Multi-target association and fusion positioning method and device

Abstract

The invention provides a multi-target association and fusion positioning method and equipment. The method comprises the following steps: acquiring a receiving measurement parameter, a transmitting station site coordinate and a receiving station site coordinate of each receiving station radar; carrying out combination processing on the received measurement parameters to obtain a combined measurement vector, and carrying out combination processing on the site coordinates of the transmitting station and the site coordinates of the receiving station to obtain a site vector; based on the combined measurement vector and the site vector, an improved least square method is adopted to solve the target position, and a target least square solution corresponding to the target position is obtained; based on the target least square solution and the station site vector, calculating homologous hypothesis measurement of each receiving station radar, and generating a likelihood function penalty term by using the homologous hypothesis measurement; constructing a multi-dimensional distribution optimization model through a likelihood function penalty term, and solving the multi-dimensional distribution optimization model by adopting a Lagrange relaxation algorithm to obtain an association result; and determining the real position of the target based on the association result.

Description

Technical Field

[0001] The present invention relates to the field of radar data processing technology, and in particular to a multi-target association and fusion positioning method and device. Background Art

[0002] With the continuous development of radar technology, multi-base radar systems have been widely used in defense and civilian fields. Compared with traditional single-base radar systems, multi-base radar systems can effectively improve the ability of target detection and identification through the collaborative work of multiple transmitting and receiving nodes. In complex three-dimensional scenes, the positioning and association of multiple targets has become a key challenge to achieve accurate target tracking and situational awareness.

[0003] The distribution of targets in three-dimensional scenes is complex, and the sensor's measurement data and sensor position coordinate data often have large errors, which makes it difficult to ensure the accuracy of target association and positioning accuracy. Secondly, since the base radar system involves the collaborative work of multiple transmitters and receivers, the amount of data transmission and processing is large, how to perform efficient calculations while ensuring real-time performance is also an important issue. However, existing technologies rarely combine target association and positioning problems in the radar field, which makes it particularly important to solve these two problems at the same time in complex environments. Summary of the invention

[0004] In order to solve the above problems existing in the prior art, the present invention provides a multi-target association and fusion positioning method and device.

[0005] The technical problem to be solved by the present invention is achieved through the following technical solutions:

[0006] In a first aspect, the present invention provides a multi-target association and fusion positioning method, which is applied to a three-dimensional scene of a multi-receiver radar collaboration, comprising:

[0007] Obtain the receiving measurement parameters of each receiving station radar, the transmitting station site coordinates and the receiving station site coordinates;

[0008] Combining and processing the received measurement parameters to obtain a combined measurement vector, and combining and processing the transmitting station site coordinates and the receiving station site coordinates to obtain a site vector;

[0009] Based on the combined measurement vector and the station vector, the improved least square method is used to solve the target position and obtain the target least square solution corresponding to the target position; the improved least square method is executed based on the approximate linear equation constructed by trigonometric function transformation, Taylor expansion processing and ignoring quadratic noise processing;

[0010] Based on the target least squares solution and the station vector, the homology hypothesis measurement of each receiving station radar is calculated, and the homology hypothesis measurement is used to generate the likelihood function penalty term;

[0011] A multi-dimensional allocation optimization model is constructed through the likelihood function penalty term, and the Lagrangian relaxation algorithm is used to solve the multi-dimensional allocation optimization model to obtain the correlation results;

[0012] The true position of the target is determined based on the association results.

[0013] Optionally, the receiving measurement parameters include: delay difference τ, pitch angle θ and azimuth angle The delay difference is the time delay between the direct wave and the reflected wave.

[0014] Optionally, combining and processing the received measurement parameters to obtain a combined measurement vector, and combining and processing the transmitting station site coordinates and the receiving station site coordinates to obtain a site vector, including:

[0015] The received measurement parameters are fused to obtain an initial measurement combination; the initial measurement combination is expressed as:

[0016]

[0017] in, represents the initial measurement combination, represents the i-th radar of the s-th receiving station s receive measurement parameters, s=1,...,N,i s =1,...,n s , n s represents the total number of received measurement parameters of the radar at the sth receiving station, represents the i-th radar of the s-th receiving station s The delay difference under the receiving measurement parameters is: represents the i-th radar of the s-th receiving station s The azimuth of the reflected wave under the receiving measurement parameters, represents the i-th radar of the s-th receiving station s The elevation angle of the reflected wave under the receiving measurement parameters, [·] T Represents the transpose of a matrix;

