A terrain matching method based on differential height point mass filtering

Abstract

The present application relates to a kind of terrain matching methods based on differential height point mass filtering, comprising: collecting the observed air pressure height of current time, observed radio height, and determining the observed terrain height of current time according to observed air pressure height, observed radio height;Obtain the inertial navigation position corresponding to the observed terrain height of current time, project the point mass filtering PMF grid with the inertial navigation position as center to the elevation map, determine the terrain height corresponding to each grid point in PMF grid, and construct projection map;According to the observed terrain height of two adjacent moments, projection map, determine the terrain height difference corresponding to the inertial navigation position of two adjacent moments, terrain height difference map;Based on terrain height difference, terrain height difference map constructs the conditional probability of bayesian estimation;And combine conditional probability with prior probability, determine edge probability;According to conditional probability, edge probability determines the posterior probability of inertial navigation position;And based on posterior probability, bayesian decision is carried out, and the matching position result is determined.

Description

Technical Field

[0001] This invention relates to the field of aircraft navigation technology, and more specifically to a terrain matching method based on differential altitude point quality filtering. Background Technology

[0002] Among related technologies, terrain-aided navigation systems are an effective means of achieving autonomous navigation for aircraft. These systems use sensors to measure altitude and calculate terrain height information. This information is then compared with a pre-loaded terrain map to achieve matching and positioning, correcting position errors in the inertial navigation system (INS), thereby suppressing INS divergence and improving navigation accuracy. Terrain-matching navigation is an autonomous navigation method with the advantage of having characteristics of navigation information sources that are not easily altered. Furthermore, most aircraft are equipped with barometric altimeters and radio altimeters, making them suitable for using terrain-matching technology.

[0003] The diverse mission requirements of modern aircraft have led to higher performance demands on terrain matching algorithms. Compared to traditional algorithms, modern terrain matching algorithms not only require higher accuracy, but also need to consider factors such as matching efficiency, sensitivity to initial errors, adaptability to sensor errors, and convergence speed when selecting an algorithm. For aircraft, inertial navigation systems gradually diverge when flying over unsuitable areas (such as the sea surface) and satellite-denied environments before matching, resulting in relatively large initial errors when the algorithm enters the terrain matching phase. Increased flight depth can cause altimeter deviations due to changes in regional air pressure. Radar altimeter noise increases with altitude, all of which pose new challenges to the performance of matching algorithms.

[0004] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of the present invention, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] This invention provides a terrain matching method based on differential elevation point quality filtering, which achieves comprehensive optimization of matching success rate and convergence speed, thereby effectively overcoming the defects existing in the prior art.

[0006] Other features and advantages of the invention will become apparent from the following detailed description, or may be learned in part by practice of the invention.

[0007] According to a first aspect of the present invention, a terrain matching method based on differential elevation point quality filtering is provided, the method comprising: Collect the observed air pressure altitude at the current moment. Observing radio altitude The observation terrain altitude at the current moment is determined based on the observed barometric altitude and observed radio altitude. ; Obtain the observed terrain height at the current moment. Corresponding inertial navigation position The point mass filter (PMF) mesh centered at the inertial navigation position is projected onto the elevation map to determine the terrain elevation corresponding to each grid point in the PMF mesh, and a projected map is constructed. ; Based on the observed terrain height and projected map at two adjacent time points, determine the terrain elevation difference corresponding to the inertial navigation positions at two adjacent time points. Topographic elevation map ; Conditional probabilities for Bayesian estimation are constructed based on topographic elevation differences and topographic elevation maps. ; and combined with conditional probability with prior probability Determine marginal probabilities The posterior probability of the inertial navigation system (INS) position is determined based on the conditional probability and marginal probability; and a Bayesian decision is made based on the posterior probability to determine the matching position result; wherein the matching position result is used to correct the INS position.

[0008] In some exemplary embodiments, the method further includes: Obtain the current inertial navigation system position of the aircraft, and configure the initial position prior distribution based on the current inertial navigation system position, including:

[0009] in, This represents the standard deviation corresponding to the current inertial navigation system position; This represents the grid coordinates corresponding to the inertial navigation system position; Represents a map The coordinates of the grid points on the grid; Represents prior probability; Initialize the PMF mesh and configure the mesh number based on the standard deviation.

[0010] In some exemplary embodiments, the observed terrain altitude at the current moment is determined based on the observed barometric altitude and the observed radio altitude. ,include:

[0011] in, This represents the observed air pressure altitude at time k; Represents the observed radio altitude at time k; This represents the observed terrain height at time k.

