CN111175740A - Building layout reconstruction optimization method based on through-wall radar - Google Patents

Building layout reconstruction optimization method based on through-wall radar Download PDF

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CN111175740A
CN111175740A CN202010021411.4A CN202010021411A CN111175740A CN 111175740 A CN111175740 A CN 111175740A CN 202010021411 A CN202010021411 A CN 202010021411A CN 111175740 A CN111175740 A CN 111175740A
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scene
building layout
unknown
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optimization method
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郭世盛
张扬
崔国龙
陈家辉
李虎泉
徐彦钦
张伟
孔令讲
杨晓波
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University of Electronic Science and Technology of China
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/88—Radar or analogous systems specially adapted for specific applications
    • G01S13/887—Radar or analogous systems specially adapted for specific applications for detection of concealed objects, e.g. contraband or weapons
    • G01S13/888—Radar or analogous systems specially adapted for specific applications for detection of concealed objects, e.g. contraband or weapons through wall detection
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/003—Bistatic radar systems; Multistatic radar systems
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/88—Radar or analogous systems specially adapted for specific applications
    • G01S13/89—Radar or analogous systems specially adapted for specific applications for mapping or imaging

Abstract

The invention discloses a building layout reconstruction optimization method based on a through-wall radar, which is applied to the technical field of through-wall radar imaging and aims to solve the problem of low operation efficiency caused by large calculated amount of the existing reconstruction algorithm; then extracting building layout and internal object edge information according to the gray gradient change of the preliminarily imaged image, and performing region segmentation on the preliminarily imaged image to obtain a plurality of sub-regions; secondly, respectively carrying out self-adaptive iteration threshold value binarization operation on the original images of different sub-regions; finally, fusing results after binarization of different sub-regions to obtain an unknown scene building layout and an internal object imaging result; the scheme of the invention can quickly and efficiently realize the reconstruction of the building layout of the unknown scene, and obviously improve the artifact problem of the initial reconstruction result.

Description

Building layout reconstruction optimization method based on through-wall radar
Technical Field
The invention belongs to the technical field of through-wall radar imaging, and particularly relates to a building layout reconstruction imaging technology for an unknown scene.
Background
The through-wall radar imaging technology mainly utilizes the electromagnetic phenomena such as reflection, diffraction and transmission when electromagnetic waves are transmitted in a building space to image targets and building layouts in unknown areas, and has important application value and social significance in the aspects of disaster relief, medical monitoring, urban wars and the like. When electromagnetic waves propagate in a building space, a more complex electromagnetic phenomenon can be generated, and when a transmitting-receiving node is on the same side, a Synthetic Aperture Radar (SAR) can be used for receiving a reflection signal on the surface of the building, so that building layout imaging is realized. A group of opposite transmitting and receiving nodes are positioned on two sides outside a building scene, electromagnetic waves can penetrate through the building space, and building layout reconstruction of an unknown scene can be achieved by utilizing transmission signals.
Research on the application of the through-wall radar to building layout imaging is carried out by a plurality of research institutions at home and abroad. The united states army laboratory introduced a computer simulation method for a Through-the-Wall Radar suitable for room imaging, simulating SAR imaging of a complex room (t.dogaru, a.subclain, c.le and c.kenyon, "Radar Signature Prediction for sensing-Through-the-Wall by Xpatch and AFDTD-Part II,"2010DoD high performance Computing modeling Program Users Group Conference, Schaumburg, IL,2010, pp.401-406.). The Canadian department of national defense research and development proved the feasibility of the multi-view fusion method of UWB radar ON the inner space mapping through some experimental results (P.S mg and D.J. DiFilippo, "Amulti-look fusion approach to through-wall radar imaging,"2013IEEE RadarConference (RadarCon13), Ottawa, ON,2013, pp.1-6.).
