Missile-borne integrated navigation method and device for adaptive switching and sequential updating

Abstract

The invention discloses a missile-borne integrated navigation method and a missile-borne integrated navigation device for adaptive switching and sequential updating. The method comprises the following steps: acquiring angular velocity data and specific force data of an IMU (Inertial Measurement Unit), and performing inertial navigation state prediction based on the angular velocity data and the specific force data to obtain a prediction error state vector and a prediction error covariance matrix at the current moment; based on the prediction error state vector and the prediction error covariance matrix, navigation mode adaptive discrimination is carried out, a navigation mode discrimination result at the current moment is obtained, and first measurement information is determined based on the discrimination result; performing sequential measurement updating based on the first measurement information to obtain a posterior error state vector and a posterior error covariance matrix at the current moment; and performing error compensation correction based on the posterior error state vector and the posterior error covariance matrix to obtain navigation information. According to the invention, the technical problems of navigation interruption of a high-dynamic missile-borne platform in a GPS denial environment and large calculated amount and poor robustness of traditional fusion filtering are solved.

Description

Technical Field

[0001] This invention relates to the field of navigation, and more specifically, to a method and apparatus for adaptive switching and sequential updating of missile-borne integrated navigation. Background Technology

[0002] Modern warfare is evolving towards informatization and intelligence, and precision strike capability has become a core indicator for measuring the effectiveness of weapon systems. As a precision strike weapon that combines cost-effectiveness and tactical flexibility, the performance of the navigation system of guided artillery shells directly determines their accuracy and combat effectiveness. However, artillery shells face harsh environments during flight, including high dynamics, high spin, strong impact, and complex electromagnetic interference, placing extremely high demands on the accuracy, reliability, and robustness of the navigation system.

[0003] Currently, the combination of Inertial Navigation System (INS) and Global Positioning System (GPS) has become the most widely used solution for missile-borne navigation. This combination utilizes the high-precision position and velocity information provided by GPS to periodically correct the accumulated errors of the INS caused by low-cost inertial devices, achieving high navigation accuracy. However, this solution has inherent drawbacks in highly contested environments: First, GPS signal power is weak and easily susceptible to deliberate interference from the enemy or loss in complex terrain. Once the GPS signal fails, the performance of the combined navigation system will drastically degrade to the level of pure inertial navigation, leading to rapid error divergence. Second, GPS does not directly observe the carrier's attitude, resulting in the inability to effectively observe the attitude errors of the INS (especially roll angle errors), which will continue to accumulate slowly. To address the navigation problem in GPS-denied environments, model-based assisted navigation has become an important research direction. Among these, using pre-established ballistic models or flight dynamics models to provide pseudo-measurement information for the INS is a highly promising technical approach, as the trajectory of a projectile is highly regular and predictable. Meanwhile, introducing auxiliary information sources such as geomagnetism can also improve the accuracy of attitude estimation to some extent.

[0004] At the information fusion architecture level, while traditional centralized Kalman filtering can theoretically achieve optimal fusion of multi-source information, it constructs all observation information into a single high-dimensional measurement vector, resulting in enormous computational costs, difficulty in meeting real-time requirements, and poor robustness. To improve the efficiency and robustness of fusion algorithms, sequential filtering methods have been introduced into the field of integrated navigation. This method effectively reduces the computational burden by decomposing high-dimensional measurements into multiple low-dimensional updates and inherently possesses fault isolation capabilities. Furthermore, to cope with complex dynamic environments, adaptive filtering and multi-model switching filtering methods have also been extensively studied.

[0005] There is currently no effective solution to the above problems. Summary of the Invention

[0006] This invention provides an adaptive switching and sequential update method and apparatus for missile-borne integrated navigation, which at least solves the technical problems of navigation interruption in high-dynamic missile-borne platforms under GPS denial environment and the large computational load and poor robustness of traditional fusion filtering.

