Outdoor target RCS measurement and mid-field to far-field conversion system and measurement method

Abstract

The present invention discloses an outdoor target RCS measurement and mid-field to far-field conversion system, comprising a vector network analyzer, wherein the output port of the vector network analyzer is connected to the input port of a power amplifier, which is in turn connected to a transmitting antenna, the receiving port of the vector network analyzer is connected to the output port of a low-noise power amplifier, which is then connected to the input port of the low-noise power amplifier. The vector network analyzer is connected to a computer and a power module, which are respectively connected to a Raspberry Pi and a power module, which are respectively connected to a test platform and a power module, and the Raspberry Pi is respectively connected to a test platform and a power module, with a transmitting antenna and a receiving antenna disposed on the test platform. The present invention also discloses an outdoor target RCS measurement and mid-field to far-field conversion method. By using mid-field data obtained in an outdoor field to obtain the target's RCS value, the accuracy and speed of outdoor field testing are improved.

Description

Technical Field

[0001] The invention belongs to the technical field of RCS measurement, relates to an outdoor field target RCS measurement and mid-field-far field conversion system, and also relates to an outdoor field target RCS measurement and mid-field-far field conversion method. Background Art

[0002] Radar Cross Section (RCS) is the most important physical quantity that reflects the electromagnetic scattering characteristics of a target. Therefore, obtaining the RCS of a target is the basis for studying its electromagnetic scattering characteristics. When simulating and experimentally measuring the RCS of a target, it is usually required that the calculation and test scenarios meet the far-field condition, that is, the minimum distance from the target center to the radar antenna is R min =2D 2 / λ, where D is the lateral dimension of the target and λ is the wavelength of the electromagnetic wave emitted by the antenna. However, as target size increases and operating frequency rises, the far-field distance can reach several kilometers or even tens of kilometers. In outdoor locations, due to confidentiality constraints and excessive far-field distances, far-field testing conditions for electrically large targets are difficult to meet. Furthermore, the vast test site and complex electromagnetic environment can significantly impact measurement accuracy. However, the data obtained from close-range measurements differs significantly from that from far-field measurements, making them unusable. Furthermore, current methods for deriving far-field RCS from mid-field data are typically used in anechoic chambers and are unsuitable for outdoor use.

[0003] Through the above analysis, there is currently no relevant work on the mid- and far-field transformation of target RCS for outdoor fields, which results in the inability to directly obtain the target RCS when measuring in outdoor fields, seriously affecting the subsequent electromagnetic characteristics analysis work. Summary of the Invention

[0004] The purpose of the present invention is to provide an outdoor field target RCS measurement and mid-field to far-field conversion system, which solves the problem that the RCS of the target cannot be directly obtained in the outdoor field.

[0005] Another object of the present invention is to provide an outdoor field target RCS measurement and mid-field-far field conversion method, which improves the accuracy and speed of outdoor field testing by obtaining the RCS value of the target using mid-field data obtained in the outdoor field.

[0006] The technical solution adopted by the present invention is an outdoor field target RCS measurement and mid-to-far field conversion system, which includes a vector network analyzer, wherein the output port of the vector network analyzer is connected to the input port of a power amplifier, the output port of the power amplifier is connected to a transmitting antenna, the receiving port of the vector network analyzer is connected to the output port of a low-noise power amplifier, the input port of the low-noise power amplifier is connected to the receiving antenna, the vector network analyzer is respectively connected to a computer and a power module, the computer is respectively connected to a Raspberry Pi and the power module, the Raspberry Pi is respectively connected to a test platform and the power module, and the transmitting antenna and the receiving antenna are arranged on the test platform.

[0007] The present invention is also characterized in that

[0008] The test platform includes a base, with pulleys symmetrically arranged at both ends of the bottom of the base, and the pulleys are arranged on guide rails. The pulleys located on the two guide rails and symmetrical are connected by a rotating shaft, and the rotating shaft is connected to the servo motor through a reducer. The servo motor is connected to a motor driver, and the motor driver is connected to the Raspberry Pi. An antenna bracket is arranged on the upper surface of the base, and a transmitting antenna and a receiving antenna are connected to the antenna bracket.

[0009] The Raspberry Pi is connected to a temperature and humidity sensor, an ultrasonic sensor, a cooling fan, and an indicator light.

[0010] Another technical solution adopted by the present invention is an outdoor field target RCS measurement and mid-to-far field conversion method, which adopts an outdoor field target RCS measurement and mid-to-far field conversion system and is specifically implemented according to the following steps:

[0011] Step 1: Select a calibration sphere, receiving and transmitting antennas of the corresponding frequency band, a power amplifier, a low-noise power amplifier, and a vector network analyzer based on the target size and test site conditions.

[0012] Step 2: Calibrate the vector network analyzer and set the measurement frequency band and frequency points of the vector network analyzer according to the measurement requirements;

[0013] Step 3: Power on the vector network analyzer, power amplifier, and low-noise power amplifier to preheat them, and turn on the host computer and Raspberry Pi.

[0014] Step 4: Measure the midfield data of the empty background, calibration sphere, and target. Subtract the midfield data of the empty background from the midfield data of the calibration sphere and target respectively using vector operation rules to obtain the midfield data of the calibration sphere and target without background clutter.

[0015] Step 5: According to the theory of physical optics, the far-field electric field of the calibration sphere and the target is obtained from the midfield data obtained in step 4, and then the RCS of the target is obtained by the comparison method.

