Unmanned aerial vehicle cluster positioning method and device in satellite denial environment

Abstract

The present application relates to the technical field of positioning, in particular to a UAV cluster positioning method and device in a satellite denial environment, which can be used in UAV cluster positioning in a satellite denial environment, a plurality of ground stations are used to position a plurality of aircrafts, since the aircrafts adopt cluster operation, the present application uses the observation data of the inertial sensors of adjacent aircrafts to check the distance change between adjacent aircrafts, and when an anomaly is found, the abnormal UAV is quickly determined by using the calculated abnormal distance. Then, the Kalman filter is called to correct the positioning of the section of abnormal positioning data of the abnormal UAV. The present application can realize accurate positioning of the aircraft cluster by screening abnormal points and using the Kalman filter, and has strong applicability.

Description

[0001] This application is a divisional application of the Chinese application with the application number 202411491316.5, the application date of October 24, 2024, and the invention name of "Positioning method and device based on multi-ground radio ranging assistance in satellite denial environment". TECHNICAL FIELD

[0002] The present application relates to the field of positioning technology, in particular to a UAV cluster positioning method and device in a satellite denial environment. BACKGROUND

[0003] Currently, the global positioning system based on satellite positioning is mostly used for aircraft positioning. However, in some scenarios, such as in an environment where satellite signals are interfered, blocked, or completely denied, the traditional navigation system relying on GPS or GNSS will fail. Therefore, in the prior art, other ways, such as ground base stations, can be used to position UAVs. However, positioning by ground base stations is prone to noise interference, so a corresponding filtering algorithm is needed to eliminate the influence of noise.

[0004] In the prior art, if a filtering algorithm is always used to eliminate noise for aircraft positioning, it will bring a huge computational burden, especially in a UAV cluster environment, the computing power of the ground base station is difficult to meet the need of always using a filtering algorithm to eliminate noise for the positioning of the aircraft cluster. SUMMARY

[0005] Therefore, the purpose of the present application is to provide a UAV cluster positioning method and device in a satellite denial environment to solve the problems in the background art.

[0006] To achieve the above purpose, the present application adopts the following technical solutions:

[0007] The UAV cluster positioning method in a satellite denial environment of the present application comprises the following steps:

[0008] Positioning a plurality of aircraft based on a plurality of ground base stations to obtain the positioning coordinates of each aircraft at a plurality of time points; and obtaining the observation data of a plurality of sensors of each aircraft at a plurality of time points, wherein the plurality of sensors include inertial sensors;

[0009] Calculate the relative distance 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 in the flight process based on the relative distance at a plurality of time points, wherein the plurality of aircraft are pre-grouped to obtain a plurality of aircraft groups, and each aircraft and its adjacent aircraft form an aircraft group;

[0010] The positioning coordinates of each group of aircrafts during flight are checked for abnormalities based on observation data of the inertial sensors of each group of aircrafts at multiple time points, to obtain target aircrafts with abnormal positioning coordinates; the inertial sensors include a three-axis gyroscope and an acceleration sensor, wherein checking the positioning coordinates of each group of aircrafts during flight for abnormalities based on observation data of the inertial sensors of each group of aircrafts at multiple time points includes: obtaining instantaneous speed and acceleration of the aircrafts at multiple time points, wherein the instantaneous speed includes a speed value and a speed direction, and the instantaneous speed is obtained by integrating observation data of the three-axis gyroscope and the acceleration sensor; for each group of aircrafts, predicting relative distances at a next time point based on instantaneous speed, acceleration and relative distances at a previous time point, to obtain predicted distances; comparing the relative distances at the next time point with the predicted distances, and determining that the relative distances at the next time point are abnormal when a difference between the relative distances at the next time point and the predicted distances exceeds a preset threshold; and screening the abnormal relative distances, and checking the positioning coordinates of all aircrafts for abnormalities based on the abnormal relative distances.

[0011] Performing Kalman filtering based on the positioning coordinates of the target aircrafts in a target time period, and observation data of multiple sensors, to obtain accurate positioning, wherein the target time period includes multiple time points within a preset time length before an abnormal time point with abnormal positioning coordinates.

[0012] In an embodiment of the present application, the positioning coordinates of the aircrafts at multiple time points are obtained by positioning the aircrafts based on multiple ground base stations, including:

[0013] Obtaining coordinates of the multiple base stations;

[0014] Measuring distances of the aircrafts based on the multiple ground base stations at multiple continuous time points, to obtain distances at the multiple continuous time points;

[0015] Constructing distance equation sets at the multiple continuous time points based on the coordinates of the multiple base stations, the positioning coordinates of the aircrafts, and the distances at the multiple continuous time points;

[0016] Fitting the distance equation sets at each continuous time point based on the least square method, to obtain the positioning coordinates of the aircrafts at the multiple time points.

[0017] In an embodiment of the present application, checking the positioning coordinates of all aircrafts for abnormalities based on the abnormal relative distances includes:

[0018] Marking the aircrafts corresponding to all abnormal relative distances as candidate aircrafts;

[0019] Marking the candidate aircrafts corresponding to multiple abnormal relative distances as target aircrafts;

[0020] The candidate aircraft satisfying the first target condition is confirmed as a normal aircraft, the first target condition including: corresponding to only one abnormal relative distance, and the corresponding abnormal relative distance corresponding to the target aircraft;

[0021] The candidate aircraft satisfying the second target condition is confirmed as a target aircraft, the second target condition including: corresponding to only one abnormal relative distance, and the corresponding abnormal relative distance not corresponding to the target aircraft.

