Positioning methods, apparatuses, devices, and media

Abstract

The application relates to a positioning method, a device, equipment and a medium, wherein the method comprises the following steps: acquiring an arrival angle of a radiation source at a first moment relative to an observation system, determining a measurement value at the first moment according to the arrival angle at the first moment, the observation system comprising two observation stations; acquiring a prediction weight of the radiation source at the first moment, the prediction weight at the first moment being determined by signal strengths of the radiation source at different observation stations; acquiring a historical motion state of the radiation source, and determining a motion state of the radiation source at the first moment according to the prediction weight at the first moment, the measurement value at the first moment and the historical motion state, the motion state comprising a position of the radiation source, the above positioning method reduces the number of observation stations and communication overhead, reduces the positioning uncertainty of a single positioning system, and improves the robustness of a radiation source positioning algorithm.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of passive positioning, in particular to a positioning method, device, equipment and medium. BACKGROUND

[0002] In the commonly used multi-station cooperative passive positioning technology, the passive positioning based on FDOA depends on relative speed and frequency, and there may be non-linear problems in the use process, and the application range is very limited. At present, the main means are TDOA and AOA systems, but both still have some shortcomings in use. The passive positioning algorithm based on TDOA has high accuracy, but the communication overhead is large, and at least 3 stations or more are needed to realize positioning measurement, and multiple solutions may occur in the solving process. In addition, the quality of the multi-station parameter measurement of cooperative positioning may be uneven, which may easily lead to an increase in positioning error in the data matching and association process. The passive positioning technology based on AOA is relatively mature, and only two observation stations are needed to realize positioning capability, but such method has low positioning accuracy and may have direction finding ambiguity.

[0003] Therefore, there is an urgent need in the prior art for a positioning method with less number of observation stations and high positioning accuracy. SUMMARY

[0004] To solve at least one of the technical problems in the background art, the present application provides a positioning method, device, equipment and medium to reduce the number of observation stations and communication overhead, reduce the positioning uncertainty of a single positioning system, and improve the robustness of the radiation source positioning algorithm.

[0005] The first aspect of the present application provides a positioning method, which comprises: obtaining the angle of arrival of a radiation source at a first time with respect to an observation system, determining a measurement value at the first time according to the angle of arrival at the first time, the observation system comprising two observation stations; obtaining a prediction weight of the radiation source at the first time, the prediction weight at the first time being determined by the signal strength of the radiation source at different observation stations; obtaining a historical motion state of the radiation source, determining a motion state of the radiation source at the first time according to the prediction weight at the first time, the measurement value at the first time and the historical motion state, the motion state comprising the position of the radiation source.

[0006] Optionally, determining the measurement value at the first time according to the angle of arrival at the first time comprises: inputting the angle of arrival at the first time into a preset conversion relationship to obtain an observation value at the first time; and adding a preset observation noise to the observation value at the first time to obtain the measurement value at the first time.

[0007] Optionally, the acquiring the predicted weight of the radiation source at the first time point comprises: acquiring a weight at a previous time point of the first time point, and an initial value of the weight is a variance of signal strengths of the radiation source at different observation stations at an initial time point; and determining the predicted weight of the radiation source at the first time point according to a motion model of the radiation source and the weight at the previous time point.

[0008] Optionally, the determining the motion state of the radiation source at the first time point according to the predicted weight of the radiation source at the first time point, the observation value at the first time point and the setting state comprises: determining an optimal Kalman gain at the first time point according to the predicted weight of the radiation source at the first time point and an observation equation at the first time point; determining a predicted motion state at the first time point according to the historical motion state and the motion model; and determining the motion state of the radiation source at the first time point according to the predicted motion state at the first time point, the observation value at the first time point and the optimal Kalman gain at the first time point.

[0009] Optionally, the acquiring the observation model at the first time point comprises: acquiring the motion state at the first time point and a predicted motion state of the radiation source; performing first-order Taylor expansion on the conversion relationship to acquire a first-order approximate linear equation, wherein a first-order Taylor expansion point of the conversion relationship is a point at which the motion state is equal to the predicted motion state; and taking the first-order approximate linear equation as the observation model at the first time point.

[0010] Optionally, the method further comprises: acquiring a difference between the motion state of the radiation source at the first time point and the setting state; and generating error information in a case where the difference is greater than a preset threshold.

