Positioning method and device based on multi-ground radio station ranging assistance in satellite denial environment

Abstract

The present invention relates to the field of positioning technology, and specifically to a positioning method and device based on multi-ground radio station ranging assistance in a satellite-denied environment. The present application can be used in the positioning of drone clusters in a satellite-denied environment, and multiple ground base stations are used to first locate multiple aircraft. Since the aircraft adopt cluster operation, the present application uses the observation data of the inertial sensors of adjacent aircraft during clustering to verify the distance change between adjacent aircraft, and when the verification is abnormal, the calculated abnormal distance is used to quickly determine the abnormal drone. Then call Kalman filtering to perform positioning correction on the section where the abnormal positioning data of the abnormal drone appears. The present application performs Kalman filtering by screening abnormal points, so that the aircraft cluster can be accurately positioned using base stations with lower computing power, and has strong applicability.

Description

Technical Field

[0001] The present invention relates to the field of positioning technology, and in particular to a positioning method and device based on multi-ground radio station ranging assistance in a satellite denial environment. Background Art

[0002] Currently, most aircraft positioning uses a global positioning system based on satellite positioning. However, in some scenarios, such as when satellite signals are interfered with, blocked, or completely denied, traditional navigation systems that rely on GPS or GNSS will fail. Therefore, existing technologies may use other methods, such as ground base stations, to locate drones. However, positioning through ground base stations is susceptible to noise interference, so a corresponding filtering algorithm is needed to eliminate the impact of noise.

[0003] In the prior art, if filtering algorithms are used all the time to eliminate noise in aircraft positioning, it will bring a huge computational burden, especially in a drone cluster environment. The computing power of the ground base station is difficult to meet the needs of using filtering algorithms to eliminate noise in the positioning of aircraft clusters. Summary of the invention

[0004] In view of this, an object of the present invention is to provide a positioning method and device based on multi-ground radio station ranging assistance in a satellite denial environment to solve the problems in the background technology.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] The positioning method based on multi-ground radio station ranging assistance in a satellite-denied environment of the present invention comprises the steps of:

[0007] Positioning multiple aircraft based on multiple ground base stations to obtain the positioning coordinates of each aircraft at multiple time points; and obtaining observation data of multiple sensors of each aircraft at multiple time points, wherein the multiple sensors include inertial sensors;

[0008] Calculating the relative distances of the positioning coordinates of the same group of aircraft at the same time point; and determining the relative position data of each group of aircraft during the flight process based on the relative distances at multiple time points, wherein the multiple aircraft are grouped in advance to obtain multiple aircraft groups, and each aircraft and its adjacent aircraft respectively form an aircraft group;

[0009] Based on the observation data of the inertial sensors of each group of aircraft at multiple time points, the positioning coordinates of each group of aircraft during flight are checked for abnormalities, and the target aircraft with abnormal positioning coordinates are obtained;

[0010] Kalman filtering is performed based on the positioning coordinates of the target aircraft in a target time period and observation data from multiple sensors to obtain precise positioning, wherein the target time period includes multiple time points within a preset time period before an abnormal time point when the positioning coordinates are abnormal.

[0011] In one embodiment of the present application, positioning an aircraft based on multiple ground base stations to obtain positioning coordinates of the aircraft at multiple time points includes:

[0012] Get the coordinates of multiple base stations;

[0013] At multiple consecutive time points, the aircraft is measured based on multiple ground base stations to obtain the distances at the multiple consecutive time points;

[0014] Constructing a distance equation group for multiple consecutive time points based on the coordinates of the multiple base stations, the positioning coordinates of the aircraft, and the distances of multiple consecutive time points;

[0015] The distance equation group of each continuous time point is fitted based on the least square method to obtain the positioning coordinates of the aircraft at multiple time points.

[0016] In one embodiment of the present application, the inertial sensor includes a three-axis gyroscope and an acceleration sensor, wherein the positioning coordinates of each group of aircraft during flight are checked for abnormalities based on the observation data of the inertial sensor of each group of aircraft at multiple time points, including:

[0017] Obtaining instantaneous speed and acceleration of the aircraft at multiple time points, wherein the instantaneous speed includes a speed value and a speed direction, and the instantaneous speed is obtained based on integrating observation data of a three-axis gyroscope and an acceleration sensor;

[0018] For each group of aircraft, the relative distance at the next time point is predicted based on the instantaneous speed, acceleration and relative distance at the previous time point to obtain the predicted distance;

[0019] The relative distance at the next time point is compared with the predicted distance, and when the difference between the relative distance at the next time point and the predicted distance exceeds a preset threshold, it is determined that the relative distance at the next time point is abnormal;

[0020] The relative distances with abnormalities are screened out, and the positioning coordinates of all aircraft are checked for abnormalities based on the relative distances with abnormalities.

[0021] In one embodiment of the present application, the positioning coordinates of all aircraft are checked for abnormalities based on the relative distances where abnormalities exist, including:

[0022] Mark all aircraft corresponding to abnormal relative distances as candidate aircraft;

[0023] marking candidate aircraft corresponding to relative distances of the plurality of anomalies as target aircraft;

[0024] Confirming a candidate aircraft that meets a first target condition as a normal aircraft, wherein the first target condition includes: corresponding to only one abnormal relative distance, and the corresponding abnormal relative distance corresponds to the target aircraft;

[0025] A candidate aircraft that meets a second target condition is confirmed as a target aircraft, wherein the second target condition includes: corresponding to only one abnormal relative distance, and the corresponding abnormal relative distance does not correspond to the target aircraft either.