[0022] In an embodiment of the present application, 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 a predicted distance, including:

[0023] The positioning coordinates of the two aircrafts in the aircraft group are connected to obtain a reference horizontal axis, and a reference vertical axis and a reference longitudinal axis perpendicular to the reference horizontal axis are constructed with one of the aircrafts as the origin;

[0024] The instantaneous speed and acceleration of the two aircrafts are decomposed into the reference horizontal axis, the reference vertical axis and the reference longitudinal axis, and the differences are calculated respectively to obtain the horizontal axis initial speed v h , the horizontal axis acceleration a h , the vertical axis initial speed v v , the vertical axis acceleration a v , the longitudinal axis initial speed v l and the longitudinal axis acceleration a l ;

[0025] The horizontal axis relative displacement amount S h , the vertical axis relative displacement amount S v and the longitudinal axis relative displacement amount S l at the next time point are calculated based on the time length T of the previous time point and the next time point, the horizontal axis initial speed v h , the horizontal axis acceleration a h , the vertical axis initial speed v v , the vertical axis acceleration a v , the longitudinal axis initial speed v l and the longitudinal axis acceleration a l ;

[0026] The predicted distance S k+1 ′ is calculated based on the horizontal axis relative displacement amount S h and the longitudinal axis relative displacement amount S l , and the mathematical expression of the predicted distance S k+1 ′ is:

[0027]

[0028] In the formula, Sk a relative distance of a previous time point.

[0029] In an embodiment of the present application, Kalman filtering is performed based on the positioning coordinates of the target aircraft at the target time period, and the observation data of the plurality of sensors, to obtain accurate positioning, comprising:

[0030] establishing a process model and an observation model of the aircraft during flight;

[0031] selecting a target time point from the target time period, and constructing an initial state of the target aircraft at the target time point based on the positioning coordinates of the target aircraft at the target time point and the observation data of the plurality of sensors, and constructing an initial covariance matrix based on the initial state of the aircraft;

[0032] performing Kalman filtering based on the initial state and the initial covariance matrix of the target aircraft to obtain accurate positioning of the target aircraft at a plurality of time points after the target time point.

[0033] In an embodiment of the present application, selecting a target time point from the target time period comprises:

[0034] obtaining stability basic data of the target aircraft at a plurality of time points within the target time period, wherein the stability basic data comprises ranging signal strength and acceleration value on each axis;

[0035] mapping the stability basic data at a plurality of time points to a two-dimensional coordinate system, wherein the longitudinal 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;

[0036] sliding along the time axis based on a pre-constructed sliding window, and at each sliding time, calculating the ranging signal strength mean value, ranging signal strength variance, acceleration mean value and acceleration variance of a plurality of time points within the sliding window;

[0037] selecting a candidate window with a ranging signal strength mean value greater than a preset signal strength threshold value and an acceleration mean value less than or equal to a preset acceleration threshold value;

[0038] performing weighted summation on the ranging signal strength variance and the acceleration variance to obtain a stability value of each candidate window;

[0039] selecting a candidate window with the largest stability value as a target window, and selecting a middle time point of the target window as a target time point.

[0040] In an embodiment of the present application, the stability value W step The mathematical expression of the stability value W

[0041]

[0042] In the formula, step is the serial number of the candidate window, is the ranging signal strength variance of the stepth candidate window, is the acceleration variance of the stepth candidate window, a is the first weight, and b is the second weight.

[0043] In an embodiment of the present application, the method further comprises:

[0044] After obtaining the accurate positioning, the Kalman filter is exited, and the positioning of the plurality of aircraft based on the plurality of ground base stations is returned to.

[0045] The present application also provides a UAV cluster positioning device in a satellite denial environment, comprising:

[0046] The positioning module is configured to position the plurality of aircraft based on the plurality of ground base stations to obtain the positioning coordinates of each aircraft at a plurality of time points; and obtain the observation data of a plurality of sensors of each aircraft at a plurality of time points, wherein the plurality of sensors include an inertial sensor.

[0047] The distance calculation module is configured to calculate the relative distance 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 in the flight process based on the relative distance at a plurality of time points, wherein the plurality of aircraft are pre-grouped to obtain a plurality of aircraft groups, and each aircraft and its adjacent aircraft form an aircraft group.

[0048] The anomaly verification module is configured to perform anomaly verification on the positioning coordinates of each group of aircraft in the flight process based on the observation data of the inertial sensor of each group of aircraft at a plurality of time points to obtain a target aircraft with abnormal positioning coordinates; the inertial sensor includes a three-axis gyroscope and an acceleration sensor, wherein the anomaly verification on the positioning coordinates of each group of aircraft in the flight process based on the observation data of the inertial sensor of each group of aircraft at a plurality of time points includes: obtaining the instantaneous speed and acceleration of the aircraft at a plurality of time points, wherein the instantaneous speed includes a speed value and a speed direction, and the instantaneous speed is obtained by integrating the observation data of the three-axis gyroscope and the acceleration sensor; for each group of aircraft, the relative distance at a next time point is predicted based on the instantaneous speed, the acceleration at a previous time point, and the relative distance to obtain a 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 distance with the anomaly is screened out, and the positioning coordinates of all aircraft are anomaly verified based on the relative distance with the anomaly.