[0011] Optionally, the method further comprises: determining an optimal weight of the radiation source at the first time point according to the observation equation at the first time point, the predicted weight of the radiation source at the first time point and the optimal Kalman gain at the first time point; taking the optimal weight at the first time point and the motion state at the first time point as an optimal weight and a motion state at a previous time point; and performing a preset iteration step, wherein the preset iteration step comprises: acquiring an angle of arrival of the radiation source at a current time point, and determining a measurement value at the current time point according to the angle of arrival at the current time point; acquiring a predicted weight of the radiation source at the current time point, wherein the predicted weight at the current time point is determined according to the optimal weight at the previous time point; acquiring a motion state of the radiation source at the previous time point, and determining a motion state of the radiation source at the current time point according to the weight at the current time point, the measurement value at the current time point and the motion state at the previous time point; acquiring a difference between the motion state at the current time point and the motion state at the previous time point; continuing the iteration in a case where the difference is less than the preset threshold; and stopping the iteration until a preset stop condition is met, wherein the preset stop condition comprises: a number of iterations reaching a preset number, the motion state meeting a preset accuracy, and the difference being greater than the preset threshold.

[0012] The second aspect of the present application provides a positioning device, the device comprising: a measurement value acquisition module, configured to acquire an angle of arrival of a radiation source at a first time relative to an observation system, to determine a measurement value at the first time according to the angle of arrival at the first time, the observation system comprising two observation stations; a weight acquisition module, configured to acquire a prediction weight of the radiation source at the first time, the prediction weight at the first time being determined by signal strengths of the radiation source at different observation stations; and an estimation module, configured to acquire a historical motion state of the radiation source, to determine a motion state of the radiation source at the first time according to the prediction weight at the first time, the measurement value at the first time and the historical motion state, the motion state comprising a position of the radiation source.

[0013] The third aspect of the present application provides a computer readable storage medium, having a computer program stored thereon, the program being executed by a processor to implement the steps of the method of any one of the first aspect.

[0014] The fourth aspect of the present application provides an electronic device, comprising: a memory having a computer program stored thereon; and a processor configured to execute the computer program in the memory to implement the steps of the method of any one of the first aspect.

[0015] Through the above technical solution, the observation value of the radiation source is calculated by the angle of arrival, only two observation stations are used, the prediction weight at the first time and the historical motion state are used to eliminate noise in the motion state estimation process, and the positioning accuracy of the radiation source is ensured. The above method reduces the number of observation stations and communication overheads under the premise of ensuring the positioning accuracy, effectively reduces the positioning uncertainty of a single positioning system, and improves the robustness of the radiation source positioning algorithm.

[0016] Other features and advantages of the present application will be described in detail in the following specific embodiments. BRIEF DESCRIPTION OF DRAWINGS

[0017] The accompanying drawings are included to provide a further understanding of the present application, and constitute a part of the specification, and are used together with the following specific embodiments to explain the present application, but do not constitute a limitation on the present application. In the drawings:

[0018] Figure 1 is a schematic diagram of an application scenario of a positioning method according to an exemplary embodiment;

[0019] Figure 2 is a schematic diagram of an implementation flow of a positioning method according to an exemplary embodiment;

[0020] Figure 3is an implementation flow diagram of another positioning method according to an exemplary embodiment;

[0021] Figure 4 is a double-station AOA direction-finding cross-locating geometry precision distribution diagram according to an exemplary embodiment;

[0022] Figure 5 is a relationship between positioning precision and iteration number according to an exemplary embodiment;

[0023] Figure 6 is a schematic block diagram of a positioning apparatus according to an exemplary embodiment;

[0024] Figure 7 is a schematic block diagram of an electronic device according to an exemplary embodiment. DETAILED DESCRIPTION

[0025] The specific embodiments of the application will be described below in detail with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are merely intended to illustrate and explain the application, and are not intended to limit the application.

[0026] It should be noted that all actions of obtaining signals, information or data in the present application are performed in compliance with the corresponding data protection regulations and policies of the country where the device is located, and with the authorization of the corresponding device owner.

[0027] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the application belongs. The terms used herein are only for the purpose of describing the embodiments of the application and are not intended to limit the application.

[0028] In the following description, "some embodiments" are described, which describe a subset of all possible embodiments, but it can be understood that "some embodiments" can be the same subset or different subsets of all possible embodiments, and can be combined with each other without conflict.

[0029] It should be noted that the terms "first", "second", "third" used in the embodiments of the present application are used to distinguish similar or different objects, and do not represent a specific order of the objects. It can be understood that "first", "second", "third" can be interchanged in a specific order or sequence as allowed, so that the embodiments of the application described herein can be implemented in an order other than that illustrated or described herein.

[0030] Before the embodiments of the present application are further described in detail, the terms and terms involved in the embodiments of the present application are explained, which are applicable to the following explanations.

