Unmanned aerial vehicle positioning method and apparatus based on sequence observation, device, and medium

Abstract

The application provides a sequence observation-based unmanned aerial vehicle positioning method, device, equipment and medium, comprising the following steps: constructing an unmanned aerial vehicle tracking model according to state information of an observation station; obtaining current observed unmanned aerial vehicle state data, and initializing the unmanned aerial vehicle tracking model; judging whether the current heat of each mode is 0, and obtaining a predicted state of the current mode; obtaining an observation prediction value based on the predicted state of the current mode, and calculating an observation residual variance based on the observation prediction value and the predicted state of each current mode; calculating a mode attribution probability of the current observation value; judging a probability that the current observation value belongs to a new mode in the mode attribution probability of the current observation value; outputting state information of a target unmanned aerial vehicle corresponding to a mode with the maximum heat as tracking positioning information; and updating the state of each mode. The application can simply and efficiently complete positioning and tracking of a target unmanned aerial vehicle.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of unmanned aerial vehicle multi-station positioning, and in particular to an unmanned aerial vehicle positioning method and device based on sequence observation, equipment and a medium. BACKGROUND

[0002] Passive detection, identification and positioning of unmanned aerial vehicles through passive detection of electromagnetic signals emitted by unmanned aerial vehicles is one of the main technical means for unmanned aerial vehicle detection. Radio positioning generally requires the use of multiple stations, and the typical system is AOA (Angle of Arrival) intersection positioning, which solves the absolute position of the target by simultaneously solving the geometric direction equations of multiple stations (at least two stations).

[0003] This AOA intersection positioning algorithm requires the collection of multiple station synchronous direction finding data, and the synchronization requirement depends on the target mobility. When the synchronization time of the multiple station direction finding data does not match the target mobility, a large positioning error occurs, but too strict synchronization control will lead to a decrease in the update rate of the positioning data. Due to this reason, AOA intersection positioning is generally difficult to use in the intersection positioning of a mobile single station virtual multi-station. Therefore, there is an urgent need for an AOA intersection positioning method that can effectively deal with mobile target positioning and is suitable for both real multi-station and single station mobile virtual multi-station. SUMMARY

[0004] In view of the defects of the prior art, the present application provides an unmanned aerial vehicle positioning method and device based on sequence observation, equipment and a medium.

[0005] To achieve the above-mentioned purpose, the technical solution adopted by the present application is as follows:

[0006] On the one hand, the present application provides an unmanned aerial vehicle positioning method based on sequence observation, comprising the following steps:

[0007] S1, constructing an unmanned aerial vehicle tracking model according to the latitude and longitude information of the observation station, the unmanned aerial vehicle tracking model comprising an unmanned aerial vehicle state representation model, a state update model and an observation model;

[0008] S2, obtaining current unmanned aerial vehicle state data and initializing the unmanned aerial vehicle tracking model;

[0009] S3, determining whether the current heat of each mode in the unmanned aerial vehicle state data is 0, if the current heat of the mode is 0, skipping the current mode; if the current heat of the mode is not 0, obtaining the predicted state of the current mode according to the state prediction model; if the current heat of all modes is 0, determining that the probability of the current observation value belonging to a new mode is 1, and turning to S6;

[0010] S4. Obtain the observation prediction value based on the prediction state of the current mode, the observation prediction value includes the observation matrix approximation value and the azimuth angle approximation value; and calculate the observation margin variance based on the observation matrix approximation value and the prediction state of each current mode;

[0011] S5. Calculate the mode assignment probability of the current observation based on the azimuth approximation, the observation margin variance, and the current heat of each mode.

[0012] S6. Determine the probability that the current observation belongs to the new mode in the mode assignment probability of the current observation; if the probability that the current observation belongs to the new mode is greater than 0.5, then compare the probability that the current observation belongs to the new mode with the popularity of the mode with the lowest popularity; if the probability that the current observation belongs to the new mode is greater than the popularity of the mode with the lowest popularity, then construct the new mode based on the mode with the lowest popularity.

[0013] S7. Update the state of each mode based on the mode assignment probability of the current observation and the current observation, then go to S3 until the target UAV leaves the observation range.

