Non-vision-field target positioning method based on minimum weight variance

Abstract

The invention relates to the technical field of single photon detection, in particular to a non-vision-field target positioning method based on minimum weight variance, which comprises the following steps: establishing a space coordinate system for a detection space, and discretizing the detection space into a plurality of rough split elements in the space coordinate system; obtaining histograms of a plurality of detection points on the detection wall surface, and obtaining a first compensation histogram of the detection points based on the positions of the coarse split elements; obtaining a positioning coarse split element from the plurality of coarse split elements; discretizing the positioning coarse split elements into a plurality of subdivided voxels, and obtaining a second compensation histogram of the detection point based on the positions of the subdivided voxels; obtaining weight values of the subdivided voxels; obtaining a weight variance value of each subdivided voxel according to the weight value of each subdivided voxel corresponding to each detection point; and determining a space region where the subdivision voxel with the minimum weight variance value is located as a non-vision-field target positioning region. According to the non-vision-field target positioning method, the precision and the efficiency of non-vision-field target positioning can be improved.

Description

Technical Field

[0001] The invention relates to the technical field of single photon detection, and in particular to a non-line-of-sight target positioning method based on minimum weight variance. Background Art

[0002] Non-line-of-sight target positioning is an emerging technology that breaks through the limitation of traditional positioning methods that can only detect targets within the direct line of sight. This technology is mainly based on the principle of laser transient positioning. First, the laser is emitted by a pulsed laser, which is reflected by the intermediate surface and irradiated onto the target object. Then the single-photon detector receives the photons scattered back from the target object. By recording the flight time of these photons, a histogram containing photon counts and flight time information is generated. Using the positioning algorithm, the spatial position of the target object can be accurately calculated.

[0003] In most current studies on non-visual target positioning, the more traditional back-projection algorithm is usually used. The back-projection algorithm is based on the probability multiplication method, that is, the hidden objects are equally likely to be located on an ellipsoid with the detection point and the illumination point as the focus and the product of the light flight time and the speed of light as the focal length. The signal collected by each detection point can be used to obtain the expression of the probability of the hidden object's distribution in space. The probability density distribution obtained by selecting several detection points can be multiplied to obtain the joint probability density function of the hidden object's distribution in the entire hidden space. The specific implementation steps are to divide the space where the hidden object is located into several voxel grids, calculate the distance from each grid point to the detection point and the illumination point, and then obtain the light flight time value, and compensate for the distance attenuation according to the corresponding photon count value, so as to obtain the weight value of the voxel grid; traverse multiple detection points, superimpose the weight values, and take the point with the largest weight as the location of the hidden object. This method has shortcomings. The algorithm needs to traverse all voxel grids. The finer the grid division, the greater the amount of calculation, that is, the positioning accuracy and speed cannot be taken into account at the same time. In addition, there will be a problem that the position without hidden objects will obtain a larger weight value due to the large distance compensation, while the position with objects will obtain a smaller weight value due to the small distance compensation. Points where there is no object but obtain a larger weight value due to the large distance compensation are called pseudo points.

[0004] At present, single-point and array single-photon detectors are the two main types of detectors. Single-point detectors are theoretically complete and have high positioning accuracy, but they usually require multi-point scanning and take a long time to collect data. Although the data quality of array detectors is relatively poor compared to single-point detectors, they can collect data from multiple imaging points at the same time, significantly shortening the data acquisition time and having greater practical potential. Summary of the invention

[0005] In view of the above problems, the present invention provides a non-line-of-sight target positioning method based on minimum weight variance, which solves the technical problems of low positioning accuracy and long time consumption caused by pseudo points in the existing back-projection method.

[0006] The present invention provides a non-line-of-sight target positioning method based on minimum weight variance, comprising the following steps:

[0007] Step S1: Establish a spatial coordinate system for the detection space, and discretize the detection space into a plurality of coarse voxels in the spatial coordinate system;

[0008] Step S2: Obtain the histogram of a plurality of detection points on the detection wall based on a single-photon detector, compensate the histogram of each detection point based on the position of the coarse voxel, and obtain the first compensated histogram of each detection point; obtain the positioning coarse voxel from the plurality of coarse voxels based on the first compensated histograms of all detection points;

[0009] Step S3: Discretize the positioning coarse voxel into a plurality of fine voxels, compensate the histogram of each detection point based on the position of the fine voxel, and obtain the second compensated histogram of each detection point; obtain the weight value of each fine voxel corresponding to each detection point based on the second compensated histogram of each detection point;

[0010] Step S4: Obtain the weight variance value of each fine voxel from the weight values of each fine voxel corresponding to each detection point;

[0011] Step S5: Determine the spatial region where the fine voxel with the minimum weight variance value is located as the non-line-of-sight target positioning region.