[0012] In some exemplary embodiments, the terrain elevation difference corresponding to the inertial navigation position at two adjacent time points is determined based on the observed terrain elevation and the projected map at two adjacent time points. Topographic elevation map ,include: Based on the difference in observed terrain height between time k-1 and time k, the terrain elevation difference is configured, including:

[0013] in, This represents the observed terrain elevation at time k; This represents the observed terrain elevation at time k-1; Indicates the difference in terrain elevation; Read the elevation map and determine the inertial navigation position based on two adjacent time points. and The difference between the corresponding projected maps, and the configuration of the terrain elevation difference map, including:

[0014] in, Map showing terrain elevation differences; Indicates the inertial navigation position at time k Corresponding projection map; Indicates the inertial navigation position at time k-1 The corresponding projected map.

[0015] In some exemplary implementations, Bayesian estimation of conditional probabilities is constructed based on terrain elevation differences and terrain elevation maps. ,include:

[0016] in, For terrain elevation difference The error variance is determined based on the measurement error variances corresponding to barometric altitude and radio altitude; Indicates the difference in terrain elevation; Indicated on the elevation difference map middle The terrain elevation difference found at the location.

[0017] In some exemplary embodiments, the method further includes: Based on the error models for barometric altitude and wireless altitude, a terrain altitude error model is configured, including:

[0018] in, Indicates altitude; This indicates the altitude deviation of the barometric altimeter when measuring altitude; Indicates relative height; This indicates the noise level of the barometric altimeter when measuring altitude. This indicates the measurement noise of the radio altimeter when measuring relative altitude. Configure a terrain elevation difference observation error model based on the terrain elevation error model, including:

[0019] The standard deviation is determined based on the error model of the topographic elevation difference observations and configured as the topographic elevation variance, including:

[0020] in, This represents the standard deviation of the error in the measurement of terrain elevation differences, which is contributed by the errors of the barometric altimeter and the radio altimeter. This is the coefficient for the noise of a constant radio altimeter as it increases with relative altitude; express The variance; This represents the relative height measurement at time k; This represents the relative height measurement at time k-1.

[0021] In some exemplary implementations, conditional probability is combined with prior probability Determine marginal probabilities ,include:

[0022] in, This represents the prior probability of a location; Represents conditional probability; This represents the marginal probability.

[0023] In some exemplary embodiments, the posterior probability is determined based on the conditional probability and the marginal probability; and a Bayesian decision is performed based on the posterior probability to determine the matching position result, including: The posterior probability is determined based on conditional probability and marginal probability, including:

[0024] in, Represents the posterior probability of the inertial navigation system's position; Represents marginal probability; Bayesian decision-making based on posterior probability determines the matching location result, including:

[0025] in, Represents grid coordinates; This indicates the matching position result.

[0026] In some exemplary embodiments, the method further includes: The inertial navigation position error is determined based on the matching position result and the inertial navigation position, including:

[0027] in, This indicates position information derived from the inertial navigation system; Indicates the matching position result; Indicates the inertial navigation system position error; The inertial navigation position error is compared with the preset validity criteria, and the validity of the matching position result is determined based on the comparison result; wherein, the preset validity criteria determine the corresponding matching position variance configuration based on the matching position result.

[0028] In some exemplary embodiments, the method includes: The variance of the corresponding matching position is determined based on the matching position results, including:

[0029] in, Represents the posterior probability; This indicates the current matching position result; Represents grid coordinates; Centered on the current inertial navigation position, the grid boundary of the PMF grid is determined based on the matching position variance, so as to be used for the next round of position matching based on the grid within the grid boundary.

[0030] According to a second aspect of the present invention, a computer program product is provided, on which a computer program is stored, wherein the computer program, when executed by a processor, implements the above-described terrain matching method based on differential elevation point quality filtering.

[0031] According to a third aspect of the present invention, a storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the above-described terrain matching method based on differential elevation point quality filtering.

[0032] The terrain matching method based on differential altitude point quality filtering provided in the embodiments of the present invention uses observed barometric high speed and observed radio altitude as data foundations to calculate the corresponding terrain elevation difference and terrain elevation difference map. It uses terrain elevation difference as an observation and calculates conditional probabilities. During Bayesian estimation, it effectively compensates for the cumulative effects of inertial navigation errors, thereby ensuring the stability of the matching method in long-endurance scenarios. Bayesian decision-making based on posterior probabilities ensures that each matching operation only needs to process the current observation information, effectively improving the overall performance of the matching method.