[0014] S8. Traverse the updated states of each mode and output the state information of the target UAV corresponding to the mode with the highest popularity as the tracking and positioning information.

[0015] Furthermore, the UAV status data includes longitude, latitude, meridional speed, and zonal speed.

[0016] Furthermore, the UAV state representation model is as follows:

[0017] ;

[0018] in, For the first k The current popularity of a modality; For the first k The variance of each modality; For the first k The mean of each mode, , Longitude For the meridional velocity, Latitude This represents the latitudinal velocity.

[0019] Furthermore, based on the predicted state of the current mode, the observed predicted values ​​are obtained, including:

[0020] At the predicted state of the current mode, a Taylor expansion of the observation model is performed to obtain an approximate model of the observation matrix and an approximate model of the azimuth angle.

[0021] Input the predicted state of the current mode to obtain the approximate values ​​of the observation matrix and azimuth angle;

[0022] The observation model is as follows:

[0023] ;

[0024] in, For the first i The observations of this observation; For the first i The azimuth angle of the second observation; For the first i The azimuth observation noise of the second observation; For the first i The longitude of the second observation; For the first i The latitude of the second observation; For the first i The longitude of the observation station for this observation; For the first i The latitude of the observation station for this observation;

[0025] The approximate model of the observation matrix is ​​as follows:

[0026] ;

[0027] The azimuth approximation model is as follows:

[0028] ;

[0029] in, For the first k The modality of the first i Approximate value of the observation matrix for this observation; For the first k The modality of the first i Approximate azimuth angle value for the second observation; For the first i Predicted longitude for the next observation; For the first i The predicted latitude of the second observation.

[0030] Furthermore, based on the state prediction model, the predicted state of the current mode is obtained as follows:

[0031] The predicted UAV state data is obtained based on the state prediction model;

[0032] The predicted state of the current mode is obtained based on the predicted state data of the drone.

[0033] The state update model is as follows:

[0034] ;

[0035] in, Here is the state transition matrix. ; is the tracking state for the i th observation; is the longitude for the i th observation; is the longitudinal velocity for the i th observation; is the latitude for the i th observation; is the latitudinal velocity for the i th observation; is the state transition uncertainty for the i th observation, following a Gaussian distribution with mean 0 and variance , , , and are the longitude uncertainty variance, the longitudinal velocity uncertainty variance, the latitude uncertainty variance and the latitudinal velocity uncertainty variance, respectively;

[0036] The predicted state of the current mode is obtained according to the following formula:

[0037] ;

[0038] wherein, is the temperature prediction value for the k th observation of the i th mode; is the temperature for the k th observation of the i th mode; is the forgetting factor; is the mean prediction value for the k th observation of the i th mode; is the mean for the k th observation of the i th mode; is the variance prediction value for the k th observation of the i th mode; is the variance for the k th observation of the i th mode.

[0039] Further, the observation residual variance is calculated according to the following formula:

[0040] ;

[0041] wherein, is the observation residual variance for the k th observation of the i th mode. is the variance of the observation noise; is the predicted value of the variance of the kth observation of the mth modality; k is the predicted value of the variance of the kth observation of the mth modality; i is the predicted value of the variance of the kth observation of the mth modality; is the predicted value of the variance of the kth observation of the mth modality; k is the predicted value of the variance of the kth observation of the mth modality; i is the predicted value of the variance of the kth observation of the mth modality; is the transpose of .

[0042] Further, the modality attribution probability of the current observation value is predicted based on the azimuth angle approximation value, the observation residual variance and the current heat of each modality, including:

[0043] S61, judging whether the current heat of each modality is 0, if yes, the modality attribution probability of the current observation value is k ; if not, the modality attribution probability of the current observation value is , which is subject to a Gaussian distribution , and the process goes to S62;

[0044] S62, calculating the modality attribution probability of the current observation value according to the posterior probability, specifically according to the following formula:

[0045] ;

[0046] ;

[0047] ;

[0048] ;

[0049] ;

[0050] wherein, is the probability of the current observation value belonging to the mth modality; k is the probability of the current observation value belonging to the new modality; is the prior probability of the modality ; k is the prior probability of the new modality; is the probability of the new modality; is the probability of the new modality; is the likelihood probability of the current observation value belonging to the new modality.