[0049] The positioning correction module is configured to perform Kalman filtering based on the positioning coordinates of the target time period of the target aircraft and the observation data of the plurality of sensors to obtain accurate positioning, wherein the target time period includes a plurality of time points within a preset time period before an abnormal time point at which the positioning coordinates are abnormal.

[0050] The satellite denial environment unmanned aerial vehicle cluster positioning method and device of the present application can be used in satellite denial environment unmanned aerial vehicle cluster positioning, and a plurality of ground base stations are used to position a plurality of aircrafts. Since the aircrafts adopt cluster operation, the observation data of the inertial sensors of adjacent aircrafts are used to verify the distance change between adjacent aircrafts, and when the verification is abnormal, the abnormal unmanned aerial vehicle is quickly determined by using the calculated abnormal distance. Then, Kalman filtering is called to correct the positioning of the section of abnormal unmanned aerial vehicle with abnormal positioning data. The present application can realize accurate positioning of the aircraft cluster by using a low-power base station by screening abnormal points, and has strong applicability. BRIEF DESCRIPTION OF DRAWINGS

[0051] The present application will be further described below in conjunction with the drawings and embodiments:

[0052] Figure 1 The application scenario diagram in an embodiment of the present application is shown in the figure;

[0053] Figure 2 The flowchart of the satellite denial environment unmanned aerial vehicle cluster positioning method in an embodiment of the present application is shown in the figure;

[0054] Figure 3 The abnormal aircraft discrimination principle diagram in an embodiment of the present application is shown in the figure;

[0055] Figure 4 The structure diagram of the satellite denial environment unmanned aerial vehicle cluster positioning device in an embodiment of the present application is shown in the figure. DETAILED DESCRIPTION

[0056] The embodiments of the present application will be described below through specific concrete examples, and those skilled in the art can easily understand other advantages and effects of the present application from the disclosure of the present specification. The present application can also be implemented or applied by different specific embodiments, and the details in the present specification can be modified or changed based on different views and applications without departing from the spirit of the present application. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict.

[0057] It should be noted that the diagrams provided in the following embodiments only schematically illustrate the basic concepts of the present application, and only the layers related to the present application are shown in the diagrams, rather than being drawn according to the number, shape and size of the layers in actual implementation. The actual implementation of each layer can be a random change, and the layer layout pattern can be more complex.

[0058] In the following description, a large number of details are discussed to provide a more thorough explanation of the embodiments of the present application, however, it is obvious to those skilled in the art that the embodiments of the present application can be implemented without these specific details.

[0059] Figure 1 For the application scenario of an embodiment of the present application, as shown in Figure 1 The present application measures the distance of the aircraft to be positioned 1 through multiple ground base stations, such as base station 1 (x1, y1, z1), base station 2 (x2, y2, z2), base station 3 (x3, y3, z3), base station 4 (x4, y4, z4), for example, corresponding to d1, d2, d3, d4. Using the above data, the present application can be positioned, and then the inertial measurement unit (IMU) is used to predict the distance between adjacent aircraft at the next time point, and the predicted distance is used to verify and correct the distance at the next time point. The specific process is as follows:

[0060] Figure 2 The flowchart of the unmanned aerial vehicle cluster positioning method in the satellite denial environment of an embodiment of the present application is shown in Figure 2 The satellite denial environment unmanned aerial vehicle cluster positioning method of the present embodiment can include steps S210 to S240:

[0061] S210, positioning a plurality of aircraft based on a plurality of ground base stations to obtain the positioning coordinates of each aircraft at a plurality of time points; and obtaining observation data of a plurality of sensors of each aircraft at a plurality of time points, wherein the plurality of sensors include inertial sensors;

[0062] In the present 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 station. Three-dimensional space positioning requires at least four sets of ground base stations for assistance. After obtaining the ranging values between the airborne station and multiple ground base stations, the approximate position of the aircraft can be obtained.

[0063] The method for obtaining the approximate position of the aircraft is as follows:

[0064] S211, obtaining the coordinates of a plurality of base stations, for example: base station 1 (x1, y1, z1), base station 2 (x2, y2, z2), base station 3 (x3, y3, z3), base station 4 (x4, y4, z4);

[0065] S212, ranging the aircraft based on the plurality of ground base stations at a plurality of continuous time points to obtain distances at the plurality of continuous time points; for example, at each time point, the ranging values of the 4 base stations are d1, d2, d3, and d4, respectively;

[0066] S213, constructing a distance equation group at the plurality of continuous time points based on the coordinates of the plurality of base stations, the positioning coordinates of the aircraft, and the distances at the plurality of continuous time points;

[0067] The distance equation group is: i is 1, 2, 3, or 4;

[0068] Wherein, (x, y, z) is the position of the aircraft.

[0069] S214, fitting the distance equation group at each continuous time point based on the least squares method to obtain the positioning coordinates of the aircraft at the plurality of time points.

[0070] The specific solution is as follows:

[0071] Let r = x 2 +y 2 +z 2

[0072] Then

[0073] Let

[0074] According to the least squares method, we have

[0075] P dt =(A T A) -1 A T Y

[0076] The rough position coordinates P of the aircraft can be obtained. dt .

