A target localization method based on PCL-PET hybrid heterogeneous passive detection network

Abstract

The present application discloses a method for target positioning in a PCL-PET hybrid heterogeneous passive detection network, which relates to the field of passive detection positioning technology. The method comprises: obtaining multiple measurement combinations of the PCL-PET hybrid heterogeneous passive detection network, determining an association hypothesis between each measurement combination and the position of a target, and performing target positioning based on each association hypothesis to obtain a positioning result; calculating a test statistic for the association hypothesis based on the positioning result; if the test statistic is less than a test threshold, the association hypothesis is true, and the positioning result of the target is output; if the test statistic is greater than the test threshold, the association hypothesis is false, and the positioning result is excluded. The present application calculates the target positioning result corresponding to each association hypothesis, and determines whether the association hypothesis is correct based on each positioning result, thereby eliminating ghosting caused by incorrect associations between the measurement combination and the target, which is beneficial to improving the efficiency of correct association.

Description

Technical Field

[0001] The present application relates to the field of passive detection and positioning technology, and in particular to a target positioning method of a PCL-PET hybrid heterogeneous passive detection network. Background Art

[0002] Passive coherent localization (PCL) and passive emitter tracking (PET) are both passive detection systems. PCL uses existing third-party signals in space to detect targets, while PET detects targets by directly receiving the emitted signals of the radiating source. A hybrid PCL-PET heterogeneous network not only maintains radio silence but also promises higher detection accuracy.

[0003] Most target localization algorithms typically consider only a single sensor network and fail to account for heterogeneous PCL-PET networks. Related technologies typically assume that any transmitter and receiver in a PCL network can form a measurement combination. However, in more general scenarios, such as missed target detection or different frequency bands between transmitters and receivers, only specific transmitter-receiver pairs can obtain target measurements, or a receiver can only receive signals from specific transmitters.

[0004] For PCL-PET hybrid heterogeneous networks, some related technologies require that the coordinates of the PET receiving node in the hybrid heterogeneous network be consistent with those of the PCL receiving station. It also assumes that measurements can be made between any transmitting station and receiving station in the PCL network. This means that this algorithm can only be applied to special cases and is not universal.

[0005] In addition, related technologies find it difficult to distinguish whether the measurement combination for target positioning comes from the same target, and the wrong measurement combination will cause the final positioning result of the target to produce "ghosts", which will lead to wrong positioning results and seriously affect the detection performance of the network.

[0006] Therefore, there is a need for a target localization method that can be applied to PCL-PET hybrid heterogeneous passive detection networks and has both low positioning error and high association accuracy. Summary of the Invention

[0007] The present application provides a target positioning method for a PCL-PET hybrid heterogeneous passive detection network to address the defects of the above-mentioned related technologies. The technical solution is as follows:

[0008] In a first aspect, an embodiment of the present application provides a target positioning method for a PCL-PET hybrid heterogeneous passive detection network, the method comprising:

[0009] Acquire multiple measurement combinations detected by the PCL-PET hybrid heterogeneous passive detection network, determine an association hypothesis between each measurement combination and a position of a target, and perform target positioning based on each association hypothesis to obtain a positioning result of the corresponding target;

[0010] Calculating a test statistic of a corresponding association hypothesis according to the positioning result, and comparing the test statistic with a test threshold;

[0011] If the test statistic is less than the test threshold, the corresponding association hypothesis is true, a positioning result obtained based on the corresponding association hypothesis corresponds to the target, and a positioning result of the target is output;

[0012] If the test statistic is greater than the test threshold, the corresponding association hypothesis is false and the corresponding positioning result is excluded;

[0013] The PCL-PET hybrid heterogeneous passive detection network includes multiple PCL transmitting stations, multiple PCL receiving stations and multiple PET receiving nodes, one of the multiple PET receiving nodes is a PET reference node; one of the PCL transmitting station and one of the PCL receiving stations constitutes a PCL measurement, one of the PET receiving node and the PET reference node constitutes a PET measurement, and each of the measurement combinations can be composed of any number of PCL measurements and any number of PET measurements.