[0033] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit the invention. Attached Figure Description

[0034] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention. It is obvious that the drawings described below are merely some embodiments of the invention, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0035] Figure 1 This diagram illustrates an exemplary embodiment of the present invention: a terrain matching method based on differential elevation point quality filtering. Figure 2 This diagram illustrates an exemplary embodiment of the principle of terrain matching and terrain height measurement according to the present invention. Figure 3 This diagram illustrates a flowchart of a terrain matching method based on differential elevation point quality filtering, an exemplary embodiment of the present invention. Figure 4 The illustration shows a schematic diagram of an exemplary embodiment of the present invention, namely, a differential height point quality filtering algorithm. Figure 5 This diagram schematically illustrates a matching trajectory result of an exemplary embodiment of the present invention; Figure 6 This diagram schematically illustrates a matching error result according to an exemplary embodiment of the present invention. Figure 7 This diagram illustrates a position error curve for a high-pressure deviation matching integrated navigation system, as shown in an exemplary embodiment of the present invention. Figure 8 This diagram illustrates a statistical result of the mean and extreme values ​​of errors according to an exemplary embodiment of the present invention. Figure 9 The diagram illustrates an exemplary embodiment of the present invention: a combined navigation latitude and longitude error box. Detailed Implementation

[0036] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that the invention will be more comprehensive and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0037] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0038] To address the shortcomings and deficiencies of existing technologies, this example embodiment provides a terrain matching method based on differential altitude point quality filtering, which can be applied to the terrain matching system within an aircraft inertial / terrain integrated navigation system. Specifically, the inertial navigation system provides prior position information to the terrain matching system. Prior information on positional error The terrain matching system executes the method of this invention and provides positioning information to the inertial navigation system. Ultimately, this achieves the goal of long-endurance stable navigation using an inertial / terrain integrated navigation system. (Reference) Figure 1 As shown, the method provided by this invention may specifically include: Step S11: Collect the observed air pressure altitude at the current moment. Observing radio altitude The observation terrain altitude at the current moment is determined based on the observed barometric altitude and observed radio altitude. ; Step S12: Obtain the observed terrain height at the current moment. Corresponding inertial navigation position The point mass filter (PMF) mesh centered at the inertial navigation position is projected onto the elevation map to determine the terrain elevation corresponding to each grid point in the PMF mesh, and a projected map is constructed. ; Step S13: Based on the observed terrain height and projected map at two adjacent time points, determine the terrain elevation difference corresponding to the inertial navigation position at two adjacent time points. Topographic elevation map ; Step S14: Construct the conditional probability of Bayesian estimation based on terrain elevation difference and terrain elevation difference map. ; and combined with conditional probability with prior probability Determine marginal probabilities The posterior probability of the inertial navigation system (INS) position is determined based on the conditional probability and marginal probability; and a Bayesian decision is made based on the posterior probability to determine the matching position result; wherein the matching position result is used to correct the INS position.

[0039] The method provided in this embodiment can effectively handle terrain matching tasks in satellite-denied environments. It can effectively cope with application environments with inertial navigation position divergence, large initial errors, large altimeter deviations, and high radar altimeter noise, while still maintaining high terrain matching accuracy and stability.

[0040] The following will describe in more detail each step of the terrain matching method based on differential elevation point quality filtering in this exemplary embodiment, with reference to the accompanying drawings and embodiments.

[0041] In this example implementation, the method described above may include: Step S101: Obtain the current inertial navigation system position of the aircraft and configure the initial position prior distribution based on the current inertial navigation system position; Step S102: Initialize the PMF mesh and configure the number of meshes based on the standard deviation.

[0042] Specifically, the terrain matching system can communicate with the inertial navigation system and receive its inertial navigation position information. (Reference) Figure 3 As shown, the terrain matching system can be initialized first. During the initial operation of the terrain matching system, in response to the received current inertial navigation position, the standard deviation of the current inertial navigation position can be estimated based on the prior information of the position error of the inertial navigation position. This allows for the configuration of the initial carrier position prior distribution of the aircraft, which serves as the prior probability. The formula includes: (1) in, This represents the standard deviation corresponding to the current inertial navigation system position; This represents the grid coordinates corresponding to the inertial navigation system position; Represents a map The coordinates of the grid points on the grid; This represents the prior probability.