[0007] According to one aspect of the present invention, an adaptive switching and sequential update method for missile-borne integrated navigation is provided, comprising: acquiring angular velocity data and specific force data output by an inertial measurement unit (IMU); performing inertial navigation state prediction based on the angular velocity data and specific force data to obtain a prediction error state vector and a prediction error covariance matrix at the current moment; performing adaptive navigation mode discrimination based on the prediction error state vector and the prediction error covariance matrix to obtain a navigation mode discrimination result at the current moment, and selecting GPS observation information or ballistic model pseudo-measurement information as the first measurement information for subsequent measurement updates based on the navigation mode discrimination result; performing sequential measurement updates based on the first measurement information to obtain a posterior error state vector and a posterior error covariance matrix at the current moment; and performing error compensation correction on the missile's attitude information, velocity information, and position information based on the posterior error state vector and the posterior error covariance matrix to obtain integrated navigation information at the current moment.

[0008] According to another aspect of the present invention, an adaptive switching and sequential update missile-borne integrated navigation device is also provided, comprising: a prediction module configured to acquire angular velocity data and specific force data output by an inertial measurement unit (IMU), and based on the angular velocity data and specific force data, perform inertial navigation state prediction to obtain a prediction error state vector and a prediction error covariance matrix at the current moment; a discrimination module configured to perform adaptive navigation mode discrimination based on the prediction error state vector and the prediction error covariance matrix to obtain a navigation mode discrimination result at the current moment, and select GPS observation information or ballistic model pseudo-measurement information as the first measurement information for subsequent measurement updates based on the navigation mode discrimination result; a measurement update module configured to perform sequential measurement updates based on the first measurement information to obtain a posterior error state vector and a posterior error covariance matrix at the current moment; and a closed-loop correction module configured to perform error compensation correction on the missile's attitude information, velocity information, and position information based on the posterior error state vector and the posterior error covariance matrix to obtain integrated navigation information at the current moment.

[0009] In this embodiment of the invention, angular velocity data and specific force data output by the inertial measurement unit (IMU) are acquired. Based on the angular velocity data and specific force data, inertial navigation state prediction is performed to obtain the prediction error state vector and prediction error covariance matrix at the current moment. Based on the prediction error state vector and prediction error covariance matrix, adaptive navigation mode discrimination is performed to obtain the navigation mode discrimination result at the current moment. Based on the navigation mode discrimination result, GPS observation information or ballistic model pseudo-measurement information is selected as the first measurement information for subsequent measurement updates. Based on the first measurement information, sequential measurement updates are performed to obtain the posterior error state vector and posterior error covariance matrix at the current moment. Based on the posterior error state vector and posterior error covariance matrix, error compensation correction is performed on the attitude information, velocity information, and position information of the missile to obtain the combined navigation information at the current moment. Through the above scheme, the technical problems of navigation interruption of high dynamic missile platforms in GPS denied environments and the large computational load and poor robustness of traditional fusion filtering are solved. Attached Figure Description

[0010] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0011] Figure 1 This is a flowchart of an optional adaptive switching and sequential update airborne integrated navigation method according to an embodiment of the present invention;

[0012] Figure 2 This is a flowchart of an optional adaptive switching and sequential update airborne integrated navigation method according to an embodiment of the present invention;

[0013] Figure 3 This is an optional SSKF overall architecture flowchart according to an embodiment of the present invention;

[0014] Figure 4 This is a simulation trajectory diagram according to an embodiment of the present invention;

[0015] Figure 5 This is an optional comparison of the position error between the SSKF and S-IG schemes after GPS failure, according to an embodiment of the present invention;

[0016] Figure 6 This is a comparison of the three optional schemes of the three-axis velocity error in the northeast and south directions according to an embodiment of the present invention;

[0017] Figure 7 This is a comparison chart of the root mean square errors of three schemes according to an embodiment of the present invention;

[0018] Figure 8This is a structural diagram of an adaptive switching and sequential update missile-borne integrated navigation device according to an embodiment of the present invention;

[0019] Figure 9 A schematic diagram of the structure of a computer device suitable for implementing embodiments of the present disclosure is shown. Detailed Implementation

[0020] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0021] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0022] According to an embodiment of the present invention, a method embodiment of an adaptive switching and sequential update missile-borne integrated navigation method is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0023] Figure 1 This is an adaptive switching and sequential update missile-borne integrated navigation method according to an embodiment of the present invention, such as... Figure 1 As shown, the method includes the following steps:

[0024] Step S102: Obtain angular velocity data and force data output by the inertial measurement unit (IMU). Based on the angular velocity data and force data, perform inertial navigation state prediction to obtain the prediction error state vector and prediction error covariance matrix at the current moment.