[0077] 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 in the flight process based on the relative distances at the plurality of time points, wherein the plurality of aircraft are pre-grouped to obtain a plurality of aircraft groups, and each aircraft and its adjacent aircraft form an aircraft group;

[0078] Wherein, assuming that the positioning coordinates of the same group of aircraft at the same time point are (x, y, z) and (x', y', z'), then the corresponding relative distance is

[0079] The reason why the relative distance is used as a reference for verification in the present application is that in the UAV cluster, each UAV may have a positioning error. By verifying based on the relative distance, these errors can be discovered and corrected in time, avoiding the accumulation of errors in the cluster, thereby improving the overall positioning accuracy.

[0080] In addition, more attention is paid to the cooperative work between UAVs in the UAV cluster, so using the relative distance as a reference can provide a more effective data basis for subsequent control of the cooperative work between UAVs.

[0081] S230, based on the observation data of the inertial sensor of each group of aircraft at multiple time points, the positioning coordinates of each group of aircraft in the flight process are verified;

[0082] In the present application, in view of the influence of measurement error and signal multipath propagation in the radio ranging process, which directly affects the accuracy of the positioning result and the robustness of the system. Therefore, since the take-off, the relative position of the aircraft at the next time point is verified by using the observation data of the inertial sensor at multiple time points at the previous time point. Thus, the positioning points with large deviations can be discovered in time so as to be corrected in time by using Kalman filtering subsequently. Thus, while improving the positioning accuracy, the calculation amount caused by Kalman filtering can be reduced, which specifically includes:

[0083] S231, obtaining the instantaneous speed and acceleration of the aircraft at multiple time points, wherein the instantaneous speed includes speed value and speed direction, and the instantaneous speed is obtained by integrating the observation data of the three-axis gyroscope and the acceleration sensor;

[0084] The inertial detection unit IMU in the aircraft in the present application includes an accelerometer and a three-axis gyroscope. The acceleration on three axes is integrated to obtain the instantaneous speed of the aircraft during flight. The acceleration at multiple time points can be directly read by the accelerometer.

[0085] S232, for each group of aircraft, based on the instantaneous speed, acceleration and relative distance at the previous time point, the relative distance at the next time point is predicted to obtain a predicted distance;

[0086] In the present application, the parameter of interest is the distance between adjacent aircraft, so it is necessary to analyze the change of the distance between adjacent aircraft by using the instantaneous speed and acceleration to obtain the predicted distance, which specifically includes:

[0087] S2321, connecting the positioning coordinates of the 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;

[0088] 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 respectively and calculate the difference to obtain the horizontal axis initial speed v h , horizontal axis accelerator a h , vertical axis initial speed v v , vertical axis acceleration a v , longitudinal axis initial speed v l and longitudinal axis acceleration a l ;

[0089] In order to facilitate displacement analysis in the above-mentioned coordinate system, the flight direction and acceleration direction of the aircraft do not coincide with the above-mentioned reference axes 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 speed v h , horizontal axis accelerator a h , vertical axis initial speed v v , vertical axis acceleration a v , longitudinal axis initial speed v l and longitudinal axis acceleration a l .

[0090] S2323, based on the time length T of the previous time point and the next time point, the horizontal axis initial speed v h , the horizontal axis accelerator a h , the vertical axis initial speed v v , the vertical axis acceleration a v , the longitudinal axis initial speed v l and the longitudinal axis acceleration a l , calculate the horizontal axis relative displacement amount S h , the vertical axis relative displacement amount S v and the longitudinal axis relative displacement amount S l ;

[0091] The interval of the two time points in the present application is relatively short, about 0.1-0.5 seconds, therefore, the displacement of each time is regarded as uniform acceleration motion, thereby using the horizontal axis initial speed v h , horizontal axis accelerator a h , vertical axis initial speed v v , vertical axis acceleration a v , longitudinal axis initial speed v l and longitudinal axis acceleration a l obtained by orthogonal decomposition to calculate the horizontal axis relative displacement amount S h , the vertical axis relative displacement amount S v and the longitudinal axis relative displacement amount S l , wherein the horizontal axis relative displacement amount S h and the longitudinal axis relative displacement amount Sl The mathematical expression of S

[0092]

[0093] S2324, based on the horizontal axis relative displacement amount S h and the vertical axis relative displacement amount S l calculating the predicted distance S k+1 ', wherein the mathematical expression of the predicted distance S k+1 ' is:

[0094]

[0095] S k is the relative distance at the previous time point.

[0096] Finally, the predicted distance S k+1 ' at the next time point is calculated, and the predicted distance of the adjacent aircraft from the inertial measurement unit is obtained. Since the inertial measurement unit is not easily disturbed by noise, the relative displacement state at the next time point can be accurately predicted by performing inertial prediction at each time point, and error accumulation caused by using inertial navigation all the time can be avoided.

[0097] S233, comparing the relative distance S k+1 at the next time point with the predicted distance S k+1 ', and determining that the relative distance at the next time point is abnormal when the difference between the relative distance at the next time point and the predicted distance exceeds a preset threshold S th .

[0098] When |S k+1 -S k+1 '|>S th , it indicates that there is a large deviation between the relative displacement amount predicted by the inertial observation unit and the relative displacement amount measured by the ground base station. Since the accuracy of the inertial system in a short time is high, the relative distance S k+1 is verified based on the predicted distance S k+1 . Thus, the abnormal relative distance is checked.