[0018] The initial measurement combination is processed by data reorganization to obtain a combined measurement vector; the combined measurement vector is expressed as:

[0019]

[0020] represents the combined measurement vector, τ1,...,τ N Indicated in Under the assumption of homology, the delay differences corresponding to the radars of the 1st to Nth receiving stations are θ1,...,θ N Indicated in Under the homology assumption, the elevation angles corresponding to the radars of the 1st to Nth receiving stations are: Indicated in The azimuths corresponding to the radars of the 1st to Nth receiving stations under the assumption of homology;

[0021] The site coordinates of the transmitting station and the receiving station are combined and processed by a preset method to obtain the site vector; the site vector is expressed as:

[0022]

[0023] Among them, p represents the site vector, t represents the site coordinates of the transmitting station, r1,...,r N Indicates the receiving station site coordinates corresponding to the 1st to Nth receiving station radars.

[0024] Optionally, based on the combined measurement vector and the site vector, an improved least square method is used to solve the target position, and a target least square solution corresponding to the target position is obtained, including:

[0025] Based on the first-order Taylor expansion and neglecting the quadratic noise term, the time delay difference in the combined measurement vector is linearly transformed to obtain the first approximate linear equation;

[0026] Based on trigonometric function transformation, first-order Taylor expansion and neglect of quadratic noise term processing, the azimuth and elevation angles in the combined measurement vector are linearly transformed to obtain the second approximate linear equation;

[0027] The first approximate linear equation and the second approximate linear equation are combined to obtain the total linear equation;

[0028] The target preliminary least squares solution is obtained by calculating the total linear equation;

[0029] The third approximate linear equation is obtained based on the first-order Taylor expansion, the neglect of the quadratic noise term processing and the relationship between the elements in the target preliminary least squares solution;

[0030] The target least squares solution is obtained by calculating the third approximate linear equation.

[0031] Alternatively, the first approximate linear equation is expressed as:

[0032] B τ n τ =h τ -G τ α o ;

[0033] Among them, B τ represents the correlation matrix corresponding to the real position of the target and the coordinates of the receiving station, n τrepresents the error vector associated with the site error vector Δp ​​and the delay error vector Δτ, h τ G represents the vector related to the delay error vector, the transmitting station site coordinates and the receiving station site coordinates. τ represents the matrix related to the delay error vector, the transmitting station site coordinates and the receiving station site coordinates, α o Represents the first parameter;

[0034] α o =[u oT ,||u o -t|| 2 ] T ;

[0035] u o represents the real position of the target, t represents the coordinates of the transmitting station,

[0036] The second approximate linear equation is expressed as:

[0037]

[0038] in, Represents the pitch angle error vector Δθ, azimuth angle error vector and the error vector associated with the site error vector Δp, Represents the vector related to the site coordinates, elevation angle and azimuth angle of the transmitting station, Represents a matrix related to the site coordinates, elevation angle, and azimuth angle of the transmitting station.

[0039] Alternatively, the total linear equation is expressed as:

[0040] n=h1-G1α o ;

[0041] n represents the total error vector, h1 represents the second parameter, G1 represents the third parameter,

[0042]

[0043] Among them, 0 N represents an N×1-dimensional all-zero column vector.

[0044] Optionally, the target least squares solution is expressed as:

[0045]

[0046] in, represents the target least squares solution, W2 represents the weighting matrix; G2 represents the fourth parameter, h2 represents the fifth parameter, (·) -1 Indicates taking the inverse of a matrix;

[0047]

[0048]

[0049] Indicates taking The first three items of represents the target preliminary least squares solution, Indicates taking The fourth term, I3, represents the identity matrix of dimension 3×3.

[0050] Optionally, the likelihood function penalty term is expressed as:

[0051]

[0052] represents the likelihood function penalty term, represents the detection probability of the radar at the sth receiving station, represents the volume of the observation airspace of the sth receiving station radar, represents the error covariance matrix of the received measurement parameters corresponding to the radar at the sth receiving station, In different initial measurement combinations The following corresponds to the homology hypothesis measurement of the receiving station radar.

[0053] Optionally, the multi-dimensional allocation optimization model is expressed as:

[0054]

[0055] in, Indicates the result of association. represents the likelihood function penalty term, n N represents the total number of received measurement parameters of the radar at the Nth receiving station, where It is a binary variable with the value of 0 or 1.