The research is to acquire echo signals on the surface of a building to reconstruct the layout of the building by using the synthetic aperture form of the ultra-wideband radar. The Santa Barbara university of California uses a transceiving narrowband signal radar node to obtain the receiving power of different positions, and proposes a building layout imaging method based on total variation minimization (A. Gonzalez-Ruiz, A. Ghaffarkhah and Y. Mostofi, "An Integrated frame for Obstaclm applying With continuous vias Using Laser and Wireless ChannelMeasurements," in IEEE Sensors Journal, vol.14, No.1, pp.25-38, Jan.2014.). The university of electronic technology proposes a method of Data Fusion by using different paths (l.x.cao, g.l.cui, l.j.kong, et al, "Narrow-Band Through-Wall Imaging with Received Signal Strength Data", International Conference on Information Fusion, Cambridge, the United Kingdom,2018.), which obtains the Received power of signals from multiple paths, then reconstructs the building layout by using a Radon inverse transformation method, and simultaneously realizes the Imaging of objects in the building by using a connected domain extraction method. However, the total variation minimization method needs to process a large amount of data for a scene with a large scale or a three-dimensional scene, which increases the calculation burden and causes low reconstruction efficiency. The reconstruction efficiency is improved by the method based on the Radon inverse transformation, but when the multi-path fused sampling data is reconstructed, the incoherent superposition imaging can cause the problem of partial artifacts of a building scene and internal objects. In practical applications, it is often necessary to quickly and efficiently image the architectural layout and internal objects. Therefore, the method for quickly and efficiently imaging the building layout is researched, and the problem of imaging artifacts of internal objects is reduced as much as possible, so that the method has important research significance.
Disclosure of Invention
In order to solve the technical problems, the invention provides a building layout reconstruction optimization method based on a through-wall radar.
The technical scheme adopted by the invention is as follows: a building layout reconstruction optimization method based on a through-wall radar comprises the following steps:
s1, preliminarily imaging the unknown scene according to the relation between the received signal power of the positions and the system sampling matrix;
s2, extracting building layout and internal object edge information according to the gray gradient change of the preliminarily imaged image, and performing region segmentation on the preliminarily imaged image to obtain a plurality of sub-regions;
s3, respectively carrying out adaptive iteration threshold value binarization operation on the original images of different sub-regions;
and S4, fusing results of binarization of different sub-regions to obtain an unknown scene building layout and an internal object imaging result.
Before step S1, initializing unknown parameters of the unknown environment and the radar node, specifically: the radar nodes comprise transmitting nodes and receiving nodes, the transmitting nodes and the receiving nodes move synchronously at equal intervals outside an unknown scene along a plurality of groups of planned paths (at least 4 groups of paths can cover the 360-degree range of the scene), and the moving intervals are usually smaller than or equal to the size of a scene resolution unit grid. The set of planning paths comprises a transmitting node path and a receiving node path which are positioned on two sides of the unknown scene, and an area formed between the transmitting node path and the receiving node path completely covers the unknown scene.
Before the step S1, a simulation scene is established, where the size of the simulation scene is at least that of the unknown scene, a center point of the simulation scene is used as a coordinate origin, and a resolution of the simulation scene is determined.
The interval of each movement is determined according to the resolution of the simulation scene.
The radar beam direction is that the transmitting node is opposite to the receiving node.
The relation between the received signal power and the system sampling matrix is expressed as follows:
P≈ΨO
wherein, P tableShowing a normalized decaying power matrix, P ═ P1,...PM]TM represents the sampling times, and the sampling points are determined according to the sampling path covering the unknown scene and each moving interval; o represents the attenuation ratio of all cells, O ═ O (r)1),...O(rN)]T，O(rn) Represents the attenuation rate of cell N, N being 1,2, 3.Ψ denotes a system sampling matrix of M × N order.
The preliminary imaging of the unknown scene specifically comprises: and performing one-dimensional Fourier transform on the normalized attenuation power matrix P, then performing filtering operation, and then performing two-dimensional Fourier inverse transform to obtain an initial reconstruction result O.
The plurality of sub-regions in step S2 specifically include: and segmenting the initial reconstruction result to obtain a building layout area and an internal object area.
The invention has the beneficial effects that: firstly, placing a group of transceiving split radar nodes on two sides outside an unknown scene, enabling the transceiving split radar nodes to synchronously move at equal intervals along a plurality of groups of planned paths, sampling an electromagnetic signal once at each position, establishing a system sampling matrix according to the position relation between the unknown scene and the radar transceiving nodes, solving a relation equation between the received electromagnetic signal and the system sampling matrix by using a filtering back-projection algorithm to obtain an initial reconstruction result of the building layout, then extracting edge information of the building layout according to the gradient change of image gray level to carry out region segmentation, respectively carrying out self-adaptive iteration threshold value binarization operation on a plurality of sub-regions, and finally fusing the results after the plurality of sub-regions are binarized to obtain an imaging result of the building layout of the unknown scene; the invention has the following advantages:
1. the reconstruction of the building layout of the unknown scene is realized quickly and efficiently;
2. the artifact problem of an initial reconstruction result is remarkably improved;
3. the side length of a building with an unknown scene does not need to be measured, and reconstruction can be realized by establishing a simulation scene covering the unknown scene and a corresponding coordinate system;
4. the invention can be applied to the fields of disaster rescue, anti-terrorism stability maintenance and the like.