[0025] For example, angular velocity data and specific force data output by the inertial measurement unit (IMU) are acquired, and inertial navigation calculations are performed based on the angular velocity data and specific force data to obtain the attitude information, velocity information and position information at the current moment; based on the attitude information, velocity information and position information at the current moment, the error state vector and error covariance matrix of the previous moment are propagated over time using a pre-established inertial navigation error state model to obtain the prediction error state vector and prediction error covariance matrix at the current moment.

[0026] Step S104: Based on the predicted error state vector and the predicted error covariance matrix, perform adaptive navigation mode discrimination to obtain the navigation mode discrimination result at the current time, and select GPS observation information or ballistic model pseudo-measurement information as the first measurement information for subsequent measurement updates based on the navigation mode discrimination result.

[0027] For example, based on the prediction error state vector and prediction error covariance matrix, the INS constructs the predicted observations of GPS; the actual GPS observations at the current time are obtained, the observation innovation between the actual GPS observations and the predicted observations is calculated, and a normalized innovation squared (NIS) statistic is constructed based on the observation innovation; the NIS statistic is compared with a preset discrimination threshold to obtain the navigation mode discrimination result at the current time; based on the navigation mode discrimination result, GPS observation information or ballistic model pseudo-measurement information is selected as the first measurement information for subsequent measurement updates. For example, when the NIS statistic is less than or equal to the preset discrimination threshold, the GPS observation information is determined to be valid; otherwise, the ballistic model pseudo-measurement information is determined to be valid.

[0028] Step S106: Based on the first measurement information, perform sequential measurement update to obtain the posterior error state vector and posterior error covariance matrix at the current time.

[0029] Using the prediction error state vector and prediction error covariance matrix at the current moment as prior information, a sequential Kalman filter is used to perform a first measurement update on the prediction error state vector and prediction error covariance matrix at the current moment based on the first measurement information, to obtain an intermediate error state vector and intermediate error covariance matrix; using the intermediate error state vector and intermediate error covariance matrix as prior information, a second measurement update is performed based on geomagnetic observation information, to obtain the posterior error state vector and posterior error covariance matrix at the current moment.

[0030] Step S108: Based on the posterior error state vector and the posterior error covariance matrix, error compensation correction is performed on the attitude information, velocity information and position information of the missile to obtain the combined navigation information at the current moment.

[0031] For example, using the prediction error state vector and prediction error covariance matrix at the current moment as prior information, a sequential Kalman filter is used to perform a first measurement update on the error state based on the first measurement information to obtain an intermediate error state vector and an intermediate error covariance matrix; using the intermediate error state vector and intermediate error covariance matrix as prior information, a second measurement update is performed based on geomagnetic observation information to obtain a posterior error state vector and posterior error covariance matrix at the current moment.

[0032] To address the issues of navigation interruption on high-dynamic missile-borne platforms under GPS denial environments and the high computational cost and poor robustness of traditional fusion filtering, this application proposes an adaptive switching sequential Kalman filter (SSKF) navigation method. This method uses an inertial navigation system (INS) as its core, constructing a dual-mode navigation framework of "GPS / geomagnetic" and "ballistic model / geomagnetic," and achieves autonomous and smooth switching of navigation modes through a self-consistency check criterion based on the internal state of the filter. Its core sequential update architecture decouples multi-source heterogeneous information, improving robustness and computational efficiency. Simulation results show that the proposed SSKF method can effectively suppress navigation error divergence during GPS interruptions, ensuring navigation continuity. Compared with centralized filtering, it improves computational efficiency and exhibits stronger robustness while maintaining similar accuracy. This provides an efficient solution for missile-borne multi-source navigation systems that balances accuracy, robustness, and real-time performance.

[0033] Figure 2 This is an adaptive switching sequential Kalman filter navigation method according to embodiments of this application, such as... Figure 2 The method shown includes the following steps:

[0034] Step S202: Construct the integrated navigation system model.

[0035] 1) Construct an error model for the inertial navigation system.