[0031] Passive positioning technology does not require external radiation of electromagnetic waves and can directly use the radiation information of the target source to locate it. It plays a very important role in the field of electromagnetic spectrum monitoring.

[0032] The core idea of ​​the passive positioning system is to extract and analyze the characteristic parameters of the detection signal, and then obtain the spatial parameters of the radiation source signal, including the arrival direction and position of the radiation source. This has important applications in electronic attack direction guidance, radiation source reconnaissance and warning, signal sorting and identification, etc.

[0033] How to obtain high-precision radiation source position estimates in complex and diverse environments is a technical challenge faced by passive positioning systems.

[0034] Existing passive positioning methods mainly focus on locating the radiation source based on time difference of arrival (TDOA), frequency difference of arrival (FDOA), angle of arrival (AOA) and signal strength of arrival (RSSI). These methods mainly determine the location of the radiation source through the known base station location coordinates and various time differences, frequency differences, angle measurements, signal strength, etc.

[0035] Traditional single-station passive direction-finding positioning requires the detection platform to move at a large angle over a period of time to achieve good results. In complex environments, there are problems such as long positioning time, limited positioning accuracy, and insufficient three-dimensional spatial positioning capabilities. It is difficult to quickly understand the movement status of potential threat targets, and it cannot resist complex environments and malicious interference.

[0036] Among multi-station collaborative passive positioning technologies, passive positioning based on FDOA (Frequency Difference of Arrival) relies on relative speed and frequency, and may have nonlinear problems during use, resulting in a very limited scope of application. Traditional passive positioning algorithms based on TDOA (Time Difference of Arrival) have high accuracy, but the communication overhead is large, and at least three stations need to collaborate to achieve positioning measurements. Multiple solutions are also prone to occur during the solution process. In addition, the quality of multi-station parameter measurements in collaborative positioning may vary, which can easily lead to increased positioning errors during data matching and association. Although traditional passive positioning based on AOA (Angle of Arrival) only requires two observation stations to achieve positioning capabilities, such methods require low positioning accuracy and may cause direction finding ambiguity.

[0037] In order to avoid the problems existing in the single-station passive positioning system and the single passive positioning system, this application proposes a positioning method to further reduce the number of observation stations and communication overhead while ensuring positioning accuracy, effectively reduce the positioning uncertainty of the single positioning system, and improve the robustness of the radiation source positioning algorithm.

[0038] Therefore, the embodiment of the present application provides a positioning method, which is applied to an electronic device. The electronic device can be various types of devices with information processing capability in the implementation process. For example, the electronic device can include a personal computer, a notebook computer, a palm computer, a server, or the like; and the electronic device can also be a mobile terminal, for example, the mobile terminal can include a mobile phone, a vehicle-mounted computer, a tablet computer, a projector, or the like. The function implemented by the method can be realized by calling program code by a processor in the electronic device, and of course, the program code can be saved in a computer storage medium. Therefore, the electronic device at least includes a processor and a storage medium.

[0039] Figure 1 FIG. 1 is a schematic diagram of an application scenario of a positioning method in the embodiment of the present application, Figure 1 The application scenario includes an observation station 110, an observation station 120, and a radiation source 130.

[0040] The observation station 110 and the observation station 120 are dispersedly arranged in a space, which is represented by an (X, Y, Z) coordinate system. The coordinate of the observation station 110 in the space is (x1, y1, z1), and the coordinate of the observation station 120 in the space is (x2, y2, z2). The observation station 110 and the observation station 120 are provided with an antenna array to obtain direction information of the radiation source 130. For example, Figure 1 In the application scenario, the observation station 110 and the observation station 120 each use a 5-element circular array antenna. The coordinate of the radiation source 130 in the space is (x, y, z). The direction information obtained by the observation station 110 includes a horizontal angle θ2 and a pitch angle ψ1. The direction information obtained by the observation station 120 includes a horizontal angle θ1 and a pitch angle ψ2. The observation stations aggregate the direction finding results to a processing unit through a communication link for cooperative positioning. The processing unit can be arranged in the observation stations or outside the observation stations. Figure 1 In the application scenario, the processing unit is arranged in the observation station 110. The observation station 120 sends the direction finding result to the processing unit through the communication link, and the processing unit receives the direction finding result reported by the observation station 110 to perform cooperative positioning.

[0041] Figure 2 FIG. 2 is a schematic diagram of an implementation process of a positioning method provided by the embodiment of the present application. The method can be applied to the processing unit in the application scenario. Figure 1 As shown in FIG. 2, the method can include the following steps 210 to 230. Figure 2

[0042] S210, obtaining an angle of arrival of a radiation source at a first time relative to an observation system, determining a measurement value at the first time according to the angle of arrival at the first time, and the observation system includes two observation stations.