Drawings
Fig. 1 is a working schematic diagram of a transmitting-receiving split radar node.
FIG. 2 is a diagram of a simulation scenario and a scan path in an embodiment (two sampling paths are labeled for illustration).
FIG. 3 is a diagram illustrating an edge detection result of a simulation scenario in an exemplary embodiment;
wherein, fig. 3(a) is the edge detection result of the subregion 1; fig. 3(b) shows the edge detection result of the subregion 2.
Fig. 4 shows the result of binarization of adaptive iteration threshold values of different sub-regions of a simulation scene in a specific embodiment.
FIG. 5 is a reconstruction result of a simulation scenario in an exemplary embodiment;
wherein, fig. 5(a) is the result after the initial reconstruction result is adaptively binarized; fig. 5(b) shows the result of the algorithm reconstruction after adaptive binarization.
Detailed Description
The following gives specific embodiments of the present invention according to a MATLAB numerical simulation example:
the operation principle of the transceiving split radar node is shown in fig. 1, the radar node comprises a transmitting node and a receiving node, the transmitting node and the receiving node move synchronously at equal intervals along a plurality of groups of planning paths outside an unknown scene, the group of planning paths comprises a transmitting node path (a → b) and a receiving node path (c → d) which are positioned at two sides of the unknown scene, and an area formed between the transmitting node path and the receiving node path completely covers the unknown scene.
In this embodiment, a simulation scenario shown in fig. 2 is taken as an example to explain the content of the present invention, and a specific implementation process includes the following steps:
step 1: building environment and radar node location parameter initialization
For a square building scene with a cylindrical object in the center of the scene, a simulated scene is shown in fig. 2, the origin of the coordinate system is located at the center of the scene, wall _1 and wall _3 in the four walls are parallel to the x axis, and wall _2 and wall _4 are parallel to the y axis. And is provided withDistance d of outer wall surface from original point1＝1m、d2The thickness of the wall is 0.12m, a cylinder with the radius r of 0.3m is arranged at the origin, and the relative dielectric constants of the wall and the internal objects are set to be 4. In the simulation experiment, the size of a resolution grid of a simulation scene is set to be 0.02m, and the simulation scene is a square of-2 to-2 m and-2 to-2 m; the center of the square is taken as the origin (0,0), and the distance from the origin to each sampling path is d3At least 4 sets of sampling paths are needed to cover the 360 ° range of the scene, 2 m. In order to obtain a better reconstruction effect, 8 sampling paths with an inclination angle of 0 ° -180 ° are equally divided in the transmitting path (only two paths are labeled in fig. 2 for illustration).
Taking a sampling path with an inclination angle of 45 ° as an example, if an area surrounded by the sampling path covers an unknown scene shown in fig. 2, the length of the sampling path is at least 3m, and according to the size of a resolution grid, the sampling interval is usually equal to or smaller than the size of a scene resolution cell, and the interval between two adjacent sampling points in this embodiment is 0.015m, then the number of sampling points in a single movement path is 201.
The total number of simulation scenes selected in this embodiment is
Figure BDA0002360884340000041
The sampling point number of the 8 sampling paths is 1608, that is, the actual sampling point number of the invention only accounts for 4.02% of the total point number of the scene. By adopting the method of the invention, the recovery degree of 80% of the simulation scene in the embodiment can be realized under the condition of using a small amount of sampling data.
The person skilled in the art should note that the method of the present invention is not limited to the closed building scene composed of four walls as shown in fig. 2, and the present invention only needs the set simulation scene to cover the unknown scene, establishes a coordinate system through the set simulation scene, and completes the building layout and the internal object imaging of the unknown scene through a small amount of sampling data.