[0036] The inertial navigation system (INS) is the core of missile-borne navigation. It integrates the outputs of gyroscopes and accelerometers to autonomously provide the vehicle's attitude, velocity, and position information at high frequency. However, due to inherent errors such as drift and bias in the IMU itself, the calculation of navigation parameters accumulates rapidly over time, failing to meet the requirements for high accuracy over extended periods. To estimate and compensate for these accumulated errors using external information, a 15-dimensional error state vector is used to construct the INS system model.

[0037]

[0038] In the formula: Represents the system state vector. This indicates the attitude angle errors in the east, north, and azimuth directions. This indicates the speed errors in the east, north, and sky directions. Indicates the error in latitude, longitude, and altitude. This represents the constant drift error of the gyroscope across its three axes. This indicates the constant zero bias error of the accelerometer across its three axes.

[0039] The linearized and discretized inertial navigation error equation is written as follows:

[0040]

[0041] It is a 15×15 state transition matrix. It is a process noise mapping. . The results are derived from the random walk of the gyroscope angle, the random walk of the accelerometer velocity, and the zero-partial correlation time constant.

[0042] 2) Constructing an observation information model

[0043] The general expression for the measurement update step in Kalman filtering is:

[0044]

[0045] exist time, It is the observation vector. It is the observation matrix. It is the system state vector. It is the observation noise vector.

[0046] GPS receivers provide high-precision, absolute geographic location and velocity information. This information is unaffected by accumulated time errors and can be used as a benchmark to observe and correct for position and velocity errors that may occur after long-term INS operation.

[0047] When GPS signals are available, the constructed observation vector The difference between the position and velocity calculated by INS and the GPS measurements constitutes the following:

[0048]

[0049] Represents a location vector (latitude, longitude, altitude). Represents the velocity vector (East, North, Sky).

[0050] GPS observation matrix The state vector With GPS observation vectors Connecting them:

[0051]

[0052] in, It is a 3×3 identity matrix. It is a 3×3 zero matrix.

[0053] For high-speed spinning projectiles, the GPS / INS combined mode is insufficient for effectively observing roll angle errors. A geomagnetic sensor, by measuring the local geomagnetic field vector, can provide an absolute reference direction in the navigation coordinate system. By comparing the geomagnetic sensor's measurements in the projectile's coordinate system with the known geomagnetic field reference vector in the navigation coordinate system, the projectile's attitude information, especially the precise measurement of the roll angle, can be calculated.

[0054] Establish geomagnetic vector in carrier coordinate system Geomagnetic vector in navigation coordinate system The relationship between them:

[0055]

[0056] in, It is the actual attitude transformation matrix from the navigation system to the carrier system. This is the attitude matrix estimate calculated by INS. It is composed of the attitude error vector The resulting antisymmetric matrix.

[0057] The difference between the geomagnetic observations and the reference magnetic field value predicted by the INS attitude is linearized to construct a linear observation equation:

[0058]

[0059] in, This is the actual measurement value from the magnetometer. It is a vector The resulting antisymmetric matrix.

[0060] Geomagnetic observation matrix The state vector With geomagnetic observation vector Connecting them:

[0061]

[0062] Because the flight trajectory of guided projectiles is highly regular and predictable, a dynamic or kinematic model of its flight can be pre-established. In the event of GPS failure, this trajectory model can calculate the theoretical position and velocity of the projectile at its current moment based on the initial launch parameters and flight time. This serves as a "pseudo-measurement" information, providing a reasonable constraint for the INS (Instrument Navigation System), effectively suppressing the divergence of its position and velocity errors, thereby ensuring navigation continuity.

[0063] The structure of the ballistic model pseudo-measurement vector is similar to that of the GPS observation vector, consisting of the difference between the INS calculated value and the ballistic model predicted value. The observation matrix of the ballistic model pseudo-measurement. and They are also completely identical, although the structures are the same, but their corresponding observation noise covariance matrices are different. and There are significant differences, usually The value is greater than The value of is used to reflect the uncertainty in the ballistic model prediction.

[0064] Step S204: Design an adaptive switching sequential filtering algorithm.

[0065] 1) Sequential filtering

[0066] Traditional centralized Kalman filtering constructs all observation information into a single high-dimensional vector for updating. This not only results in a huge computational burden of inverting the high-dimensional matrix in the measurement update step, but also means that the failure of a single sensor can contaminate the entire filtering process, leading to poor robustness.