[0043] ​The radiation source refers to an object that emits wireless signals, such as a stealth fighter, a drone, a missile, etc. When the processing unit receives a positioning instruction at a first time to position the radiation source, the processing unit obtains the angle of arrival of the radiation source relative to each observation station observed by the observation station, and calculates the measurement value of the radiation source at the first time through the angle of arrival at the first time.

[0044] S220, obtain the prediction weight of the radiation source at the first time, and the prediction weight at the first time is determined by the signal strength of the radiation source at different observation stations.

[0045] The prediction weight of the radiation source at the first time is obtained by comprehensively calculating the signal strength of the radiation source received by each observation station. For example, each observation station calculates the RSSI (Received Signal Strength Indicator) of the radiation source. RSSI is an indication of the strength of the received signal, which is obtained after the reverse channel baseband receiver filter. The RSSI values of all observation stations are comprehensively calculated to obtain the prediction weight.

[0046] S230, obtain the historical motion state of the radiation source, determine the motion state of the radiation source at the first time according to the prediction weight at the first time, the measurement value at the first time and the historical motion state, and the motion state includes the position of the radiation source.

[0047] The historical motion state of the radiation source refers to the motion state of the radiation source at a time before the first time, such as the previous time of the first time. The motion state includes the position, speed, etc. of the radiation source.

[0048] The motion state of the radiation source at the first time is predicted through the prediction weight at the first time, the measurement value at the first time and the historical motion state. For example, it is assumed that there is noise and incomplete estimation in the prediction process, for example, the measurement value contains a certain degree of error, including random noise, or there is an error in the motion state, etc. The weighted average value is calculated through the prediction weight at the first time and the historical motion state, and the estimated value of the motion state at the first time is calculated through the measurement value at the first time and the weighted average value, so as to eliminate the noise in the estimation process and improve the estimation accuracy of the motion state of the radiation source.

[0049] The positioning method calculates the observation of the radiation source by the angle of arrival, only needs to use two observation stations, uses the prediction weight at the first time and the historical motion state to eliminate the noise in the motion state estimation process, ensures the positioning accuracy of the radiation source, reduces the number of observation stations and communication overhead under the premise of ensuring the positioning accuracy, effectively reduces the positioning uncertainty of a single positioning system, and improves the robustness of the radiation source positioning algorithm.

[0050] The Kalman filtering is based on the motion equation of the radiation source object, uses the linear system state equation contained therein, calculates the optimal estimation of the state of the measured radiation source system by recycling the input and output observation data based on the best criterion to achieve the minimum mean square error effect. It has good noise reduction and smoothing effect for position estimation of static or moving nodes. For the convenience of actual application, the first-order extended Kalman filtering model is used to realize the following positioning method.

[0051] Figure 3 Another implementation process diagram of the positioning method provided by the embodiment of the application, the method can be applied to the processing unit in Figure 1 , as shown in Figure 3 , the method can include the following steps 310 to step 310:

[0052] S310, obtaining the angle of arrival of the radiation source at the first time relative to the observation system, inputting the angle of arrival at the first time into the preset conversion relationship to obtain the observation value at the first time; adding the observation noise to the observation value at the first time to obtain the measurement value at the first time, the observation system includes two observation stations.

[0053] In the process of positioning the radiation source, the angle of arrival of the radiation source relative to each observation station at the first time is obtained, in the best embodiment of the application, the first time is usually the first time of positioning.

[0054] The angle of arrival observed at the first time is output into the preset conversion relationship, and the conversion relationship is used to represent the conversion relationship between the measured angle of arrival and the motion object state. The observation value of the radiation source at the first time is calculated through the conversion relationship.

[0055] The observation value at the first time is added to the preset observation noise to obtain the measurement value at the first time.

[0056] For example, a measurement equation is used to represent the measurement value calculation process, and the measurement equation can be represented as:

[0057]

[0058] Zij,k represents a measurement value, AOA can be expressed as a two-dimensional angle between a radiation source and an observation station, i.e. the observation model is expressed as a conversion relationship between a radiation source state and measurement values obtained by different positioning methods, v ij,k is a zero-mean, Gaussian white noise with a variance, x k is a state vector of the radiation source at time t k .

[0059] When the observation variable is AOA, taking a two-station as an example, at this time is expressed as

[0060] h ij (χ k )=[angle1(χ k )angle2(χ k )] T

[0061] wherein θ i (χ k ), φ i (χ k ) are horizontal angle and pitch angle respectively.