[0026] In one embodiment of the present application, the relative distance at a later time point is predicted based on the instantaneous speed, acceleration and relative distance at a previous time point to obtain the predicted distance, including:

[0027] Connect the positioning coordinates of two aircraft in the aircraft group to obtain a reference horizontal axis, and construct a reference vertical axis and a reference longitudinal axis perpendicular to the reference horizontal axis with one of the aircraft as the origin;

[0028] Decompose the instantaneous speed and acceleration of the two aircraft into the reference horizontal axis, the reference vertical axis and the reference longitudinal axis and calculate the difference respectively to obtain the initial velocity v of the horizontal axis h , horizontal axis acceleratora h , initial velocity of vertical axis v v , vertical axis acceleration a v , initial velocity of the longitudinal axis v l and the longitudinal acceleration a l ;

[0029] Based on the time length T between the previous time point and the next time point, the horizontal axis initial velocity v h , the horizontal axis acceleration a h , the vertical axis initial velocity v v , the vertical axis acceleration a v , the initial velocity of the longitudinal axis v l and the longitudinal acceleration a l Calculate the relative displacement S of the horizontal axis at the next time point h , vertical axis relative displacement S v and the relative displacement S of the longitudinal axis l ;

[0030] Based on the horizontal axis relative displacement S h and the longitudinal axis relative displacement S l Calculate the prediction distance S k+1 ', where the predicted distance S k+1 The mathematical expression of ' is:

[0031]

[0032] In the formula, S k is the relative distance to the previous time point.

[0033] In one embodiment of the present application, Kalman filtering is performed based on the positioning coordinates of the target aircraft in the target time period and the observation data of multiple sensors to obtain accurate positioning, including:

[0034] Establish the process model and observation model of the aircraft during flight;

[0035] Selecting a target time point within the target time period, and constructing an initial state of the target aircraft using the positioning coordinates of the target aircraft at the target time point and observation data of multiple sensors, and constructing an initial covariance matrix based on the initial state of the aircraft;

[0036] Kalman filtering is performed based on the initial state and initial covariance matrix of the target aircraft to obtain accurate positioning of the target aircraft at multiple time points after the target time point.

[0037] In an embodiment of the present application, selecting a target time point from the target time period includes:

[0038] Acquire basic stability data of the target aircraft at multiple time points within the target time period, wherein the basic stability data includes ranging signal strength and acceleration values ​​on each axis;

[0039] Mapping the stability basic data of multiple time points into a two-dimensional coordinate system, wherein the vertical axis of the two-dimensional coordinate system is the data axis and the horizontal axis of the two-dimensional coordinate system is the time axis;

[0040] Sliding along the time axis based on a pre-constructed sliding window, and calculating the ranging signal strength mean, ranging signal strength variance, acceleration mean and acceleration variance at multiple time points in the sliding window during each sliding;

[0041] Filter out candidate windows whose mean ranging signal strength is greater than a preset signal strength threshold and whose mean acceleration is less than or equal to a preset acceleration threshold;

[0042] Perform weighted summation on the ranging signal strength variance and the acceleration variance to obtain the stability value of each candidate window;

[0043] The candidate window with the largest stability value is used as the target window, and the middle time point of the target window is used as the target time point.

[0044] In one embodiment of the present application, the stability value W step The mathematical expression is:

[0045]

[0046] In the formula, step is the sequence number of the candidate window, is the ranging signal strength variance of the step-th candidate window, The acceleration variance of the step-th candidate window, α is the first weight, and β is the second weight.

[0047] In one embodiment of the present application, it also includes:

[0048] After accurate positioning is obtained, Kalman filtering is exited and the positioning of multiple aircraft based on multiple ground base stations is returned to.

[0049] The present application also provides a positioning device based on multi-ground radio station ranging assistance in a satellite denial environment, including:

[0050] A positioning module, used to locate multiple aircraft based on multiple ground base stations, obtain the positioning coordinates of each aircraft at multiple time points; and obtain observation data of multiple sensors of each aircraft at multiple time points, wherein the multiple sensors include inertial sensors;

[0051] A distance calculation module is used to calculate the relative distances of the positioning coordinates of the same group of aircraft at the same time point; and determine the relative position data of each group of aircraft during the flight process based on the relative distances at multiple time points, wherein the multiple aircraft are grouped in advance to obtain multiple aircraft groups, and each aircraft and its adjacent aircraft respectively form an aircraft group;

[0052] An abnormality checking module is used to perform abnormality checking on the positioning coordinates of each group of aircraft during flight based on the observation data of the inertial sensors of each group of aircraft at multiple time points, and obtain the target aircraft with abnormal positioning coordinates;

[0053] The positioning correction module is used to perform Kalman filtering based on the positioning coordinates of the target time period of the target aircraft and the observation data of multiple sensors to obtain accurate positioning, wherein the target time period includes multiple time points within a preset time period before the abnormal time point when the positioning coordinates are abnormal.