[0056] In a second aspect, the present invention provides a multi-target association and fusion positioning device, comprising: a processor, a storage medium and a bus, the storage medium storing machine-readable instructions executable by the processor, when the multi-target association and fusion positioning device is running, the processor and the storage medium communicate through the bus, and the processor executes the machine-readable instructions to perform the steps of the multi-target association and fusion positioning method as described in the first aspect above.

[0057] The present invention provides a multi-target association and fusion positioning method and device. Among them, a multi-target association and fusion positioning method is applied to a three-dimensional scene of a one-transmitter-multiple-receiver radar collaboration, including: obtaining the receiving measurement parameters of each receiving station radar, the transmitting station site coordinates and the receiving station site coordinates; combining the receiving measurement parameters to obtain a combined measurement vector, combining the transmitting station site coordinates and the receiving station site coordinates to obtain a site vector; based on the combined measurement vector and the site vector, using an improved least squares method to solve the target position, and obtaining a target least squares solution corresponding to the target position; the improved least squares method is executed based on an approximate linear equation constructed by trigonometric function transformation, Taylor expansion processing and ignoring secondary noise processing; based on the target least squares solution and the site vector, calculating the homologous hypothesis measurement of each receiving station radar, and using the homologous hypothesis measurement to generate a likelihood function penalty term; constructing a multidimensional allocation optimization model through the likelihood function penalty term, and using the Lagrangian relaxation algorithm to solve the multidimensional allocation optimization model to obtain an association result; determining the true position of the target based on the association result. In an embodiment of the present invention, an improved least squares method is used to solve the target position based on the combined measurement vector and the site vector, and a closed-form positioning solution - the target least squares solution is obtained, which improves the positioning accuracy and positioning speed of subsequent targets; in addition, by constructing a multi-dimensional allocation optimization model and using the Lagrangian relaxation algorithm to solve the optimization problem, the fusion calculation process is less computationally intensive, which improves the calculation speed of the true position of the target.

[0058] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 A flowchart of a multi-target association and fusion positioning method provided by an embodiment of the present invention;

[0060] Figure 2 A schematic diagram of a three-dimensional scene in a one-transmit-three-receive mode is schematically shown;

[0061] Figure 3 A schematic diagram of a positioning result obtained according to the method of the present invention when there is a single target in a one-transmit-three-receive mode is shown schematically;

[0062] Figure 4 A diagram schematically shows the relationship between target spacing and target association success probability;

[0063] Figure 5 The relationship between different distance difference measurement errors (obtained from the delay difference) and the probability of successful target association is schematically shown;

[0064] Figure 6 The relationship between different azimuth measurement errors and the probability of successful target association is schematically shown;

[0065] Figure 7 A schematic diagram of the structure of a multi-target association and fusion positioning device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0066] The present invention is further described in detail below with reference to specific embodiments, but the embodiments of the present invention are not limited thereto.

[0067] In order to improve the calculation speed and accuracy of the real position of a target, an embodiment of the present invention provides a multi-target association and fusion positioning method. Figure 1 The following is a flow chart of a multi-target association and fusion positioning method provided by an embodiment of the present invention. Figure 1 As shown, including:

[0068] S101, obtaining receiving measurement parameters of each receiving station radar, the transmitting station site coordinates and the receiving station site coordinates.

[0069] Optionally, the receiving measurement parameters include: delay difference τ, pitch angle θ and azimuth angle The delay difference is the time delay between the direct wave and the reflected wave.

[0070] S102, combining and processing the received measurement parameters to obtain a combined measurement vector, and combining and processing the transmitting station site coordinates and the receiving station site coordinates to obtain a site vector.

[0071] Optionally, S102 may specifically include:

[0072] The received measurement parameters are fused to obtain an initial measurement combination; the initial measurement combination is expressed as:

[0073]

[0074] in, represents the initial measurement combination, z sis represents the i-th radar of the s-th receiving station s receive measurement parameters, s=1,...,N,i s =1,...,n s , n s represents the total number of received measurement parameters of the radar at the sth receiving station, represents the i-th radar of the s-th receiving station s The delay difference under the receiving measurement parameters is represents the i-th radar of the s-th receiving station s The azimuth of the reflected wave under the receiving measurement parameters, represents the i-th radar of the s-th receiving station sThe elevation angle of the reflected wave under the receiving measurement parameters, [·] T Represents the transpose of a matrix;