[0099] S234, screening the abnormal relative distance, and performing abnormality checking on the positioning coordinates of all aircrafts based on the abnormal relative distance, to obtain the target aircraft with abnormal positioning coordinates.

[0100] Since each relative distance corresponds to two aircrafts, it is impossible to directly determine the target aircraft with abnormal positioning at this time. Therefore, the following process is adopted to screen the aircraft with abnormal positioning, including:

[0101] Figure 3 This is a schematic diagram illustrating the discrimination principle of an abnormal aircraft in one embodiment of this application, as shown below. 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 criteria:

[0102] (1) Mark the candidate aircraft corresponding to the relative distances of multiple anomalies as the target aircraft; if an aircraft corresponds to the relative distances of multiple anomalies, then it is highly likely that the aircraft's positioning is abnormal, causing multiple relative distance anomalies with adjacent aircraft. For example, Figure 3 If the t9 positioning is abnormal, it will most likely cause all the relative distances S9-S16 to be abnormal.

[0103] (2) The candidate aircraft that meets the first target condition is identified as a normal aircraft. The first target condition includes: it corresponds to only one abnormal relative distance, and the relative distance of the corresponding abnormality corresponds to the target aircraft.

[0104] For example, Figure 3 If t9 is the target aircraft, then if only S9 is abnormal at t1, but S1 and S8 are not abnormal, then the relative distance abnormality is caused by t9, and t1 is the normal aircraft.

[0105] (3) The candidate aircraft that meets the second target condition is identified as the target aircraft. The second target condition includes: it corresponds to the relative distance of only one anomaly, and the relative distance of the corresponding anomaly does not correspond to the target aircraft.

[0106] For example, Figure 3 S1 is abnormal, but there are no other relative distance anomalies in t1 and t2. At this time, it is impossible to determine whether the positioning is abnormal in t1 or t2. Therefore, both t1 and t2 are marked as target aircraft for positioning correction.

[0107] If a candidate aircraft has only one abnormal relative distance with the target aircraft, then the cause of the abnormal relative distance is attributed to the target aircraft. However, if neither of the aircraft causing the abnormal relative distance is the target aircraft, then both are marked as the target aircraft when it is impossible to determine which aircraft caused the abnormal relative distance.

[0108] S240, 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. The target time period includes multiple time points within a preset time period before the abnormal time point where the positioning coordinates are abnormal.

[0109] After determining the target aircraft, the positioning coordinates of the target aircraft are Kalman filtered in combination with the time point of the positioning coordinates with the abnormality to obtain accurate positioning, specifically as follows:

[0110] S241, a process model and an observation model of the aircraft in the flight process are established;

[0111] The process model is:

[0112] x k =f(x k-1 ,u k-1 ,w k-1 ),w k ~N(0,Q k )

[0113] The observation model is:

[0114] z k =h(x k ,v k ),v k ~N(0,R k )

[0115] Where x k and x k-1 are system state variables at time k and time k-1 respectively, and z k is the system observation at time k. f() is a nonlinear state transition function, h() is a 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, both of which are subject to zero-mean Gaussian white noise, N(0,Q k ) and N(0,R k ) represent zero-mean Gaussian white noise.

[0116] S242, a target time point is selected from the target time period, and an initial state of the target aircraft is constructed with the positioning coordinates of the target aircraft at the target time point and the observation data of the plurality of sensors, and an initial covariance matrix is constructed based on the initial state of the aircraft;

[0117] Since the present 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 positioning coordinates of the target aircraft are abnormal, in order to make the Kalman filtering fit as soon as possible, and then exit. In order to avoid long-time occupation of high computing power. Therefore, the present application needs to select the most accurate positioning coordinates as the initial value to perform Kalman filtering.

[0118] In the positioning composition, the accuracy of the positioning is related to the flight attitude of the aircraft in the flight process and the signal, and therefore the application selects a target time point in a target time period based on the flight attitude and the signal, and performs Kalman filtering with the positioning coordinates of the target time point as the initial value, so that the filtering result is fitted as soon as possible to obtain an accurate result, and specifically includes:

[0119] S2421, acquiring stability basic data of the target aircraft at multiple time points in the target time period, wherein the stability basic data includes ranging signal strength and acceleration value on each axis;

[0120] The application collects inertial data of an inertial measurement unit (IMU) of the aircraft at each time point, including acceleration values. When positioning, the ranging signal strength is also automatically collected, so that the flight attitude of the unmanned aerial vehicle at each time point and the ranging signal are analyzed based on the above two parameters to determine whether the positioning coordinates are at risk.

[0121] S2422, mapping the stability basic data at multiple time points to a two-dimensional coordinate system, wherein the longitudinal 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;

[0122] S2423, based on a pre-constructed sliding window, sliding along the time axis, and at each sliding, calculating the ranging signal strength mean, ranging signal strength variance, acceleration mean and acceleration variance of multiple time points in the sliding window;

[0123] Wherein, the sliding window can collect signal strength characteristics (mean and variance) in a short period of time, and flight attitude characteristics (acceleration mean and variance) in a short period of time, so as to avoid the analysis result error caused by the data fluctuation of a single time point.

