An auxiliary altimetry method for stellar positioning

Abstract

This invention belongs to the field of stellar positioning and navigation technology, and particularly relates to an auxiliary altimetry method for stellar positioning. The method includes constructing a three-dimensional spatiotemporal coordinate system, collecting raw altimetry data, constructing a three-dimensional spatiotemporal database, analyzing the spatial and temporal correlations of measurement points, calculating the spatial and temporal correlations between different measurement point areas and surrounding measurement point areas in the latitudinal and meridional directions, identifying correlated data based on the correlation of the altimetry data, eliminating other invalid samples without correlation, and performing spatiotemporal weighted fusion based on the spatial correlation of adjacent measurement points and the temporal correlation at different times to obtain fused altimetry data. This application integrates the spatial correlation of measurement points during the altimetry data acquisition process, as well as the temporal correlation attributes of the same measurement point at different times, into the verification statistics of the altimetry data results, providing a data foundation for subsequent output verification of altimetry data and correction of erroneous data.

Description

Technical Field

[0001] This invention belongs to the field of stellar positioning and navigation technology, and particularly relates to an auxiliary altimetry method for stellar positioning. Background Technology

[0002] Stellar positioning and navigation technology relies on stable and easily observable information such as the azimuth and altitude of stars to assist in navigation and positioning. It can ensure the necessary navigation and positioning needs of ocean-going navigation lights in special situations without relying on various electronic technologies. In this process, the analysis cloud needs to comprehensively acquire the position, altitude and other parameter information of various stellar targets for analysis and judgment. However, in the actual measurement process, due to the influence of the ship as a mobile platform and the instability of sea waves, the altimeter data acquired by various sensors or altimeter methods will inevitably be subject to various interferences, resulting in inaccurate altimeter data acquired by traditional altimeter acquisition schemes, which is difficult to meet the needs of continuous and accurate positioning analysis. Summary of the Invention

[0003] The purpose of this invention is to provide a method for optimizing the accuracy of altimetry data during stellar positioning. This method utilizes the spatial and temporal continuity characteristics between different measurement points to fuse altimetry data, thereby establishing a statistical verification, analysis, and validation method for continuous altimetry data.

[0004] To achieve the above objectives, the present invention adopts the following technical solution.

[0005] An auxiliary altimeter method for stellar positioning includes the following steps:

[0006] Step 1: Construct a three-dimensional spatiotemporal coordinate system, collect raw altimetry data, construct a three-dimensional spatiotemporal database with latitude and longitude coordinates of the measurement points and time as the three-dimensional coordinate axes, and collect raw altimetry data to fill it in;

[0007] Step 2: Analyze the spatial and temporal correlations of the measuring points, and calculate the spatial and temporal correlations between different measuring point areas and surrounding measuring point areas in the latitudinal and longitudinal directions;

[0008] Step 3: Valid data screening. Based on the correlation of the altimetry data, determine the relevant associated data. This includes: calculating the spatial offset correlation coefficient, using the preset maximum spatial correlation threshold as a benchmark to determine the maximum effective offset of the measuring point; calculating the temporal correlation coefficient under different lag times, using the preset maximum temporal correlation threshold as a benchmark to determine the maximum effective temporal offset; retaining altimetry data samples that have both spatial and temporal correlation, and removing other invalid samples that are not related.

[0009] Step 4: Altitude measurement data fusion and optimization. Collect valid data at different times to construct an altitude measurement dataset. Perform spatiotemporal weighted fusion based on the spatial correlation of adjacent measurement points and the temporal correlation at different times to obtain fused altitude measurement data.

