Multi-sensor fusion positioning method, system and equipment based on probability statistics and medium

Abstract

The invention provides a multi-sensor fusion positioning method, system and device based on probability statistics and a medium. The method comprises the following steps: acquiring positioning result data of a same target by a plurality of time difference positioning systems, and inversely calculating azimuth information of the target relative to each time difference positioning system according to the positioning result data; based on each time difference positioning system, constructing a statistical distribution model of a corresponding sensor measurement error; according to the plurality of statistical distribution models, constructing a joint probability distribution model of sensor measurement errors; and estimating the position of the target by using the maximum likelihood parameter of the joint probability distribution model. On the basis of each time difference positioning system, the positioning data and the position characteristics of the sensor are fully mined, the joint probability distribution model is constructed, the target position is estimated by using the maximum likelihood parameter meeting the distribution, the data characteristics are fused into the fusion algorithm, and the positioning fusion precision is improved.

Description

Technical Field

[0001] The present invention relates to the field of navigation technology, and in particular to a multi-sensor fusion positioning method, device and medium based on probability statistics. Background Art

[0002] With the advancement of information technology, multi-sensor fusion positioning technology (also known as distributed fusion) has become an indispensable part of the command and control system. It can more accurately estimate the actual position trajectory of the target by integrating target track data from different sensors and combining mathematical models and algorithms. Multi-sensor fusion positioning is not only crucial for monitoring, tracking and controlling air or sea targets, but also provides strong support for target guidance in the field of electronic reconnaissance.

[0003] Compared with the traditional centralized measurement fusion method, the multi-sensor fusion positioning solution significantly reduces the demand for communication bandwidth. Due to the flexibility of its algorithm design, the system has better reliability and scalability, and stronger feasibility, so it has broad application prospects.

[0004] However, the multi-sensor fusion positioning technology in related technologies mostly relies on the optimal track fusion algorithm. Although it can effectively process data from multiple sensors, it uses a general method to calculate the cross-covariance matrix after fusion, which may lead to a decrease in positioning accuracy in certain complex environments. Specifically, the traditional multi-sensor fusion positioning algorithm fails to fully utilize the characteristics of positioning data and the location information of the sensor itself, thus limiting its performance in complex environments. Summary of the invention

[0005] The present invention provides a multi-sensor fusion positioning method, system, device and medium based on probability statistics, which solves the problem of how to improve the positioning accuracy of the multi-sensor fusion positioning algorithm.

[0006] In order to achieve the above objectives, this application adopts the following technical solutions:

[0007] In a first aspect, a multi-sensor fusion positioning method based on probability statistics is provided, comprising:

[0008] Acquire positioning result data of multiple time difference positioning systems for the same target, and reversely calculate the azimuth information of the target relative to each of the time difference positioning systems according to the positioning result data;

[0009] Based on each of the time difference positioning systems, a statistical distribution model of the corresponding sensor measurement error is constructed;

[0010] Constructing a joint probability distribution model of sensor measurement errors according to the plurality of statistical distribution models;

[0011] The position of the target is estimated using the maximum likelihood parameters of the joint probability distribution model.

[0012] In a first possible implementation manner of the first aspect, the time difference positioning system is used to locate a target, including:

[0013] Estimate the target's position based on the time difference between signal transmission and reception;

[0014] Performing Kalman filtering on the estimated position information;

[0015] Back-calculating the azimuth of the target relative to the center point of the sensor according to the processed position information;

[0016] Based on the azimuth angle, the position information of the fusion initial point is obtained by using the intersection of azimuth lines.

[0017] In a second possible implementation manner of the first aspect, constructing a statistical distribution model of a corresponding sensor measurement error based on each of the time difference positioning systems includes:

[0018] The exponential function that defines the probability distribution is:

[0019] F(x,y)=-(w-w0) 2 / 2σ 2 ,

[0020] Among them, w is the index value, which is a binary function of the positioning result, that is, w = f(x, y), w0 is the true value, σ 2 is the variance;

[0021] Assume that the positioning fusion result is (x0, y0),

[0022] The standard form of the polynomial F(x,y) is:

[0023] F(x,y)=a1x 2 +a2y 2 +a3xy+a4x+a5y+a6,

[0024] The polynomial coefficients can be expressed as:

[0025]

[0026] Among them, f' x (x0,y0) and f' y (x0, y0) represent the partial derivatives of f(x, y) at (x0, y0) with respect to x and y respectively. The distance D can be expressed as:

[0027] D=f(x0,y0)-w0.