[0012] Preferably, the step S1 specifically includes:

[0013] Step S1-1: Determine the upper left corner of the field of view in the detection space as the origin of the coordinate system, determine the right direction along the horizontal direction from the origin as the positive direction of the y-axis, determine the direction perpendicular to the y-axis on the field of view plane as the x-axis direction, and determine the direction perpendicular to the field of view plane as the z-axis direction to establish a spatial coordinate system;

[0014] Step S1-2: Average the detection space and discretize it into n×n×n coarse voxels, and the sizes of the coarse voxels are the same, all not greater than 0.15m.

[0015] Preferably, the step S2 specifically includes:

[0016] Step S2-1: Use a single-photon detector to obtain the histogram of a detection point on the detection wall, and obtain the photon flight time range in the histogram;

[0017] Step S2-2: Calculate the sum of the distances from each coarse voxel to the detection point and the illumination point, and divide it by the speed of light to convert it into the photon flight time;

[0018] Step S2-3: Judge the photon flight time corresponding to each coarse voxel according to the flight time range, and compensate the photon count value of the histogram of the detection point according to the judgment result and the position of the coarse voxel to obtain the first compensated histogram of the detection point, and obtain the weight value of each coarse voxel according to the first compensated histogram;

[0019] Step S2-4: Return to Step S2-1 until the calculation of all detection points is completed to obtain the weights of each coarse voxel at each detection point, and superimpose the weight values at the same coarse voxel position of different detection points; Determine the coarse voxel with the largest weight value as the positioning coarse voxel.

[0020] Preferably, in Step S2-1, the abscissa of the histogram is the photon flight time, and the ordinate is the photon count value;

[0021] The photon flight time range in the histogram specifically includes: the maximum value T of the photon flight time in the histogram max and the minimum value T min .

[0022] Preferably, judge the photon flight time T corresponding to each coarse voxel. If the photon flight time T of the coarse voxel is not within T max and T min , then the weight value of the coarse voxel is set to zero;

[0023] If the photon flight time T of the coarse voxel is within T max and T min , then compensate the photon count value by the distance between the coarse voxel and the illumination point and the distance between the coarse voxel and the detection point to obtain the first compensated histogram, and use the compensated photon count value in the first compensated histogram as the weight value of the coarse voxel. The expression is:

[0024]

[0025] where C c is the photon count value after compensation of the coarse voxel, C0 is the photon count value before compensation, n c is the distance between the position of the coarse voxel and the detection point, and q c is the distance between the illumination point and the position of the coarse voxel.

[0026] Preferably, Step S3 specifically includes:

[0027] Step S3-1: Discretize the positioning coarse voxel into a subdivided voxel of m×m×m on average. Each subdivided voxel has the same size, which is not greater than 0.028m.

[0028] Step S3-2: Select a detection point on the detection wall, obtain the histogram of this detection point, compensate the photon count value of the histogram according to the position of the subdivided voxel, obtain the second compensated histogram, and obtain the weight value of each subdivided voxel according to the second compensated histogram.

[0029] Step S3-3: Return to Step S3-2 until the calculation of at least 4 detection points on the detection wall is completed, and obtain the weight value of each subdivided voxel corresponding to each detection point.

[0030] Preferably, Step S3-2 specifically includes: Select a detection point on the detection wall, obtain the histogram of this detection point, compensate the photon count value of the histogram according to the distance between the subdivided voxel and the illumination point and the distance between the subdivided voxel and the detection point, obtain the second compensated histogram, and use the compensated photon count value in the second compensated histogram as the weight value of the subdivided voxel. The expression is:

[0031]

[0032] where C f is the compensated photon count value of the subdivided voxel, C0 is the photon count value before compensation, n f is the distance between the position of the subdivided voxel and the detection point, and q f is the distance between the illumination point and the position of the subdivided voxel.