[0067] The core idea of ​​the sequential filtering architecture is to decompose high-dimensional measurements into a series of independent, low-dimensional sequential update processes. Within each filtering cycle, a time update is performed first, followed by a measurement update using information from the first observation source. The updated state estimate and covariance serve as prior information for the next update, which is then performed by the second observation source. This "serialized" update method transforms a single high-dimensional matrix inversion operation into multiple low-dimensional matrix inversions, significantly improving computational efficiency and enhancing the robustness of the entire system while maintaining filtering accuracy.

[0068] 2) Adaptive switching criteria

[0069] To enable the navigation system to autonomously and reliably switch between "GPS available" and "GPS denied" modes in complex electromagnetic environments, an adaptive switching criterion was designed. A statistical hypothesis testing method based on the internal state of the filter—the Normalized Innovation Squared (NIS) test—was employed to achieve real-time monitoring of GPS signal quality and navigation mode switching decisions.

[0070] In Kalman filtering, innovation is defined as the difference between the actual measured value and the system's predicted value, i.e. Assuming the system model is accurate and the sensors are functioning correctly, the innovation sequence is a zero-mean Gaussian white noise sequence with a theoretical covariance of... When GPS signals are interfered with or malfunction, their actual measurements will deviate from the normal trajectory, causing changes in the statistical characteristics of the information.

[0071] Based on this principle, a chi-square ( NIS statistic test for the distribution :

[0072]

[0073] Obeying the degree of freedom Chi-square ( ) distribution, in which Let be the dimension of the GPS measurement vector.

[0074] according to The distribution table is configured with a set confidence level, and the corresponding decision threshold is then found. In each filtering cycle, the NIS value measured by GPS is calculated. And execute the following switching logic:

[0075] like If the GPS measurement matches the system prediction, the signal is valid, and the system adopts the "GPS available" mode; otherwise... If the GPS measurement is inconsistent with the system prediction, the signal is invalid or unreliable, and the system switches to "GPS denial" mode, using ballistic model pseudo-measurement to replace GPS.

[0076] This switching logic, based on internal consistency checks, can not only detect "hard faults" where the signal is completely interrupted, but also effectively identify "soft faults" where the signal indicators are normal but the data is inaccurate, greatly improving the robustness of the entire system.

[0077] Step S206: Navigate the overall algorithm flow.

[0078] The Switched Sequential Kalman Filter (SSKF) algorithm, with INS as its core, selects different external information sources through an adaptive switching criterion and performs measurement updates in a sequential manner. The overall process is as follows: Figure 3 As shown, a single filtering cycle mainly consists of four parts: time update, navigation mode determination, sequential measurement update, and closed-loop correction.

[0079] Step S302, time update.

[0080] according to Optimal estimate of time and its covariance ,predict State and covariance at time:

[0081]

[0082] It is the system dynamic matrix The single-step state transition matrix obtained by discretization It is the system process noise covariance matrix.

[0083] Step S304, Navigation mode determination.

[0084] Obtain the current GPS observation value Calculate its new information relative to the system prediction. Calculate the statistic according to the NIS test method described in Section 2.2. and threshold Compare and determine whether the "GPS available" mode or "GPS denied" mode should be used at the current time.

[0085] Step S306, sequential measurement update.

[0086] In "GPS Available" mode, GPS observation vectors are used first. The first measurement update is performed on the prior estimate, letting Calculate the Kalman gain of GPS measurements. :

[0087]

[0088] Update state estimates and covariance:

[0089]

[0090] The second measurement update uses the state and covariance from the previous update as prior information to calculate the Kalman gain of the geomagnetic measurement. :

[0091]

[0092] Update the state and covariance to obtain The final result at that moment:

[0093]

[0094] In "GPS Denied" mode, pseudo-measurement vectors generated using the ballistic model are used. Perform the first measurement update to obtain the intermediate state estimate. Covariance The second update uses geomagnetic observation vectors to obtain the final posterior optimal estimate. Covariance .

[0095] Step S308, closed-loop calibration.

[0096] Estimating the optimal error of the filtered output The data is fed back to the INS navigation solution module, which corrects the calculated attitude, velocity, and position. Simultaneously, some or all components of the error state vector are set to zero, completing the closed-loop correction and preparing for the next filtering cycle.