[0051] On the other hand, the application provides a UAV positioning device based on sequence observation, including:

[0052] A first module is configured to construct a UAV tracking model according to the longitude and latitude information of an observation site, wherein the UAV tracking model includes a UAV state representation model, a state update model and an observation model; ​

[0053] a second module configured to obtain current observed UAV state data, and initialize a UAV tracking model;

[0054] a third module configured to determine whether the current heat of each mode in the UAV state data is 0, if the current heat of the mode is 0, skip the current mode, if the current heat of the mode is not 0, obtain the predicted state of the current mode according to the state prediction model, if the current heat of all modes is 0, determine that the probability of the current observation belonging to a new mode is 1, and turn to a sixth module;

[0055] a fourth module configured to obtain an observation prediction value based on the predicted state of the current mode, the observation prediction value including an observation matrix approximation value and an azimuth approximation value, and calculate an observation residual variance based on the observation matrix approximation value and the predicted state of each current mode;

[0056] a fifth module configured to calculate the mode belonging probability of the current observation value based on the azimuth approximation value, the observation residual variance and the current heat of each mode;

[0057] a sixth module configured to determine the probability of the current observation belonging to a new mode in the mode belonging probability of the current observation value, if the probability of the current observation belonging to a new mode is greater than 0.5, compare the probability of the current observation belonging to a new mode with the heat of the mode with the smallest heat, if the probability of the current observation belonging to a new mode is greater than the heat of the mode with the smallest heat, construct a new mode based on the mode with the smallest heat;

[0058] a seventh module configured to update the state of each mode based on the mode belonging probability of the current observation value and the current observation value, and turn to the third module until the target UAV drives out of the observation range;

[0059] an eighth module configured to traverse the updated state of each mode, and output the state information of the target UAV corresponding to the mode with the largest heat as the tracking positioning information.

[0060] In another aspect, the present application provides a computer device comprising a memory and a processor, the memory stores a computer program, and the processor implements the steps of the UAV positioning method based on sequence observation when executing the computer program.

[0061] In another aspect, the present application provides a computer readable storage medium, which stores a computer program, and the computer program implements the steps of the UAV positioning method based on sequence observation when executed by a processor.

[0062] Compared with the prior art, the present application has the following advantages:

[0063] The unmanned aerial vehicle positioning method based on sequence observation provided by the application, device, equipment and medium, the state information of the observation station is used to construct an unmanned aerial vehicle tracking model, the predicted state of the current mode is obtained through the state update model in the unmanned aerial vehicle tracking model, the predicted value of the observation is further obtained through the predicted state of the current mode, and then the mode attribution probability of the current observation value is calculated; the state of the new mode or the updated mode is created through the mode attribution probability of the current observation value, and finally the state information of the target unmanned aerial vehicle corresponding to the mode with the maximum heat is output as the tracking positioning information by traversing the updated state of each mode, so that the state information of the target unmanned aerial vehicle corresponding to the mode is used as the tracking positioning information to complete the positioning of the target unmanned aerial vehicle. The application can effectively complete the positioning and tracking of the target unmanned aerial vehicle without simultaneously solving multiple station geometric equations, and is suitable for mobile target radio positioning and single station mobile virtual multi-station intersection positioning scenarios. BRIEF DESCRIPTION OF DRAWINGS

[0064] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or the prior art description will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor on the basis of the drawings shown.

[0065] Figure 1 The flowchart of the unmanned aerial vehicle positioning method based on sequence observation provided by an embodiment is provided.