[0043] Synchronously, a point mass filter (PMF) mesh can be created, with the mesh being an n×n rectangular mesh. Centered on the inertial navigation system position, the number of PMF meshes can be configured according to the standard deviation in formula (1). For example, the PMF mesh can be configured to be at least six times the standard deviation of n. Let the coordinates of any grid point in the PMF mesh be . The probability that the actual location of the aircraft appears at this grid point is calculated.

[0044] In step S11, the observed air pressure altitude at the current moment is collected. Observing radio altitude The observation terrain altitude at the current moment is determined based on the observed barometric altitude and observed radio altitude. .

[0045] For example, in step S11 above, the observed terrain altitude at the current moment is determined based on the observed barometric altitude and the observed radio altitude. ,include: (2) in, This represents the observed air pressure altitude at time k; Represents the observed radio altitude at time k; This represents the observed terrain height at time k.

[0046] For details, please refer to Figure 2 As shown, altitude can be observed using a barometer and recorded as the observed barometric altitude. This represents the observed value. The altitude measured by a barometer includes atmospheric deviation and instrument error. k The barometric altitude observation value at time is recorded as Additionally, relative altitude can be observed using a radio altimeter, and the observed value can be recorded as the observed radio altitude. The measurement results from a radio altimeter may also include various errors of the radio altimeter. Terrain height is the vertical distance from a ground point to the geoid, and is the height value read from a map, which can be denoted as... ;in, Indicates the position coordinates.

[0047] The terrain height observed by the terrain matching system can be determined by the difference between the observed barometric altitude and the observed radio altitude, denoted as . .

[0048] In step S12, the observed terrain height at the current moment is obtained. Corresponding inertial navigation position The point mass filter (PMF) mesh centered at the inertial navigation position is projected onto the elevation map to determine the terrain elevation corresponding to each grid point in the PMF mesh, and a projected map is constructed. .

[0049] Specifically, after obtaining the observed terrain height at the current moment, the inertial navigation system (INS) position received by the INS can be recorded, and the correlation between the observed terrain height and the INS position can be configured. Simultaneously, a pre-installed terrain map is read, and based on the PMF's grid coverage area, an elevation map is read centered on the INS position. .

[0050] Specifically, it could be the inertial navigation position at time k. PMF grid centered Each grid point is projected onto a pre-stored topographic map, and each grid point corresponds to a topographic height value, resulting in a map corresponding to that grid point, denoted as . .

[0051] In step S13, the terrain elevation difference corresponding to the inertial navigation position at two adjacent time points is determined based on the observed terrain elevation and projected map at two adjacent time points. Topographic elevation map .

[0052] For example, step S13 above specifically includes: Step S131: Configure the terrain elevation difference based on the difference between the observed terrain elevation at time k-1 and time k; Step S132: Read the elevation map and determine the position of the inertial navigation system based on two adjacent time points. and The difference between the corresponding projected maps is used to configure the terrain elevation difference map.

[0053] Specifically, considering the observation terrain height The altitude is calculated based on observed barometric altitude and observed radio altitude. However, the measurement process is susceptible to pressure deviation and barometric altimeter zero bias, resulting in constant deviations that affect the matching accuracy. Therefore, considering the impact of barometric altimeter deviation on matching accuracy, differential altitude is selected as the observed measurement. The observed terrain altitude is measured at times k-1 and k, respectively. , Obtain the inertial navigation position and The formula for the difference in elevation between two points includes: (3) in, This represents the observed terrain elevation at time k; This represents the observed terrain elevation at time k-1; This represents the elevation difference; the value is an observed value.

[0054] Elevation map reads from the inertial navigation system (INS) position, with two INS positions as the center. and Two maps were read at the two points. , The terrain is high. The corresponding terrain elevation difference map and calculation methods include: (4) in, Map showing terrain elevation differences; Indicates the inertial navigation position at time k Corresponding projection map; Indicates the inertial navigation position at time k-1 The corresponding projected map.

[0055] In step S14, the conditional probability of Bayesian estimation is constructed based on the terrain elevation difference and the terrain elevation difference map. ; and combined with conditional probability with prior probability Determine marginal probabilities The posterior probability of the inertial navigation system (INS) position is determined based on the conditional probability and marginal probability; and a Bayesian decision is made based on the posterior probability to determine the matching position result; wherein the matching position result is used to correct the INS position.