[0014] In an optional solution of the first aspect, determining an association hypothesis between each measurement combination and a position of a target, and performing target positioning based on each association hypothesis to obtain a positioning result of the corresponding target includes:

[0015] Obtaining an initial position value of the target;

[0016] The positioning result of the target is calculated using the initial position value as the initial value of the iteration, and the formula is applied:

[0017]

[0018] Obtaining a maximum likelihood solution for each measurement combination through iterative calculation, and outputting a positioning result of the target under the corresponding measurement combination according to the maximum likelihood solution;

[0019] Where Q is the error covariance matrix, u is the target position, and g(·) is the measurement function.

[0020] In an optional solution of the first aspect, determining an association hypothesis between each measurement combination and a position of a target, and performing target positioning based on each association hypothesis to obtain a positioning result of the corresponding target includes:

[0021] Obtaining an initial position value of the target according to calculation based on the SDP algorithm;

[0022] Calculating a position error of the initial position value according to the initial position value;

[0023] The initial position value is corrected by the position error, and the positioning result of the target is output.

[0024] In an optional solution of the first aspect, the initial position value of the target is calculated based on the distance between the target and the PCL transmitting station and the distance between the target and the PET receiving node, and the formula is applied:

[0025]

[0026]

[0027]

[0028] The linear equation for the position estimation error is obtained:

[0029]

[0030]

[0031] The PCL-PET hybrid heterogeneous passive detection network PCL +N PET The linear equation is written in matrix form:

[0032]

[0033]

[0034]

[0035]

[0036] The weighted least squares solution is calculated to obtain the position error:

[0037] Δu=(H T W -1 H) -1 H T W -1 d;

[0038] Where W = CQC T , Q = blkdiag(Q PCL , Q PET );

[0039] Output error-corrected positioning results

[0040]

[0041] in, Indicates the actual calculated position measurement value, u 0 represents the true value of the position, Δu represents the position error, r PCL，i and r PET，j Represents the true value of the measurement, δ PCL and δ PET Respectively represent the measurement error of the corresponding measurement, δ PCL and δ PET The covariance matrices are Q PCL and Q PET , For measurement combination, measurement combination By N PCL PCL measurements and N PET PET measurements, the coordinates of the i-th PCL transmitting station are t i , the coordinates of the i-th PCL receiving station are s i ; The coordinates of the jth PET receiving node are q j , the coordinates of the PET reference node are p, L PCL，i is the distance between the target and the PCL receiving station, L PET is the distance between the target and the PCL receiving node.

[0042] In an optional solution of the first aspect, a test statistic of the corresponding association hypothesis is calculated according to the positioning result, and the formula is applied:

[0043]

[0044] in, is the positioning result of the target, g(·) is the measurement function, is the measurement combination, Q is the error covariance matrix, and γ is the test statistic.

[0045] In a second aspect, an embodiment of the present application further provides a target positioning device of a PCL-PET hybrid heterogeneous passive detection network, comprising:

[0046] The first positioning module is configured to obtain multiple measurement combinations detected by the PCL-PET hybrid heterogeneous passive detection network, determine an association hypothesis between each measurement combination and the position of a target, and perform target positioning based on each association hypothesis to obtain a positioning result of the corresponding target:

[0047] a second positioning module, configured to calculate a test statistic of a corresponding association hypothesis according to the positioning result, and compare the test statistic with a test threshold;

[0048] If the test statistic is less than the test threshold, the corresponding association hypothesis is true, a positioning result obtained based on the corresponding association hypothesis corresponds to the target, and a positioning result of the target is output;

[0049] If the test statistic is greater than the test threshold, the corresponding association hypothesis is false and the corresponding positioning result is excluded;

[0050] The PCL-PET hybrid heterogeneous passive detection network includes multiple PCL transmitting stations, multiple PCL receiving stations and multiple PET receiving nodes, one of the multiple PET receiving nodes is a PET reference node; one PCL transmitting station and one PCL receiving station constitute a PCL measurement, one PET receiving node and one PET reference node constitute a PET measurement, and each measurement combination is composed of any number of PCL measurements and any number of PET measurements.

[0051] In a third aspect, an embodiment of the present application further provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method provided in the first aspect of the embodiment of the present application or any one of the implementations of the first aspect is implemented.

[0052] In a fourth aspect, the present application also provides a non-transitory computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, it implements the method provided by the first aspect of the embodiment of the present application or any one of the implementation methods of the first aspect.