[0028] Based on the second possible implementation manner of the first aspect, in a third possible implementation manner of the first aspect, constructing a joint probability distribution model of sensor measurement errors according to the multiple statistical distribution models includes:

[0029] Exponential function F of joint probability distribution ∑ (x,y) should be the sum of the statistical distribution models, that is:

[0030]

[0031] F ∑ The polynomial form of (x,y) is:

[0032] F ∑ (x,y)=a1x 2 +a2y 2 +a3xy+a4x+a5y+a6,

[0033] The corresponding polynomial coefficients should be F i The sum of the coefficients of each statistical distribution model (x, y), that is:

[0034]

[0035] Among them, a i,j , (i=1…N, j=1…5) represents the jth polynomial coefficient of the i-th time difference positioning system, f' i,x (x0,y0) and f' i,y (x0, y0) represent the exponential function f of the i-th time difference positioning system. i The partial derivative of (x,y) with respect to x and y at (x0,y0); distance D i It is expressed as:

[0036] D i =f i (x0,y0)-w0.

[0037] Based on any possible implementation manner of the first aspect, in a fourth possible implementation manner of the first aspect, estimating the position of the target by using a maximum likelihood parameter of a joint probability distribution model includes:

[0038] For each time difference positioning system's statistical distribution model, find the partial derivative value of its exponential function;

[0039] The distance value is expressed by the time difference between the target and the center point of the sensor;

[0040] Setting the partial derivative of the exponential function of the joint probability distribution equal to zero, and using the partial derivative value and the distance value to represent the polynomial coefficients in the linear equation;

[0041] Use the method of adding linear equation coefficients to find the fusion coefficient;

[0042] Based on the initial fusion point, the fusion coefficient combination method is used to calculate the fusion target position.

[0043] Based on the fourth possible implementation manner of the first aspect, in a fifth possible implementation manner of the first aspect, the partial derivative value is:

[0044]

[0045] The distance values ​​are:

[0046]

[0047] Among them, the positions of the sensor center points (called point a and point b) of the i-th time difference positioning system are (x i,a ,y i,a ) and (x i,b ,y i,b ), the azimuth angles of the target relative to the center points of the two sensors are θ i,a and θ i,a , the time difference of the signal reaching the center point of the sensor is Δt i The result of time difference positioning is (x i ,y i ), (i=1…N), N is the number of time difference positioning systems, c represents the speed of light in vacuum;

[0048] The linear equation is:

[0049]

[0050] The polynomial coefficients are:

[0051]

[0052] The fusion coefficient is:

[0053]

[0054] Wherein, j = 1…5;

[0055] The target location:

[0056]

[0057] In a second aspect, a multi-sensor fusion positioning system based on probability statistics is provided, comprising:

[0058] A data preprocessing module, used to obtain positioning result data of multiple time difference positioning systems for the same target, and reversely calculate the azimuth information of the target relative to each of the time difference positioning systems according to the positioning result data;

[0059] A statistical distribution model module, used to construct a statistical distribution model of the corresponding sensor measurement error based on each of the time difference positioning systems;

[0060] A joint probability distribution model module, used to construct a joint probability distribution model of sensor measurement errors according to a plurality of said statistical distribution models;

[0061] The target position determination module is used to estimate the position of the target using the maximum likelihood parameters of the joint probability distribution model.

[0062] In a first possible implementation manner of the second aspect, the target position determination module is specifically configured to:

[0063] For each time difference positioning system's statistical distribution model, find the partial derivative value of its exponential function;

[0064] The distance value is expressed by the time difference between the target and the center point of the sensor;

[0065] Setting the partial derivative of the exponential function of the joint probability distribution equal to zero, and using the partial derivative value and the distance value to represent the polynomial coefficients in the linear equation;

[0066] Use the method of adding linear equation coefficients to find the fusion coefficient;

[0067] Based on the initial fusion point, the fusion coefficient combination method is used to calculate the fusion target position.

[0068] According to a third aspect, an electronic device is provided, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the computer program is executed by the processor, the steps of the multi-sensor fusion positioning method based on probability statistics as described in the first aspect are implemented.