[0033] Preferably, Step S4 specifically includes:

[0034] Based on the weight value of each subdivided voxel corresponding to each detection point obtained in Step S3, for each subdivided voxel, obtain the weight values of this subdivided voxel at all detection points, and calculate the variance of the weight values of each subdivided voxel at all detection points; finally, obtain the weight variance value corresponding to each subdivided voxel.

[0035] Compared with the prior art, the present invention has at least the following beneficial effects:

[0036] (1) By adopting a two-stage voxel division strategy of first coarse division and then fine division, the present invention first roughly divides the entire detection space and determines the coarse voxel area where the target may be located, and then makes a detailed division of this area, which can improve the efficiency of non-line-of-sight target positioning, avoid the waste of computing resources for detailed calculation of the entire detection space, and reduce the data processing time.

[0037] (2) The present invention provides a reliable evaluation criterion for target positioning by calculating the variance of the weight values at the same sub-voxel position of different detection points. The smaller the variance of the weight values, the more likely it is that this position is the true position of the target. This judgment method based on the consistency of multiple detection points significantly improves the accuracy and reliability of positioning.

[0038] (3) The present invention not only considers the influence of the detection distance on the histogram and makes corresponding compensation, but also can adapt to non-ideal illumination and detection conditions, which enables this method to be flexibly applied to various actual scenarios. By providing fast and accurate non-line-of-sight target positioning capabilities, it promotes the practical process of non-line-of-sight target positioning technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The drawings are only for the purpose of showing specific embodiments and are not considered to be a limitation of the present invention.

[0040] Figure 1 It is a flowchart of the non-line-of-sight target positioning method provided by the present invention.

[0041] Figure 2 It is a schematic diagram of the line-of-sight and non-line-of-sight ranges provided by the present invention.

[0042] Figure 3 It is a schematic diagram of the non-line-of-sight target positioning system provided by the present invention.

[0043] Figure 4 It is a schematic diagram of the simplified model for target detection provided by the present invention.

[0044] Figure 5 It is a flowchart of the non-line-of-sight target positioning algorithm provided by the present invention.

[0045] Figure 6 It is a schematic diagram of the rough division and subdivision of the voxel in the positioning area provided by the present invention.

[0046] Figure 7 It is a schematic diagram of the positioning strategy based on the variance of the weight values provided by the present invention.

[0047] Figure 8 It is a comparison chart of the lateral positioning errors of each algorithm provided by the present invention.

[0048] Figure 9 It is a comparison chart of the depth positioning errors of each algorithm provided by the present invention.

[0049] Figure 10 It is a comparison chart of the positioning times of each algorithm provided by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0050] To better understand the above objects, features, and advantages of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, without conflict, the embodiments of the present invention and the features in the embodiments can be combined with each other. Additionally, the present invention can also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited by the specific embodiments disclosed below.

[0051] The present invention is a technology for non-line-of-sight target detection systems. The non-line-of-sight range refers to the area outside the line-of-sight range, such as Figure 2 shown. The non-line-of-sight target positioning technology can achieve precise three-dimensional positioning of targets outside the line of sight, enabling the detector to observe the target while maintaining a certain distance from the obstacle. A specific illustration is as Figure 3 shown. The working process of the non-line-of-sight target positioning system is as follows: First, a pulsed laser emits laser light, which scatters with the target object. Then, a single-photon detector receives the scattered photons and records their flight time. From this, a histogram representing the distribution of photon counts over flight time can be constructed. Based on the measured photon flight time, the flight distance of the photons can be calculated. As Figure 4 shown, the non-line-of-sight target is located on an ellipsoid surface. This ellipsoid has the detection point and the illumination point as foci, and the photon flight path length as the focal length. By intersecting multiple such ellipsoid surfaces, the specific position of the non-line-of-sight target can be determined.

[0052] To illustrate the effectiveness of the method proposed by the present invention, the above technical solution of the present invention will be described in detail through a specific embodiment below, as Figure 1 shown, which discloses a non-line-of-sight target positioning method based on minimum weight variance. The specific implementation steps are as follows:

[0053] Step S1: Establish a spatial coordinate system for the detection space, and discretize the detection space into multiple coarse voxels in the spatial coordinate system;