[0097] The simulation experiment and results analysis will be described in detail below.

[0098] To comprehensively and objectively evaluate the actual performance of the switched sequential Kalman filter (SSKF) proposed in this application, three schemes were designed for comparative simulation: Scheme 1 is the standard INS / GPS loose combination (S-IG), Scheme 2 is the centralized Kalman filter (SCKF) using the same switching logic, and Scheme 3 is the switched sequential Kalman filter (SSKF) proposed in this application. The effectiveness of the method in this application was verified from multiple dimensions, including navigation continuity, filtering accuracy, computational efficiency, and robustness.

[0099] 1) Simulation condition settings.

[0100] First, a highly dynamic flight reference trajectory is generated using a seven-degree-of-freedom ballistic model. For example... Figure 4 As shown, the initial launch velocity of the projectile was 805 m / s, the initial launch angle was 30°, the total simulation time was 60.54 s, the flight distance was 22.5 km, and the highest point of the trajectory was approximately 4.6 km. To simulate a typical scenario under strong interference, the GPS signal was set to fail after t=30 s during the simulation to verify the performance of the algorithm under GPS denial conditions.

[0101] The main error parameter settings of the IMU and GPS sensors used in the simulation are shown in Table 1. The parameter settings refer to the typical values ​​used in similar missile-borne navigation systems.

[0102] Table 1 Sensor error parameter settings

[0103]

[0104] The main physical parameters of the projectile are shown in Table 2.

[0105] Table 2 Projectile and Launch Parameters

[0106]

[0107] 2) Results Analysis and Discussion

[0108] 2.1) Navigation continuity

[0109] like Figure 5 As shown, the standard INS / GPS integrated navigation system has a small error before the GPS signal is lost, but after the GPS signal is lost, the error of pure inertial navigation begins to accumulate rapidly, and the curve shows a sharp and unbounded divergence trend. The astronomical position error has reached as high as 180 meters at the end of the simulation. However, the switching sequential filtering method proposed in this application successfully switches to the "ballistic model / geomagnetic" auxiliary mode after the GPS signal is lost, ensuring the continuity and reliability of navigation.

[0110] 2.2) Error Comparison Analysis

[0111] The speed error of the INS / GPS integrated navigation method under GPS signal availability is compared with that of centralized SCKF and switched sequential SSKF, such as... Figure 6 As shown, the standard INS / GPS can maintain error convergence, but its velocity error curve fluctuates significantly more than the other two schemes. SCKF and SSKF both exhibit superior performance, with smoother error curves that converge near zero. The RMSEs of the three-axis INS / GPS are 0.779, 0.927, and 1.132 m / s, respectively, while the RMSEs of SSKF are 0.382, 0.459, and 0.809 m / s, respectively. SSKF has the lowest RMSE, at 0.298, 0.314, and 0.433 m / s, respectively, proving that the SSKF method proposed in this application achieves the theoretically optimal accuracy.

[0112] like Figure 7 As shown, a comparative analysis of the root mean square errors (RMSE) of the three schemes shows that the S-IG scheme has the highest RMSE among the three, with the error fluctuating between 2 and 4 meters; the SCKF scheme has a stable RMSE within 3 meters, and the SSKF scheme has a stable RMSE within 1 meter, demonstrating excellent positioning accuracy.

[0113] 3) Overall performance evaluation

[0114] Table 3 Comparison of performance parameters of the three filtering schemes

[0115]

[0116] To provide a more precise quantitative comparison, Table 3 summarizes the root mean square error of navigation parameters and computation time for each scheme. In terms of computational efficiency, the S-IG scheme has the lowest computation time because it only performs INS recursion during GPS outages, incurring almost no overhead for measurement updates. Compared to the SCKF and SSKF schemes, the SSKF scheme has a significant advantage in computational efficiency, with its average computation time reduced by approximately 30.7% compared to SCKF, and its maximum computation time reduced by approximately 51.0%. This demonstrates that sequential updates, by decomposing the inversion of a high-dimensional matrix into multiple low-dimensional operations, can significantly reduce the peak and average computational burden of the algorithm, making it more suitable for real-time deployment on computationally limited missile-borne platforms.