[0069] In a fourth aspect, a readable storage medium is provided, on which a program or instruction is stored. When the program or instruction is executed by a processor, the steps of the multi-sensor fusion positioning method based on probability statistics as described in the first aspect are implemented.

[0070] This application is based on each time difference positioning system, fully exploits the location characteristics of the positioning data and the sensor itself, derives the joint probability distribution of measurement errors of multiple passive sensors, and uses the maximum likelihood parameters that satisfy this distribution to estimate the target position. It integrates the data characteristics into the fusion algorithm, improves the accuracy of positioning fusion, and can adapt to high-precision positioning of target positions in complex environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] Figure 1 A schematic flowchart of a multi-sensor fusion positioning method based on probability statistics provided in an embodiment of the present application;

[0072] Figure 2 A schematic flowchart of another multi-sensor fusion positioning method based on probability statistics provided in an embodiment of the present application;

[0073] Figure 3 This is a diagram showing the positioning effect of a fixed target according to an embodiment of the present application;

[0074] Figure 4 This is the positioning effect diagram of the traditional method on the fixed target;

[0075] Figure 5 This is the positioning effect diagram of the linear motion target using the traditional method;

[0076] Figure 6 This is the positioning effect diagram of the curved motion target using the traditional method;

[0077] Figure 7 This is the positioning effect diagram of the circular motion target using the traditional method;

[0078] Figure 8 A structural block diagram of a fusion positioning system of a hypergraph and multi-behavior comparative learning provided in an embodiment of the present application;

[0079] Fig. 9 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0080] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined purpose, the technical solutions in the embodiments of the present application are clearly described. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field belong to the scope of protection of the present application.

[0081] The terms "first", "second", etc. in the specification and claims of the present application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the terms used in this way are interchangeable where appropriate, so that the embodiments of the present application can be implemented in an order other than those illustrated or described herein, and the objects distinguished by "first", "second", etc. are generally of one type, and the number of objects is not limited. For example, the first object can be one or more. In addition, "and / or" in the specification and claims represents at least one of the connected objects, and the character " / " generally indicates that the objects associated with each other are in an "or" relationship.

[0082] The description of the method flow in the specification of the present application and the steps of the flowchart in the drawings of the present specification do not have to be strictly executed according to the step numbers, and the method steps can be executed in a different order. Moreover, some steps can be omitted, multiple steps can be combined into one step, and / or one step can be decomposed into multiple steps.

[0083] The following is a detailed description of the multi-sensor fusion positioning method, system, device and medium based on probability statistics provided by the embodiments of the present application in combination with the accompanying drawings and preferred embodiments.

[0084] First, the application scenario of the multi-sensor fusion positioning method based on probability statistics in an embodiment of the present application is described in detail.

[0085] The multi-sensor fusion positioning in related technologies mainly adopts the optimal track fusion algorithm, whose essence is to use the covariance of each sensor to calculate the fusion mutual covariance to achieve the fusion of measurement values. This method only uses the results obtained by each sensor and often focuses on simple data fusion. It lacks in-depth analysis and optimization processing of different sensor characteristics and error models, resulting in limited fusion effect.

[0086] For example, in the field of electronic reconnaissance, passive time difference positioning is usually used to determine the target position. As an important means of electronic reconnaissance, passive time difference positioning determines the target position by measuring the time difference between the target signal reaching different sensors. It has the advantages of good concealment and strong anti-interference ability. However, due to factors such as multipath effects, non-line-of-sight propagation, time synchronization, sensor failures, and dynamic environmental changes in the signal propagation process, when traditional fusion algorithms are directly applied for data processing, the fusion accuracy is often difficult to reach the ideal level, and it is difficult to obtain high-precision positioning results.

[0087] Based on this, the present application provides a multi-sensor fusion positioning method based on probability statistics, which makes full use of the characteristics of passive sensor data and further improves the accuracy of positioning fusion by deeply exploring the inherent laws of passive sensor data and the location characteristics of the sensors themselves.

[0088] Based on the statistical distribution model of single sensor measurement error, this application derives the joint probability distribution of multiple passive sensor measurement errors, and uses the maximum likelihood parameters that satisfy this distribution to estimate the target position. The method has a solid theoretical basis. At the same time, based on parameter estimation theory, a set of simple and easy calculation methods has been developed to facilitate engineering implementation.