[0056] Specifically, the terrain elevation difference at time k-1 and time k has been obtained in the aforementioned steps. Simultaneously, terrain elevation maps of the region centered on the inertial navigation position were obtained at times k-1 and k. The map covers an area containing the inertial navigation system's actual location. Therefore, it's necessary to locate the aircraft's true position on the map based on elevation measurements obtained from the aircraft's terrain matching system. However, the terrain elevation difference (i.e., elevation difference) is... The points are not unique on the map, and the observed elevation differences contain noise; therefore, elevation difference analysis is required. The position of the inertial navigation system is estimated through multiple observations, combined with the Bayesian estimation principle and recursive process design.

[0057] For example, constructing Bayesian estimation of conditional probabilities based on terrain elevation differences and terrain elevation maps. ,include: (5) in, For terrain elevation difference The error variance is determined based on the measurement error variances corresponding to barometric altitude and radio altitude; Indicates the difference in terrain elevation; Indicated on the elevation difference map middle The terrain elevation difference found at the location.

[0058] Among them, topographical variance It can be obtained by summing the variances of the measurement errors of barometric altitude and radio altitude.

[0059] For example, the method further includes: Step S21: Configure the terrain height error model based on the pressure height error model and the wireless height error model; Step S22: Configure the terrain elevation difference observation error model based on the terrain elevation error model; Step S23: Determine the corresponding standard deviation based on the terrain elevation difference observation error model, and configure it as the terrain elevation variance.

[0060] Specifically, considering the application scenario of altitude-fixed aircraft matching in environments with large terrain elevation differences, the influence of radar altimeter beam angle on the variance of altimeter noise is derived, and the calculation of conditional probability is improved. The specific calculation methods may include: First, establish the standard deviation. The error models for various related observations replace the traditional topographic height matching with the height difference between two altimeters to mitigate the impact of barometric altitude deviation. Specifically, the error models for radio altimeters and barometric altimeters are expressed as follows: (6) in, Indicates altitude; This indicates the altitude deviation of the barometric altimeter when measuring altitude; This indicates barometric altitude noise, which is the noise generated when the barometric altimeter measures altitude. This represents the relative altitude of the aircraft with respect to the ground, and is the actual true value. This indicates the noise of the radio altimeter, which is the measurement noise when the radio altimeter measures radio altitude.

[0061] Based on this error model, a terrain height error model can be obtained, including: (7) in, Indicates altitude; This indicates the altitude deviation of the barometric altimeter when measuring altitude; Indicates relative height; This indicates the noise level of the barometric altimeter when measuring altitude. This indicates the measurement noise of the radio altimeter when measuring relative altitude. Therefore, the error model for deriving the topographic elevation difference observations can be obtained, including: (8) Further calculations yield the following:

[0062]

[0063] Furthermore, the terrain elevation difference was derived. Standard deviation Specifically, it includes: (9) in, The standard deviation of the measurement error of the terrain elevation difference is represented by the contributions of the barometric altimeter error and the radio altimeter error. is a constant representing the coefficient by which radio altimeter noise increases with relative altitude; Indicates the noise level of the barometric altimeter. The variance; This represents the relative height measurement at time k; This represents the relative height measurement at time k-1.

[0064] Substituting formula (9) into formula (5) completes the conditional probability. The calculation.

[0065] In one exemplary embodiment, the standard deviation can be set as the measured height value. Double, and take It is 1%.

[0066] For example, in step S14, conditional probability is combined. with prior probability Determine marginal probabilities ,include: (10) in, This represents the prior probability of a location; Represents conditional probability; This represents the marginal probability.

[0067] Specifically, elevation map At each point, in the terrain elevation difference Corresponding inertial navigation position A specific conditional probability value Then, given the prior probability is... The conditional probability is When the marginal probability is determined according to formula (10), the marginal probability is determined.

[0068] For example, in step S14, the posterior probability is determined based on the conditional probability and the marginal probability; and a Bayesian decision is performed based on the posterior probability to determine the matching position result, including: The posterior probability is determined based on conditional probability and marginal probability, including: (11) in, Represents the posterior probability of the inertial navigation system's position; Represents marginal probability; Bayesian decision-making based on posterior probability determines the matching location result, including: (12) in, Represents grid coordinates; This indicates the matching position result.