[0010] A further improvement or preferred implementation of the aforementioned auxiliary altimetry method for stellar positioning also includes step five, altimetry data verification, which specifically refers to:

[0011] The height measurement data of the measuring point can be expressed as the actual height value. And the normally distributed noise interference caused by the acquisition equipment or acquisition scheme at two acquisition times. The fusion value; it is easy to see the noise interference. They are independent random variables, derived from continuous altimetry data at time t and time t+1. ;

[0012] The mean height measurement data at time t is obtained from the height measurement data of all valid measuring point areas. and its variance The optimal height estimate at time t+1 is obtained based on the mean and variance of the height measurement at time t. and variance ;

[0013]

[0014]

[0015] After each altimeter data collection, the optimal side altimeter estimate and variance are calculated and compared with the measurement data at the next time step to determine whether the current altimeter data result conforms to statistical characteristics. Simultaneously, the newly calculated estimate is used to correct the deviation of the previous estimate. This process is continuously cyclically updated to obtain a sequence of optimal estimates for each time step. and mean squared error sequence ;

[0016] Based on the optimal estimate sequence of each measuring point at each time point and mean squared error sequence Further verification of the corresponding altimetry data was conducted, and invalid or erroneous data was removed.

[0017] In a further improved or preferred embodiment of the aforementioned auxiliary altimetry method for stellar positioning, in step one, the three-dimensional spatiotemporal database consists of the position coordinates of different measuring points and altimetry data of the same measuring point collected at different times.

[0018] A further improvement or preferred embodiment of the aforementioned auxiliary altimetry method for stellar positioning, step two, analyzing the spatial and temporal correlation of the measurement point, specifically includes:

[0019] To determine the spatial correlation between different measuring point areas and surrounding measuring point areas in the latitudinal and longitudinal directions, the maximum offset of each measuring point in the latitudinal and longitudinal directions is calculated and analyzed based on the maximum correlation threshold, and the spatial correlation coefficient of the measuring points is obtained from this, expressed as follows: ;

[0020] Where i refers to the longitude or latitude deviation; This refers to the height measurement data collected at the measurement point location with coordinates (x, y). This refers to the height measurement data collected in the corresponding coordinate measurement point area;

[0021] The spatial offset correlation coefficients between the measuring point and its neighboring grid points are calculated along the latitudinal (x) and longitudinal (y) directions, respectively. Using a preset maximum spatial correlation threshold as a benchmark, when the spatial offset correlation coefficient first falls below the maximum spatial correlation threshold, the measuring point is considered to no longer have a significant spatial correlation with the benchmark measuring point. The maximum achievable effective offset in the latitudinal (x) and longitudinal (y) directions is then obtained by comparing the measuring point data that satisfy the spatial correlation requirement. , ;

[0022] To determine the temporal correlation of the measurement point area at different time intervals within the data acquisition period, the temporal correlation coefficient of the measurement points is determined based on the height offset at different lag times of each measurement point, and is expressed as follows: ;

[0023] in , indicating in The height measurement data is obtained from the measurement point area with time coordinates of (x, y); , indicating in Time lag The height measurement data is obtained from the measurement point area with coordinates (x, y) after a certain time.

[0024] Calculate the spatial correlation of the measurement point area with coordinates (x, y). ;in It refers to the maximum effective spatial offset in the latitudinal direction of the measurement point area with coordinates (x, y). It refers to the maximum effective spatial offset in the longitude direction of the measurement point area with coordinates (x, y). Latitude resolution, Longitude resolution.

[0025] In a further improvement or preferred embodiment of the aforementioned auxiliary altimetry method for stellar positioning, step three, effective data screening, specifically includes:

[0026] According to different lag times The collected altimetry data are used to calculate the time correlation coefficient at different lag times. Using a preset maximum time correlation threshold as a benchmark, when the time correlation coefficient first falls below the maximum threshold, it is considered that the lag data at that moment and the last moment no longer have a significant time correlation with the altimetry data at the benchmark time. Based on the moments preceding this point... Determine the maximum effective time offset;

[0027] According to different lag times The collected altimetry data are used to calculate the time correlation coefficient at different lag times. Using a preset maximum time correlation threshold as a benchmark, when the time correlation coefficient first falls below the maximum threshold, it is considered that the lag data at that moment and the last moment no longer have a significant time correlation with the altimetry data at the benchmark time. Based on the moments preceding this point... By determining the maximum effective time offset, the time correlation of the measurement point area with coordinates (x, y) is obtained. , For time resolution;

[0028] Preservation of spatially relevant ( ) and time correlation ( The height measurement data samples were used to remove other invalid samples that were not related.