[0053] The present application provides a target positioning method for a PCL-PET hybrid heterogeneous passive detection network. By calculating the target positioning result corresponding to each association hypothesis and determining the degree of association with the target based on each positioning result, ghosting caused by incorrect associations between the measurement combination and the target can be eliminated, thereby improving the efficiency of correct association. In addition, the present application adopts a "position first, then judge" approach, with its input data being the association hypothesis constructed from the measurement combination in the PCL-PET hybrid heterogeneous passive detection network. The positioning accuracy approaches the theoretical upper limit, and the measured association accuracy is highly consistent with the preset value, combining the advantages of low positioning error and high association accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the technical solutions in this application or related technologies, the following briefly introduces the drawings required for use in the embodiments or related technical descriptions. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0055] Figure 1 1 is a flow chart of a target localization method of a PCL-PET hybrid heterogeneous passive detection network according to an embodiment of the present application;

[0056] Figure 2 1 is a flow chart of a target localization method of a PCL-PET hybrid heterogeneous passive detection network according to an embodiment of the present application;

[0057] Figure 3 Schematic diagram of a PCL-PET hybrid heterogeneous passive detection network according to an embodiment of the present application;

[0058] Figure 4 Schematic diagram of simulation results of a target localization method of a PCL-PET hybrid heterogeneous passive detection network according to an embodiment of the present application;

[0059] Figure 5 Schematic diagram of simulation results of a target localization method of a PCL-PET hybrid heterogeneous passive detection network according to an embodiment of the present application;

[0060] Figure 6 This is a schematic structural diagram of a target positioning device of a PCL-PET hybrid heterogeneous passive detection network according to an embodiment of the present application;

[0061] Figure 7 It is a structural diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0062] To make the objectives, technical solutions, and advantages of this application more clear, the technical solutions of this application will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only part of the embodiments of this application, not all of them. Based on the embodiments of this application, all other embodiments obtained by ordinary technicians in this field without making any creative efforts are within the scope of protection of this application.

[0063] The terms "including" and "having," and any variations thereof, in the specification and claims of this application and the accompanying drawings are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus comprising a series of steps or modules is not limited to the listed steps or modules, but may optionally include steps or modules not listed, or may optionally include other steps or modules inherent to the process, method, product, or apparatus.

[0064] It should be noted that the terms "first" and "second" used in this application are merely used to distinguish similar objects and do not represent a specific ordering of the objects. It is understood that the terms "first" and "second" may interchangeably represent a specific order or precedence, where permitted. It should be understood that the objects distinguished by "first" and "second" may interchangeably represent a specific order or precedence, where appropriate, such that the embodiments of the present application described herein can be implemented in an order other than that described or illustrated herein.

[0065] It should be noted that the PCL-PET hybrid heterogeneous passive detection network includes multiple PCL transmitting stations, multiple PCL receiving stations and multiple PET receiving nodes, one of the multiple PET receiving nodes is a PET reference node; one of the PCL transmitting station and one of the PCL receiving stations constitute a PCL measurement, one of the PET receiving node and the PET reference node constitute a PET measurement, and each of the measurement combinations can be composed of any number of PCL measurements and any number of PET measurements.

[0066] It should be noted that the total number of measurements should be greater than the spatial dimension.

[0067] It should be noted that since the signals between multiple radiation source targets are different, they can be used as a basis for distinguishing the sources of PET measurements. However, the correspondence between PCL measurements and between PCL and PET measurements is unknown and requires additional judgment. Therefore, the PCL-PET hybrid heterogeneous network in related technologies faces problems in both target positioning and measurement correlation.

[0068] Among them, the measurement association problem is that it is difficult to determine whether the measurement combination used for target positioning comes from the same target. The target positioning problem is that the relevant technology is difficult to obtain stable convergence results under large initial position value errors. The target positioning problem also includes the problem of low solution convergence efficiency due to the excessive number of auxiliary variables under the premise that it is difficult to obtain the initial position value.

[0069] The present application is described in detail below with reference to specific embodiments.