[0062] angle i (χ k )=[θ i (χ k )φ i (χ k )]

[0063] S320, obtaining a weight at a previous time of the first time, an initial value of the weight being a variance of signal strength of the radiation source at different observation stations at an initial time; determining a predicted weight of the radiation source at the first time by a motion model of the radiation source and the weight at the previous time.

[0064] obtaining a weight used in a motion state estimation process at a previous time of the first time;

[0065] In the best embodiment of the present application, the first time is a time when calculation is first performed, at the first time, a preset initial weight is obtained as the weight at the previous time for calculation, the initial weight being a variance of signal strength of the radiation source at different observation stations at the initial time, i.e. at the first time when positioning is first performed, the variance of signal strength of the radiation source at different observation stations at the first time is obtained as the weight at the previous time, so as to calculate the predicted weight at the present time.

[0066] The predicted weight of the radiation source at the first time is determined through the motion model and the weight at the previous time.

[0067] The motion model of the radiation source in the Cartesian coordinate system can be expressed as

[0068] x k = Φ k-1 x k-1 + Γ k-1 w k-1

[0069] wherein x k is a state vector of the radiation source at time t k , which is a 6-dimensional vector containing three-dimensional position information and corresponding velocity information , and is composed as follows:

[0070]

[0071] u k = [x k y k z k ] T

[0072]

[0073] wherein x, y, and z respectively represent the coordinate axes in the Cartesian coordinate system, and k represents the time index, wherein the above x k represents the coordinate of the radiation source at time t k on the X-axis, y k represents the coordinate of the radiation source at time t k on the Y-axis, z k represents the coordinate of the radiation source at time t k on the Z-axis, represents the velocity of the radiation source at time t k on the X-axis, represents the velocity of the radiation source at time t k on the Y-axis, represents the velocity of the radiation source at time t k on the Z-axis.

[0074] Φ k-1 is a state transition matrix of the radiation source at time t k ~ t k-1 , and is expressed as:

[0075]

[0076] wherein I 3×3 represents a 3x3 unit matrix.

[0077] where w k-1 is the zero-mean, variance Q k-1 , and Gaussian white noise with a multivariate normal distribution, which is specifically expressed as:

[0078] w k-1 = [w x,k-1 w y,k-1 w z,k-1 ] T

[0079] E[w k-1 ] = 0

[0080]

[0081] Γ k-1 is the noise contained in the state transition matrix part, which is expressed as:

[0082]

[0083] If K represents the first time, the weight of the previous time is expressed as P s,k-1 .

[0084] The predicted weight of the radiation source at the first time can be determined by the motion model and the weight of the previous time, and is expressed as:

[0085]

[0086] where, in order to represent the predicted value and the actually calculated optimal value, different weights calculated are represented by different subscripts, the S subscript represents the optimal weight, the optimal weight of the previous time is expressed as P s,k-1 , the P subscript represents the predicted value, and P p,k-1 represents the predicted weight of the previous time.

[0087] S330, the observation equation at the first time is obtained, the optimal Kalman gain at the first time is determined according to the predicted weight at the first time and the observation equation at the first time, the predicted motion state at the first time is determined by the historical motion state and the motion model, and the motion state of the radiation source at the first time is determined according to the predicted motion state at the first time, the observation at the first time and the optimal Kalman gain at the first time.

[0088] The optimal Kalman gain at the first time can be determined by the predicted weight at the first time and the observation equation at the first time, and is expressed as:

[0089]

[0090] where Hij,p,k Represents the observation equation at the first moment, R k Indicates the above

[0091] Then, the predicted motion state at the first moment is determined by using the historical motion state and the motion model. This process can be expressed as:

[0092]

[0093] After obtaining the optimal Kalman gain and the predicted motion state at the first moment, the motion state of the radiation source at the first moment is estimated in combination with the observed value at the first moment, which can be expressed as

[0094] χ s,k =χ p,k +K k [Z ij,k -h ij (χ p,k )]

[0095] In one embodiment of the present application, obtaining the observation model at the first moment includes: obtaining the motion state and predicted motion state of the radiation source at the first moment; performing a first-order Taylor expansion on the conversion relationship to obtain a first-order approximate linear equation, and the first-order Taylor expansion point of the conversion relationship is where the motion state is equal to the predicted motion state; and using the first-order approximate linear equation as the observation model at the first moment.

[0096] Will In χ k =χ p,k Perform a first-order Taylor expansion near the , and get a first-order approximate linear equation.

[0097] Among them, χ p,k Refers to t in the following text k Moment χ k The predicted value of .