[0054] The processing steps of the minimum weight variance positioning method of the present invention mainly include two parts: coarse voxel positioning and variance calculation of fine voxels. The algorithm flow of the present invention is as Figure 5 shown. The schematic diagram of the coarse and fine division of the detection space in the present invention is as Figure 6 shown. The steps of coarse voxel positioning need to delimit a general area for the hidden object to obtain a relatively large three-dimensional cube with known coordinates, and the hidden object is within the cube. The specific steps of the discretization of the coarse voxels include:

[0055] As Figure 3As shown in the figure, a spatial rectangular coordinate system is established, where the origin of the coordinate system is the upper left corner of the detection field of view; the positive direction of the y-axis is the direction from the origin along the horizontal direction to the right, which is the direction of column increase; the x-axis is perpendicular to the y-axis on the field of view plane, and the positive direction is the direction of row increase; the z-axis is perpendicular to the field of view and conforms to the right-hand rule with the x-axis and y-axis. The object space is discretized into grid_x, grid_y, and grid_z. The discretization degree does not need to be too fine, and only the approximate spatial range of the hidden object needs to be determined. In some embodiments, the detection space can be evenly discretized into a coarse voxel of n×n×n, and the sizes of all the coarse voxels are the same and not greater than 0.15m. In some embodiments, n can take the value of 8.

[0056] Step S2: Obtain the histograms of multiple detection points on the detection wall based on the single-photon detector, compensate the histogram of each detection point based on the position of the coarse voxel, and obtain the first compensated histogram of each detection point; obtain the positioning coarse voxel from the multiple coarse voxels based on the first compensated histograms of all detection points;

[0057] (1) Obtain the histogram of a detection point on the detection wall by the single-photon detector. The abscissa in the histogram is the flight time of the photon, and the ordinate is the photon count value. Screen the maximum value T of the photon flight time in the histogram max and the minimum value T min , the coordinate values of the detection point are grid_fov_x, grid_fov_y, and the coordinate values of the illumination point are source_x, source_y

[0058] (2) Calculate the sum of the distances from each coarse voxel in the detection space to the detection point and the illumination point, divide it by the speed of light, and convert it into the photon flight time T.

[0059] (3) Judge the photon flight time T corresponding to each coarse voxel. If the photon flight time T of the coarse voxel is not within T max and T min , then set the weight value of the coarse voxel to zero.

[0060] If the photon flight time T of the coarse voxel is within T max and T min , then compensate the photon count value by the distance between the coarse voxel and the illumination point and the distance between the coarse voxel and the detection point to obtain the first compensated histogram, and use the compensated photon count value in the first compensated histogram as the weight value of the coarse voxel. The expression is, the expression is:

[0061]

[0062] Among them, C cis the photon count value after coarse voxel compensation, C0 is the photon count value before compensation, and n c is the distance between the position of the coarse voxel and the detection point, q c is the distance between the illumination point and the position of the coarse voxel.

[0063] Take the compensated photon count value C as the weight value of the corresponding current coarse voxel at this detection point.

[0064] (4) Go back to step (1) until the calculations for all detection points are completed, obtain the weights of each coarse voxel at each detection point, and superimpose the weight values of the same coarse voxel position at different detection points; determine the coarse voxel with the largest weight value as the positioning coarse voxel, and take the coordinate value distribution range of the positioning coarse voxel as the approximate positioning area of the hidden object.

[0065] In the above way, the present invention takes the coordinate value distribution range of the coarse voxel with the largest weight as the approximate positioning area of the hidden object, which can improve the calculation speed of the algorithm on the basis of ensuring the positioning accuracy.

[0066] Step S3: Discretize the positioning coarse voxel into multiple sub-voxels, compensate the histogram of each detection point based on the position of the sub-voxels, and obtain the second compensated histogram of each detection point; obtain the weight value of each sub-voxel corresponding to each detection point based on the second compensated histogram of each detection point;

[0067] The second part of the present invention further subdivides the positioning coarse voxel into several sub-voxels, calculates the variance of the weight values of different detection points in the same sub-voxel, so as to complete the final positioning.

[0068] In the traditional back-projection algorithm, it is necessary to traverse all voxel grids, with a large amount of calculation, and there will be a problem that points that may not have an object but have a large distance compensation value will obtain a large weight value, while the positions where there are actually objects will obtain a small weight value because the distance compensation value is too small, that is, pseudo points are generated. In view of the above problems existing in the existing positioning algorithms, the present invention proposes a positioning algorithm relying on the variance of weight values to optimize these problems, eliminate pseudo points, and improve the positioning accuracy.