[0070] Next, combine Figure 1-2 , introduces the target positioning method of PCL-PET hybrid heterogeneous passive detection network provided by the embodiment of this application. Figure 1 , Figure 1 A flow chart of a target localization method for a PCL-PET hybrid heterogeneous passive detection network provided in an embodiment of the present application is shown, and the method includes the following steps:

[0071] S101, obtaining multiple measurement combinations detected by the PCL-PET hybrid heterogeneous passive detection network, determining an association hypothesis between each measurement combination and a position of a target, and performing target positioning based on each association hypothesis to obtain a positioning result of the corresponding target;

[0072] S102, calculating a test statistic of a corresponding association hypothesis according to the positioning result, and comparing the test statistic with a test threshold;

[0073] If the test statistic is less than the test threshold, the corresponding association hypothesis is true, and then S103 is executed, including:

[0074] A positioning result obtained based on the corresponding association hypothesis corresponds to the target, and a positioning result of the target is output;

[0075] If the test statistic is greater than the test threshold, the corresponding association hypothesis is false, and then S104 is executed to exclude the corresponding positioning result.

[0076] Specifically, in S101, the association hypothesis between each measurement combination and the position of a target is determined, and the target positioning result of the corresponding target is obtained based on each of the association hypotheses. It is necessary to judge whether the initial position of the target, that is, the initial value of the target position, can be directly obtained.

[0077] In some cases, the initial position value of the target can be obtained directly. Generally, the initial position value at the time of positioning can be inferred by analyzing the position information of the target in the historical period, or the initial position value given by the target through precise single-point positioning and other methods can be combined. The embodiment of the present application does not limit the method of obtaining the initial position value of the target.

[0078] Furthermore, the positioning position of the target is iteratively calculated in combination with the known initial position value to obtain the positioning result. Specifically, the homotopy function of the target positioning problem can be constructed based on the homotopy idea, and then the target position is accurately estimated iteratively, that is, the positioning result of the target is output, including:

[0079] remember is a measurement combination in an association hypothesis, the corresponding error covariance matrix is ​​recorded as Q, the target position is recorded as u, and the measurement function is represented by g(·). For the measurement combination, under the assumption that the measurement error obeys the normal distribution, the likelihood function of the measurement combination can be expressed as:

[0080]

[0081] The corresponding log-likelihood function is:

[0082]

[0083] Among them, const represents a constant.

[0084] Specifically, you can take Therefore, under the maximum likelihood criterion, the target positioning problem can be described as:

[0085]

[0086] in, is the maximum likelihood solution for the target location;

[0087] Through the function Compute the value of the independent variable u when f(u) reaches its minimum value.

[0088] Define the first-order partial derivative of f(u) with respect to the target position u as Convert the positioning problem into solving the following function:

[0089]

[0090] in, is an ND-dimensional all-zero column vector.

[0091] Constructing the homotopy function we get:

[0092]

[0093] Discretize the value interval of g in the formula to 0=g0 <g1<…<g n =1, and solve a series of nonlinear equations by Newton's method:

[0094]

[0095] Among them, the initial value of each iteration is the result of the previous iteration The iteration formula is:

[0096]

[0097] in,

[0098] Through the above formula, the final iterative result is the maximum likelihood solution after convergence, that is, the positioning result with the maximum probability that the target is located at the corresponding coordinates, and the positioning result of the target under the corresponding association hypothesis is output.

[0099] It can be understood that the above iterative process can set the maximum number of iterations as the convergence termination condition, which is not limited in the embodiments of the present application.

[0100] Specifically, when the initial value of the target position is known or can be obtained, the difficulty of target positioning lies in how to obtain a stable convergence result under a large initial value error. Although the Newton-Raphson method has a fast convergence speed, it has high requirements for the accuracy of the initial value. For this reason, this application proposes a new method based on the homotopy idea, denoted as the Newton-Homotopy positioning method, which specifically converts the positioning problem into a simple problem with a known solution, which can significantly improve the probability of convergence of the solution under large initial value errors.

[0101] In some cases, it is difficult to directly obtain the initial position value of the target. The positioning problem can be described as a constrained optimization problem and converted into a convex optimization problem through semidefinite relaxation technology. That is, the initial position value of the target is calculated through the semidefinite programming (SDP) method, and then the positioning result of the target is calculated based on the initial position value, including:

[0102] Measurement combination By N PCL PCL measurements and N PET PET measurements consist of:

[0103]

[0104]

[0105]

[0106] The coordinates of the i-th PCL transmitting station are t i , the coordinates of the i-th PCL receiving station are s i ; The coordinates of the jth PET receiving node are q j , the coordinate of the PET reference node is p.