[0117] This application addresses the challenge of continuous navigation for missile-borne platforms in GPS-denied environments by proposing an adaptive switching sequential Kalman filter method. A dual-mode switching framework, combining "GPS / Geomagnetic" and "Ballistic Model / Geomagnetic," is constructed, and a sequential update strategy is employed to fuse multi-source information. Simulation results demonstrate that the proposed switching architecture effectively suppresses navigation error divergence during GPS outages. Compared to centralized filtering, the sequential filtering architecture exhibits stronger robustness while maintaining high accuracy, and also boasts higher computational efficiency.

[0118] While simulations have verified the effectiveness of the methods in the embodiments of this application, their performance depends on the accuracy of the ballistic model. Future research could involve porting the algorithm to a semi-physical simulation platform for verification and exploring adaptive filtering techniques to improve its adaptability to the model.

[0119] This application also provides an adaptive switching and sequential update missile-borne integrated navigation device, such as... Figure 8 As shown, the system includes: a prediction module 82, configured to acquire angular velocity data and specific force data output by the inertial measurement unit (IMU), and based on the angular velocity data and specific force data, perform inertial navigation state prediction to obtain the prediction error state vector and prediction error covariance matrix at the current moment; a discrimination module 84, configured to perform adaptive navigation mode discrimination based on the prediction error state vector and prediction error covariance matrix to obtain the navigation mode discrimination result at the current moment, and select GPS observation information or ballistic model pseudo-measurement information as the first measurement information for subsequent measurement updates based on the navigation mode discrimination result; a measurement update module 86, configured to perform sequential measurement updates based on the first measurement information to obtain the posterior error state vector and posterior error covariance matrix at the current moment; and a closed-loop correction module 88, configured to perform error compensation correction on the attitude information, velocity information, and position information of the missile based on the posterior error state vector and posterior error covariance matrix to obtain the combined navigation information at the current moment.

[0120] It should be noted that the adaptive switching and sequential update missile-borne integrated navigation device provided in the above embodiments is only an example of the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the adaptive switching and sequential update missile-borne integrated navigation device and the adaptive switching and sequential update missile-borne integrated navigation method embodiments provided in the above embodiments belong to the same concept, and their specific implementation process can be found in the method embodiments, which will not be repeated here.

[0121] Figure 9 A schematic diagram of a computer device suitable for implementing embodiments of the present disclosure is shown. It should be noted that... Figure 9 The computer device shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments disclosed herein.

[0122] like Figure 9 As shown, the computer device includes a central processing unit (CPU) 1001, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage section 1008 into a random access memory (RAM) 1003. The RAM 1003 also stores various programs and data required for system operation. The CPU 1001, ROM 1002, and RAM 1003 are interconnected via a bus 1004. An input / output (I / O) interface 1005 is also connected to the bus 1004.

[0123] The following components are connected to I / O interface 1005: an input section 1006 including a keyboard, mouse, etc.; an output section 1007 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 1008 including a hard disk, etc.; and a communication section 1009 including a network interface card such as a LAN card, modem, etc. The communication section 1009 performs communication processing via a network such as the Internet. A drive 1010 is also connected to I / O interface 1005 as needed. A removable medium 1011, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on drive 1010 as needed so that computer programs read from it can be installed into storage section 1008 as needed.

[0124] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A method for adaptive switching and sequential updating of missile-borne integrated navigation, characterized in that, include: Acquire angular velocity and force data output by the inertial measurement unit (IMU), and based on the angular velocity and force data, perform inertial navigation state prediction to obtain the prediction error state vector and prediction error covariance matrix at the current moment. Based on the predicted error state vector and the predicted error covariance matrix, adaptive navigation mode discrimination is performed to obtain the navigation mode discrimination result at the current time. Based on the navigation mode discrimination result, GPS observation information or ballistic model pseudo measurement information is selected as the first measurement information for subsequent measurement updates. Based on the first measurement information, sequential measurement updates are performed to obtain the posterior error state vector and posterior error covariance matrix at the current time. Based on the posterior error state vector and the posterior error covariance matrix, error compensation and correction are performed on the attitude, velocity, and position information of the missile to obtain the combined navigation information at the current moment.