[0054] The beneficial effects of the present invention are as follows: the positioning method and device based on multi-ground radio station ranging assistance in a satellite-denied environment of the present invention can be used in the positioning of drone clusters in a satellite-denied environment, and multiple ground base stations are used to first locate multiple aircraft. Since the aircraft adopt cluster operation, the present application uses the observation data of the inertial sensors of adjacent aircraft during clustering to verify the distance change between adjacent aircraft, and when the verification is abnormal, the abnormal drone is quickly identified using the calculated abnormal distance. The Kalman filter is then called to perform positioning correction on the section where the abnormal positioning data of the abnormal drone appears. The present application performs Kalman filtering by screening abnormal points, so that the aircraft cluster can be accurately positioned using base stations with lower computing power, and has strong applicability. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] The present invention will be further described below in conjunction with the accompanying drawings and embodiments:

[0056] Figure 1 This is an application scenario diagram in an embodiment of the present application;

[0057] Figure 2 It is a flow chart of a positioning method based on multi-ground radio station ranging assistance in a satellite-denied environment shown in an embodiment of the present application;

[0058] Figure 3 A schematic diagram of the principle of distinguishing an abnormal aircraft in an embodiment of the present application;

[0059] Figure 4 It is a structural diagram of a positioning device based on multi-ground radio station ranging assistance in a satellite-denied environment shown in one embodiment of the present application. DETAILED DESCRIPTION

[0060] The following describes the embodiments of the present invention by specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict.

[0061] It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present invention, and thus the drawings only show the layers related to the present invention rather than being drawn according to the number, shape and size of the layers in actual implementation. In actual implementation, the type, quantity and proportion of each layer may be changed arbitrarily, and the layer layout may also be more complicated.

[0062] In the following description, numerous details are discussed to provide a more thorough explanation of embodiments of the present invention; however, it is apparent to one skilled in the art that embodiments of the present invention may be practiced without these specific details.

[0063] Figure 1 This is an application scenario diagram in an embodiment of the present application, such as Figure 1 As shown, the present application uses multiple ground base station radio stations, such as base station radio station 1 (x1, y1, z1), base station radio station 2 (x2, y2, z2), base station radio station 3 (x3, y3, z3), and base station radio station 4 (x4, y4, z4) to measure the distance of the aircraft 1 to be positioned, such as the corresponding d1, d2, d3, and d4. Using the above data, the present application can perform positioning, and then use the inertial measurement unit IMU to predict the distance between adjacent aircraft at the next time point, and use the predicted distance to verify and correct the distance at the next time point. The specific process is as follows:

[0064] Figure 2 FIG. 1 is a flowchart of a positioning method based on multi-ground radio station ranging assistance in a satellite-denied environment shown in an embodiment of the present application. Figure 2 As shown: the positioning method based on multi-ground radio station ranging assistance in a satellite-denied environment of this embodiment may include steps S210 to S240:

[0065] S210, positioning multiple aircrafts based on multiple ground base stations to obtain positioning coordinates of each aircraft at multiple time points; and obtaining observation data of multiple sensors of each aircraft at multiple time points, wherein the multiple sensors include inertial sensors;

[0066] In this application, the aircraft obtains the relative distance, that is, the ranging value, by calculating the signal transmission time between the ground base station and the airborne radio station. Three-dimensional spatial positioning requires at least four groups of ground base stations to assist. After obtaining the ranging values ​​between the airborne radio station and multiple ground base stations, the approximate position of the aircraft can be obtained.

[0067] The method to calculate the approximate position of the aircraft is as follows:

[0068] S211, obtaining coordinates of multiple base stations, for example: base station radio 1 (x1, y1, z1), base station radio 2 (x2, y2, z2), base station radio 3 (x3, y3, z3), base station radio 4 (x4, y4, z4);

[0069] S212, measuring the distance of the aircraft based on multiple ground base stations at multiple consecutive time points to obtain the distances at multiple consecutive time points; for example, at each time point, the distance measurement values ​​of the four base station radio stations are d1, d2, d3, and d4 respectively;

[0070] S213, constructing a distance equation group of multiple consecutive time points based on the coordinates of the multiple base stations, the positioning coordinates of the aircraft, and the distances of multiple consecutive time points;

[0071] The distance equations are: i is 1, 2, 3, or 4;

[0072] Among them, (x, y, z) is the position of the aircraft.

[0073] S214, fitting the distance equation group of each continuous time point based on the least square method to obtain the positioning coordinates of the aircraft at multiple time points.

[0074] The specific solution is as follows:

[0075] make r = x 2 +y 2 +z 2

[0076] but

[0077] make

[0078] According to the least squares method, we can get

[0079] P dt =(A T A) -1 A T Y

[0080] The rough position coordinates P of the aircraft can be obtained dt .

[0081] S220, calculating the relative distances of the positioning coordinates of the same group of aircraft at the same time point; and determining the relative position data of each group of aircraft during the flight process based on the relative distances at multiple time points, wherein the multiple aircraft are grouped in advance to obtain multiple aircraft groups, and each aircraft and its adjacent aircraft respectively form an aircraft group;

[0082] Assuming that the positioning coordinates of the same group of aircraft at the same time point are (x, y, z) and (x', y', z'), the corresponding relative distance is

[0083] The reason why this application uses relative distance as the benchmark for verification is that in a drone cluster, each drone may have positioning errors. By using relative distance as the benchmark for verification, these errors can be discovered and corrected in a timely manner to avoid error accumulation in the cluster, thereby improving the overall positioning accuracy.

[0084] In addition, drone swarms place more emphasis on collaborative operations between drones, so using relative distance as a benchmark can provide a more effective data basis for subsequent control of collaborative operations between drones.