[0107] Take L PCL，i =||u 0 -s i ||,L PET =||u 0 -p||, where u 0 Indicates the true value of the target position, L PCL，i is the distance between the target and the PCL receiving station, L PET is the distance between the target and the PCL receiving node. After a simple mathematical transformation, the measurement equation can be converted into a pseudo-linear equation system:

[0108] h-Gθ 0 =0;

[0109]

[0110]

[0111]

[0112] r PCL，i and r PET，j Indicates the true value of the measurement,

[0113] Among them, δ PCL and δ PET Respectively represent the measurement error of the corresponding measurement, δ PCL and δ PET The covariance matrices are Q PCL and Q PET .

[0114] Replace h-Gθ with actual measurement with error 0 =0, we get:

[0115]

[0116] Where ε is the error term,

[0117]

[0118]

[0119] According to the stochastic least squares criterion, the The positioning problem is described as a constrained optimization problem:

[0120]

[0121] sL PCL，i =||s i -u||i=1,2,...,N PCL ;

[0122] L PET =||qu||;

[0123] In order to transform the optimization problem into a convex optimization problem, the semi-positive definite relaxation technique is used here, and the resulting convex optimization problem is:

[0124]

[0125]

[0126]

[0127]

[0128] Where Θ = θθ T , A is G T G+C G The square root of T ) -1 (G T h+η Gh );

[0129]

[0130]

[0131] Specifically, the target position estimation can be obtained by solving the above convex optimization problem The target position estimate As the initial position value for calculation.

[0132] Furthermore, the target positioning accuracy is further improved by considering the error distribution of the position measurement value, and the positioning result is expressed as the form of the position true value superimposed error, and the auxiliary variable L PCL，i With L PET exist Perform a first-order Taylor expansion at and ignore higher-order terms. Indicates the actual calculated position measurement value, u 0 Represents the true position value, Δu represents the position error, and the application formula is:

[0133]

[0134]

[0135]

[0136] Substitute the above three equations into h-Gθ 0 = 0, we get the linear equation for the position estimation error:

[0137]

[0138]

[0139] The N in the hybrid network PCL +N PET The linear equation is written in matrix form:

[0140]

[0141]

[0142]

[0143]

[0144] The weighted least squares solution is calculated as Δu=(H T W -1 H) -1 H T W -1 d, where W = CQC T , Q = blkdiag(Q PCL , Q PET );

[0145] Therefore, the final target positioning result after error correction is:

[0146]

[0147] Output error-corrected positioning results

[0148] Specifically, when the initial value of the target position is unknown, related technologies face the problem of too many auxiliary variables, which may lead to an underdetermined pseudo-linear system of equations. To this end, this application formulates the positioning problem as a constrained optimization problem and converts it into a convex optimization problem using a semidefinite relaxation technique, effectively improving computational efficiency.

[0149] In some embodiments, after calculating the positioning result corresponding to each association hypothesis, it is necessary to judge each association hypothesis and then determine the correspondence between the measurement combination in each association hypothesis and the target, that is, to determine whether the position coordinates measured by the measurement combination involved in the association hypothesis are the position coordinates of the corresponding target.

[0150] Specifically, based on the positioning result calculated in S101, the test statistic of the corresponding association hypothesis is further calculated using the formula:

[0151]

[0152] in, The positioning result of the target can be verified. The test statistic approximately obeys the chi-square distribution. Different degrees of freedom can be taken for 2D scenes and 3D scenes. For example, the degrees of freedom can be taken as N in the 2D scene. PCL +N PET -2, in 3D scenes, its degree of freedom is N PCL +N PET -3.

[0153] The test statistic is compared with the test threshold and the following association hypothesis decision criteria are applied:

[0154]

[0155] Among them, γ is the test threshold, and the value of the test threshold is determined by the required association probability P A The degrees of freedom are determined jointly with the chi-square distribution.

[0156] Specifically, if the test statistic is less than the test threshold, the corresponding association hypothesis is true, and step S103 is executed. The positioning result obtained based on the corresponding association hypothesis corresponds to the target, and the positioning result of the target is output.

[0157] Otherwise, executing step S104, the corresponding association hypothesis is false, and the corresponding positioning result is excluded;

[0158] It should be noted that for an association hypothesis that is determined to be true, it can be considered that in the measurement combination corresponding to the association hypothesis, the target measured by the PCL measurement and / or PET measurement is the target to be located, and the positioning result obtained by the measurement is the positioning position of the target to be located, thereby eliminating erroneous association hypotheses, improving the probability of association between the measurement and the target, and avoiding the defects of low accuracy and poor robustness of correct association between the measurement and the target in related technologies.