[0124] S2424, screening out a candidate window whose ranging signal strength mean is greater than a preset signal strength threshold and whose acceleration mean is less than or equal to a preset acceleration threshold;

[0125] S2425, performing weighted summation on the ranging signal strength variance and the acceleration variance to obtain a stability value of each candidate window; the stability value W step The mathematical expression of the stability value W

[0126]

[0127] In the formula, step is the serial number of the candidate window, is the ranging signal strength variance of the stepth candidate window, is the acceleration variance of the stepth candidate window, and a is the first weight and β is the second weight.

[0128] In this embodiment, the ranging signal strength mean value, ranging signal strength variance, acceleration mean value and acceleration variance in a small period of time corresponding to each sliding of the sliding window are used to reflect the strength and stability of the ranging signal and the size and stability of the acceleration. First, the window with strong ranging signal and small acceleration is screened to obtain the candidate window. The window with the strongest stability is selected in the candidate window, and the weighted sum of the ranging signal strength variance and the acceleration variance is used to obtain the comprehensive stability.

[0129] S2426, the candidate window with the largest stability value is taken as the target window, and the middle time point of the target window is taken as the target time point.

[0130] The candidate window with the largest stability has the smallest ranging bias risk value, so the time point in this window is selected as the target time point to perform the subsequent Kalman filtering.

[0131] S243, based on the initial state and the initial covariance matrix of the target aircraft, Kalman filtering is performed to obtain the accurate positioning of the target aircraft at multiple time points after the target time point.

[0132] The present application adopts the existing Kalman filtering, and the steps can be summarized as:

[0133] The positioning coordinates of the target aircraft corresponding to the target time point, the observation parameters of the inertial sensor, and the sensor parameters such as the magnetometer are taken as the initial state of the Kalman filtering;

[0134] The system covariance matrix and the observation noise covariance matrix are set, which are used to describe the uncertainty of the system state and the observation data.

[0135] According to the motion state estimation of the unmanned aerial vehicle at the last moment, the state change matrix is used to predict the state of the unmanned aerial vehicle at the current moment. At the same time, the system covariance matrix is predicted to reflect the uncertainty of the predicted state.

[0136] The observation data at the current moment, such as GPS positioning information or other sensor data, is obtained.

[0137] The Kalman filtering gain is calculated, which is used to weigh the credibility of the predicted value and the observation value.

[0138] The estimated value of the unmanned aerial vehicle state is updated in combination with the Kalman filtering gain and the observation data.

[0139] The system covariance matrix is updated to reflect the uncertainty of the updated state.

[0140] Assuming that the motion of the target unmanned aerial vehicle in a short period of time is uniform motion, the current motion state matrix of the unmanned aerial vehicle is corrected according to the optimal estimation value of the current unmanned aerial vehicle positioning system.

[0141] Repeat the above prediction and update steps, and continuously iterate the Kalman filtering algorithm to obtain continuous UAV positioning correction results.

[0142] When the deviation of the continuous multiple observation values and the prediction values does not exceed the preset threshold, the determination result is fitted, and accurate positioning is obtained. The time point of accurate positioning is uncertain, and can be performed in real time during subsequent flight.

[0143] After obtaining accurate positioning, the Kalman filter is exited and returned to step S210. In this process, the Kalman filter is continuously performed on part of the aircrafts for part of the time period. Compared with performing the Kalman filter on all aircrafts throughout the whole process, the present application can reduce the large amount of computing power required and has stronger adaptability.

[0144] The satellite denial environment-based UAV cluster positioning method of the present application can be used in satellite denial environment-based UAV cluster positioning. A plurality of ground base stations are used to first position a plurality of aircrafts. Since the aircrafts adopt cluster operation, the present application uses the observation data of the inertial sensors of adjacent aircrafts in the cluster to verify the distance change between adjacent aircrafts, and when an anomaly is verified, the abnormal UAV is quickly determined using the calculated abnormal distance. Then, the Kalman filter is called to correct the positioning of the section of abnormal positioning data of the abnormal UAV. The present application performs Kalman filtering by screening abnormal points, so that accurate positioning of the aircraft cluster can be achieved using a base station with lower computing power, and has strong applicability.

[0145] As shown in Figure 4 The present application also provides a satellite denial environment-based UAV cluster positioning device, which comprises:

[0146] A positioning module is configured to position a plurality of aircrafts based on a plurality of ground base stations to obtain positioning coordinates of each aircraft at a plurality of time points; and obtain observation data of a plurality of sensors of each aircraft at a plurality of time points, wherein the plurality of sensors comprise inertial sensors.

[0147] A distance calculation module is configured to calculate the relative distance of the positioning coordinates of the same group of aircrafts at the same time point; and determine the relative position data of each group of aircrafts in the flight process based on the relative distance at a plurality of time points, wherein the plurality of aircrafts are pre-grouped to obtain a plurality of aircraft groups, and each aircraft and its adjacent aircraft form an aircraft group.

[0148] An anomaly verification module is configured to perform anomaly verification on the positioning coordinates of each group of aircrafts in the flight process based on the observation data of the inertial sensors of each group of aircrafts at a plurality of time points, to obtain a target aircraft with abnormal positioning coordinates.

[0149] The positioning correction module is configured to perform Kalman filtering based on the positioning coordinates of the target time period of the target aircraft and the observation data of the plurality of sensors to obtain accurate positioning, wherein the target time period includes a plurality of time points within a preset time period before an abnormal time point at which the positioning coordinates are abnormal.