[0085] S230, performing anomaly verification on the positioning coordinates of each group of aircraft during flight based on observation data of the inertial sensors of each group of aircraft at multiple time points;

[0086] In this application, since the radio ranging process is susceptible to measurement errors and signal multipath propagation, this directly affects the accuracy of the positioning results and the robustness of the system. Therefore, starting from takeoff, this application uses the observation data of the inertial sensor at the previous time point at multiple time points to verify the relative position of the aircraft at the next time point. In this way, positioning points with large deviations can be found in time, so that they can be corrected in time using Kalman filtering later. In this way, while improving the positioning accuracy, the amount of calculation caused by Kalman filtering can be reduced, including:

[0087] S231, obtaining instantaneous speed and acceleration of the aircraft at multiple time points, wherein the instantaneous speed includes a speed value and a speed direction, and the instantaneous speed is obtained based on integrating observation data of a three-axis gyroscope and an acceleration sensor;

[0088] The inertial measurement unit (IMU) in the aircraft in this application includes an accelerometer and a three-axis gyroscope, which integrates the acceleration on the three axes to obtain the instantaneous speed of the aircraft during flight. The acceleration at multiple time points can be directly read by the accelerometer.

[0089] S232, for each group of aircraft, predicting the relative distance at a later time point based on the instantaneous speed, acceleration and relative distance at a previous time point to obtain a predicted distance;

[0090] In this application, the parameter of interest is the distance between adjacent aircraft. Therefore, it is necessary to use instantaneous velocity and acceleration to analyze the change in distance between adjacent aircraft to obtain the predicted distance, which specifically includes:

[0091] S2321, connecting the positioning coordinates of two aircraft in the aircraft group to obtain a reference horizontal axis (h axis), and constructing a reference vertical axis (v axis) and a reference longitudinal axis (l axis) perpendicular to the reference horizontal axis with one of the aircraft as the origin;

[0092] S2322, decompose the instantaneous speed and acceleration of the two aircraft into the reference horizontal axis, the reference vertical axis and the reference longitudinal axis and calculate the difference respectively to obtain the initial speed v of the horizontal axis h , horizontal axis acceleratora h , initial velocity of vertical axis v v , vertical axis acceleration av , initial velocity of the longitudinal axis v l and the longitudinal acceleration a l ;

[0093] In order to facilitate displacement analysis in the above coordinate system, the flight direction and acceleration direction of the aircraft do not coincide with the above reference axis in most cases. Therefore, the velocity vector and acceleration vector are decomposed into the reference horizontal axis (h axis), the reference vertical axis (v axis) and the reference longitudinal axis (l axis) by orthogonal decomposition, thereby obtaining the horizontal axis initial velocity v h , horizontal axis acceleratora h , initial velocity of vertical axis v v , vertical axis acceleration a v , initial velocity of the longitudinal axis v l and the longitudinal acceleration a l .

[0094] S2323, based on the time length T between the previous time point and the next time point, the horizontal axis initial velocity v h , the horizontal axis accelerator a h , the vertical axis initial velocity v v , the vertical axis acceleration a v , the initial velocity of the longitudinal axis v l and the longitudinal acceleration a l Calculate the relative displacement S of the horizontal axis at the next time point h , vertical axis relative displacement S v and the relative displacement of the longitudinal axis S l ;

[0095] The interval between the two time points in this application is relatively short, about 0.1-0.5 seconds. Therefore, this application regards each displacement as uniformly accelerated motion, and uses the horizontal axis initial velocity v obtained by orthogonal decomposition. h , horizontal axis acceleratora h , initial velocity of vertical axis v v , vertical axis acceleration a v , initial velocity of the longitudinal axis v l and the longitudinal acceleration a l , calculate the relative displacement S of the horizontal axis respectively h , vertical axis relative displacement S v and the relative displacement of the longitudinal axis S l , where the horizontal axis relative displacement S h and the relative displacement of the longitudinal axis S l The mathematical expression is:

[0096]

[0097] S2324, based on the horizontal axis relative displacement S hand the longitudinal axis relative displacement S l Calculate the predicted distance S k+1 ', where the predicted distance S k+1 The mathematical expression of ' is:

[0098]

[0099] In the formula, S k is the relative distance to the previous time point.

[0100] Finally, calculate the predicted distance S at the next time point k+1 ', we can get the predicted distance of the adjacent aircraft from the perspective of the inertial measurement unit. Since the inertial measurement unit is not easily disturbed by noise, inertial prediction at each time point can accurately predict the relative displacement state at the next time point and avoid the error accumulation caused by the continuous use of inertial navigation.

[0101] S233, the relative distance S at the next time point k+1 The predicted distance S k+1 'Compare, and the difference between the relative distance at the next time point and the predicted distance exceeds the preset threshold S th When , it is determined that the relative distance at the next time point is abnormal;

[0102] In |S k+1 -S k+1 '|>S th When , it means that there is a large deviation between the relative displacement predicted by the inertial observation unit and the relative displacement measured by the ground base station. In view of the accuracy of the inertial system in a short time, the predicted distance S k+1 ' as a benchmark to verify the relative distance S k+1 . Thus, the relative distance where the anomaly exists can be verified.

[0103] S234, screen out the relative distances with abnormalities, and perform abnormality check on the positioning coordinates of all aircraft based on the relative distances with abnormalities to obtain the target aircraft with abnormal positioning coordinates.