[0069] The coordinate system in this step is the same as the coordinate system in step S1.

[0070] (1) In this step, refine the voxels in the area where the positioning coarse voxel is located, as Figure 5 shown, evenly discretize the area where the positioning coarse voxel is located into sub-voxels of m×m×m, and the size of each sub-voxel is the same, all not greater than 0.028m. In some embodiments, m can take the value of 8.

[0071] (2) Select a detection point and obtain the histogram of this detection point. Compensate the photon count value of the histogram according to the distance between the subdivision voxel and the illumination point and the distance between the subdivision voxel and the detection point to obtain a second compensated histogram, and use the compensated photon count value in the second compensated histogram as the weight value of the subdivision voxel. The expression is:

[0072]

[0073] where C f is the compensated photon count value of the subdivision voxel, C0 is the photon count value before compensation, n f is the distance between the position of the subdivision voxel and the detection point, and q f is the distance between the illumination point and the position of the subdivision voxel.

[0074] (3) Return to step (2) until the calculation of at least 4 detection points on the detection wall is completed.

[0075] Step S4: Obtain the weight variance value of each subdivision voxel corresponding to each detection point;

[0076] Figure 7 shows a schematic diagram of locating hidden objects according to the variance of weight values. For each detection point, each subdivision voxel in the detection space has a weight value. In this step, for each subdivision voxel, obtain the weight values of this subdivision voxel at all detection points, and calculate the variance of the weight values of this subdivision voxel at all detection points, that is, each subdivision voxel corresponds to a weight variance value.

[0077] Step S5: Determine the area where the subdivision voxel with the smallest variance of weight values is located as the area where the hidden object is located.

[0078] For the variance value of each subdivision voxel obtained in the previous step, determine the area where the subdivision voxel with the smallest weight variance value is located as the area where the hidden object is located.

[0079] Under the dark light conditions in the laboratory, use a bright cardboard with a length and width of 60 cm × 80 cm as the hidden target, and use a matrix single-photon detector to obtain source data. First, use the positioning result of the traditional back-projection algorithm as the control group, and then use the minimum weight variance positioning algorithm for target positioning. In order to evaluate the positioning results of each algorithm, the lateral error, depth error, and positioning time consumption in multiple positioning results are selected for comparison. The results are as Figure 8 、 Figure 9 and Figure 10 shown. The feasibility and superiority of the non-line-of-sight target positioning algorithm proposed by the present invention are verified.

[0080] Although the specific embodiments of the present invention depict various actions or steps in a particular order, it should be understood that such actions or steps are required to be performed in the particular order shown or in a sequential order, or that all of the illustrated actions or steps should be performed to achieve the desired result. In certain circumstances, multitasking and parallel processing may be advantageous. Similarly, although a number of specific implementation details are included in the above discussion, these should not be construed as limiting the scope of the present disclosure. Certain features described in the context of separate embodiments may also be implemented in combination in a single implementation. Conversely, the various features described in the context of a single implementation may also be implemented separately or in any suitable sub-combination in multiple implementations.

[0081] The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed by the present invention should be covered within the protection scope of the present invention.

Claims

1. A method for locating non-line-of-sight targets based on minimum weighted variance, characterized in that: The following steps are involved: Step S1, establishing a spatial coordinate system for the detection space, and discretizing the detection space into a plurality of coarse voxels in the spatial coordinate system; Step S2, acquiring histograms of multiple detection points on the detection wall based on the single-photon detector, and compensating the histogram of each detection point based on the coarse voxel position to obtain a first compensated histogram of each detection point; Acquire a positioning coarse voxel from the plurality of coarse voxels based on a first compensated histogram of all detection points; Step S3, discretizing the coarse positioning voxel into a plurality of subdivided voxels, and compensating the histogram of each detection point based on the position of the subdivided voxel to obtain a second compensated histogram of each detection point; Acquire a weight value of each subdivided voxel corresponding to each detection point based on the second compensated histogram of each detection point; Step S4, obtaining a weight variance value of each subdivided voxel from the weight value of each subdivided voxel corresponding to each detection point; Step S5: determine the spatial region where the subdivided voxel with the smallest weight variance value is located as the non-viewing area target positioning region.