[0089] See also Figure 1-2 , the embodiment of the present application provides a multi-sensor fusion positioning method based on probability statistics, such as Figure 1-2 As shown, the fusion positioning method of the embodiment of the present application includes:

[0090] Step S1, obtaining positioning result data of multiple time difference positioning systems for the same target, and back-calculating the azimuth information of the target relative to each time difference positioning system based on the positioning result data.

[0091] Assume that there are N time difference positioning systems for passive positioning of the same target, and the positions of the center points of the reconnaissance stations of the i-th passive positioning system (called points a and b) are (x i,a ,y i,a ) and (x i,b ,y i,b ), the azimuths of the target relative to the center points of the two reconnaissance stations are θ i,a and θ i,a , the time difference of the signal reaching the center point of the reconnaissance station is Δt i The result of time difference positioning is (x i ,y i ),(i=1…N).

[0092] Specifically, each time difference positioning system locates the target, including:

[0093] Step S101, estimating the position of the target according to the time difference from signal transmission to signal reception.

[0094] Step S102: performing Kalman filtering on the estimated position information.

[0095] Step S103: inversely calculate the azimuth of the target relative to the center point of the sensor based on the processed position information.

[0096] Step S104, based on the azimuth angle, the position information of the fusion initial point is obtained by using the intersection of the azimuth lines.

[0097] This step intersects the target orientation lines obtained by the two systems to obtain a preliminary fusion point as the initial value.

[0098] The time difference positioning system is based on the pre-arranged sensors (or reconnaissance stations) receiving the signals (such as radar pulses, sound waves or electromagnetic waves) emitted by the target; the time difference of the signal reaching different sensors is calculated based on the arrival time of the received signal; the time difference positioning equation group is based on the measured time difference and signal propagation speed; the equation group is solved by mathematical methods to obtain the estimated position coordinates of the target. In the application of time difference positioning, Kalman filtering can help correct the positioning error caused by sensor noise or other factors, and improve the accuracy and stability of positioning. According to the position coordinates, the azimuth of the target relative to each reconnaissance station can be calculated.

[0099] Step S2: construct a statistical distribution model of the corresponding sensor measurement error based on each time difference positioning system. Specifically, it includes:

[0100] Step S201, define the index value w with the true value w0 as the expectation and σ 2 is a normal distribution with a variance of .

[0101] In the measurement, each indicator value w is usually a random value, which satisfies the expectation of the true value w0 and the σ 2 is a normal distribution with variance, so its probability density function can be expressed as follows:

[0102]

[0103] Step S202, based on the binary function representation of the positioning result (x, y),

[0104] In the positioning system, the index value w is a binary function of the positioning result (x, y), that is:

[0105] w=f(x,y),

[0106] Assuming that the positioning fusion result is (x0, y0), the above function can be approximated by Taylor expansion near (x0, y0), that is:

[0107] w=f(x0,y0)+f′ x (x0,y0)(x-x0)+f′ y (x0,y0)(y-y0),

[0108] Among them, f' x (x0,y0) and f' y (x0, y0) represent the partial derivatives of f(x, y) with respect to x and y at (x0, y0) respectively.

[0109] In this application, the exponential function of the probability distribution is defined as:

[0110] F(x,y)=-(w-w0) 2 / 2σ2 ,

[0111] but:

[0112]

[0113] Then the probability density function of the above index value w can be rewritten as,

[0114]

[0115] The exponential function F(x,y) can be regarded as a quadratic polynomial, which can be written in the standard form of the polynomial, namely:

[0116] F(x,y)=a1x 2 +a2y 2 +a3xy+a4x+a5y+a6,

[0117] Among them, the polynomial coefficients can be expressed as:

[0118]

[0119] The distance D can be expressed as:

[0120] D=f(x0,y0)-w0.

[0121] Step S3: constructing a joint probability distribution model of sensor measurement errors according to multiple statistical distribution models.