[0104] Since each relative distance corresponds to two aircrafts, it is impossible to directly determine the target aircraft with abnormal positioning. Therefore, the present application adopts the following process to screen out aircrafts with abnormal positioning, including:

[0105] Figure 3 FIG. 1 is a schematic diagram of the principle of distinguishing abnormal aircraft in an embodiment of the present application, such as Figure 3 As shown, all aircraft corresponding to abnormal relative distances are marked as candidate aircraft; and the target aircraft and ordinary aircraft are identified by the following discrimination conditions:

[0106] (1) The candidate aircraft corresponding to multiple abnormal relative distances are marked as target aircraft; if an aircraft corresponds to multiple abnormal relative distances, it is likely that the positioning of the aircraft is abnormal, causing multiple abnormal relative distances between adjacent aircraft. For example, Figure 3 If the positioning of t9 is abnormal, it is highly likely that the relative distances S9-S16 will all be abnormal.

[0107] (2) confirming a candidate aircraft that meets a first target condition as a normal aircraft, wherein the first target condition includes: corresponding to only one abnormal relative distance, and the corresponding abnormal relative distance corresponds to the target aircraft;

[0108] For example, Figure 3 In the figure, t9 is the target aircraft. If only S9 is abnormal at t1, but S1 and S8 are not abnormal, it is determined that the relative distance abnormality is caused by t9, and t1 is a normal aircraft.

[0109] (3) Confirming a candidate aircraft that meets a second target condition as a target aircraft, wherein the second target condition includes: corresponding to only one abnormal relative distance, and the corresponding abnormal relative distance does not correspond to the target aircraft.

[0110] For example, Figure 3 S1 is abnormal, but there are no other relative distance anomalies for t1 and t2. At this time, it is impossible to determine whether t1 or t2 is abnormal. Therefore, both t1 and t2 are marked as target aircraft for positioning correction.

[0111] If a candidate aircraft has only one abnormal relative distance with the target aircraft, the cause of the abnormal relative distance is attributed to the target aircraft. If none of the aircraft that cause an abnormal relative distance is the target aircraft, then when it is not possible to determine which aircraft caused the abnormal relative distance, both are marked as the target aircraft.

[0112] S240, performing Kalman filtering based on the positioning coordinates of the target aircraft in a target time period and observation data from multiple sensors to obtain precise positioning, wherein the target time period includes multiple time points within a preset time period before an abnormal time point when the positioning coordinates are abnormal.

[0113] After the target aircraft is determined, the Kalman filter is used to determine the target aircraft's positioning coordinates in combination with the time point at which the abnormal positioning coordinates exist, and accurate positioning is obtained, as follows:

[0114] S241, establishing a process model and an observation model of the aircraft during flight;

[0115] The process model is:

[0116] x k =f(x k-1 ,u k-1 ,w k-1 ),w k ~N(0,Q k )

[0117] The observation model is:

[0118] z k =h(x k ,v k ),v k ~N(0,R k )

[0119] where x k and x k-1 are the system state variables at time k and time k-1 respectively, z k is the system observation quantity at time k. f() is the nonlinear state transfer function, h() is the nonlinear observation function, u k-1 is the control vector at time k-1, w k is the process noise, v k is the measurement noise, all of which are zero-mean Gaussian white noise, N(0,Q k ) and N(0,R k ) represents zero-mean Gaussian white noise.

[0120] S242, selecting a target time point from the target time period, and constructing an initial state of the target aircraft using the positioning coordinates of the target aircraft at the target time point and observation data of multiple sensors, and constructing an initial covariance matrix based on the initial state of the aircraft;

[0121] Since this application does not perform Kalman filtering on the entire flight process of the target aircraft, but starts Kalman filtering from a certain time point before the abnormal time point after the target aircraft positioning coordinates are abnormal, in order to make the Kalman filter fit as quickly as possible and then exit. To avoid occupying high computing power for a long time. Therefore, this application needs to select the most accurate positioning coordinates as the initial value to perform Kalman filtering.

[0122] In the positioning structure, the accuracy of positioning is related to the flight attitude and signal of the aircraft during the flight process. Therefore, this application selects the target time point within the target time period based on the flight attitude and signal, and uses the positioning coordinates of the target time point as the initial value to perform Kalman filtering, so that the filtering result can be fitted as quickly as possible to obtain accurate results, specifically including:

[0123] S2421, acquiring basic stability data of the target aircraft at multiple time points within the target time period, wherein the basic stability data includes ranging signal strength and acceleration values ​​on each axis;

[0124] This application collects inertial data from the aircraft's inertial observation unit (IMU), including acceleration values, at each time point. During positioning, the ranging signal strength is also automatically collected. Therefore, based on the above two parameters, the flight attitude and ranging signal of the drone at each time point are analyzed to determine whether there is a risk in the positioning coordinates.

[0125] S2422, mapping the stability basic data of multiple time points into a two-dimensional coordinate system, wherein the vertical axis of the two-dimensional coordinate system is a data axis, and the horizontal axis of the two-dimensional coordinate system is a time axis;

[0126] S2423, sliding along the time axis based on a pre-built sliding window, and calculating the mean of the ranging signal strength, the variance of the ranging signal strength, the mean of the acceleration, and the variance of the acceleration at multiple time points in the sliding window during each sliding;

[0127] Among them, the sliding window can be used to collect the signal strength characteristics (mean and variance) within a short period of time, as well as the flight attitude characteristics (acceleration mean and variance) within a short period of time, so as to avoid the analysis result errors caused by data fluctuations at a single time point.