[0075] The initial measurement combination is processed by data reorganization to obtain a combined measurement vector; the combined measurement vector is expressed as:

[0076]

[0077] represents the combined measurement vector, τ1,...,τ N Indicated in Under the assumption of homology, the delay differences corresponding to the radars of the 1st to Nth receiving stations are θ1,...,θ N Indicated in Under the homology assumption, the elevation angles corresponding to the radars of the 1st to Nth receiving stations are: Indicated in The azimuths corresponding to the radars of the 1st to Nth receiving stations under the assumption of homology;

[0078] The site coordinates of the transmitting station and the receiving station are combined and processed by a preset method to obtain the site vector; the site vector is expressed as:

[0079]

[0080] Among them, p represents the site vector, t represents the site coordinates of the transmitting station, r1,...,r N Indicates the receiving station site coordinates corresponding to the 1st to Nth receiving station radars.

[0081] S103. Based on the combined measurement vector and the site vector, an improved least squares method is used to solve the target position, and a target least squares solution corresponding to the target position is obtained.

[0082] The improved least square method is implemented based on the approximate linear equation constructed by trigonometric function transformation, Taylor expansion processing and ignoring quadratic noise processing.

[0083] Optionally, S103 may specifically include:

[0084] Based on the first-order Taylor expansion and neglecting the quadratic noise term, the time delay difference in the combined measurement vector is linearly transformed to obtain the first approximate linear equation;

[0085] Based on trigonometric function transformation, first-order Taylor expansion and neglect of quadratic noise term processing, the azimuth and elevation angles in the combined measurement vector are linearly transformed to obtain the second approximate linear equation;

[0086] The first approximate linear equation and the second approximate linear equation are combined to obtain the total linear equation;

[0087] The target preliminary least squares solution is obtained by calculating the total linear equation;

[0088] The third approximate linear equation is obtained based on the first-order Taylor expansion, the neglect of the quadratic noise term processing and the relationship between the elements in the target preliminary least squares solution;

[0089] The target least squares solution is obtained by calculating the third approximate linear equation.

[0090] Alternatively, the first approximate linear equation is expressed as:

[0091] B τ n τ =h τ -G τ α o ;

[0092] Among them, B τ represents the correlation matrix corresponding to the real position of the target and the coordinates of the receiving station, n τ represents the error vector associated with the site error vector Δp ​​and the delay error vector Δτ, h τ G represents the vector related to the delay error vector, the transmitting station site coordinates and the receiving station site coordinates. τ represents the matrix related to the delay error vector, the transmitting station site coordinates and the receiving station site coordinates, α o Represents the first parameter;

[0093] α o =[u oT ,||u o -t|| 2 ] T ;

[0094] u o represents the real position of the target, t represents the coordinates of the transmitting station,

[0095] The second approximate linear equation is expressed as:

[0096]

[0097] in, Represents the pitch angle error vector Δθ, azimuth angle error vector and the error vector associated with the site error vector Δp, Represents the vector related to the site coordinates, elevation angle and azimuth angle of the transmitting station, Represents a matrix related to the site coordinates, elevation angle, and azimuth angle of the transmitting station.

[0098] It should be noted that, in the embodiment of the present invention, n τ Expressed as, n τ=cΔτ+D1Δp.

[0099]

[0100]

[0101] c is the speed of light, Indicates that from u o The normalized direction vector to t, and Similarly. In addition, It can also be expressed as: Specifically, B θ , D2, as well as They are respectively expressed as follows:

[0102]

[0103]

[0104] Alternatively, the total linear equation is expressed as:

[0105] n=h1-G1α o ;

[0106] n represents the total error vector, h1 represents the second parameter, G1 represents the third parameter,

[0107]

[0108] Among them, 0 N represents an N×1-dimensional all-zero column vector.

[0109] Furthermore, using ordinary least squares, we can get α o with u o The initial estimate of

[0110] in Indicates taking The first three items.