[0122] For multiple time difference positioning systems, the positioning result obtained by considering the i-th time difference positioning system should obey the true value w0 as the expectation and is the normal distribution of variance, that is:

[0123]

[0124] Since multiple systems operate independently, the joint probability distribution of the results of all positioning systems should be the product of their respective probabilities, so the exponential function F of the joint probability distribution is ∑ (x,y) should be the sum of each, that is:

[0125]

[0126] Therefore, F ∑ The polynomial of (x,y) can still be written in polynomial form, that is:

[0127] F ∑ (x,y)=a1x 2 +a2y 2 +a3xy+a4x+a5y+a6,

[0128] The corresponding polynomial coefficients should be F i The sum of the coefficients of (x, y), that is:

[0129]

[0130] Among them, a i,j , (i=1…N, j=1…5) represents the jth polynomial coefficient of the i-th time difference positioning system, f' i,x (x0,y0) and f' i,y (x0, y0) represent the exponential function f of the i-th time difference positioning system. i The partial derivative of (x,y) with respect to x and y at (x0,y0); distance D i It can be expressed as:

[0131] D i =f i (x0,y0)-w0.

[0132] Step S4, using the maximum likelihood parameters of the joint probability distribution model to estimate the position of the target.

[0133] Maximum likelihood estimation is a statistical method used to estimate model parameters that maximize the probability of observed data. In this application, the goal is to find a set of model parameters that maximizes the probability of these passive sensors measuring known results. In actual operation, the maximum point, i.e., the fusion point, can be found by solving the partial derivative and setting it equal to zero.

[0134] The process of finding the fusion point can be regarded as making F ∑ The process in which (x,y) reaches a maximum value.

[0135] F ∑ Taking the partial derivative of (x,y) and setting it to zero gives:

[0136]

[0137] Therefore, the location of the fusion point is solved as:

[0138]

[0139] Specifically, it includes:

[0140] Step S401, for each time difference positioning system statistical distribution model, calculate the partial derivative value of its exponential function.

[0141] Exponential function f i The specific forms of (x,y) and its derivatives are as follows:

[0142] The positioning result (x, y) obtained by the i-th time difference positioning system, the position of the center points of the two sensors of the system (called point a and point b) (x i,a ,y i,a )、(x i,b ,y i,b ), the exponential function of the time difference positioning system can be defined as the distance difference between the fusion point and the center points of the two sensors. The distance difference w i is a two-dimensional function f about x,y i (x,y), which can be expressed as,

[0143]

[0144] Assume that the azimuth angles of the two sensors a and b to the fusion point are θ i,a ,θ i,b , then f i The partial derivative of (x,y) can be expressed as:

[0145]

[0146] Right now:

[0147]

[0148] Step S402: Use the time difference between the target and the center point of the sensor to represent its distance value.

[0149] Distance D i It can be expressed as:

[0150]

[0151] Among them, the positions of the sensor center points (called point a and point b) of the i-th time difference positioning system are (x i,a ,y i,a ) and (x i,b ,y i,b ), the azimuth angles of the target relative to the center points of the two sensors are θ i,a and θ i,b , the time difference of the signal reaching the center point of the sensor is Δt i The result of time difference positioning is (x i ,y i ), (i=1…N), N is the number of time difference positioning systems, and c represents the speed of light in vacuum.

[0152] Step S403, setting the partial derivative of the exponential function of the joint probability distribution equal to zero, and using the partial derivative value and the distance value to represent the polynomial coefficients in the linear equation.

[0153] The partial derivative of the exponential function based on each time difference positioning system can be calculated through the joint probability distribution model to obtain the partial derivative of the exponential function of the joint probability distribution. The linear equation is:

[0154]

[0155] The polynomial coefficients are:

[0156]

[0157] Step S404, using the method of adding linear equation coefficients to calculate the fusion coefficient.

[0158]

[0159] Among them, j=1…5.

[0160] Step S405, based on the fusion initial point, the fusion coefficient combination method is used to calculate the fusion target position:

[0161]

[0162] This application is based on each time difference positioning system, fully exploits the location characteristics of positioning data and the sensor itself, derives the joint probability distribution of multiple passive sensor measurement errors, and uses the maximum likelihood parameters that satisfy this distribution to estimate the target position, integrates data characteristics into the fusion algorithm, improves the accuracy of positioning fusion, and can adapt to high-precision positioning of target positions in complex environments. The following simulation conclusions explain in detail that this application can achieve better fusion effects than traditional methods.