[0069] Specifically, based on the marginal probabilities, the corresponding posterior probabilities can be determined. The posterior probability is represented by the terrain elevation difference at the current time t. The posterior probability of the inertial navigation system position under the information constraint.

[0070] To extract matching points from the distribution information, Bayesian decision-making is required. The purpose of Bayesian decision-making is to calculate the expected value of the optimal grid point from the PMF grid and the posterior probability of the grid, which serves as the matching location result. Specifically, as shown in formula (12), the calculation result is the matching location result.

[0071] For example, the method further includes: Step S31, determine the corresponding matching position variance based on the matching position results, including: (13) in, Represents the posterior probability; This indicates the current matching position result; Indicates the position of the inertial navigation system; Step S32: Using the current inertial navigation position as the center, determine the grid boundary of the PMF grid based on the matching position variance, so as to perform the next round of position matching based on the grid within the grid boundary.

[0072] Specifically, after determining the matching position, the corresponding variance can be calculated according to formula (13), and the computational load of the matching process can be adjusted based on this variance. Specifically, the variance can be used to configure the grid boundaries. For example, the distance between the grid in the x and y directions and the central grid can be equal to 6, with the current inertial navigation position as the center. The grid serves as the boundary, and points outside the grid boundary do not participate in the matching calculation, thereby reducing the computational load of Bayesian estimation and Bayesian decision-making processes and speeding up the operation.

[0073] For example, the method further includes: Step S41, determine the inertial navigation position error based on the matching position result and the inertial navigation position, including: (14) in, Indicates the position of the inertial navigation system; Indicates the matching position result; Indicates the inertial navigation system position error; Step S42: Compare the inertial navigation position error with the preset validity discrimination conditions, and determine the validity of the matching position result based on the comparison result; wherein, the preset validity discrimination conditions determine the corresponding matching position variance configuration based on the matching position result.

[0074] Specifically, before using the position matching results to correct the inertial navigation system, it is necessary to determine the validity of the matching to ensure that erroneous matching results do not contaminate the inertial navigation information and avoid deviations in subsequent matching. Specifically, formula (14) can be used to subtract the matching position from the inertial navigation position to obtain the inertial navigation error observation information. A validity determination rule can be defined in advance using variance; for example, the validity determination rule can be defined as follows: When the inertial navigation position error is less than or equal to If the match is successful, the formula is as follows:

[0075] Alternatively, if the inertial navigation system position error is determined to be greater than If the condition is met, the match is considered a failure. In the event of a failed match, the prior probabilities are re-initialized, and the output is invalidated.

[0076] For example, refer to Figure 4 As shown, probability transition calculations can be performed after obtaining the posterior probability, or after determining the validity of the match and confirming a successful match. The probability transition involves shifting the posterior probability of the current match... Converted into the prior probability of the next match The process.

[0077] Specifically, this can be achieved by reading the velocity error variance from the Kalman filter in the integrated navigation system, and calculating the change in probability density of each grid cell in the PMF grid from the current time to the next time when the next observation information is valid (e.g., responding to the inertial navigation system's position). The posterior probability estimated at the current time is used as the prior probability in the estimation at the next time; that is, the posterior probability at the current time is configured as the prior probability at the next time. Since the inertial navigation system has accumulated errors, the prior probability at the next time should be the result of probability transfer calculation based on the posterior probability at the previous time, taking into account process noise. Macroscopically, this can improve the PMF's tracking performance of inertial navigation position changes and avoid excessive convergence of each probability in the PMF.

[0078] random variable (k = 1,2,…n) is defined in map space. arrive The change in probability is called the transition probability distribution, which is represented by the probability transition kernel.

[0079] Assume the spacecraft is located at time k-1. Position, inertial navigation displacement is However, inertial navigation itself has velocity errors. Then time k-1 appears Given the position, time k occurs The probability is: (15) So what appears The total probability is expressed as: (16) For example, refer to Figure 5 , Figure 6 The diagram shows the results of terrain matching performed by the UAV using the method of the present invention, as well as the matching error. The matching results of the method of the present invention effectively eliminate the influence of inertial navigation error divergence, with an average matching error of no more than 3 grids and a maximum matching error of no more than 10 grids.