[0159] It is understandable that S101 does not limit the specific positioning method performed by S101. The results obtained by both positioning methods can be used as input, and this embodiment of the present application does not limit this.

[0160] In a specific embodiment, the effect of the present application can be verified through the following simulation experiments.

[0161] The simulation considers a two-dimensional scene. Figure 3 The following diagram shows a scenario for an embodiment. The PCL-PET hybrid heterogeneous passive detection network consists of two PCL transmitting stations, a receiving station, a PET receiving node, and a PET reference node. PCL receiving station 1 can only receive signals from transmitting station 1, and receiving station 2 can only receive signals from transmitting station 2. Therefore, if no missed detections are detected, two bistatic distance measurements and one TDOA measurement will be generated. The specific station coordinates and simulated target coordinates are shown in Table 1.

[0162] Table 1 Station deployment and target coordinates of the simulation example

[0163]

[0164] The number of simulations in the simulation is set to 5000, the measurement standard deviation of the bistatic distance is set to σ, and the measurement standard deviation of TDOA is set to 1 / 4σ. After simulation, the positioning error changes with σ as shown in the following figure: Figure 4 As shown in the figure, "SDP" is the result obtained by solving the convex optimization problem using the SDP method, and "SDP+Improvement" is the final output of the SDP method, which is the result of bias correction based on "SDP". The "Initial Value" in the figure is obtained through iteration based on the initial position value.

[0165] It can be observed that both the disclosed SDP and Newton-Homotopy positioning methods have positioning accuracy close to the Cramér-Rao Lower Bound (CRLB). When σ = 95m, the difference between the two methods and the CRLB is only 1m.

[0166] The performance of the association hypothesis judgment in S102 is further verified by simulation. The station deployment is kept unchanged, the number of targets is increased to 3, and the specific target coordinate information is shown in Table 2. The number of simulations is 100,000. A =0.99. According to the chi-square distribution with a degree of freedom of 1, the decision threshold λ is ≈ 6.635.

[0167] When the test statistic is less than 6.635, the corresponding measurement interconnection hypothesis is accepted, otherwise the interconnection hypothesis is rejected. Let the number of times the correct association is accepted be N A , the number of Monte Carlo simulations is N MC , then the actual correct association probability can be obtained by calculate.

[0168] Figure 5 The variation of the measured correct association probability of the three targets with σ is shown.

[0169] Table 2 Target echo parameters simulated in the embodiment

[0170]

[0171] As can be seen from the figure, when σ<1000m, the correct association probability of the three targets is close to the preset value P A =0.99, which is basically consistent. This shows that the disclosed measurement association decision method can not only effectively decide the measurement interconnection hypothesis, but also provide a reference for the selection of the decision threshold.

[0172] Therefore, the target positioning and measurement association method proposed in this application has strong robustness and excellent accuracy.

[0173] The following are device embodiments of the present application, which can be used to implement the method embodiments of the present application. For details not disclosed in the device embodiments of the present application, please refer to the method embodiments of the present application.

[0174] See next Figure 6 , a schematic diagram of the structure of a target positioning device for a PCL-PET hybrid heterogeneous passive detection network provided in an exemplary embodiment of the present application. This device can be implemented as all or part of a terminal through software, hardware, or a combination of both, or can be integrated into a server as an independent module. The target positioning device 60 for a PCL-PET hybrid heterogeneous passive detection network in this embodiment of the present application includes a first positioning module 610 and a second positioning module 620, wherein:

[0175] The first positioning module 610 is configured to obtain a plurality of measurement combinations detected by the PCL-PET hybrid heterogeneous passive detection network, determine an association hypothesis between each measurement combination and a position of a target, and perform target positioning based on each association hypothesis to obtain a positioning result of the corresponding target;

[0176] The second positioning module 620 is used to calculate a test statistic of the corresponding association hypothesis according to the positioning result, and compare the test statistic with a test threshold;

[0177] If the test statistic is less than the test threshold, the corresponding association hypothesis is true, a positioning result obtained based on the corresponding association hypothesis corresponds to the target, and a positioning result of the target is output;

[0178] If the test statistic is greater than the test threshold, the corresponding association hypothesis is false and the corresponding positioning result is excluded.