[0163] Exemplarily, the present application uses a numerical simulation method to illustrate the fusion effect. Set up two time difference positioning systems, the position coordinates of the reconnaissance station of system 1 are (-70, 0), (-50, 0), (-30, 0), and the position coordinates of the reconnaissance station of system 2 are (30, 0), (50, 0), (70, 0), and the unit is kilometers (km). Set the time difference error between system 1 and system 2 to 100 nanoseconds (ns). Set four simulation targets, namely: 1. A fixed target with a position coordinate of (20, 150) km; 2. A linear motion target about 150 km away from the two systems; 3. A curved motion target about 100 km away from the two systems; 4. A circular motion target about 100 km away from the two systems. For the same scene, the method of the present application and the traditional fusion positioning method are used for simulation, and the results are compared, such as Figure 3 to Figure 7 shown.

[0164] Figure 3 This is a diagram showing the positioning effect of a fixed target in an embodiment of the present application. Figure 4 This is the positioning effect diagram of the traditional method for fixed targets; comparison Figure 3 , Figure 4 It can be seen that the result of the fusion algorithm of the present application for fixed targets converges to the intersection of the positioning error ellipses of the two time difference positioning systems, while the fusion result of the traditional method is more divergent.

[0165] Figure 5 This is the positioning effect of the traditional method for linear motion targets. Figure 6 This is the positioning effect diagram of the traditional method for the curved motion target. Figure 7 This is the positioning effect diagram of the circular motion target using the traditional method; Figure 5 to Figure 7 From the comparison of the fusion results of the moving target, it can be seen that the fusion result of the present application is closer to the real track of the target.

[0166] The calculation comparison of the positioning root mean square error of three positioning methods (single time difference positioning system, fusion algorithm of the embodiment of the present application, and traditional fusion method) is shown in Table 1:

[0167] System 1 System 2 This application Traditional methods fixed 1.559 1.5124 0.41288 1.1017 Linear motion 1.4338 1.552 0.42519 1.04 Curved motion 1.2249 1.2033 0.32353 0.92035 Circle Movement 0.97719 0.93859 0.26476 0.62867

[0168] It can be seen that both the fusion algorithm of the embodiment of the present application and the traditional fusion algorithm can improve the positioning accuracy of the target through data fusion, and the fusion algorithm of the embodiment of the present application has smaller error and better performance.

[0169] See also Figure 8 , corresponding to the above-mentioned multi-sensor fusion positioning method embodiment based on probability statistics, the embodiment of the present application provides a multi-sensor fusion positioning system based on probability statistics, and the fusion positioning system includes:

[0170] The data preprocessing module 1001 is used to obtain positioning result data of multiple time difference positioning systems for the same target, and reversely calculate the position information of the target relative to each of the time difference positioning systems according to the positioning result data;

[0171] A statistical distribution model module 1002 is used to construct a statistical distribution model of the corresponding sensor measurement error based on each of the time difference positioning systems;

[0172] A joint probability distribution model module 1003 is used to construct a joint probability distribution model of sensor measurement errors according to the multiple statistical distribution models;

[0173] The target position determination module 1004 is used to estimate the position of the target using the maximum likelihood parameters of the joint probability distribution model.

[0174] Furthermore, the target location determination module 1004 is specifically used for:

[0175] For each time difference positioning system's statistical distribution model, find the partial derivative value of its exponential function;

[0176] The distance value is expressed by the time difference between the target and the center point of the sensor;

[0177] Setting the partial derivative of the exponential function of the joint probability distribution equal to zero, and using the partial derivative value and the distance value to represent the polynomial coefficients in the linear equation;

[0178] Use the method of adding linear equation coefficients to find the fusion coefficient;

[0179] Based on the initial fusion point, the fusion coefficient combination method is used to calculate the fusion target position.

[0180] The above-mentioned multi-sensor fusion positioning system based on probability statistics implements the steps and various processes of the above-mentioned multi-sensor fusion positioning method embodiment based on probability statistics, and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0181] See also Fig. 9 Corresponding to the above-mentioned embodiment of the multi-sensor fusion positioning method based on probability statistics, an embodiment of the present application provides an electronic device, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, the steps of the above-mentioned embodiment of the multi-sensor fusion positioning method based on probability statistics and the various processes of the embodiment are implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0182] The memory 1009 can be used to store software programs and various data. The memory 1009 may mainly include a first storage area for storing programs or instructions and a second storage area for storing data, wherein the first storage area may store an operating system, an application program or instructions required for at least one function (such as a sound playback function, an image playback function, etc.), etc. In addition, the memory 1009 may include a volatile memory or a non-volatile memory, or the memory 1009 may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), a static random access memory (SRAM), a dynamic random access memory (DRAM), a synchronous dynamic random access memory (SDRAM), a double data rate synchronous dynamic random access memory (DDRSDRAM), an enhanced synchronous dynamic random access memory (ESDRAM), a synchronous link dynamic random access memory (SLDRAM) and a direct memory bus random access memory (DRRAM). The memory 1009 in the embodiment of the present application includes but is not limited to these and any other suitable types of memory.