[0111] Will Replace u o Calculation parameters Obtain the weight matrix have

[0112]

[0113] Among them, Q τ is [τ1,...,τ N ] T The corresponding noise covariance matrix, With Qθ Similarly, Q P for The corresponding noise covariance matrix obtains the target preliminary least squares solution based on the required parameters:

[0114]

[0115] according to The relationship between the first term and the last three terms is derived through Taylor expansion and neglecting the quadratic noise term to obtain the third approximate linear equation as follows:

[0116] B2Δα=h2-G2u o ;

[0117] in,

[0118]

[0119] Finally get The least squares solution of the target position under

[0120] Optionally, the target least squares solution is expressed as:

[0121]

[0122] in, represents the target least squares solution, W2 represents the weighting matrix; G2 represents the fourth parameter, h2 represents the fifth parameter, (·) -1 Indicates taking the inverse of a matrix;

[0123]

[0124] Indicates taking The first three items of represents the target preliminary least squares solution, Indicates taking The fourth term, I3, represents the identity matrix of dimension 3×3.

[0125] Among them, W2 is composed of Calculated.

[0126] S104, based on the target least squares solution and the station location vector, calculating the homologous hypothesis measurement of each receiving station radar, and using the homologous hypothesis measurement to generate a likelihood function penalty term.

[0127] Specifically, the initial measurement combination The corresponding receiving station radar homology measurement It is expressed by the following formula:

[0128]

[0129] in, The i-th signal obtained by the radar of the s-th receiving station s A measuring moment, Indicated in The mapping from the geometric position of the target and the receiving station radar to the measurement is obtained by the following formulas:

[0130]

[0131] in Respectively The coordinates of the three dimensions in the rectangular coordinate system, r s,x ,r s,y ,r s,z They represent the coordinates of the three dimensions of the radar at the sth receiving station in the rectangular coordinate system, ||·|| represents the second norm of the vector. Indicates that only vectors are taken The first two elements of .

[0132] Based on the following formula, calculate the initial measurement combination is the likelihood function penalty term for homology measurement

[0133] Optionally, the likelihood function penalty term is expressed as:

[0134]

[0135] represents the likelihood function penalty term, represents the detection probability of the radar at the sth receiving station, represents the volume of the observation airspace of the sth receiving station radar, represents the error covariance matrix of the received measurement parameters corresponding to the radar at the sth receiving station, In different initial measurement combinations The following corresponds to the homology hypothesis measurement of the receiving station radar.

[0136] S105. Construct a multidimensional allocation optimization model through a likelihood function penalty term, and use a Lagrangian relaxation algorithm to solve the multidimensional allocation optimization model to obtain a correlation result.

[0137] Optionally, the multi-dimensional allocation optimization model is expressed as:

[0138]

[0139] in, Indicates the result of association. represents the likelihood function penalty term, n Nrepresents the total number of received measurement parameters of the radar at the Nth receiving station, where It is a binary variable with the value of 0 or 1.

[0140] S106: Determine the real position of the target based on the association result.

[0141] Specifically, in the embodiment of the present invention, The corresponding measurement combination when the value is 1 As a homologous measurement.

[0142] Will The corresponding homologous measurement is expressed as Will The corresponding least squares solution of the target The actual location of the target.

[0143] The embodiment of the present invention provides a multi-target association and fusion positioning method, which is applied to a one-transmitter-multiple-receiver radar collaborative three-dimensional scenario, including: using an improved least squares method based on a combined measurement vector and a station vector to solve the target position, and obtaining a closed-form positioning solution - a target least squares solution, thereby improving the positioning accuracy and positioning speed of subsequent targets; in addition, by constructing a multi-dimensional allocation optimization model and using the Lagrangian relaxation algorithm to solve the optimization problem, the fusion calculation process is less computationally intensive, thereby improving the calculation speed of the true position of the target.

[0144] In order to verify the effectiveness of a multi-target association and fusion positioning method, the embodiment of the present invention also conducted a simulation experiment as follows:

[0145] 1. Simulation conditions

[0146] The simulation experiment of the present invention uses an AMD Ryzen 9 7945HX CPU @ 2.50 GHz, a 64-bit Windows operating system, and the simulation software uses MATLAB (R 2023b).