[0066] Figure 2 The positioning result and actual trajectory comparison diagram provided by an embodiment is provided. DETAILED DESCRIPTION

[0067] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0068] REFERENCE Figure 1 An embodiment provides an unmanned aerial vehicle positioning method based on sequence observation, comprising the following steps:

[0069] S1, constructing an unmanned aerial vehicle tracking model according to the latitude and longitude information of the observation station, wherein the unmanned aerial vehicle tracking model comprises an unmanned aerial vehicle state representation model, a state update model and an observation model;

[0070] S2, obtaining current observation data of the unmanned aerial vehicle, and initializing the unmanned aerial vehicle tracking model;

[0071] S3, judging whether the current heat of each mode in the unmanned aerial vehicle state data is 0, if the current heat of the mode is 0, skipping the current mode; if the current heat of the mode is not 0, obtaining the predicted state of the current mode according to the state prediction model; if the current heat of all modes is 0, determining that the probability of the current observation value belonging to a new mode is 1, and turning to S6;

[0072] S4, obtaining an observation prediction value based on the predicted state of the current mode, the observation prediction value including an observation matrix approximation value and an azimuth approximation value; and calculating an observation residual variance based on the observation matrix approximation value and the predicted state of the current mode;

[0073] S5, calculating the mode belonging probability of the current observation value based on the azimuth approximation value, the observation residual variance and the current heat of each mode;

[0074] S6, judging the probability of the current observation value belonging to a new mode in the mode belonging probability of the current observation value; if the probability of the current observation value belonging to a new mode is greater than 0.5, comparing the probability of the current observation value belonging to a new mode with the heat of the mode with the smallest heat, if the probability of the current observation value belonging to a new mode is greater than the heat of the mode with the smallest heat, constructing a new mode based on the mode with the smallest heat;

[0075] S7, updating the state of each mode based on the mode belonging probability of the current observation value and the current observation value, and turning to S3 until the target unmanned aerial vehicle drives out of the observation range;

[0076] S8, traversing the updated state of each mode, and outputting the state information of the target unmanned aerial vehicle corresponding to the mode with the largest heat as the tracking positioning information.

[0077] By using the state information of the observation site to construct an unmanned aerial vehicle tracking model, the predicted state of the current mode is obtained through the state update model in the unmanned aerial vehicle tracking model, the observation prediction value is further obtained through the predicted state of the current mode, and then the mode belonging probability of the current observation value is calculated; then the mode belonging probability of the current observation value is used to create a new mode or update the state of the mode, and finally the updated state of each mode is traversed, and the state information of the target unmanned aerial vehicle corresponding to the mode with the largest heat is output as the tracking positioning information, so that the state information of the target unmanned aerial vehicle corresponding to the mode is used as the tracking positioning information to complete the positioning of the target unmanned aerial vehicle. At the same time, the current heat of each mode is used as a judgment mark to determine whether the current mode needs to be predicted, and when the current heat of all modes of the target unmanned aerial vehicle is 0 (which means that all current modes are not activated), the construction of a new mode is directly performed, so as to adaptively expand the range of the current mode to cope with the change of the unmanned aerial vehicle maneuverability.

[0078] In a preferred embodiment, the UAV state data comprises longitude, latitude, longitudinal velocity and latitudinal velocity.

[0079] The UAV state representation model is:

[0080] ;

[0081] wherein, is the current hotness of the k th modality; is the variance of the k th modality; is the mean of the k th modality, , is the longitude, is the longitudinal velocity, is the latitude, is the latitudinal velocity.

[0082] Specifically, in the embodiment, the longitude, latitude, longitudinal velocity and latitudinal velocity of the target UAV are used as the state information of the target UAV to represent the maneuverability of the target UAV by the position and velocity of the target, so that the geometric direction equation of multiple observation sites is not needed to be solved simultaneously, and the state is labeled by the above UAV state representation model, which can adapt to the direction finding outliers.

[0083] The observation prediction value is obtained based on the predicted state of the current modality, comprising:

[0084] Taylor expansion is performed on the observation model at the predicted state of the current modality to obtain an observation matrix approximation model and an azimuth angle approximation model;

[0085] The predicted state of the current modality is input to obtain the observation matrix approximation value and the azimuth angle approximation value;

[0086] The observation model is:

[0087] ;

[0088] wherein, is the observation value of the i th observation; is the azimuth angle of the i th observation; is the azimuth observation noise of the i th observation; is the longitude of the i th observation; is the latitude of the i th observation; is the longitude of the observation site of the i th observation; is thei The latitude of the observation station for this observation;

[0089] The approximate model of the observation matrix is ​​as follows:

[0090] ;

[0091] The azimuth approximation model is as follows:

[0092] ;

[0093] in, For the first k The modality of the first i Approximate value of the observation matrix for this observation; For the first k The modality of the first i Approximate azimuth angle value for the second observation; For the first i Predicted longitude for the next observation; For the first i The predicted latitude of the second observation.