[0183] The processor 1010 may include one or more processing units; optionally, the processor 1010 integrates an application processor and a modem processor, wherein the application processor mainly processes operations related to an operating system, a user interface, and application programs, and the modem processor mainly processes wireless communication signals, such as a baseband processor. It is understandable that the modem processor may not be integrated into the processor 1010.

[0184] Corresponding to the above-mentioned multi-sensor fusion positioning method embodiment based on probability statistics, the embodiment of the present application also provides a readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by the processor, the steps of the above-mentioned multi-sensor fusion positioning method embodiment based on probability statistics and the various processes of the embodiment are implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0185] The processor is the processor in the electronic device described in the above embodiment of the present application. The readable storage medium includes a computer readable storage medium, such as a computer read-only memory ROM, a random access memory RAM, a magnetic disk or an optical disk.

[0186] It should be noted that, in this article, the terms "comprise", "include" or any other variant thereof are intended to cover non-exclusive inclusion, so that the process, method, article or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise one..." do not exclude the presence of other identical elements in the process, method, article or device including the element. In addition, it should be pointed out that the scope of the methods and devices in the embodiments of the present application is not limited to performing functions in the order shown or discussed, and may also include performing functions in a substantially simultaneous manner or in reverse order according to the functions involved, for example, the described method may be performed in an order different from that described, and various steps may also be added, omitted, or combined. In addition, the features described with reference to certain examples may be combined in other examples.

[0187] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a computer software product, which is stored in a storage medium (such as ROM / RAM, a disk, or an optical disk), and includes a number of instructions for a terminal (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present application.

[0188] The embodiments of the present application are described above in conjunction with the accompanying drawings, but the present application is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present application, ordinary technicians in this field can also make many forms without departing from the purpose of the present application and the scope of protection of the claims, all of which are within the protection of the present application.

Claims

1. A multi-sensor fusion positioning method based on probability statistics, characterized in that: include: Acquire positioning result data of multiple time difference positioning systems for the same target, and reversely calculate the azimuth information of the target relative to each of the time difference positioning systems according to the positioning result data; Based on each of the time difference positioning systems, a statistical distribution model of the corresponding sensor measurement error is constructed; Constructing a joint probability distribution model of sensor measurement errors according to the plurality of statistical distribution models; The position of the target is estimated using the maximum likelihood parameters of the joint probability distribution model.

2. The multi-sensor fusion positioning method based on probability statistics according to claim 1 is characterized in that: The time difference positioning system is used to locate a target, and includes: Estimate the target's position based on the time difference between signal transmission and reception; Performing Kalman filtering on the estimated position information; Back-calculating the azimuth of the target relative to the center point of the sensor according to the processed position information; Based on the azimuth angle, the position information of the fusion initial point is obtained by using the intersection of azimuth lines.

3. The multi-sensor fusion positioning method based on probability statistics according to claim 1 is characterized in that: The method of constructing a statistical distribution model of the corresponding sensor measurement error based on each of the time difference positioning systems includes: The exponential function that defines the probability distribution is: F(x,y)=-(w-w0) 2 / 2σ 2 , Among them, w is the index value, which is a binary function of the positioning result, that is, w = f(x, y), w0 is the true value, σ 2 is the variance; Assume that the positioning fusion result is (x0, y0), The standard form of the polynomial F(x, y) is: <h2 style=";text-align:left;direction:ltr">F(x, y) = a1x<h2 style=";text-align:left;direction:ltr"> 2 <h2 style=";text-align:left;direction:ltr"> +a2y<h2 style=";text-align:left;direction:ltr"> 2 <h2 style=";text-align:left;direction:ltr"> +a3xy+a4x+a5y+a6, The polynomial coefficients can be expressed as: Among them, f′ x (x0, y0) and f′ y (x0, y0) represent the partial derivatives of f(x, y) at (x0, y0) with respect to x and y respectively. The distance D can be expressed as: D=f(x0,y0)-w0.