[0147] 2. Simulation experiment content

[0148] The simulation experiment conditions and experimental parameters of the process of the present invention are set as follows:

[0149] The radar network is in a one-transmit-three-receive working mode. Figure 2 The schematic diagram of a three-dimensional scene in a one-transmit-three-receive mode is shown schematically, where Figure 2 Figure (a) is the main view of the three-dimensional scene in the one-transmit-three-receive mode. Figure 2Figure (b) is a top view of the three-dimensional scene in the one-transmit-three-receive mode. The coordinates of the transmitting station radar are [0,0,0]km, the coordinates of the receiving station radar 1 are [-40,0,29]km, the coordinates of the receiving station radar 2 are [36,15,1]km, and the coordinates of the receiving station radar 3 are [0,20,1]km. There are 4 densely packed targets in the space observation area, forming a square. The spatial coordinates are [50,50,70]km, [50.5,50,70]km, [50,50.5,70]km, and [50.5,50.5,70]km. At this time, the target spacing The distance between the two targets is 500m. The electromagnetic waves emitted by the transmitting station radar can be received and detected by the receiving radar station after being reflected by the four targets, and the target's delay difference (measurement), azimuth (measurement), and elevation angle (measurement) are obtained. The measurement errors of the three receiving station radars are the same. The error of the delay difference measurement converted to the distance difference measurement is 50m, the azimuth measurement error is 0.06°, the elevation measurement error is 0.06°, the station site error is 50m, the detection probability of each receiving station radar is 0.9, and the airspace observation volume is 12,200 cubic kilometers.

[0150] Figure 3 The schematic diagram of the positioning result obtained according to the method of the present invention when there is a single target in the one-transmit-three-receive mode is schematically shown. Figure 3 The corresponding figure (a) is Figure 2 The result from the perspective of Figure (a) is Figure 3 The corresponding figure (b) is Figure 2 The actual spatial position of the target is [50,50,70] km. The measurement error and site error are the same as those in the above experimental conditions. The blue star-shaped points in the space represent the positioning results repeated 500 times. Figure 3 (a) and Figure 3 (b) It can be seen that in the case of a single target, the average positioning result obtained by using the distance difference, pitch angle, azimuth angle measurement and station coordinates of multiple receiving stations is close to the true value and has a high positioning accuracy.

[0151] Figure 4-Figure 6 The following schematically shows the association effect diagram obtained by the method of the present invention when there are four targets in the one-transmit-three-receive mode. Specifically, Figure 4 The relationship between target distance and target association success probability is schematically shown. Figure 5 The relationship between different distance difference measurement errors (obtained from the delay difference) and the probability of successful target association is schematically shown. Figure 6 The relationship between different azimuth angle measurement errors and the probability of successful target association is schematically shown. Figure 4The horizontal axis is the target spacing, i.e., the sparsity, in meters, and the vertical axis is the probability of successful association, in percent. In this experiment, except for the target spacing changing from 40m to 500m, the other experimental conditions remain unchanged. Figure 4 It can be seen that when the target distance is small (such as 0-100m), the probability of successful association is low. As the target distance increases, the probability of successful association increases rapidly. At about 200m, the probability of successful association reaches above 0.8, and gradually stabilizes after 300m, approaching 1. Figure 5 The target spacing is set to 300m, and the experiment of varying the distance difference measurement error from 10m to 300m is conducted, while the other experimental conditions remain unchanged. Figure 5 The horizontal axis is the distance difference error, in meters, and the vertical axis is the probability of successful association, in percent. Figure 5 It can be seen that when the distance difference measurement error is within 50m, the probability of successful association is above 90%. When the distance difference measurement error is greater than 80m, the probability drops rapidly. When the error is greater than 200m, it tends to be stable and gradually approaches 0. Figure 6 The target spacing was set to 300 m, and the other experimental conditions remained unchanged. Experiments were conducted to change the azimuth angle measurement error from 0.05° to 1.2°. Figure 6 The horizontal coordinate is the azimuth measurement error, in meters, and the vertical coordinate is the probability of successful association, in percent. Figure 6 It can be seen that with the increase of the azimuth measurement error, the probability of successful association decreases rapidly. When the azimuth measurement error is greater than 0.45°, the impact of the measurement error slows down, and the probability of successful association is basically stable at 0.45.

[0152] In summary, the embodiment of the present invention provides a multi-target association and fusion positioning method, which utilizes delay difference measurement and two-dimensional angle measurement, obtains a closed-form positioning solution through linear approximation and multiple least squares, and has good positioning accuracy and positioning speed for single targets with site errors and measurement errors. Based on the maximum likelihood criterion, an association optimization problem is established and solved. When the measurement error is within a certain range and the multi-target sparsity is high, the probability of successful association is close to 1. The Lagrangian relaxation algorithm is used to solve the optimization problem, which effectively reduces the amount of calculation and basically meets the real-time requirements of the radar system.