[0080] In this exemplary embodiment, a specific example is provided below to verify and illustrate the technical effects of the present invention. Specifically, the verification scenario assumes that the terrain matching system is operating in a denied environment, without high-pressure deviation calibration, and has a 50m elevation deviation. The simulation results of four different algorithms are as follows: refer to Figure 7 The ICCP (Iterative Closest Contour Point) algorithm has a positional error of 499.16m, the RTCAN (Rapid Terrain Contour Alignment Network) algorithm has a positional error of 122.12m, and the Bayes algorithm has a positional error of 206.75m. The DHPMF algorithm (the method of this invention), which uses differential terrain height as the observation, has a positional error of 38.92m, making it the best performing algorithm among the four. (Reference) Figure 8 As shown, the RMSE (Root Mean Square Error) and maximum value statistics of the method of the present invention are both minimized.

[0081] In another set of comparisons, reference Figure 9 The following is an explanation of the error ablation analysis. Specifically, four operating conditions were set according to the sensor error addition, and four algorithms were simulated and tested under each of the four conditions. Condition 1 included both radio altimeter noise and barometric altimeter constant deviation; Condition 2 removed the barometric altimeter deviation; Condition 3 removed the radio noise error; and Condition 4 removed both radio noise error and barometric altimeter deviation. Navigation error statistics are shown in Table 1.

[0082] Table 1

[0083] In summary, when system noise is low and characteristics are good, the DHPMF method and PMF positioning error provided by this invention are approximately 28m, equivalent to 1 grid; the RTCAN position error RMS is approximately 2 grids, and the ICCP algorithm is no more than 3 grids. When the system is simultaneously affected by radio altitude noise and high pressure deviation, all four algorithms show varying degrees of accuracy decline. In comparison, the DHPMF algorithm provided by this invention improves accuracy by 96.5% compared to the traditional ICCP algorithm, 82.8% compared to the RTCAN algorithm, and 87.4% compared to the conventional PMF algorithm. This is because the DHPMF algorithm, which uses differential observation, is least affected, and the final RMSE increase does not exceed 10m, maintaining good accuracy.

[0084] The method provided in this invention presents a differential altitude terrain matching scheme based on point quality filtering. It proposes using terrain altitude differences as observations, corresponding to new observations, and completes a conditional probability calculation scheme, eliminating the influence of air pressure deviation. A probability transfer formula is used to establish a mathematical model of the influence of inertial navigation error on prior probability, compensating for the cumulative effect of inertial navigation error in Bayesian estimation and ensuring the long-term stability of the matching algorithm. The matching boundary is calculated, and mismatch detection and dynamic grid adjustment are completed. A recursive Bayesian estimation process is designed, allowing the matching algorithm to process only one observation at a time, converging the positioning error through multiple recursions.

[0085] It should be noted that the above figures are merely illustrative of the processes included in the method according to exemplary embodiments of the present invention, and are not intended to be limiting. It is readily understood that the processes shown in the above figures do not indicate or limit the temporal order of these processes. Furthermore, it is readily understood that these processes may, for example, be executed synchronously or asynchronously in multiple modules.

[0086] It should be noted that although several modules or units of the device for performing actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to embodiments of the present invention, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0087] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0088] The units described in the embodiments of the present invention can be implemented in software or hardware, and the described units can also be located in a processor. The names of these units do not necessarily limit the specific unit itself.

[0089] It should be noted that, as another aspect, this application also provides a storage medium, which may be included in an electronic device or may exist independently without being assembled into the electronic device. The aforementioned storage medium carries one or more programs, which, when executed by an electronic device, cause the electronic device to perform the methods described in the following embodiments. For example, the electronic device may perform... Figure 1 The steps of the method shown.

[0090] In one embodiment, this application provides a computer program product including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0091] Furthermore, the above figures are merely illustrative of the processes included in the method according to exemplary embodiments of the present invention, and are not intended to be limiting. It is readily understood that the processes shown in the above figures do not indicate or limit the temporal order of these processes. Additionally, it is readily understood that these processes may be executed synchronously or asynchronously, for example, in multiple modules.

[0092] Other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention herein. This application is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein. The specification and embodiments are to be considered exemplary only, and the true scope and spirit of the invention are indicated by the claims.