2. The method for non-visual area target positioning based on minimum weighted variance according to claim 1, characterized in that: The step S1 specifically includes: Step S1-1, determine the upper left corner of the field of view in the detection space as the origin of the coordinate system, determine the right direction of the origin along the horizontal direction as the positive direction of the y-axis, determine the direction on the field of view plane and perpendicular to the y-axis as the x-axis direction, and determine the direction perpendicular to the field of view plane as the z-axis direction, and establish a spatial coordinate system; Step S1-2: discretize the detection space into n×n×n coarse voxels on average, and the size of each coarse voxel is the same and no larger than 0.15 m.

3. The method for non-visual area target positioning based on minimum weighted variance according to claim 2, characterized in that: The step S2 specifically includes: Step S2-1, using a single-photon detector to obtain a histogram of a detection point on the wall, and obtaining a photon flight time range in the histogram; Step S2-2, the sum of the distances from each coarse voxel to the detection point and the illumination point is divided by the speed of light to convert it into the photon flight time; Step S2-3, judging the photon flight time corresponding to each coarse voxel according to the flight time range, compensating the photon count value of the histogram of the detection point according to the judgment result and the coarse voxel position, obtaining a first compensated histogram of the detection point, and obtaining the weight value of each coarse voxel according to the first compensated histogram; Step S2-4, return to step S2-1, until the calculation of all detection points is completed, the weight of each coarse voxel of each detection point is obtained, and the weight values ​​of the same coarse voxel position of different detection points are superimposed; the coarse voxel with the largest weight value is determined as the positioning coarse voxel.

4. The method for non-visual area target positioning based on minimum weighted variance according to claim 3, characterized in that: In step S2-1, the abscissa of the histogram is the flight time of the photons, and the ordinate is the photon count value; The range of the photon flight time in the histogram specifically includes: the maximum value T of the photon flight time in the histogram max With the minimum value T min .

5. The method for non-visual area target positioning based on minimum weighted variance according to claim 4, characterized in that: The step S2-3 specifically includes: The photon flight time T corresponding to each coarse-divided voxel is judged. If the photon flight time T of the coarse-divided voxel is not within T max and T min If , the weight value of the coarse-divided voxel is set to zero; If the photon flight time T of the coarse-divided voxel is T max and T min The photon count value is compensated by the distance between the coarse voxel and the illumination point and the distance between the coarse voxel and the detection point to obtain a first compensation histogram. The compensated photon count value in the first compensation histogram is used as the weight value of the coarse voxel. The expression is: Among them, C c is the photon count value after coarse voxel compensation, C0 is the photon count value before compensation, n c is the distance between the coarse voxel position and the detection point, q c is the distance between the illumination point and the coarse voxel position.

6. The method for non-visual area target positioning based on minimum weighted variance according to claim 5, characterized in that: The step S3 specifically includes: Step S3-1, discretizing the coarse positioning voxels into m×m×m subdivided voxels on average, with each subdivided voxel having the same size and no larger than 0.028 m; Step S3-2, selecting a detection point on the detection wall, obtaining the histogram of the detection point, compensating the photon count value of the histogram according to the position of the subdivided voxel to obtain a second compensated histogram, and obtaining the weight value of each subdivided voxel according to the second compensated histogram; Step S3-3, return to step S3-2, until the calculation of at least 4 detection points on the wall is completed, and the weight value of each subdivided voxel corresponding to each detection point is obtained.

7. The method for non-visual area target positioning based on minimum weighted variance according to claim 6, characterized in that: The step S3-2 specifically includes: selecting a detection point on the detection wall, obtaining the histogram of the detection point, compensating the photon count value of the histogram according to the distance between the subdivided voxel and the illumination point and the distance between the subdivided voxel and the detection point, obtaining a second compensated histogram, and using the compensated photon count value in the second compensated histogram as the weight value of the subdivided voxel, expressed as: Among them, C f is the photon count value after compensation of the subdivided voxel, C0 is the photon count value before compensation, n f is the distance between the subdivided voxel position and the detection point, q f is the distance between the illumination point and the subdivision voxel position.

8. The method for non-visual area target positioning based on minimum weighted variance according to claim 7, characterized in that: The step S4 specifically includes: Based on the weight value of each subdivided voxel corresponding to each detection point obtained in step S3, for each subdivided voxel, the weight value of the subdivided voxel at all detection points is obtained, and the variance of the weight value of each subdivided voxel at all detection points is calculated; finally, the weight variance value corresponding to each subdivided voxel is obtained.

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