[0128] S2424, screening out candidate windows whose mean ranging signal strength is greater than a preset signal strength threshold and whose mean acceleration is less than or equal to a preset acceleration threshold;

[0129] S2425, performing weighted summation on the ranging signal strength variance and the acceleration variance to obtain a stability value for each candidate window; the stability value W step The mathematical expression is:

[0130]

[0131] In the formula, step is the sequence number of the candidate window, is the ranging signal strength variance of the step-th candidate window, The acceleration variance of the step-th candidate window, α is the first weight, and β is the second weight.

[0132] In this embodiment, the strength and stability of the ranging signal, the magnitude and stability of the acceleration are reflected by the mean value of the ranging signal strength, the variance of the ranging signal strength, the mean value of the acceleration and the variance of the acceleration in a short period of time corresponding to each sliding of the sliding window. First, the windows with relatively strong ranging signals and relatively small acceleration are screened out to obtain candidate windows. The window with the strongest stability is selected from the candidate windows, and the weighted summation of the ranging signal strength variance and the acceleration variance is performed to obtain the comprehensive stability.

[0133] S2426, taking the candidate window with the largest stability value as the target window, and taking the middle time point of the target window as the target time point.

[0134] The candidate window with the greatest stability has the smallest corresponding ranging deviation risk value, so the time point in this window is selected as the target time point to perform subsequent Kalman filtering.

[0135] S243, performing Kalman filtering based on the initial state and initial covariance matrix of the target aircraft to obtain accurate positioning of the target aircraft at multiple time points after the target time point.

[0136] This application adopts the existing Kalman filter, and its steps can be summarized as follows:

[0137] The positioning coordinates of the target aircraft corresponding to the target time point, the observation parameters of the inertial sensor, the magnetometer and other sensor parameters are used as the initial state of the Kalman filter;

[0138] Set the system covariance matrix and the observation noise covariance matrix, which are used to describe the uncertainty of the system state and the observation data.

[0139] According to the UAV motion state estimation at the previous moment, the state change matrix is ​​used to predict the UAV state at the current moment. At the same time, the system covariance matrix is ​​predicted to reflect the uncertainty of the predicted state.

[0140] Get the current observation data, such as GPS location information or other sensor data.

[0141] Calculates the Kalman filter gain, which is used to weigh the confidence of the predictions and observations.

[0142] Combine the Kalman filter gain and observation data to update the estimate of the drone state.

[0143] Update the system covariance matrix to reflect the uncertainty of the updated state.

[0144] Assuming that the target UAV moves at a uniform speed in a short period of time, the current motion state matrix of the UAV is corrected according to the optimal estimation value of the current UAV positioning system.

[0145] Repeat the above prediction and update steps, and continuously iterate the Kalman filter algorithm to obtain continuous drone positioning correction results.

[0146] When the deviation between multiple consecutive observations and predicted values ​​does not exceed the preset threshold, the result is judged to be fitted and accurate positioning is obtained. The time point of accurate positioning is uncertain and can be performed in real time during subsequent flights.

[0147] After accurate positioning is obtained, Kalman filtering is exited and the process returns to step S210. In this process, Kalman filtering is continuously performed on some time periods of some aircraft. Compared with performing Kalman filtering on all aircraft throughout the entire process, this application can reduce a large amount of computing power requirements and has stronger adaptability.

[0148] The positioning method based on multi-ground radio station ranging assistance in a satellite-denied environment of the present invention can be used in the positioning of drone clusters in a satellite-denied environment. Multiple ground base stations are used to first locate multiple aircraft. Since the aircraft adopt cluster operation, the present application uses the observation data of the inertial sensors of adjacent aircraft during clustering to verify the distance change between adjacent aircraft, and when the verification is abnormal, the abnormal drone is quickly identified using the calculated abnormal distance. Kalman filtering is then called to perform positioning correction on the section where the abnormal positioning data of the abnormal drone appears. The present application performs Kalman filtering by screening abnormal points, so that the aircraft cluster can be accurately located using base stations with lower computing power, and has strong applicability.

[0149] like Figure 4 As shown, the present application also provides a positioning device based on multi-ground radio station ranging assistance in a satellite denial environment, including:

[0150] A positioning module, used to locate multiple aircraft based on multiple ground base stations, obtain the positioning coordinates of each aircraft at multiple time points; and obtain observation data of multiple sensors of each aircraft at multiple time points, wherein the multiple sensors include inertial sensors;

[0151] A distance calculation module is used to calculate the relative distances of the positioning coordinates of the same group of aircraft at the same time point; and determine the relative position data of each group of aircraft during the flight process based on the relative distances at multiple time points, wherein the multiple aircraft are grouped in advance to obtain multiple aircraft groups, and each aircraft and its adjacent aircraft respectively form an aircraft group;

[0152] An abnormality checking module is used to perform abnormality checking on the positioning coordinates of each group of aircraft during flight based on the observation data of the inertial sensors of each group of aircraft at multiple time points, and obtain the target aircraft with abnormal positioning coordinates;

[0153] The positioning correction module is used to perform Kalman filtering based on the positioning coordinates of the target time period of the target aircraft and the observation data of multiple sensors to obtain accurate positioning, wherein the target time period includes multiple time points within a preset time period before the abnormal time point when the positioning coordinates are abnormal.