[0098]

[0099]

[0100] When using AOA positioning in this application, the dual stations Expressed as:

[0101] h ij (χ k )=[angle1(χ k ) angle2(χ k )] T

[0102] Among them, θi (χ k ),φ i (χ k ) respectively are horizontal angle and pitch angle.

[0103] angle i (χ k )=[θ i (χ k ) φ i (χ k )]

[0104] Then the corresponding first-order differential matrix representation is

[0105]

[0106] In the formula

[0107]

[0108]

[0109]

[0110]

[0111] In the above method, the conversion relationship is first-order Taylor expanded to obtain the observation model, which can reduce the calculation amount in the calculation process, and also linearizes the observation model.

[0112] S340, obtain the difference between the motion state of the radiation source at the first time and the historical motion state; in the case where the difference is greater than a preset threshold, generate an error message.

[0113] After the motion state of the radiation source at the first time is estimated, the estimated value can be screened, and the difference between the motion state of the radiation source at the first time and the historical motion state is obtained. The difference reflects the deviation degree between the estimated motion state and the historical motion at the last time. When the difference is greater than a preset threshold, it means that the motion state at the last time and the motion state at the present time differ greatly, which may be an estimation error, and an error message is generated to prompt the user to correct.

[0114] In an embodiment of the present application, the method further comprises: determining the optimal weight of the radiation source at the first time according to the observation equation at the first time, the prediction weight at the first time, and the optimal Kalman gain at the first time; taking the optimal weight at the first time and the motion state at the first time as the optimal weight and the motion state at the last time; performing a preset iteration step, the preset iteration step comprising: obtaining the angle of arrival of the radiation source at a current time, and determining a measurement value at the current time according to the angle of arrival at the current time; obtaining the prediction weight of the radiation source at the current time, the prediction weight at the current time being determined by the optimal weight at the last time; obtaining the motion state of the radiation source at the last time, and determining the motion state of the radiation source at the current time according to the weight at the current time, the measurement value at the current time, and the motion state at the last time; obtaining a difference between the motion state at the current time and the motion state at the last time; continuing the iteration if the difference is less than the preset threshold; and stopping the iteration until a preset stop condition is met, the preset stop condition comprising: the number of iterations reaching a preset number, the motion state meeting a preset accuracy, or the difference being greater than the preset threshold.

[0115] After obtaining the motion state at the first time, the optimal weight of the radiation source at the first time is determined according to the observation equation at the first time, the prediction weight at the first time, and the optimal Kalman gain at the first time, which can be expressed by the following formula:

[0116] P s,k =[I-K k H ij,p,k-1 ]P p,k

[0117] In an embodiment of the present application, the positioning process is an iterative process, that is, in each iteration, the observation at the current time and the observation model at the current time are obtained, the optimal Kalman gain at the current time, the predicted motion state, and the predicted weight are predicted by the motion state at the last time, the optimal weight, and the optimal Kalman gain at the last time, and the optimal weight at the current time is calculated.

[0118] So, taking the first time as an example, the motion state and the optimal weight of the first time are taken as the motion state and the optimal weight of the previous time of the next time such as the second time, the angle of arrival of the second time is obtained, the measurement value of the second time is determined according to the angle of arrival of the second time, the prediction weight of the radiation source at the second time is obtained according to the optimal weight of the first time, the motion state of the radiation source at the first time is obtained, the motion state of the radiation source at the second time is determined according to the prediction weight of the second time, the measurement value of the current time and the motion state of the previous time, the optimal weight of the second time is calculated, the difference between the motion state of the second time and the motion state of the first time is calculated, and the next round of iteration is continued to be performed in the case that the difference is less than the preset threshold.

[0119] The iteration is stopped until a preset stop condition is met, and the preset stop condition includes that the number of iterations reaches a preset number, the motion state meets a preset accuracy, and the difference is greater than the preset threshold.

[0120] In an embodiment of the present application, the above positioning method is simulated. The simulation conditions are as follows: the azimuth error of each observation station is 2°, the elevation error is 3°, the double-station positions (km) are (0, 1, 0) and (0, 1, 0) in turn, the initial position (km) of the radiation source is (3, 3, 2), and the velocity vector (m / s) of the radiation source is (-10, -10, 0). The initial position value is provided by a traditional direction finding cross positioning algorithm, and the corresponding spatial geometric position error distribution is as shown in Figure 4 Figure 4 It is a geometric precision distribution diagram of the double-station AOA direction finding cross positioning provided by the present application. It can be found that when the coordinate (km) of the radiation source is (3, 3, 2), the positioning error at the position is as high as 750 m when a simple direction finding cross positioning algorithm is used for three-dimensional space position estimation. When the extended Kalman filter is used, considering a certain initial positioning error allowance, the iteration initial position (km) is set to (3.5, 3.5, 1.6).