[0179] It should be noted that the device 60 provided in the above embodiment, when executing the target localization method for a PCL-PET hybrid heterogeneous passive detection network, only uses the division of the aforementioned functional modules as an example. In actual applications, the aforementioned functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the device provided in the above embodiment and the target localization method embodiment for a PCL-PET hybrid heterogeneous passive detection network are based on the same concept. The implementation process is detailed in the method embodiment and will not be repeated here.

[0180] An embodiment of the present application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps of the method of any of the above embodiments are implemented.

[0181] See Figure 7, is a structural block diagram of an electronic device provided in an embodiment of the present application.

[0182] like Figure 7 As shown, the electronic device 700 includes a processor 701 and a memory 702 .

[0183] In the embodiment of the present application, the processor 701 is the control center of the computer system and can be the processor of a physical machine or the processor of a virtual machine. The processor 701 may include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor 701 can be implemented in the form of at least one hardware selected from the group consisting of a DSP (Digital Signal Processing), an FPGA (Field-Programmable Gate Array), and a PLA (Programmable Logic Array).

[0184] The processor 701 may also include a main processor and a coprocessor. The main processor is a processor for processing data in the awake state, also known as a CPU (Central Processing Unit); the coprocessor is a low-power processor for processing data in the standby state.

[0185] The memory 702 may include one or more computer-readable storage media, which may be non-transitory. The memory 702 may also include high-speed random access memory and non-volatile memory, such as one or more disk storage devices and flash memory storage devices. In some embodiments of the present application, the non-transitory computer-readable storage medium in the memory 702 is used to store at least one instruction, which is used to be executed by the processor 701 to implement the method in the embodiment of the present application.

[0186] In some embodiments, the electronic device 700 further includes: a peripheral device interface 703 and at least one peripheral device 704. The processor 701, the memory 702, and the peripheral device interface 703 can be connected via a bus or signal lines. Each peripheral device 704 can be connected to the peripheral device interface 703 via a bus, signal lines, or a circuit board. Specifically, the peripheral devices 704 include: a display screen, a camera, and an audio circuit. The peripheral device interface 703 can be used to connect at least one I / O (Input / Output)-related peripheral device to the processor 701 and the memory 702.

[0187] In some embodiments of the present application, the processor 701, the memory 702, and the peripheral device interface 703 are integrated on the same chip or circuit board; in some other embodiments of the present application, any one or two of the processor 701, the memory 702, and the peripheral device interface 703 may be implemented on separate chips or circuit boards. This embodiment of the present application is not specifically limited to this.

[0188] The electronic device structure block diagram shown in the embodiment of the present application does not constitute a limitation on the electronic device 700. The electronic device 700 may include more or fewer components than shown in the figure, or combine certain components, or adopt a different component arrangement.

[0189] The present application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method of any of the aforementioned embodiments. The computer-readable storage medium may include, but is not limited to, any type of disk, including a floppy disk, an optical disk, a DVD, a CD-ROM, a microdrive, a magneto-optical disk, a ROM, a RAM, an EPROM, an EEPROM, a DRAM, a VRAM, a flash memory device, a magnetic card or an optical card, a nanosystem (including a molecular memory IC), or any type of medium or device suitable for storing instructions and / or data.

[0190] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, or of course, by hardware. Based on this understanding, the above technical solution, in essence, or the part that contributes to the relevant technology, can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or certain parts of the embodiments.

[0191] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A target positioning method for a PCL-PET hybrid heterogeneous passive detection network, characterized in that: include: Acquire multiple measurement combinations detected by the PCL-PET hybrid heterogeneous passive detection network, determine an association hypothesis between each measurement combination and a position of a target, and perform target positioning based on each association hypothesis to obtain a positioning result of the corresponding target; Calculating a test statistic of a corresponding association hypothesis according to the positioning result, and comparing the test statistic with a test threshold; If the test statistic is less than the test threshold, the corresponding association hypothesis is true, a positioning result obtained based on the corresponding association hypothesis corresponds to the target, and a positioning result of the target is output; If the test statistic is greater than the test threshold, the corresponding association hypothesis is false and the corresponding positioning result is excluded; The PCL-PET hybrid heterogeneous passive detection network includes multiple PCL transmitting stations, multiple PCL receiving stations and multiple PET receiving nodes, one of the multiple PET receiving nodes is a PET reference node; one PCL transmitting station and one PCL receiving station constitute a PCL measurement, one PET receiving node and one PET reference node constitute a PET measurement, and each measurement combination is composed of any number of PCL measurements and any number of PET measurements.