[0094] Specifically, the Taylor expansion of the observation model at the predicted state of the current mode is as follows:

[0095] ;

[0096] in, .

[0097] In a preferred embodiment, obtaining the predicted state of the current mode according to the state prediction model includes:

[0098] The predicted UAV state data is obtained based on the state prediction model;

[0099] The predicted state of the current mode is obtained based on the predicted state data of the drone.

[0100] The state update model is as follows:

[0101] ;

[0102] in, Here is the state transition matrix. ; For the first i The tracking status of the next observation; For the first i Longitude of -1 observation; For the first i Meridional velocity of -1st observation; For the first i -1 latitude of the observation; For the firsti -1st observed longitudinal velocity; is the state transition uncertainty for the i th observation, which is subject to a Gaussian distribution with mean 0 and variance , , , and are the longitude uncertainty variance, the longitudinal velocity uncertainty variance, the latitude uncertainty variance and the longitudinal velocity uncertainty variance, respectively;

[0103] The predicted state of the current mode is obtained according to the following formula:

[0104] ;

[0105] wherein, is the temperature prediction value of the k th observation of the i th mode; is the temperature of the k th observation of the i -1th mode; is the forgetting factor; is the mean prediction value of the k th observation of the i th mode; is the mean of the k th observation of the i th mode; is the variance prediction value of the k th observation of the i th mode; is the variance of the k th observation of the i -1th mode.

[0106] In an embodiment, the initialization of the unmanned aerial vehicle tracking model includes parameter initialization and state initialization of each mode; the parameters include the maximum number of modes K , the forgetting factor , the variance of the observation noise, the longitude uncertainty variance , the longitudinal velocity uncertainty variance , the latitude uncertainty variance and the longitudinal velocity uncertainty variance .

[0107] In a preferred embodiment, the parameters further include the state variance .

[0108] In an embodiment, indicates that the time window is 20 times, , (30 m per second of longitude, 2 m uncertainty of longitude) , , .

[0109] In an embodiment, (corresponding to 5 km), (corresponding to 30 m / s), (corresponding to 5 km), (corresponding to 30 m / s). represents the velocity; represents the position; wherein, represents seconds, 1° = 6 minutes ; 1 minute = 60 seconds .

[0110] The observation residual variance is calculated according to the following formula:

[0111] ;

[0112] wherein, is the observation residual variance of the n-th observation of the m-th mode; k is the variance of the observation noise; i is the variance prediction value of the n-th observation of the m-th mode; is the observation matrix approximation value of the n-th observation of the m-th mode; is the transpose of k . i In a preferred embodiment, the mode belonging probability of the current observation value is predicted based on the azimuth angle approximation value, the observation residual variance and the current hotness of each mode, comprising: k i S61, judging whether the current hotness of each mode is 0, if yes, the mode belonging probability of the current observation value is ; if not, the mode belonging probability of the current observation value is calculated according to the posterior probability, specifically according to the following formula:

[0113] S62, calculating the mode belonging probability of the current observation value according to the posterior probability, specifically according to the following formula:

[0114] k ; if not, the mode belonging probability of the current observation value is calculated according to the posterior probability, specifically according to the following formula:

[0115] S62, calculating the mode belonging probability of the current observation value according to the posterior probability, specifically according to the following formula:

[0116] ;

[0117] ;​​​​​​​

[0118] ;

[0119] ;

[0120] ;

[0121] wherein, is the probability of the current observation belonging to the k modal; is the probability of the current observation belonging to the new modal; is the prior probability of the modal k ; is the prior probability of the new modal; is the probability of the new modal, which is generally taken as 0.2; is the likelihood probability of the current observation belonging to the new modal.