[0121] The observation noise variance of the two observation stations is estimated by RSSI, and the estimated value is 0.005. The relationship between the positioning precision and the number of iterations of the AOA double-station cooperative positioning algorithm based on the extended Kalman filter is as shown in Figure 5 Figure 5 It is a relationship between the AOA double-station cooperative positioning precision and the number of tracking times when the azimuth error is 2° and the elevation error is 3° using the method of the present application. It can be found that the precision of the extended Kalman filter increases with the increase of the number of tracking times, and the number of tracking times only needs to be about 10, that is, the detection precision can be improved from 750 m to about 10 m, which is about 75 times higher than the traditional single direction finding cross positioning. ​​

[0122] Figure 6 A schematic block diagram of a positioning device provided by an embodiment of the present application is shown in Figure 6 The device 600 includes a measurement obtaining module 610, a weight obtaining module 620, and an estimation module 630, wherein:

[0123] The measurement obtaining module is configured to obtain an angle of arrival of a radiation source at a first time relative to an observation system, and determine a measurement at the first time according to the angle of arrival at the first time, the observation system including two observation stations;

[0124] The weight obtaining module is configured to obtain a prediction weight of the radiation source at the first time, the prediction weight at the first time being determined by signal strengths of the radiation source at different observation stations;

[0125] The estimation module is configured to obtain a historical motion state of the radiation source, and determine a motion state of the radiation source at the first time according to the prediction weight at the first time, the measurement at the first time, and the historical motion state, the motion state including a position of the radiation source.

[0126] As to the device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiments of the method, and will not be described in detail here.

[0127] Figure 7 is a block diagram of an electronic device 700 according to an example embodiment. As shown in Figure 7 The electronic device 700 can include one or more of a processor 701, a memory 702. The electronic device 700 can also include one or more of a multimedia component 703, an input / output (I / O) interface 704, and a communication component 705.

[0128] The processor 701 is configured to control overall operations of the electronic device 700 to complete all or part of the steps of the positioning method described above. The memory 702 is configured to store various types of data to support operations of the electronic device 700, which can include, for example, instructions for any application or method operating on the electronic device 700, and application-related data, such as contact data, transmitted and received messages, pictures, audio, video, and the like. The memory 702 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as a static random access memory (SRAM), an electrically erasable programmable read-only memory (EEPROM), an erasable programmable read-only memory (EPROM), a programmable read-only memory (PROM), a read-only memory (ROM), a magnetic storage, a flash memory, a magnetic disk, or an optical disk. The multimedia component 703 can include a screen and an audio component. The screen can be, for example, a touch screen, and the audio component is configured to output and / or input audio signals. For example, the audio component can include a microphone configured to receive external audio signals. The received audio signals can be further stored in the memory 702 or transmitted through the communication component 705. The audio component further includes at least one speaker configured to output audio signals. The I / O interface 704 provides an interface between the processor 701 and other interface modules, which can be a keyboard, a mouse, a button, and the like. The buttons can be virtual buttons or physical buttons. The communication component 705 is configured to perform wired or wireless communication between the electronic device 700 and other devices. The wireless communication, such as Wi-Fi, Bluetooth, near field communication (NFC), 2G, 3G, 4G, NB-IOT, eMTC, or other 7G, and the like, or a combination of one or more of them, is not limited herein. Therefore, the communication component 705 can include, for example, a Wi-Fi module, a Bluetooth module, an NFC module, and the like.

[0129] In an exemplary embodiment, the electronic device 700 can be implemented by one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), controllers, micro-controllers, microprocessors, or other electronic elements for performing the positioning method described above.

[0130] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided, which, when executed by a processor, implement the steps of the positioning method described above. For example, the computer-readable storage medium can be the memory 702 described above including program instructions executable by the processor 701 of the electronic device 700 to complete the positioning method described above.

[0131] In another exemplary embodiment, a computer program product is also provided, which contains a computer program executable by a programmable apparatus, the computer program having code portions for performing the positioning method described above when executed by the programmable apparatus.

[0132] The preferred embodiments of the present application are described in detail above with reference to the accompanying drawings, but the present application is not limited to the specific details described in the above embodiments. Various simple modifications can be made to the technical solutions of the present application within the scope of the technical concept of the present application, and these simple modifications all belong to the protection scope of the present application.

[0133] It should be further noted that each of the specific technical features described in the above specific embodiments can be combined in any appropriate manner without contradiction. In order to avoid unnecessary repetition, various possible combinations are not described again in the present application.