[0154] The positioning device based on multi-ground radio station ranging assistance in a satellite-denied environment of the present invention can be used in the positioning of drone clusters in a satellite-denied environment. Multiple ground base stations are used to first locate multiple aircraft. Since the aircraft adopt cluster operation, the present application uses the observation data of the inertial sensors of adjacent aircraft during clustering to verify the distance change between adjacent aircraft, and when the verification is abnormal, the calculated abnormal distance is used to quickly determine the abnormal drone. Then call Kalman filtering to perform positioning correction on the section where the abnormal positioning data of the abnormal drone appears. The present application performs Kalman filtering by screening abnormal points, so that the aircraft cluster can be accurately positioned using base stations with lower computing power, and has strong applicability.

[0155] This embodiment further provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, any one of the methods in this embodiment is implemented, wherein the method is the execution logic of this system.

[0156] This embodiment also provides an electronic terminal, including: a processor and a memory;

[0157] The memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory, so that the terminal executes any one of the methods in this embodiment.

[0158] The computer-readable storage medium in this embodiment can be understood by ordinary technicians in this field: all or part of the steps of implementing the above-mentioned method embodiments can be completed by hardware related to the computer program. The aforementioned computer program can be stored in a computer-readable storage medium. When the program is executed, the steps of the above-mentioned method embodiments are executed; and the aforementioned storage medium includes: ROM, RAM, magnetic disk or optical disk and other media that can store program codes.

[0159] The electronic terminal provided in this embodiment includes a processor, a memory, a transceiver and a communication interface. The memory and the communication interface are connected to the processor and the transceiver and complete communication with each other. The memory is used to store computer programs, the communication interface is used to communicate, and the processor and the transceiver are used to run computer programs so that the electronic terminal executes each step of the above method.

[0160] In this embodiment, the memory may include a random access memory (RAM), and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0161] The above-mentioned processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0162] In the above-mentioned embodiments, although the present invention has been described in conjunction with the specific embodiments of the present invention, many replacements, modifications and variations of these embodiments will be apparent to those of ordinary skill in the art based on the foregoing description. The embodiments of the present invention are intended to cover all such replacements, modifications and variations falling within the broad scope of the appended claims.

[0163] The above embodiments are merely illustrative of the principles and effects of the present invention, and are not intended to limit the present invention. Anyone familiar with the art may modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by a person of ordinary skill in the art without departing from the spirit and technical concept disclosed by the present invention shall still be covered by the claims of the present invention.

Claims

1. A positioning method based on multi-ground radio station ranging assistance in a satellite-denied environment, characterized in that: include: Position multiple aircraft based on multiple ground base stations to obtain the positioning coordinates of each aircraft at multiple time points; and obtaining observation data of multiple sensors of each aircraft at multiple time points, wherein the multiple sensors include inertial sensors, including: obtaining coordinates of multiple base stations; measuring the distance of the aircraft based on multiple ground base stations at multiple consecutive time points to obtain distances at multiple consecutive time points; constructing a distance equation group for multiple consecutive time points based on the coordinates of the multiple base stations, the positioning coordinates of the aircraft, and the distances at multiple consecutive time points; fitting the distance equation group for each consecutive time point based on the least squares method to obtain the positioning coordinates of the aircraft at multiple time points; Calculating the relative distances of the positioning coordinates of the same group of aircraft at the same time point; and determining the relative position data of each group of aircraft during the flight process based on the relative distances at multiple time points, wherein the multiple aircraft are grouped in advance to obtain multiple aircraft groups, and each aircraft and its adjacent aircraft respectively form an aircraft group; Based on the observation data of the inertial sensors of each group of aircraft at multiple time points, the positioning coordinates of each group of aircraft during flight are checked for abnormalities, and the target aircraft with abnormal positioning coordinates are obtained; Kalman filtering is performed based on the positioning coordinates of the target aircraft in a target time period and observation data from multiple sensors to obtain precise positioning, wherein the target time period includes multiple time points within a preset time period before an abnormal time point when the positioning coordinates are abnormal.

2. The positioning method based on multi-ground radio station ranging assistance in a satellite-denied environment according to claim 1 is characterized in that: The inertial sensor includes a three-axis gyroscope and an acceleration sensor. The positioning coordinates of each group of aircraft during flight are checked for abnormalities based on the observation data of the inertial sensor of each group of aircraft at multiple time points, including: Obtaining instantaneous speed and acceleration of the aircraft at multiple time points, wherein the instantaneous speed includes a speed value and a speed direction, and the instantaneous speed is obtained based on integrating observation data of a three-axis gyroscope and an acceleration sensor; For each group of aircraft, the relative distance at the next time point is predicted based on the instantaneous speed, acceleration and relative distance at the previous time point to obtain the predicted distance; The relative distance at the next time point is compared with the predicted distance, and when the difference between the relative distance at the next time point and the predicted distance exceeds a preset threshold, it is determined that the relative distance at the next time point is abnormal; The relative distances with abnormalities are screened out, and the positioning coordinates of all aircraft are checked for abnormalities based on the relative distances with abnormalities.

3. The positioning method based on multi-ground radio station ranging assistance in a satellite-denied environment according to claim 2 is characterized in that: Perform anomaly checks on the positioning coordinates of all aircraft based on the relative distances where anomalies exist, including: Mark all aircraft corresponding to abnormal relative distances as candidate aircraft; marking candidate aircraft corresponding to relative distances of the plurality of anomalies as target aircraft; Confirming a candidate aircraft that meets a first target condition as a normal aircraft, wherein the first target condition includes: corresponding to only one abnormal relative distance, and the corresponding abnormal relative distance corresponds to the target aircraft; A candidate aircraft that meets a second target condition is confirmed as a target aircraft, wherein the second target condition includes: corresponding to only one abnormal relative distance, and the corresponding abnormal relative distance does not correspond to the target aircraft either.