Step 2: building layout initial reconstruction imaging
2.1 for a sampling path inclined at 45 DEG, the transmitting node and the receiving nodeRespectively placed at a position a and a position c, the beam direction is opposite to the receiving node from the transmitting node, as shown in fig. 2, the initial coordinate a of the transmitting node is (0m, -2.82m), the initial coordinate c of the receiving node is (-2.82m, 0m), the distance between the sampling path and the origin is d32 m. The transmitting node and the receiving node synchronously move at equal intervals along the planned path.
2.2, establishing a received signal power model according to the coordinate position of each sampling point planned in practice and the transmission electromagnetic signal obtained by the receiving node at the sampling position, taking the ith sampling received signal as an example, and the power expression of the received signal during the ith sampling is as follows:
P(pi,qi)＝PE(pi,qi)+PA(pi,qi)+η(pi,qi) (1)
wherein p isi,qiRespectively representing the coordinate positions of the transmitting node and the receiving node at the ith sampling, P (P)i,qi) For the received signal power at the ith sample, PE(pi,qi) Is the received power, P, of electromagnetic waves passing through the airA(pi,qi) representing the attenuation power, η (p), caused by objects in the scenei,qi) Representing model errors due to system noise.
2.3, after the electromagnetic wave passes through the unknown scene, the power attenuation caused by the objects in the scene is PA(pi,qi). To reconstruct an unknown scene, the entire scene is discretized into 200 × 200 cells with their center point coordinates rnCharacterization, where n ∈ { 1.. 40000 }. From the Wentzel-Kramers-Brillouin (WKB) linear approximation model, one can obtain:
Figure BDA0002360884340000051
wherein λ is a constant, Oi,jRepresents the attenuation ratio of the jth cell, di,jRepresenting the side length of the jth cell.
Substituting equation (2) into equation (1) results in a simplified model:
Figure BDA0002360884340000052
wherein, PiRepresents the attenuation power caused by buildings and internal objects at the ith sampling, and Δ d represents the side length of each cell. The attenuation power of the received signal of M times of sampling is expressed in the form of vector, so that:
P≈ΨO (4)
wherein, P ═ P1,...P1608]TTo normalize the attenuated power matrix, O ═ O (r)1),...O(r40000)]TRepresenting the decay rate of all cells, Ψ represents a system sampling matrix of order 1608 × 40000.
Therefore, the building layout and the internal static objects of the unknown scene can be reconstructed by solving the attenuation rates of all grids in the unknown region, and the reconstruction result is subjected to adaptive binarization operation, wherein the number of the objects is 1, and the number of the objects is 0 if the objects are not.
2.4, the line integral of the function O (x, y) on the two-dimensional plane is represented by p (r, θ), whose one-dimensional fourier transform is equal to the two-dimensional fourier transform of the function O (x, y), which can be represented by:
Figure BDA0002360884340000061
therefore, the normalized attenuation power matrix P is subjected to one-dimensional fourier transform, then to filtering operation, and then to two-dimensional inverse fourier transform, so as to obtain an initial reconstruction result O. The result of the adaptive iterative threshold binarization of the initial reconstruction result O is shown in fig. 5 (a).
Those skilled in the art should note that, in this embodiment, for convenience of calculation, the transmitting node path and the receiving node path of the sampling path are set to be parallel paths, so that the same spatial distance of signal propagation in an unknown scene is ensured, and comparison with the same distance of signal propagation in air is facilitated; in practical application, the transmitting node path and the receiving node path may be in other position relationships, and the propagation distance of the acquired signal in the scene needs to be calculated separately each time the signal is acquired.
And step 3: subregion segmentation and optimization
3.1, extracting the edge information of the building layout and the internal objects from the initial reconstruction result O, wherein the edge detection result of the simulation scene is shown in FIG. 3, and FIG. 3(a) is the edge detection result of the subregion 1; fig. 3(b) shows the edge detection result of the subregion 2.
The gradient of the function O (x, y) is calculated as:
Figure BDA0002360884340000062
when the gradient of a certain pixel changes
Figure BDA0002360884340000063
Above a defined threshold, the pixel may be detected as an edge.
3.2 architectural layout area O1And an inner object region O2Respectively carrying out adaptive iteration threshold value binarization operation on the two regions, wherein the result is shown in figure 4; the problem of serious artifacts of internal objects when the whole area is subjected to self-adaptive binarization operation is avoided.
And 3.3, respectively performing adaptive iteration threshold binarization operation on the plurality of sub-regions, and then performing fusion on the plurality of sub-regions to obtain a final unknown scene building layout and an internal object imaging result, as shown in fig. 5 (b).