[0029] A further improvement or preferred implementation of the aforementioned auxiliary altimetry method for stellar positioning, specifically step four, altimetry data fusion and optimization, refers to:

[0030] Construct a height measurement dataset using valid data acquired at different measurement points and at different times. ,in This represents the height measurement data of the i-th measuring point at time t;

[0031] For height measurement data at a certain point and time, spatiotemporal weighted fusion is performed based on the spatial correlation between adjacent measurement points and the temporal correlation between different times to obtain fused height measurement data. ;

[0032] Indicates the spatiotemporal fusion weights. Indicates spatial fusion weights, Indicates time fusion weights

[0033] Its beneficial effects are as follows:

[0034] The assisted altimetry method for star positioning proposed in this application is mainly used to perform data fusion and verification of continuously acquired altimetry data during the assisted altimetry process. By incorporating the spatial correlation of measuring points during the altimetry data acquisition process, as well as the temporal correlation attributes of the same measuring point at different times, into the verification statistics of the altimetry data results, a data foundation is provided for the subsequent output verification of altimetry data and the correction of erroneous data, thereby further improving the effectiveness of altimetry data output and reducing the probability of erroneous data output. Attached Figure Description

[0035] Figure 1 This is a flowchart illustrating an auxiliary altimetry method used for stellar positioning.

[0036] Figure 2 This is a schematic diagram of a three-dimensional spatiotemporal database structure. Detailed Implementation

[0037] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0038] like Figure 1 As shown, this application relates to an auxiliary altimetry method for star positioning, which mainly supports the optimization processing of altimetry data during star positioning. It is used to integrate spatial correlation elements generated and hidden based on spatial location of different measuring points and temporal correlation elements of measuring point data at different times during continuous measurement into continuous measurement data, thereby constructing a method that can integrate spatiotemporal correlation attributes and can be used for error filtering of large-capacity measurement data, positioning data optimization, and other purposes.

[0039] This application mainly considers the spatial location and acquisition time information corresponding to the altimetry data acquisition. Since different star positioning systems use different altimetry data output formats or data structures, in order to achieve the above objectives and ensure the effectiveness of subsequent altimetry data processing and acquisition, it is necessary to create a unified and standardized data storage structure, namely step one.

[0040] Step 1: Construct a three-dimensional spatiotemporal coordinate system and collect raw altimetry data.

[0041] A three-dimensional spatiotemporal database is constructed, consisting of the latitude and longitude coordinates of the measuring points and time as the three-dimensional coordinate axes. The three-dimensional spatiotemporal database is composed of the position coordinates of different measuring points and the height measurement data of the same measuring point at different acquisition times. The data of different measuring point areas are merged in chronological order to obtain three-dimensional height measurement data containing position, time and height measurement data information. The original height measurement data is collected and filled in.

[0042] like Figure 2 As shown, based on the characteristics of star positioning applications such as ships, the three-dimensional spatiotemporal database in this embodiment consists of the spatial location information of the measuring points, which is composed of latitude and longitude coordinate data, and the height measurement data information of each measuring point at different times. The height measurement data of different measuring points acquired at the same time are stored in one data layer, and the data layers at different times are connected in time through the latitude and longitude coordinates of the measuring points.

[0043] In order to extract spatiotemporal correlation information from the altimetry data contained in the aforementioned three-dimensional spatiotemporal database, this application performs analysis and calculation through spatial correlation and temporal correlation, i.e., step two.