4. The multi-sensor fusion positioning method based on probability statistics according to claim 3 is characterized in that: According to the plurality of statistical distribution models, a joint probability distribution model of sensor measurement errors is constructed, including: Exponential function F of joint probability distribution ∑ (x, y) should be the sum of the statistical distribution models, that is: F ∑ The polynomial form of (x, y) is: <h2 style=";text-align:left;direction:ltr">F<h2 style=";text-align:left;direction:ltr"> ∑ <h2 style=";text-align:left;direction:ltr"> (x, y) = a1x<h2 style=";text-align:left;direction:ltr"> 2 <h2 style=";text-align:left;direction:ltr"> +a2y<h2 style=";text-align:left;direction:ltr"> 2 <h2 style=";text-align:left;direction:ltr"> +a3xy+a4x+a5y+a6, The corresponding polynomial coefficients should be F i The sum of the coefficients of each statistical distribution model of (x, y), that is: Among them, a i，j , (i=1...N, j=1...5) represents the jth polynomial coefficient of the i-th time difference positioning system, f′ i，x (x0, y0) and f′ i，y (x0, y0) represent the exponential function f of the i-th time difference positioning system. i The partial derivatives of (x, y) with respect to x and y at (x0, y0); distance D i It is expressed as: D i =f i (x0,y0)-w0。 5. The multi-sensor fusion positioning method based on probability statistics according to any one of claims 1 to 4, characterized in that: The target position is estimated using the maximum likelihood parameters of the joint probability distribution model, including: For each time difference positioning system's statistical distribution model, find the partial derivative value of its exponential function; The distance value is expressed by the time difference between the target and the center point of the sensor; Setting the partial derivative of the exponential function of the joint probability distribution equal to zero, and using the partial derivative value and the distance value to represent the polynomial coefficients in the linear equation; Use the method of adding linear equation coefficients to find the fusion coefficient; Based on the initial fusion point, the fusion coefficient combination method is used to calculate the fusion target position.

6. The multi-sensor fusion positioning method based on probability statistics according to claim 5 is characterized in that: The partial derivative value is: The distance values ​​are: Among them, the positions of the sensor center points (called point a and point b) of the i-th time difference positioning system are (x i，a ,y i，a ) and (x i，b ,y i，b ), the azimuth angles of the target relative to the center points of the two sensors are θ i，a and θ i，a , the time difference of the signal reaching the center point of the sensor is Δt i The result of time difference positioning is (x i ,y i ), (i=1...N), N is the number of time difference positioning systems, c represents the speed of light in vacuum; The linear equation is: The polynomial coefficients are: The fusion coefficient is: Wherein, j = 1...5; N is the number of time difference positioning systems; The target location:

7. A multi-sensor fusion positioning system based on probability statistics, characterized in that: include: A data preprocessing module, used to obtain positioning result data of multiple time difference positioning systems for the same target, and reversely calculate the azimuth information of the target relative to each of the time difference positioning systems according to the positioning result data; A statistical distribution model module, used to construct a statistical distribution model of the corresponding sensor measurement error based on each of the time difference positioning systems; A joint probability distribution model module, used to construct a joint probability distribution model of sensor measurement errors according to a plurality of said statistical distribution models; The target position determination module is used to estimate the position of the target using the maximum likelihood parameters of the joint probability distribution model.

8. The multi-sensor fusion positioning system based on probability statistics according to claim 7 is characterized in that: The target location determination module is specifically used for: For each time difference positioning system's statistical distribution model, find the partial derivative value of its exponential function; The distance value is expressed by the time difference between the target and the center point of the sensor; Setting the partial derivative of the exponential function of the joint probability distribution equal to zero, and using the partial derivative value and the distance value to represent the polynomial coefficients in the linear equation; Use the method of adding linear equation coefficients to find the fusion coefficient; Based on the initial fusion point, the fusion coefficient combination method is used to calculate the fusion target position.

9. An electronic device, characterized in that: The electronic device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, the steps of the multi-sensor fusion positioning method based on probability statistics as claimed in any one of claims 1 to 6 are implemented.

10. A readable storage medium, characterized in that: The readable storage medium stores a program or instruction, and when the program or instruction is executed by the processor, the steps of the multi-sensor fusion positioning method based on probability statistics as claimed in any one of claims 1 to 6 are implemented.

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