[0122] obeys the Gaussian distribution , which is calculated by the Gaussian distribution probability, and since there is a 360° wrapping problem in the direction, the observation solution needs to be unwrapped to the distribution mean value 180° orientation before being brought into the Gaussian distribution probability formula.

[0123] In an embodiment, the new modal is constructed based on the modal with the minimum heat, and the new modal is specifically:

[0124] ;

[0125] wherein, is the modal with the minimum heat; is the heat of the modal with the minimum heat; is the mean value of the modal with the minimum heat.

[0126] In an embodiment, the state of each modal is updated based on the modal belonging probability of the current observation value, and the update is specifically performed according to the following formula:

[0127] ;

[0128] wherein, is the observation error of the k th observation of the i th modal; is the gain matrix of the k th modal; is the unit matrix.

[0129] In an embodiment, to verify the practicability and effectiveness of the present application, the positioning results of the present application and the traditional AOA cross positioning method are compared, and compared with the actual trajectory, and the results in the figure are converted into the results in the local coordinate system oxy. It can be seen from Figure 2 that the positioning results obtained by the method of the present application are basically consistent with the actual trajectory, while the positioning trajectory obtained by the traditional AOA cross positioning method is relatively scattered, and there are many error data. It is shown that the method of the present application is more effective than the traditional AOA cross positioning method, and the process is more simple and efficient than the traditional AOA cross positioning method.

[0130] In an embodiment, a UAV positioning device based on sequence observation is provided, comprising:

[0131] A first module for constructing a UAV tracking model according to the latitude and longitude information of the observation site, wherein the UAV tracking model comprises a UAV state representation model, a state update model and an observation model;

[0132] A second module for obtaining the current observed UAV state data and initializing the UAV tracking model;

[0133] A third module for judging whether the current heat of each mode in the UAV state data is 0, if the current heat of the mode is 0, the current mode is skipped; if the current heat of the mode is not 0, the predicted state of the current mode is obtained according to the state prediction model; if the current heat of all modes is 0, the probability that the current observation value belongs to a new mode is 1, and the sixth module is turned to;

[0134] A fourth module for obtaining an observation prediction value based on the predicted state of the current mode, wherein the observation prediction value comprises an observation matrix approximation value and an azimuth angle approximation value; and calculating an observation residual variance based on the observation matrix approximation value and the predicted state of the current mode;

[0135] A fifth module for calculating the mode belonging probability of the current observation value based on the azimuth angle approximation value, the observation residual variance and the current heat of each mode;

[0136] A sixth module for judging the probability that the current observation value belongs to a new mode in the mode belonging probability of the current observation value; if the probability that the current observation value belongs to a new mode is greater than 0.5, the probability that the current observation value belongs to a new mode is compared with the heat of the mode with the smallest heat, if the probability that the current observation value belongs to a new mode is greater than the heat of the mode with the smallest heat, a new mode is constructed based on the mode with the smallest heat;

[0137] A seventh module for updating the state of each mode based on the mode belonging probability of the current observation value and the current observation value, and turning to the third module until the target UAV drives out of the observation range;

[0138] The eighth module is configured to traverse the updated states of the modalities, and output state information of a target UAV corresponding to a modality with the maximum heat as the tracking positioning information.

[0139] In another aspect, the present application provides a computer device including a memory and a processor, the memory storing a computer program, and the processor implementing the steps of the sequence-observation-based UAV positioning method provided in any of the above embodiments when executing the computer program. The computer device can be a server. The computer device includes a processor, a memory, a network interface and a database connected through a system bus. The processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for running the operating system and the computer program in the non-volatile storage medium. The database of the computer device is configured to store sample data. The network interface of the computer device is configured to communicate with an external terminal through a network connection.

[0140] In another aspect, the present application provides a computer-readable storage medium storing a computer program, the computer program being executed by a processor to implement the steps of the sequence-observation-based UAV positioning method provided in any of the above embodiments.

[0141] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be completed by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the above-mentioned embodiments. Any reference to a memory, storage, database or other medium used in the embodiments provided in the present application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration but not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct RAM bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0142] The details of the application are known.

