T j is the real-time coefficient of the jth navigation source, that is, the time consumed by its information solution; Θ j is the fault tolerance coefficient of the jth navigation source.

10. The intelligent multi-source autonomous navigation system according to claim 1, characterized in that: The specific steps of the availability screening module are as follows: Construct an availability matching matrix A between navigation solution algorithms and navigation sources. The availability matching matrix is ​​an M×N matrix, where M is the number of navigation sources and N is the number of navigation solution algorithms. The rows of the availability matching matrix represent navigation sources, and the columns represent navigation solution algorithms. The element of the j-th row and the i'th column of the availability matching matrix is ​​1, indicating that the i'th navigation solution algorithm is applicable to the j-th navigation source. The element of the j-th row and the j-th column is 0, indicating that the i'th navigation solution algorithm is not applicable to the j-th navigation source. j=1~M, i'=1~N; Receive the optimal navigation source configuration vector V * The input is a 1×M vector, where the jth element is 1, indicating that the jth navigation source is used, and 0, indicating that the i'th navigation source is abandoned; Construct an available algorithm vector B, which is a 1×N vector, where the i'th element is 1, indicating that the i'th information fusion algorithm is available under the current navigation source configuration, and 0, indicating that it is unavailable; Perform AND and XOR operations on the navigation source configuration vector and the availability matching matrix element by element to obtain the available algorithm vector. The specific calculation method is as follows: B=XNOR(V * ,A&V * ), In the formula, XNOR is the element-wise XNOR operation; & is the element-wise AND operation; The output of the availability screening module is a series of information fusion algorithm vectors decomposed from the available algorithm vector B, and the relationship between them conforms to the following formula: B=P1+P2+…P i′ …+P q Where P i′ is a 1×N vector, with only one element being 1 and the rest being 0, P i′ 1 indicates that it represents the i'th information fusion algorithm; q is the total number of available information fusion algorithms.

11. The intelligent multi-source autonomous navigation system according to claim 1, characterized in that: The navigation solution algorithm includes extended Kalman filter, unscented Kalman filter, cubature Kalman filter, federated Kalman filter, particle filter, and factor graph optimization.

12. The intelligent multi-source autonomous navigation system according to claim 1, characterized in that: The optimality screening module uses a multi-objective optimization algorithm to obtain the optimal information fusion algorithm: The weighted method is used to transform the problem of finding the optimal information fusion algorithm into a single-objective optimization problem. The specific form of the optimization problem is: f3(P)=P·[ζ 1 ,g 2 …g N ] T , Where J′ is the objective function to be optimized; N is the number of available information fusion algorithms; P is a 1×N vector, representing a specific information fusion algorithm, with only one element being 1 and the rest being 0; Ω′ is the decision space, i.e., all available information fusion algorithms; ω′1, ω′2, ω′3 are the weights of the performance indicators of the information fusion algorithm, representing the priorities of accuracy, real-time performance, and anti-interference of the information fusion algorithm in the task requirements, respectively; They are the quality coefficients of the information fusion algorithm after normalization by the min-max method; is the precision coefficient of the jth algorithm, that is, the average deviation of its fusion result relative to the fusion results of all algorithms; is the real-time coefficient of the jth information fusion algorithm, that is, the time consumed by its operation; ζ j is the filter observability of the jth information fusion algorithm, i.e., the anti-interference coefficient; p i′ Indicates that the i'th information fusion algorithm is available; The covariance matrix Y maintained by the information fusion algorithm can be k To define the filtered observability: Where Y 0(j′j′) Represents the j'th row and j'th column element of the given initial covariance matrix, that is, the standard deviation of the j'th filter state error, Y k(j′j′) Represents the standard deviation of the filtering state error of the state at time k; The optimal solution P * The column number i' of the only non-zero element indicates that the optimal information fusion algorithm is the i'th available information fusion algorithm.

Images