[0093] It should be understood that the present invention is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. A terrain matching method based on differential elevation point quality filtering, characterized in that, include: Collect the observed air pressure altitude at the current moment. Observing radio altitude The observation terrain altitude at the current moment is determined based on the observed barometric altitude and observed radio altitude. ; Obtain the observed terrain height at the current moment. Corresponding inertial navigation position The inertial navigation position will be used Projecting the PMF grid centered on the model onto the elevation map determines the terrain elevation corresponding to each grid point in the PMF grid, and constructs the projected map. ; Based on the observed terrain height and projected map at two adjacent time points, determine the terrain elevation difference corresponding to the inertial navigation positions at two adjacent time points. Topographic elevation map ; Conditional probabilities for Bayesian estimation are constructed based on topographic elevation differences and topographic elevation maps. ,include: in, For terrain elevation difference The error variance is determined based on the measurement error variances corresponding to barometric altitude and radio altitude; Indicates the difference in terrain elevation; Indicated on the elevation difference map middle The terrain elevation difference found at the location; And combined with conditional probability with prior probability Determine marginal probabilities ,include: in, Represents prior probability; Represents conditional probability; Represents marginal probability; The posterior probability of the inertial navigation system's position is determined based on conditional probability and marginal probability, including: in, This represents the posterior probability of the inertial navigation system's position. Represents marginal probability; And based on the posterior probability, Bayesian decision-making is performed to determine the matching location result, including: in, Represents grid coordinates; Indicates the matching position result; The matching position result is used to correct the inertial navigation system position.

2. The terrain matching method according to claim 1, characterized in that, Also includes: Obtain the current inertial navigation system position of the aircraft, and configure the initial position prior distribution based on the current inertial navigation system position, including: in, This represents the standard deviation corresponding to the current inertial navigation system position; This represents the grid coordinates corresponding to the current inertial navigation system position; Represents a map The coordinates of the grid points on the grid; Represents prior probability; Initialize the PMF mesh and configure the mesh number based on the standard deviation.

3. The terrain matching method according to claim 1, characterized in that, Determine the current terrain altitude based on observed barometric altitude and observed radio altitude. ,include: in, This represents the observed air pressure altitude at time k; Represents the observed radio altitude at time k; This represents the observed terrain height at time k.

4. The terrain matching method according to claim 1 or 3, characterized in that, Based on the observed terrain height and projected map at two adjacent time points, determine the terrain elevation difference corresponding to the inertial navigation positions at two adjacent time points. Topographic elevation map ,include: Based on the difference in observed terrain height between time k-1 and time k, the terrain elevation difference is configured, including: in, This represents the observed terrain elevation at time k; This represents the observed terrain elevation at time k-1; Indicates the difference in terrain elevation; Read the elevation map and determine the inertial navigation position based on two adjacent time points. and The difference between the corresponding projected maps, and the configuration of the terrain elevation difference map, including: in, Map showing terrain elevation differences; Indicates the inertial navigation position at time k Corresponding projection map; Indicates the inertial navigation position at time k-1 The corresponding projected map.

5. The terrain matching method according to claim 1, characterized in that, Also includes: Based on the error models for barometric altitude and wireless altitude, a terrain altitude error model is configured, including: in, Indicates altitude; This indicates the altitude deviation of the barometric altimeter when measuring altitude; Indicates relative height; This indicates the noise level of the barometric altimeter when measuring altitude. This indicates the measurement noise of a radio altimeter when measuring relative altitude. Configure a terrain elevation difference observation error model based on the terrain elevation error model, including: The standard deviation is determined based on the error model of the topographic elevation difference observations and configured as the topographic elevation variance, including: in, This represents the standard deviation of the terrain elevation measurement error, which is contributed by the barometric altimeter error and the radio altimeter error. This is the coefficient for the noise of a constant radio altimeter as it increases with relative altitude; express The variance; This represents the relative height measurement at time k; This represents the relative height measurement at time k-1.

6. The terrain matching method according to claim 1, characterized in that, Also includes: The inertial navigation position error is determined based on the matching position result and the inertial navigation position, including: in, This indicates position information derived from the inertial navigation system; Indicates the matching position result; Indicates the inertial navigation system position error; The inertial navigation position error is compared with the preset validity criteria, and the validity of the matching position result is determined based on the comparison result; wherein, the preset validity criteria determine the corresponding matching position variance configuration based on the matching position result.

7. The terrain matching method according to claim 1, characterized in that, Also includes: The variance of the corresponding matching position is determined based on the matching position results, including: in, Represents the posterior probability; This indicates the current matching position result; Represents grid coordinates; Centered on the current inertial navigation position, the grid boundary of the PMF grid is determined based on the matching position variance, so as to be used for the next round of position matching based on the grid within the grid boundary.

8. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the terrain matching method based on differential elevation point quality filtering as described in any one of claims 1 to 7.

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