[0143] The technical features of the above embodiments can be combined in any manner. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described, however, as long as the combinations of the technical features do not contradict each other, they should be considered within the scope of the present disclosure.

[0144] The above embodiments only express several implementation manners of the present application, and the description is relatively specific and detailed, but it should not be understood as a limitation on the scope of the application. It should be pointed out that, for ordinary skilled persons in the art, several modifications and improvements can be made without departing from the concept of the present application, and these are within the protection scope of the present application.

[0145] The above only describes the preferred embodiments of the present application, and is not intended to limit the present application. For those skilled in the art, the present application can have various modifications and changes. Any modification, equivalent replacement, improvement, etc. within the spirit and principle of the present application should be included in the protection scope of the present application.

Claims

1. A method for positioning a UAV based on sequence observations, characterized in that, The method comprises the following steps: S1, constructing a UAV tracking model according to the longitude and latitude information of an observation site, wherein the UAV tracking model comprises a UAV state representation model, a state update model and an observation model; S2, obtaining current observed UAV state data, and initializing the UAV tracking model; S3, judging whether the current heat of each mode in the UAV state data is 0, if the current heat of the mode is 0, skipping the current mode, if the current heat of the mode is not 0, obtaining the predicted state of the current mode according to the state prediction model, if the current heat of all modes is 0, determining that the probability of the current observation value belonging to a new mode is 1, and turning to S6; S4, obtaining an observation prediction value based on the predicted state of the current mode, wherein the observation prediction value comprises an observation matrix approximation value and an azimuth angle approximation value; and calculating an observation residual variance based on the observation matrix approximation value and the predicted state of each current mode; S5, calculating the mode belonging probability of the current observation value based on the azimuth angle approximation value, the observation residual variance and the current heat of each mode; S6, judging the probability of the current observation value belonging to a new mode in the mode belonging probability of the current observation value, if the probability of the current observation value belonging to a new mode is greater than 0.5, comparing the probability of the current observation value belonging to a new mode with the heat of the mode with the smallest heat, if the probability of the current observation value belonging to a new mode is greater than the heat of the mode with the smallest heat, constructing a new mode based on the mode with the smallest heat; S7, updating the state of each mode based on the mode belonging probability of the current observation value and the current observation value, and turning to S3 until the target UAV drives out of the observation range; S8, traversing the updated state of each mode, and outputting the state information of the target UAV corresponding to the mode with the largest heat as the tracking positioning information. 2.The sequence observation based UAV positioning method of claim 1, wherein, The UAV state data comprises longitude, latitude, longitudinal velocity and latitudinal velocity. 3.The sequence-observation-based UAV positioning method of claim 1, wherein, The UAV state representation model is: wherein, is the current heat of the k th modality; is the variance of the k th modality; is the mean of the k th modality, , is the longitude, is the longitudinal velocity, is the latitude, is the latitudinal velocity. 4.The sequence-observation-based UAV positioning method of claim 1, wherein, Obtaining the observation prediction value based on the predicted state of the current mode comprises: performing Taylor expansion on the observation model at the predicted state of the current mode to obtain an observation matrix approximation model and an azimuth angle approximation model; inputting the predicted state of the current mode to obtain the observation matrix approximation value and the azimuth angle approximation value; The observation model is: in, For the first i The observations of this observation; For the first i The azimuth angle of the second observation; For the first i The azimuth observation noise of the second observation; For the first i The longitude of the second observation; For the first i The latitude of the second observation; For the first i The longitude of the observation station for this observation; For the first i The latitude of the observation station for this observation; The observation matrix approximation model is: The azimuth angle approximation model is: wherein, is an approximation of the observation matrix for the k th observation of the i th modality; is an approximation of the azimuth angle for the k th observation of the i th modality; is a predicted longitude for the i th observation; is a predicted latitude for the i th observation. 5.The sequence-observation-based UAV positioning method of claim 1, wherein, Obtaining the predicted state of the current mode according to the state prediction model comprises: obtaining predicted UAV state data according to the state prediction model; obtaining the predicted state of the current mode according to the predicted UAV state data; The state update model is: wherein, is a state transition matrix, ; is a state vector, i is a state vector, is a longitude, i is a longitude, is a longitudinal velocity, i is a longitudinal velocity, is a latitude, i is a latitude, is a latitudinal velocity, i is a latitudinal velocity, is a state transition uncertainty for the n-th observation, following a Gaussian distribution with mean 0 and variance i , , , , and are the longitude uncertainty variance, the longitudinal velocity uncertainty variance, the latitude uncertainty variance and the latitudinal velocity uncertainty variance, respectively. obtaining the predicted state of the current mode according to the following formula: in, For the first k The modality of the first i The predicted heat value for the second observation; For the first k The modality of the first i -1 observation of heat; Forgetting factor; For the first k The modality of the first i The predicted mean value of the observations; For the first k The modality of the first i -1 mean of observations; For the first k The modality of the first i Variance predictions for each observation; For the first k The modality of the first i -1 variance of observations. 6.The sequence-observation-based UAV positioning method of claim 1, wherein, The observation residual variance is calculated according to the following formula: in, For the first k The modality of the first i The variance of the observation margin for each observation; The variance of the observed noise; For the first k The modality of the first i Variance predictions for each observation; For the first k The modality of the first i Approximate value of the observation matrix for this observation; for The transpose of . 7.The sequence-observation-based UAV positioning method of claim 1, wherein, Calculating the mode belonging probability of the current observation value based on the azimuth angle approximation value, the observation residual variance and the current heat of each mode comprises: S61, judge whether the current heat of each modality is 0, if 0, the current observation value is attributed to the modality k ; if not 0, , the current observation value is attributed to the modality with the highest likelihood probability , turn to S62; S62, calculating the mode belonging probability of the current observation value according to the posterior probability, specifically according to the following formula: where, is the probability that the current observation belongs to k the modal; is the probability that the current observation belongs to the new modal; is the prior probability of the modal k ; is the prior probability of the new modal; is the probability of a new modal; is the likelihood probability that the current observation belongs to the new modal.