[0044] Step 2: Analyze the spatial and temporal correlations of the measurement points, specifically referring to:

[0045] To determine the spatial correlation between different measuring point areas and surrounding measuring point areas in the latitudinal and longitudinal directions, the maximum offset of each measuring point in the latitudinal and longitudinal directions is calculated and analyzed based on the maximum correlation threshold, and the spatial correlation coefficient of the measuring points is obtained from this, expressed as follows: ;

[0046] Where i refers to the longitude or latitude deviation; This refers to the height measurement data collected at the measurement point location with coordinates (x, y). This refers to the height measurement data collected in the corresponding coordinate measurement point area;

[0047] The spatial offset correlation coefficients between the measuring point and its neighboring grid points are calculated along the latitudinal (x) and longitudinal (y) directions, respectively. Using a preset maximum spatial correlation threshold as a benchmark, when the spatial offset correlation coefficient first falls below the maximum spatial correlation threshold, the measuring point is considered to no longer have a significant spatial correlation with the benchmark measuring point. The maximum achievable effective offset in the latitudinal (x) and longitudinal (y) directions is then obtained by comparing the measuring point data that satisfy the spatial correlation requirement. , ;

[0048] Furthermore, to determine the temporal correlation of the measurement point area at different time periods within the data acquisition cycle, the temporal correlation coefficient of the measurement points is determined based on the height offset at different lag times of each measurement point, expressed as: ;

[0049] in , indicating in The height measurement data is obtained from the measurement point area with time coordinates of (x, y); , indicating in Time lag The height measurement data is obtained from the measurement point area with coordinates (x, y) after a certain time.

[0050] Calculate the spatial correlation of the measurement point area with coordinates (x, y).

[0051] ;

[0052] in It refers to the maximum effective spatial offset in the latitudinal direction of the measurement point area with coordinates (x, y). It refers to the maximum effective spatial offset in the longitude direction of the measurement point area with coordinates (x, y). Latitude resolution, Longitude resolution;

[0053] Due to the shift in planar spatial position and the change in the relative positional relationship between the measuring point and the star over time, altimetry data that exceed a certain spatial and temporal range often no longer have spatiotemporal correlation. Therefore, it is necessary to screen and analyze them to extract altimetry data that are correlated with the location of the measuring point of interest, i.e., step three.

[0054] Step 3: Valid data screening, determining relevant related data based on the correlation of the altimetry data;

[0055] According to different lag times The collected altimetry data are used to calculate the time correlation coefficient at different lag times. Using a preset maximum time correlation threshold as a benchmark, when the time correlation coefficient first falls below the maximum threshold, it is considered that the lag data at that moment and the last moment no longer have a significant time correlation with the altimetry data at the benchmark time. Based on the moments preceding this point... By determining the maximum effective time offset, the time correlation of the measurement point area with coordinates (x, y) is obtained. , For time resolution;

[0056] Preservation of spatially relevant ( ) and time correlation ( The height measurement data sample was used to remove other invalid samples that were not related to the data.

[0057] After removing invalid data, the correlation attributes between height measurement data at different measurement points can be obtained. By considering the spatial correlation of the measurement point data and the temporal correlation at different times, data fusion can be achieved. By replacing the original data with the fused data, statistical analysis of the fused data can express the spatial and temporal correlation features and hidden features in the height measurement data. This is specifically achieved through step four.

[0058] Step 4: Altitude Measurement Data Fusion and Optimization

[0059] Construct a height measurement dataset using valid data acquired at different measurement points and at different times. ,in This represents the height measurement data of the i-th measuring point at time t;

[0060] For height measurement data at a certain point and time, spatiotemporal weighted fusion is performed based on the spatial correlation between adjacent measurement points and the temporal correlation between different times to obtain fused height measurement data. ;

[0061] in Indicates the spatiotemporal fusion weights. Indicates spatial fusion weights, Indicates the time fusion weight, This indicates the distance between measurement points.