[0150] The satellite denial environment unmanned aerial vehicle cluster positioning device of the present application can be used in satellite denial environment unmanned aerial vehicle cluster positioning. A plurality of ground base stations are used to position a plurality of aircraft. Since the aircraft adopts cluster operation, the observation data of the inertial sensors of adjacent aircraft are used to verify the distance change between adjacent aircraft. When the verification is abnormal, the abnormal unmanned aerial vehicle is quickly determined by using the calculated abnormal distance. Then, Kalman filtering is called to correct the positioning of the section of abnormal unmanned aerial vehicle with abnormal positioning data. The present application can realize accurate positioning of the aircraft cluster by using a low-power base station by screening abnormal points, and has strong applicability.

[0151] The embodiment also provides a computer readable storage medium having a computer program stored thereon, and the program is executed by a processor to implement any of the methods in the embodiment.

[0152] The embodiment also provides an electronic terminal, including a processor and a memory.

[0153] The memory is configured to store a computer program, and the processor is configured to execute the computer program stored in the memory to enable the terminal to implement any of the methods in the embodiment.

[0154] The computer readable storage medium in the embodiment can be understood by those skilled in the art that all or part of the steps of the above-mentioned method embodiments can be completed by the hardware of the computer program. The foregoing computer program can be stored in a computer readable storage medium. The program is executed to perform the steps of the above-mentioned method embodiments; and the foregoing storage medium includes ROM, RAM, magnetic disc or optical disc and various media that can store program codes.

[0155] The electronic terminal provided in the embodiment includes a processor, a memory, a transceiver and a communication interface. The memory and the communication interface are connected with the processor and the transceiver and complete communication with each other. The memory is configured to store a computer program, the communication interface is configured to communicate, and the processor and the transceiver are configured to run the computer program to enable the electronic terminal to perform the steps of the above method.

[0156] In the present embodiment, the memory can comprise a Random Access Memory (RAM) and can also include a non-volatile memory such as at least one disk memory.

[0157] The processor described above can be a general processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA) or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component.

[0158] In the above-described embodiments, although the present application has been described in conjunction with specific embodiments thereof, it is evident that many alternatives, modifications and variations will be apparent to those skilled in the art in light of the foregoing description. Embodiments of this application are intended to embrace all such alternatives, modifications and variations as can fall within the scope of the appended claims.

[0159] The above-described embodiments are merely illustrative for the principles and effects of the present application, but are not intended to limit the present application. Any modification or change made by any person skilled in the art without departing from the spirit and scope of the present application shall be covered by the claims of the present application.

Claims

1. A method for swarm positioning of unmanned aerial vehicles (UAVs) under satellite denial conditions, characterized in that, The method comprises the following steps: Positioning a plurality of aircraft based on a plurality of ground base stations to obtain positioning coordinates of each aircraft at a plurality of time points; and obtaining observation data of a plurality of sensors of each aircraft at a plurality of time points, wherein the plurality of sensors comprise an inertial sensor, and the number of ground base stations is at least four groups; Calculating the relative distance 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 in the flight process based on the relative distance at a plurality of time points, wherein a plurality of aircraft groups are obtained by grouping a plurality of aircraft in advance, and each aircraft and its adjacent aircraft form an aircraft group; Based on the observation data of the inertial sensor of each group of aircraft at a plurality of time points, the positioning coordinates of each group of aircraft in the flight process are checked for abnormalities to obtain target aircraft with abnormal positioning coordinates; the inertial sensor comprises a three-axis gyroscope and an acceleration sensor, wherein based on the observation data of the inertial sensor of each group of aircraft at a plurality of time points, the positioning coordinates of each group of aircraft in the flight process are checked for abnormalities, comprising: obtaining the instantaneous speed and acceleration of the aircraft at a plurality of time points, wherein the instantaneous speed comprises a speed value and a speed direction, and the instantaneous speed is obtained by integrating the observation data of the three-axis gyroscope and the 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 a 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 abnormal relative distance is screened out, and the positioning coordinates of all aircraft are checked for abnormalities based on the abnormal relative distance; Performing Kalman filtering based on the positioning coordinates of the target time period of the target aircraft, the observation data of the plurality of sensors to obtain accurate positioning, wherein the target time period comprises a plurality of time points within a preset time period before the abnormal time point with abnormal positioning coordinates. 2.The UAV cluster positioning method in a satellite denial environment according to claim 1, characterized in that, Positioning the aircraft based on a plurality of ground base stations to obtain the positioning coordinates of the aircraft at a plurality of time points, comprising: Obtaining the coordinates of a plurality of base stations; Measuring the distance of the aircraft based on a plurality of ground base stations at a plurality of consecutive time points to obtain the distance at a plurality of consecutive time points; Constructing a distance equation group at a plurality of consecutive time points based on the coordinates of the plurality of base stations, the positioning coordinates of the aircraft and the distance at a plurality of consecutive time points; Fitting the distance equation group at each consecutive time point based on the least square method to obtain the positioning coordinates of the aircraft at a plurality of time points. 3.The UAV cluster positioning method in a satellite denial environment according to claim 1, wherein, Checking the positioning coordinates of all aircraft for abnormalities based on the abnormal relative distance, comprising: Marking all aircraft corresponding to the abnormal relative distance as candidate aircraft; Marking the candidate aircraft corresponding to a plurality of abnormal relative distances as target aircraft; Confirm the candidate aircraft satisfying the first target condition as a normal aircraft, the first target condition including: corresponding to only one abnormal relative distance, and the corresponding abnormal relative distance not corresponding to the target aircraft; Confirm the candidate aircraft satisfying the second target condition as the target aircraft, the second target condition including: corresponding to only one abnormal relative distance, and the corresponding abnormal relative distance not corresponding to the target aircraft.