4. The positioning method based on multi-ground radio station ranging assistance in a satellite-denied environment according to claim 2, characterized in that: The relative distance at the next time point is predicted based on the instantaneous speed, acceleration and relative distance at the previous time point to obtain the predicted distance, including: Connect the positioning coordinates of two aircraft in the aircraft group to obtain a reference horizontal axis, and construct a reference vertical axis and a reference longitudinal axis perpendicular to the reference horizontal axis with one of the aircraft as the origin; Decompose the instantaneous speed and acceleration of the two aircraft into the reference horizontal axis, the reference vertical axis and the reference longitudinal axis and calculate the difference respectively to obtain the initial velocity of the horizontal axis , horizontal axis acceleration , vertical axis initial velocity , vertical axis acceleration , initial velocity of the longitudinal axis and longitudinal acceleration ; Based on the duration between the previous time point and the next time point , the initial velocity of the horizontal axis , the horizontal axis acceleration , the vertical axis initial velocity , the vertical axis acceleration , the initial velocity of the longitudinal axis and the longitudinal acceleration Calculate the relative displacement of the horizontal axis at the next time point , vertical axis relative displacement Relative displacement of the longitudinal axis ; Based on the horizontal axis relative displacement and the relative displacement of the longitudinal axis Calculate prediction distance , where the predicted distance The mathematical expression is: In the formula, is the relative distance to the previous time point.

5. The positioning method based on multi-ground radio station ranging assistance in a satellite-denied environment according to claim 1, characterized in that: Kalman filtering is performed based on the positioning coordinates of the target aircraft during the target time period and the observation data of multiple sensors to obtain accurate positioning, including: Establish the process model and observation model of the aircraft during flight; Selecting a target time point within the target time period, and constructing an initial state of the target aircraft using the positioning coordinates of the target aircraft at the target time point and observation data of multiple sensors, and constructing an initial covariance matrix based on the initial state of the aircraft; Kalman filtering is performed based on the initial state and initial covariance matrix of the target aircraft to obtain accurate positioning of the target aircraft at multiple time points after the target time point.

6. The positioning method based on multi-ground radio station ranging assistance in a satellite-denied environment according to claim 5, characterized in that: Selecting a target time point from within the target time period includes: Acquire basic stability data of the target aircraft at multiple time points within the target time period, wherein the basic stability data includes ranging signal strength and acceleration values ​​on each axis; Mapping the stability basic data of multiple time points into a two-dimensional coordinate system, wherein the vertical axis of the two-dimensional coordinate system is the data axis and the horizontal axis of the two-dimensional coordinate system is the time axis; Sliding along the time axis based on a pre-constructed sliding window, and calculating the ranging signal strength mean, ranging signal strength variance, acceleration mean and acceleration variance at multiple time points in the sliding window during each sliding; Filter out candidate windows whose mean ranging signal strength is greater than a preset signal strength threshold and whose mean acceleration is less than or equal to a preset acceleration threshold; The weighted sum of the ranging signal strength variance and the acceleration variance is performed to obtain the stability value of each candidate window; The candidate window with the largest stability value is used as the target window, and the middle time point of the target window is used as the target time point.

7. The positioning method based on multi-ground radio station ranging assistance in a satellite-denied environment according to claim 6, characterized in that: The stability value The mathematical expression is: In the formula, is the sequence number of the candidate window, For the The variance of the ranging signal strength of candidate windows is: No. The acceleration variance of candidate windows, is the first weight, is the second weight.

8. The positioning method based on multi-ground radio station ranging assistance in a satellite-denied environment according to claim 1, characterized in that: Also includes: After accurate positioning is obtained, Kalman filtering is exited and the positioning of multiple aircraft based on multiple ground base stations is returned to.

9. A positioning device based on multi-ground radio station ranging assistance in a satellite denial environment, characterized in that: include: A positioning module is used to locate multiple aircraft based on multiple ground base stations to obtain the positioning coordinates of each aircraft at multiple time points; and obtaining observation data of multiple sensors of each aircraft at multiple time points, wherein the multiple sensors include inertial sensors, including: obtaining coordinates of multiple base stations; measuring the distance of the aircraft based on multiple ground base stations at multiple consecutive time points to obtain distances at multiple consecutive time points; constructing a distance equation group for multiple consecutive time points based on the coordinates of the multiple base stations, the positioning coordinates of the aircraft, and the distances at multiple consecutive time points; fitting the distance equation group for each consecutive time point based on the least squares method to obtain the positioning coordinates of the aircraft at multiple time points; A distance calculation module is used to calculate the relative distances of the positioning coordinates of the same group of aircraft at the same time point; and determine the relative position data of each group of aircraft during the flight process based on the relative distances at multiple time points, wherein the multiple aircraft are grouped in advance to obtain multiple aircraft groups, and each aircraft and its adjacent aircraft respectively form an aircraft group; An abnormality checking module is used to perform abnormality checking on the positioning coordinates of each group of aircraft during flight based on the observation data of the inertial sensors of each group of aircraft at multiple time points, and obtain the target aircraft with abnormal positioning coordinates; The positioning correction module is used to perform Kalman filtering based on the positioning coordinates of the target time period of the target aircraft and the observation data of multiple sensors to obtain accurate positioning, wherein the target time period includes multiple time points within a preset time period before the abnormal time point when the positioning coordinates are abnormal.

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