2. The target positioning method of a PCL-PET hybrid heterogeneous passive detection network according to claim 1 is characterized in that: The determining of an association hypothesis between each measurement combination and a position of a target, and performing target positioning based on each association hypothesis to obtain a positioning result of the corresponding target, includes: Obtaining an initial position value of the target; The positioning result of the target is calculated using the initial position value as the initial value of the iteration, and the formula is applied: Obtaining a maximum likelihood solution for each measurement combination through iterative calculation, and outputting a positioning result of the target under the corresponding measurement combination according to the maximum likelihood solution; Where Q is the error covariance matrix, u is the target position, and g(·) is the measurement function.

3. The target positioning method of a PCL-PET hybrid heterogeneous passive detection network according to claim 1 is characterized in that: The determining of an association hypothesis between each measurement combination and a position of a target, and performing target positioning based on each association hypothesis to obtain a positioning result of the corresponding target, includes: Calculate the initial position value of the target based on the SDP algorithm; Calculating a position error of the initial position value according to the initial position value; The initial position value is corrected by the position error, and the positioning result of the target is output.

4. The target positioning method of a PCL-PET hybrid heterogeneous passive detection network according to claim 3 is characterized in that: The initial position value of the target is calculated based on the distance between the target and the PCL transmitting station and the distance between the target and the PET receiving node, using the formula: The linear equation for the position estimation error is obtained: The PCL-PET hybrid heterogeneous passive detection network PCL +N PET The linear equation is written in matrix form: The weighted least squares solution is calculated to obtain the position error: Δu=(H T IN -1 H) -1 H T IN -1 d; Where W = CQC T , Q = blkdiag(Q PCL ,Q PET ); Output error-corrected positioning results in, Indicates the actual calculated position measurement value, u 0 represents the true value of the position, Δu represents the position error, r PCL,i and r PET,j Represents the true value of the measurement, δ PCL and δ PET Respectively represent the measurement error of the corresponding measurement, δ PCL and δ PET The covariance matrices are Q PCL and Q PET , For the measurement combination, Measurement combination By N PCL PCL measurements and N PET PET measurements, the coordinates of the i-th PCL transmitting station are t i , the coordinates of the i-th PCL receiving station are s i ,remember The coordinates of the jth PET receiving node are q j , the coordinates of the PET reference node are p, L PCL,i is the distance between the target and the PCL receiving station, L PET is the distance between the target and the PCL receiving node.

5. The target positioning method of a PCL-PET hybrid heterogeneous passive detection network according to claim 1 is characterized in that: Calculate the test statistic of the corresponding association hypothesis based on the positioning results, and apply the formula: in, is the positioning result of the target, g(·) is the measurement function, is the measurement combination, Q is the error covariance matrix, and Υ is the test statistic.

6. A target positioning device for a PCL-PET hybrid heterogeneous passive detection network, characterized in that: include: a first positioning module, configured to obtain a plurality of measurement combinations detected by the PCL-PET hybrid heterogeneous passive detection network, determine an association hypothesis between each measurement combination and a position of a target, and perform target positioning based on each association hypothesis to obtain a positioning result of the corresponding target; a second positioning module, configured to calculate a test statistic of a corresponding association hypothesis according to the positioning result, and compare the test statistic with a test threshold; If the test statistic is less than the test threshold, the corresponding association hypothesis is true, a positioning result obtained based on the corresponding association hypothesis corresponds to the target, and a positioning result of the target is output; If the test statistic is greater than the test threshold, the corresponding association hypothesis is false and the corresponding positioning result is excluded; The PCL-PET hybrid heterogeneous passive detection network includes multiple PCL transmitting stations, multiple PCL receiving stations and multiple PET receiving nodes, one of the multiple PET receiving nodes is a PET reference node; one PCL transmitting station and one PCL receiving station constitute a PCL measurement, one PET receiving node and one PET reference node constitute a PET measurement, and each measurement combination is composed of any number of PCL measurements and any number of PET measurements.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the steps of the method according to any one of claims 1 to 5 are implemented.

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

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