8. An unmanned aerial vehicle positioning device based on sequence observation, characterized by, comprises: A first module is configured to construct a UAV tracking model according to the longitude and latitude information of an observation site, wherein the UAV tracking model comprises a UAV state representation model, a state update model and an observation model; The second module is configured to acquire current observed UAV state data and initialize a UAV tracking model. The third module is configured to determine whether the current heat of each mode in the UAV state data is 0. If the current heat of the mode is 0, the current mode is skipped. If the current heat of the mode is not 0, a predicted state of the current mode is obtained according to a state prediction model. If the current heat of all modes is 0, it is determined that the probability of the current observation belonging to a new mode is 1, and the sixth module is switched to. The fourth module is configured to obtain an observation prediction value based on the predicted state of the current mode, wherein the observation prediction value includes an observation matrix approximation value and an azimuth angle approximation value. An observation residual variance is calculated based on the observation matrix approximation value and the predicted state of each current mode. The fifth module is configured to calculate a mode belonging probability of the current observation value based on the azimuth angle approximation value, the observation residual variance and the current heat of each mode. The sixth module is configured to determine the probability of the current observation value belonging to a new mode in the mode belonging probability of the current observation value. If the probability of the current observation value belonging to a new mode is greater than 0.5, the probability of the current observation value belonging to a new mode is compared with the heat of the mode with the smallest heat. If the probability of the current observation value belonging to a new mode is greater than the heat of the mode with the smallest heat, a new mode is constructed based on the mode with the smallest heat. The seventh module is configured to update the state of each mode based on the mode belonging probability of the current observation value and the current observation value, and switch to the third module until the target UAV drives out of the observation range. The eighth module is configured to traverse the updated state of each mode and output the state information of the target UAV corresponding to the mode with the largest heat as the tracking positioning information. 9.A computer device, comprising a memory and a processor, the memory storing a computer program, and the computer device is characterized in that, The processor executes the computer program to implement the steps of the UAV positioning method based on sequence observation according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer program is stored on the processor and executed to implement the steps of the UAV positioning method based on sequence observation according to any one of claims 1-7.

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