[0062] Furthermore, considering interference, the measurement data at the measuring point can be regarded as the superposition of real data and interference data. With the acquisition platform and acquisition scheme fixed, there is a correlation between the interference data. Based on this, the measurement value can be predicted through this correlation between the height measurement data for joint verification of the height measurement data, thereby further ensuring the accuracy of the height measurement data, i.e., step five.

[0063] Step 5: Verification of Altitude Measurement Data

[0064] The height measurement data of the measuring point is expressed as the actual height value. And the normally distributed noise interference caused by the acquisition equipment or acquisition scheme at two acquisition times. The fusion value; it is easy to see the noise interference. They are independent random variables, derived from continuous altimetry data at time t and time t+1. ;

[0065] The mean height measurement data at time t is obtained from the height measurement data of all valid measuring point areas. and its variance The optimal height estimate at time t+1 is obtained based on the mean and variance of the height measurement at time t. and variance ;

[0066]

[0067]

[0068] After each altimeter data collection, the optimal side altimeter estimate and variance are calculated and compared with the measurement data at the next time step to determine whether the current altimeter data result conforms to statistical characteristics. Simultaneously, the newly calculated estimate is used to correct the deviation of the previous estimate. This process is continuously cyclically updated to obtain a sequence of optimal estimates for each time step. and mean squared error sequence ;

[0069] In the specific verification process, the optimal estimate sequence at each measurement point at each time point is used. and mean squared error sequence Further verification of the corresponding altimetry data was conducted, and invalid or erroneous data was removed.

[0070] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit the scope of protection of the present invention. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the essence and scope of the technical solutions of the present invention.

Claims

1. An auxiliary altimeter method for stellar positioning, characterized in that, Includes the following steps: Step 1: Construct a three-dimensional spatiotemporal coordinate system, collect raw altimetry data, construct a three-dimensional spatiotemporal database with latitude and longitude coordinates of the measurement points and time as the three-dimensional coordinate axes, and collect raw altimetry data to fill it in; Step 2: Analyze the spatial and temporal correlations of the measuring points, and calculate the spatial and temporal correlations between different measuring point areas and surrounding measuring point areas in the latitudinal and longitudinal directions; Step 3: Valid data screening. Based on the correlation of the altimeter data, determine the relevant associated data; this includes: calculating the spatial offset correlation coefficient, and using the preset maximum spatial correlation threshold as a benchmark to determine the maximum effective offset of the measuring point. Calculate the time correlation coefficient under different lag times, determine the maximum effective time offset based on the preset maximum time correlation threshold, retain the altimetry data samples that have both spatial and temporal correlation, and remove other invalid samples that are not related. Step 4: Altitude measurement data fusion and optimization. Collect valid data at different times to construct an altitude measurement dataset. Perform spatiotemporal weighted fusion based on the spatial correlation of adjacent measurement points and the temporal correlation at different times to obtain fused altitude measurement data.

2. The auxiliary altimeter method for stellar positioning according to claim 1, characterized in that, It also includes step five, altimeter data verification, which specifically refers to: The height measurement data of the measuring point can be expressed as the actual height value. And the normally distributed noise interference caused by the acquisition equipment or acquisition scheme at two acquisition times. The fusion value; it is easy to see the noise interference. They are independent random variables, derived from continuous altimetry data at time t and time t+1. ; The mean height measurement data at time t is obtained from the height measurement data of all valid measuring point areas. and its variance The optimal height estimate at time t+1 is obtained based on the mean and variance of the height measurement at time t. and variance ; After each altimeter data collection, the optimal side altimeter estimate and variance are calculated and compared with the measurement data at the next time step to determine whether the current altimeter data result conforms to statistical characteristics. Simultaneously, the newly calculated estimate is used to correct the deviation of the previous estimate. This process is continuously cyclically updated to obtain a sequence of optimal estimates for each time step. and mean squared error sequence ; Based on the optimal estimate sequence of each measuring point at each time point and mean squared error sequence Further verification of the corresponding altimetry data was conducted, and invalid or erroneous data was removed.