[0153] The method provided in the embodiment of the present invention can be applied to an electronic device. Specifically, the electronic device can be: a desktop computer, a portable computer, an intelligent mobile terminal, a server, etc., which is not limited in the embodiment of the present invention.

[0154] Based on the same inventive concept, an embodiment of the present invention also provides a multi-target association and fusion positioning device. Figure 7A schematic diagram of the structure of a multi-target association and fusion positioning device provided in an embodiment of the present invention includes: a processor 710, a storage medium 720 and a bus 730, wherein the storage medium 720 stores machine-readable instructions executable by the processor 710, and when the multi-target association and fusion positioning device is running, the processor 710 communicates with the storage medium 720 via the bus 730, and the processor 710 executes the machine-readable instructions to execute the steps of the above method embodiment. The specific implementation method and technical effect are similar and will not be repeated here.

[0155] The storage medium may include a random access memory (RAM) or a non-volatile memory (NVM), such as at least one disk storage. Optionally, the storage medium may also be at least one storage device located away from the aforementioned processor.

[0156] The above-mentioned processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0157] It should be noted that the terms "first", "second", etc. are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Instead, they are merely examples of devices and methods consistent with some aspects of the present invention.

[0158] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification.

[0159] Although the present invention is described herein in conjunction with various embodiments, in the process of implementing the claimed invention, those skilled in the art can understand and implement other changes of the above disclosed embodiments by viewing the drawings and the disclosed content. In the description of the present invention, the term "comprising" does not exclude other components or steps, "one" or "an" does not exclude multiple situations, and the meaning of "multiple" is two or more, unless otherwise clearly and specifically limited. In addition, certain measures are recorded in different embodiments, but this does not mean that these measures cannot be combined to produce good results.

[0160] The above contents are further detailed descriptions of the present invention in combination with specific preferred embodiments, and it cannot be determined that the specific implementation of the present invention is limited to these descriptions. For ordinary technicians in the technical field to which the present invention belongs, several simple deductions or substitutions can be made without departing from the concept of the present invention, which should be regarded as falling within the scope of protection of the present invention.

Claims

1. A multi-target association and fusion positioning method, applied to a multi-receiver radar collaborative three-dimensional scene, characterized in that: include: Obtain the receiving measurement parameters of each receiving station radar, the transmitting station site coordinates and the receiving station site coordinates; Combining the receiving measurement parameters to obtain a combined measurement vector, and combining the transmitting station site coordinates and the receiving station site coordinates to obtain a site vector; Based on the combined measurement vector and the station vector, an improved least square method is used to solve the target position to obtain a target least square solution corresponding to the target position; the improved least square method is executed based on an approximate linear equation constructed by trigonometric function transformation, Taylor expansion processing and ignoring quadratic noise processing; Calculating a homology hypothesis measurement of each receiving station radar based on the target least squares solution and the station location vector, and generating a likelihood function penalty term using the homology hypothesis measurement; A multidimensional allocation optimization model is constructed by using the likelihood function penalty term, and the multidimensional allocation optimization model is solved by using a Lagrangian relaxation algorithm to obtain a correlation result; The real position of the target is determined based on the association result.

2. A multi-target association and fusion positioning method according to claim 1, characterized in that: The receiving measurement parameters include: delay difference τ, pitch angle θ and azimuth angle The delay difference is the time delay between the direct wave and the reflected wave.

3. A multi-target association and fusion positioning method according to claim 2, characterized in that: Combining the received measurement parameters to obtain a combined measurement vector, and combining the transmitting station site coordinates and the receiving station site coordinates to obtain a site vector, including: The received measurement parameters are fused to obtain an initial measurement combination; the initial measurement combination is expressed as: in, represents the initial measurement combination, represents the i-th radar of the s-th receiving station s receive measurement parameters, s=1,...,N,i s =1,...,n s , n s represents the total number of received measurement parameters of the radar at the sth receiving station, represents the i-th radar of the s-th receiving station s The delay difference under the receiving measurement parameters is: represents the i-th radar of the s-th receiving station s The azimuth of the reflected wave under the receiving measurement parameters, represents the i-th radar of the s-th receiving station s The elevation angle of the reflected wave under the receiving measurement parameters, [·] T Represents the transpose of a matrix; The initial measurement combination is subjected to data reorganization processing to obtain the combined measurement vector; the combined measurement vector is expressed as: represents the combined measurement vector, τ1,…,τ N Indicated in Under the assumption of homology, the delay differences corresponding to the radars of the 1st to Nth receiving stations are θ1,…,θ N Indicated in Under the homology assumption, the elevation angles corresponding to the radars of the 1st to Nth receiving stations are: Indicated in The azimuths corresponding to the radars of the 1st to Nth receiving stations under the assumption of homology; The transmitting station site coordinates and the receiving station site coordinates are processed by a preset combination to obtain the site vector; the site vector is expressed as: Wherein, p represents the site vector, t represents the site coordinates of the transmitting station, r1,…,r N Indicates the receiving station site coordinates corresponding to the 1st to Nth receiving station radars.