[0134] In addition, any combination of the various different embodiments of the present application can also be made, as long as it does not deviate from the idea of the present application, it should also be considered as disclosed in the present application.

Claims

1. A positioning method, characterized in that: The method comprises: Obtaining an arrival angle of a radiation source relative to an observation system at a first moment, inputting the arrival angle at the first moment into a preset conversion relationship to obtain an observation value at the first moment; and adding a preset observation noise to the observation value at the first moment to obtain a measurement value at the first moment, wherein the observation system includes two observation stations; Obtaining a weight at a moment before the first moment, where an initial value of the weight is the variance of the signal strength of the radiation source at different observation stations at the initial moment; determining a predicted weight of the radiation source at the first moment using a motion model of the radiation source and the weight at the previous moment, where the predicted weight at the first moment is determined by the signal strength of the radiation source at different observation stations; Obtaining a historical motion state of the radiation source; Obtaining a motion state and a predicted motion state of the radiation source at a first moment; performing a first-order Taylor expansion on the conversion relationship to obtain a first-order approximate linear equation, wherein the first-order Taylor expansion point of the conversion relationship is where the motion state is equal to the predicted motion state; and using the first-order approximate linear equation as the observation equation at the first moment; The optimal Kalman gain at the first moment is determined based on the prediction weight at the first moment and the observation equation at the first moment; the predicted motion state at the first moment is determined through the historical motion state and the motion model; the motion state of the radiation source at the first moment is determined based on the predicted motion state at the first moment, the observation value at the first moment and the optimal Kalman gain at the first moment, wherein the motion state includes the position of the radiation source.

2. The method according to claim 1, characterized in that The method further comprises: Obtaining a difference between a motion state of the radiation source at a first moment and a set state; When the difference is greater than a preset threshold, an error message is generated.

3. The method according to claim 2, characterized in that The method further comprises: Determining the optimal weight of the radiation source at the first moment according to the observation equation at the first moment, the prediction weight at the first moment, and the optimal Kalman gain at the first moment; Using the optimal weight and motion state at the first moment as the optimal weight and motion state at the previous moment; Executing preset iterative steps, the preset iterative steps including: obtaining an arrival angle of the radiation source at a current moment, and determining a measurement value at the current moment based on the arrival angle at the current moment; obtaining a predicted weight of the radiation source at the current moment, wherein the predicted weight at the current moment is determined by an optimal weight at a previous moment; obtaining a motion state of the radiation source at a previous moment, and determining a motion state of the radiation source at the current moment based on the weight at the current moment, the measurement value at the current moment, and the motion state at the previous moment; obtaining a difference between the motion state at the current moment and the motion state at the previous moment; and continuing to iterate if the difference is less than a preset threshold value; The iteration is stopped until a preset stopping condition is met, and the preset stopping condition includes: the number of iterations reaches a preset number, the motion state meets a preset accuracy, and the difference is greater than the preset threshold.

4. A positioning device, characterized in that: The device comprises: A measurement value acquisition module is configured to obtain an arrival angle of a radiation source relative to an observation system at a first moment, input the arrival angle at the first moment into a preset conversion relationship to obtain an observation value at the first moment, and add a preset observation noise to the observation value at the first moment to obtain the measurement value at the first moment, wherein the observation system includes two observation stations; a weight acquisition module, configured to acquire a weight at a moment before the first moment, where the initial value of the weight is the variance of the signal strength of the radiation source at different observation stations at the initial moment; determine a predicted weight of the radiation source at the first moment using a motion model of the radiation source and the weight at the previous moment, where the predicted weight at the first moment is determined by the signal strength of the radiation source at different observation stations; An estimation module is used to obtain the historical motion state of the radiation source; obtain the motion state and predicted motion state of the radiation source at the first moment; perform a first-order Taylor expansion on the conversion relationship to obtain a first-order approximate linear equation, and the first-order Taylor expansion point of the conversion relationship is where the motion state is equal to the predicted motion state; use the first-order approximate linear equation as the observation equation at the first moment; determine the optimal Kalman gain at the first moment based on the prediction weight at the first moment and the observation equation at the first moment; determine the predicted motion state at the first moment through the historical motion state and the motion model; determine the motion state of the radiation source at the first moment based on the predicted motion state at the first moment, the observation value at the first moment and the optimal Kalman gain at the first moment, and the motion state includes the position of the radiation source.

5. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method according to any one of claims 1 to 3 are implemented.

6. An electronic device, characterized in that: include: a memory having a computer program stored thereon; A processor, configured to execute the computer program in the memory to implement the steps of the method according to any one of claims 1 to 3.

Images