The specific parameter results are shown in table 1, and the performance of the algorithm provided by the invention is measured by three parameters, namely scene restoration degree, running time and restoration radius of an internal object. The first column is initial reconstruction result parameters when the filtering back projection algorithm reconstructs a simulation scene, and the second column is final result parameters when the algorithm reconstructs the simulation scene.
TABLE 1 results of parameters reconstructed by different algorithms
Parameter(s) Initial reconstruction results Final results of the invention
Degree of scene restitution 68.26％ 81.25％
Run time 0.1012s 0.12287s
Radius of restoration of internal object 0.4755m 0.3194m
As can be seen from the above table, the scene restoration degree of the algorithm provided by the invention reaches 81.25%, the restoration radius error of the internal object is only 0.0194m, and the running time is only 0.12287 s. Therefore, the building layout reconstruction optimization method based on the through-wall radar can complete the building layout of an unknown scene and the reconstruction of internal objects quickly and efficiently, the error of the restoration radius of the internal objects is small, the restoration degree of the whole scene is high, and the correctness and the effectiveness of the method are verified.
It will be appreciated by those of ordinary skill in the art that the embodiments described herein are intended to assist the reader in understanding the principles of the invention and are to be construed as being without limitation to such specifically recited embodiments and examples. Various modifications and alterations to this invention will become apparent to those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the scope of the claims of the present invention.

Claims (8)

1. A building layout reconstruction optimization method based on a through-wall radar is characterized by comprising the following steps:
s1, preliminarily imaging the unknown scene according to the relation between the received signal power of the positions and the system sampling matrix;
s2, extracting building layout and internal object edge information according to the gray gradient change of the preliminarily imaged image, and performing region segmentation on the preliminarily imaged image to obtain a plurality of sub-regions;
s3, respectively carrying out adaptive iteration threshold value binarization operation on the original images of different sub-regions;
and S4, fusing results of binarization of different sub-regions to obtain an unknown scene building layout and an internal object imaging result.
2. The building layout reconstruction optimization method based on the through-wall radar of claim 1, wherein before the step S1, the method further comprises initializing unknown parameters of an unknown environment and radar nodes, specifically: the radar nodes comprise transmitting nodes and receiving nodes, the transmitting nodes and the receiving nodes move synchronously at equal intervals along a plurality of groups of planning paths outside an unknown scene, one group of planning paths comprise transmitting node paths and receiving node paths which are positioned at two sides of the unknown scene, and an area formed between the transmitting node paths and the receiving node paths completely covers the unknown scene.
3. The building layout reconstruction optimization method based on the through-wall radar as claimed in claim 2, wherein step S1 is preceded by establishing a simulation scene, the simulation scene is sized to cover at least the unknown scene, and the center point of the simulation scene is used as the origin of coordinates, and the resolution of the simulation scene is determined.
4. The building layout reconstruction optimization method based on the through-wall radar as claimed in claim 3, wherein the transmitting node and the receiving node move synchronously at equal intervals along a plurality of groups of planned paths outside the unknown scene, and the intervals are determined according to the resolution of the simulation scene.
5. The building layout reconstruction optimization method based on the through-wall radar as claimed in claim 4, wherein the radar beam direction is that the transmitting node faces the receiving node.
6. The building layout reconstruction optimization method based on the through-wall radar as claimed in claim 5, wherein the relation between the received signal power and the system sampling matrix is expressed as:
P≈ΨO
where P represents a normalized decaying power matrix, and P ═ P1,...PM]TM represents the number of samplings; o represents the attenuation ratio of all cells, O ═ O (r)1),...O(rN)]T，O(rn) Represents the attenuation rate of cell N, N being 1,2,3, …, N; Ψ denotes a system sampling matrix of M × N order.
7. The building layout reconstruction optimization method based on the through-wall radar according to claim 6, wherein the preliminary imaging of the unknown scene is specifically as follows: and performing one-dimensional Fourier transform on the normalized attenuation power matrix P, then performing filtering operation, and then performing two-dimensional Fourier inverse transform to obtain an initial reconstruction result O.
8. The building layout reconstruction optimization method based on the through-wall radar as claimed in claim 7, wherein the plurality of sub-regions in step S2 specifically include: and segmenting the initial reconstruction result to obtain a building layout area and an internal object area.
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