4. The multi-target association and fusion positioning method according to claim 1, characterized in that: Based on the combined measurement vector and the station location vector, an improved least square method is used to solve the target position, and a target least square solution corresponding to the target position is obtained, including: Based on the first-order Taylor expansion and the neglect of the quadratic noise term, a linear transformation is performed on the delay difference in the combined measurement vector to obtain a first approximate linear equation; Based on trigonometric function transformation, first-order Taylor expansion and neglect of quadratic noise term processing, linear transformation is performed on the azimuth and elevation angles in the combined measurement vector to obtain a second approximate linear equation; Combining the first approximate linear equation and the second approximate linear equation to obtain a total linear equation; Obtaining a target preliminary least squares solution by calculating the total linear equation; A third approximate linear equation is obtained based on the first-order Taylor expansion, the neglect of the quadratic noise term processing and the relationship between the elements in the target preliminary least squares solution; The target least squares solution is obtained by calculating the third approximate linear equation.

5. A multi-target association and fusion positioning method according to claim 4, characterized in that: The first approximate linear equation is expressed as: B τ n τ =h τ -G τ a o ; Among them, B τ represents the correlation matrix corresponding to the real position of the target and the coordinates of the receiving station, n τ represents the error vector associated with the site error vector Δp ​​and the delay error vector Δτ, h τ G represents the vector related to the delay error vector, the transmitting station site coordinates and the receiving station site coordinates. τ represents the matrix related to the delay error vector, the transmitting station site coordinates and the receiving station site coordinates, α o Represents the first parameter; α o =[u oT ,||u o -t|| 2 ] T ; u o represents the real position of the target, t represents the coordinates of the transmitting station, The second approximate linear equation is expressed as: in, Represents the pitch angle error vector Δθ, azimuth angle error vector and the error vector associated with the site error vector Δp, Represents the vector related to the site coordinates, elevation angle and azimuth angle of the transmitting station. Represents a matrix related to the transmitting station site coordinates, elevation angle and azimuth angle.

6. A multi-target association and fusion positioning method according to claim 5, characterized in that: The total linear equation is expressed as: n=h1-G1α o ; n represents the total error vector, h1 represents the second parameter, G1 represents the third parameter, Among them, 0 N represents an N×1-dimensional all-zero column vector.

7. A multi-target association and fusion positioning method according to claim 6, characterized in that: The target least squares solution is expressed as: in, represents the target least squares solution, W2 represents the weighting matrix; G2 represents the fourth parameter, h2 represents the fifth parameter, (·) -1 Indicates taking the inverse of a matrix; Indicates taking The first three items of represents the preliminary least squares solution of the objective, Indicates taking The fourth term, I3, represents the identity matrix of dimension 3×3.

8. A multi-target association and fusion positioning method according to claim 7, characterized in that: The likelihood function penalty term is expressed as: represents the likelihood function penalty term, represents the detection probability of the radar at the sth receiving station, represents the volume of the observation airspace of the sth receiving station radar, represents the error covariance matrix of the received measurement parameters corresponding to the radar at the sth receiving station, In different initial measurement combinations The following corresponds to the homology hypothesis measurement of the receiving station radar.

9. A multi-target association and fusion positioning method according to claim 8, characterized in that: The multi-dimensional allocation optimization model is expressed as: in, represents the association result, represents the likelihood function penalty term, n N represents the total number of received measurement parameters of the radar at the Nth receiving station, where It is a binary variable with the value of 0 or 1.

10. A multi-target association and fusion positioning device, characterized in that: include: A processor, a storage medium and a bus, wherein the storage medium stores machine-readable instructions executable by the processor, and when the multi-target association and fusion positioning device is running, the processor and the storage medium communicate through the bus, and the processor executes the machine-readable instructions to perform the steps of the multi-target association and fusion positioning method as described in any one of claims 1 to 9.

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