3. The auxiliary altimeter method for stellar positioning according to claim 1, characterized in that, In step one, the three-dimensional spatiotemporal database consists of the position coordinates of different measuring points and height measurement data of the same measuring point at different acquisition times.

4. The auxiliary altimeter method for stellar positioning according to claim 1, characterized in that, Step two, analyzing the spatial and temporal correlation of the measurement points, specifically includes: To determine the spatial correlation between different measuring point areas and surrounding measuring point areas in the latitudinal and longitudinal directions, the maximum offset of each measuring point in the latitudinal and longitudinal directions is calculated and analyzed based on the maximum correlation threshold, and the spatial correlation coefficient of the measuring points is obtained from this, expressed as follows: ; Where i refers to the longitude or latitude deviation; This refers to the height measurement data collected at the measurement point location with coordinates (x, y). This refers to the height measurement data collected in the corresponding coordinate measurement point area; The spatial offset correlation coefficients between the measuring point and its neighboring grid points are calculated along the latitudinal (x) and longitudinal (y) directions, respectively. Using a preset maximum spatial correlation threshold as a benchmark, when the spatial offset correlation coefficient first falls below the maximum spatial correlation threshold, the measuring point is considered to no longer have a significant spatial correlation with the benchmark measuring point. The maximum achievable effective offset in the latitudinal (x) and longitudinal (y) directions is then obtained by comparing the measuring point data that satisfy the spatial correlation requirement. , ; To determine the temporal correlation of the measurement point area at different time intervals within the data acquisition period, the temporal correlation coefficient of the measurement points is determined based on the height offset at different lag times of each measurement point, and is expressed as follows: ; in , indicating in The height measurement data is obtained from the measurement point area with time coordinates of (x, y); , indicating in Time lag The height measurement data is obtained from the measurement point area with coordinates (x, y) after a certain time. Calculate the spatial correlation of the measurement point area with coordinates (x, y). ;in It refers to the maximum effective spatial offset in the latitudinal direction of the measurement point area with coordinates (x, y). It refers to the maximum effective spatial offset in the longitude direction of the measurement point area with coordinates (x, y). Latitude resolution, Longitude resolution.

5. The auxiliary altimeter method for stellar positioning according to claim 1, characterized in that, Step three, effective data filtering, specifically includes: According to different lag times The collected altimetry data are used to calculate the time correlation coefficient at different lag times. Using a preset maximum time correlation threshold as a benchmark, when the time correlation coefficient first falls below the maximum threshold, it is considered that the lag data at that moment and the last moment no longer have a significant time correlation with the altimetry data at the benchmark time. Based on the moments preceding this point... Determine the maximum effective time offset; According to different lag times The collected altimetry data are used to calculate the time correlation coefficient at different lag times. Using a preset maximum time correlation threshold as a benchmark, when the time correlation coefficient first falls below the maximum threshold, it is considered that the lag data at that moment and the last moment no longer have a significant time correlation with the altimetry data at the benchmark time. Based on the moments preceding this point... By determining the maximum effective time offset, the time correlation of the measurement point area with coordinates (x, y) is obtained. , For time resolution; Preservation of spatially relevant ( ) and time correlation ( The height measurement data samples were used to remove other invalid samples that were not related.

6. The auxiliary altimeter method for stellar positioning according to claim 1, characterized in that, Step four, altimeter data fusion and optimization, specifically refers to: Construct a height measurement dataset using valid data acquired at different measurement points and at different times. ,in This represents the height measurement data of the i-th measuring point at time t; For height measurement data at a certain point and time, spatiotemporal weighted fusion is performed based on the spatial correlation between adjacent measurement points and the temporal correlation between different times to obtain fused height measurement data. ; Indicates the spatiotemporal fusion weights. Indicates spatial fusion weights, This represents the time fusion weight.

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