2. The method according to claim 1, characterized in that, Acquire angular velocity and specific force data output by the inertial measurement unit (IMU). Based on the angular velocity and specific force data, perform inertial navigation state prediction to obtain the prediction error state vector and prediction error covariance matrix at the current moment, including: The angular velocity data and force data output by the inertial measurement unit (IMU) are acquired, and inertial navigation calculations are performed based on the angular velocity data and the force data to obtain the attitude information, velocity information and position information at the current moment. Based on the attitude, velocity, and position information at the current moment, the error state vector and error covariance matrix of the previous moment are propagated over time using a pre-established inertial navigation error state model to obtain the prediction error state vector and prediction error covariance matrix at the current moment.

3. The method according to claim 1, characterized in that, Based on the predicted error state vector and the predicted error covariance matrix, adaptive navigation mode discrimination is performed to obtain the navigation mode discrimination result at the current time. Based on the navigation mode discrimination result, GPS observation information or ballistic model pseudo-measurement information is selected as the first measurement information for subsequent measurement updates, including: Based on the predicted error state vector and the predicted error covariance matrix, the predicted observations of GPS by INS are constructed. Obtain the actual GPS observations at the current moment, calculate the observation information between the actual GPS observations and the predicted observations, and construct the normalized information squared (NIS) statistic based on the observation information; The NIS statistics are compared with a preset discrimination threshold to obtain the navigation mode discrimination result at the current moment; Based on the navigation mode discrimination result, GPS observation information or ballistic model pseudo-measurement information is selected as the first measurement information for subsequent measurement updates.

4. The method according to claim 3, characterized in that, The NIS statistics are compared with a preset discrimination threshold to obtain the navigation mode discrimination result at the current time, including: when the NIS statistics are less than or equal to the preset discrimination threshold, the GPS observation information is determined to be valid; otherwise, the ballistic model pseudo measurement information is determined to be valid.

5. The method according to claim 1, characterized in that, Based on the first measurement information, sequential measurement updates are performed to obtain the posterior error state vector and posterior error covariance matrix at the current time, including: Using the prediction error state vector and prediction error covariance matrix at the current moment as prior information, a sequential Kalman filter is used to perform the first measurement update on the prediction error state vector and prediction error covariance matrix at the current moment based on the first measurement information, so as to obtain the intermediate error state vector and intermediate error covariance matrix. Using the intermediate error state vector and intermediate error covariance matrix as prior information, a second measurement update is performed based on geomagnetic observation information to obtain the posterior error state vector and posterior error covariance matrix at the current time.

6. The method according to claim 1, characterized in that, Based on the posterior error state vector and the posterior error covariance matrix, error compensation and correction are performed on the missile's attitude, velocity, and position information to obtain the current integrated navigation information, including: Using the current prediction error state vector and prediction error covariance matrix as prior information, a sequential Kalman filter is used to perform the first measurement update on the error state based on the first measurement information, thereby obtaining the intermediate error state vector and intermediate error covariance matrix. Using the intermediate error state vector and intermediate error covariance matrix as prior information, a second measurement update is performed based on geomagnetic observation information to obtain the posterior error state vector and posterior error covariance matrix at the current time.

7. An adaptive switching and sequential update missile-borne integrated navigation device, characterized in that, include: The prediction module is configured to acquire angular velocity data and specific force data output by the inertial measurement unit (IMU), and based on the angular velocity data and specific force data, perform inertial navigation state prediction to obtain the prediction error state vector and prediction error covariance matrix at the current moment. The discrimination module is configured to perform adaptive discrimination of navigation mode based on the prediction error state vector and the prediction error covariance matrix, obtain the navigation mode discrimination result at the current time, and select GPS observation information or ballistic model pseudo measurement information as the first measurement information for subsequent measurement updates based on the navigation mode discrimination result. The measurement update module is configured to perform sequential measurement updates based on the first measurement information to obtain the posterior error state vector and the posterior error covariance matrix at the current time. The closed-loop correction module is configured to perform error compensation correction on the attitude, velocity and position information of the missile based on the posterior error state vector and the posterior error covariance matrix, so as to obtain the combined navigation information at the current moment.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein, when the program is executed, it controls the device on which the computer-readable storage medium is located to perform the method according to any one of claims 1 to 6.

9. A computer device, characterized in that, include: Memory and processor The memory stores computer programs; The processor is configured to execute a computer program stored in the memory, wherein when the computer program is executed, the processor performs the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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