4. The method of claim 1, wherein, Predict the relative distance at the next time point based on the instantaneous speed, acceleration and relative distance at the previous time point, to obtain a predicted distance, including: Connect the positioning coordinates of two aircrafts 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 aircrafts as the origin; The instantaneous velocities and accelerations of the two aircraft are decomposed into the reference lateral axis, the reference vertical axis and the reference longitudinal axis and are respectively differenced to obtain a lateral initial velocity , a lateral acceleration , a vertical initial velocity , a vertical acceleration , a longitudinal initial velocity and a longitudinal acceleration ; based on a time length of a previous time point and a next time point , the horizontal axis initial velocity , the horizontal axis acceleration , the vertical axis initial velocity , the vertical axis acceleration , the longitudinal axis initial velocity , and the longitudinal axis acceleration calculate a horizontal axis relative displacement amount of the next time point , a vertical axis relative displacement amount , and a longitudinal axis relative displacement amount ; based on the relative displacement amount of the horizontal axis and the relative displacement amount of the vertical axis calculating a predicted distance wherein the predicted distance is mathematically expressed as wherein, is the relative distance of the previous time point.

5. The method of claim 1, wherein, 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, including: Establish a process model and an observation model of the aircraft during flight; Select a target time point from the target time period, and construct an initial state of the target aircraft based on the positioning coordinates of the target time point of the target aircraft and the observation data of multiple sensors, and construct an initial covariance matrix based on the initial state of the aircraft; Perform Kalman filtering based on the initial state and the 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 method of claim 5, wherein, Select a target time point from the target time period, including: Obtain stability basic data of the target aircraft at multiple time points in the target time period, wherein the stability basic data includes ranging signal strength and acceleration value on each axis; Map the stability basic data at multiple time points to a two-dimensional coordinate system, wherein the longitudinal 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; Slide along the time axis based on a pre-constructed sliding window, and at each sliding time, calculate the ranging signal strength mean value, ranging signal strength variance, acceleration mean value and acceleration variance of multiple time points in the sliding window; Screen out candidate windows with ranging signal strength mean value greater than a preset signal strength threshold and acceleration mean value less than or equal to a preset acceleration threshold; Weighted sum of ranging signal strength variance and acceleration variance to obtain stability value of each candidate window; Take the candidate window with the maximum stability value as the target window, and take the middle time point of the target window as the target time point.

7. The method of claim 6, wherein, said stability value The mathematical expression of said stability value is: wherein, is a sequence number of a candidate window, is a ranging signal strength variance of the th candidate window, is an acceleration variance of the th candidate window, is a first weight, is a second weight. 8.The method of claim 1, wherein, Further comprising: After obtaining accurate positioning, exit Kalman filtering and return to positioning multiple aircrafts based on multiple ground base stations.

9. A UAV cluster positioning device in a satellite denial environment, characterized by, Including: A positioning module for positioning multiple aircrafts based on multiple ground base stations to obtain the positioning coordinates of each aircraft at multiple time points; and obtaining the observation data of multiple sensors of each aircraft at multiple time points, wherein the multiple sensors include inertial sensors, and the number of ground base stations is at least four groups; The distance calculation module is configured to calculate relative distances of positioning coordinates of the same group of aircrafts at the same time point, and determine relative position data of each group of aircrafts in the flight process based on the relative distances at multiple time points. The multiple aircrafts are grouped in advance to obtain multiple aircraft groups, and each aircraft and its adjacent aircrafts form an aircraft group. The anomaly verification module is configured to perform anomaly verification on the positioning coordinates of each group of aircrafts in the flight process based on observation data of the inertial sensor of each group of aircrafts at multiple time points, to obtain a target aircraft with abnormal positioning coordinates. The inertial sensor includes a three-axis gyroscope and an acceleration sensor. The anomaly verification on the positioning coordinates of each group of aircrafts in the flight process based on the observation data of the inertial sensor of each group of aircrafts at multiple time points includes: 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 by integrating the observation data of the three-axis gyroscope and the acceleration sensor; for each group of aircrafts, predicting the relative distance at a next time point based on the instantaneous speed, the acceleration and the relative distance at a previous time point to obtain a predicted distance; comparing the relative distance at the next time point with the predicted distance, and determining that the relative distance at the next time point is abnormal when the difference between the relative distance at the next time point and the predicted distance exceeds a preset threshold; and screening the abnormal relative distance, and performing anomaly verification on the positioning coordinates of all aircrafts based on the abnormal relative distance. The positioning correction module is configured to perform Kalman filtering based on the positioning coordinates of the target aircraft in a target time period, and observation data of multiple sensors to obtain accurate positioning, wherein the target time period includes multiple time points within a preset time length before the abnormal time point with abnormal positioning coordinates.

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