[0016] The present invention is also characterized in that

[0017] The specific process of step 4 is:

[0018] Step 4.1: Measure the lateral dimension D of the target and, based on the mid-field division conditions of the radar antenna, obtain the range of the distance R between the target and the antenna:

[0019]

[0020] In formula (1), λ is the wavelength corresponding to the center frequency used in the measurement;

[0021] After obtaining R, measure the distance from the transmitting antenna and the receiving antenna to the target so that the distance meets the range requirement of R and is a fixed value;

[0022] Step 4.2: Remove the target and use the Raspberry Pi to control the motor driver to drive the servo motor, thereby moving the base along the guide rail to measure the midfield data of the empty background, and then store the data on the vector network analyzer.

[0023] Step 4.3, placing a calibration ball and a target at the target position determined in step 4.1, repeating step 4.2 to measure the midfield data of the calibration ball and the midfield data of the target, and storing the data on the vector network analyzer;

[0024] In step 4.4, the midfield data of the calibration sphere and the target are respectively subtracted from the midfield data of the empty background using the vector operation rule, so as to obtain the midfield data of the calibration sphere and the target without background clutter.

[0025] The specific process of step 5 is:

[0026] According to the theory of physical optics, the current on the target surface can be expressed as

[0027]

[0028] In formula (2), is the normal unit vector of the target, is the unit vector of propagation direction, η0 is the wave impedance of free space, is the incident electric field on the target surface;

[0029] in,

[0030]

[0031] In formula (3), is the electric field on the target surface under ideal plane wave illumination, g(x,y) is a correction function used to correct the deviation between actual illumination and plane wave illumination. is the polarization vector of the electric field, x and y are the horizontal and vertical coordinates respectively, j is the imaginary unit, and k is the wave number;

[0032]

[0033] Then the target's mid-field electric field can be expressed as:

[0034]

[0035] In formula (5), u = 2ktan(α), k is the wave number;

[0036] The far-field electric field of the target can be expressed as

[0037]

[0038] According to the relationship between the two, the formula for obtaining the far-field electric field from the mid-field electric field is:

[0039]

[0040] Where * represents the convolution operation;

[0041] After obtaining the far-field electric field of the target, the RCS of the target can be obtained by using the comparison method:

[0042]

[0043] In formula (8), E t is the far-field electric field of the target, E s is the far-field electric field of the calibration sphere, σ s is the standard RCS of the calibration sphere.

[0044] The standard RCS of the calibration sphere is:

[0045] σ s =πa 2 (9)

[0046] In formula (9), a is the radius of the calibration sphere.

[0047] The beneficial effects of the present invention are:

[0048] (1) The outdoor field target RCS measurement and mid-field to far-field conversion system of the present invention is designed for outdoor field measurement scenarios. Based on a mobile measurement platform, the control servo motor and the Raspberry Pi are interconnected to achieve automated outdoor mid-field measurement, thereby obtaining the target's RCS.

[0049] (2) The outdoor field target RCS measurement and mid-to-far field conversion system of the present invention can complete the electric field measurement of the target in a limited outdoor space, making full use of the limited outdoor space;

[0050] (3) The outdoor field target RCS measurement and mid-field to far-field conversion system of the present invention transmits the target scattering data obtained by the vector network analyzer to the computer for processing, realizing the automatic storage and processing of the measurement data;

[0051] (4) The outdoor field target RCS measurement and mid-to-far field transformation method of the present invention applies the mid-to-far field transformation algorithm to the outdoor field, and can obtain the far-field RCS of the target using the close-range test data, while avoiding the uncontrollable influence of the environment when the test distance is too far. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 Schematic diagram of the structure of the outdoor field target RCS measurement and mid-field to far-field conversion system of the present invention;

[0053] Figure 2 Schematic diagram of the structure of the guide rail and pulley in the outdoor field target RCS measurement and mid-field to far-field conversion system of the present invention;

[0054] Figure 3 2. It is a schematic structural diagram of the base in the outdoor field target RCS measurement and mid-field to far-field conversion system of the present invention;

[0055] Figure 4 This is a flow chart of the outdoor field target RCS measurement and mid-field to far-field conversion method of the present invention;

[0056] Figure 5 This is a midfield data diagram of a calibration sphere without background clutter in an embodiment of the present invention;

[0057] Figure 6 This is a midfield data diagram of a tank model without background clutter in an embodiment of the present invention;

[0058] Figure 7 4 is an RCS data diagram of a tank model target in an embodiment of the present invention.

[0059] In the figure, 1. Transmitting antenna, 2. Receiving antenna, 3. Power amplifier, 4. Low-noise power amplifier, 5. Vector network analyzer, 6. Computer, 7. Raspberry Pi, 8. Test platform, 9. Pulley, 10. Antenna bracket, 11. Motor driver, 12. Servo motor, 13. Guide rail, 14. Reducer, 15. Temperature and humidity sensor, 16. Ultrasonic sensor, 17. Cooling fan, 18. Indicator light, 19. Power module, 20. Base;

[0060] 101. Bracket A, 102. Bracket B. DETAILED DESCRIPTION

[0061] The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0062] The present invention provides an outdoor field target RCS measurement and mid-field to far-field conversion system, the structure of which is as follows: Figure 1 As shown, it includes a vector network analyzer 5, the output port of the vector network analyzer 5 is connected to the input port of the power amplifier 3, the output port of the power amplifier 3 is connected to the transmitting antenna 1, the receiving port of the vector network analyzer 5 is connected to the output port of the low-noise power amplifier 4, the input port of the low-noise power amplifier 4 is connected to the receiving antenna 2, the vector network analyzer 5 is respectively connected to the computer 6 and the power module 19, the computer 6 is respectively connected to the Raspberry Pi 7 and the power module 19, the Raspberry Pi 7 is respectively connected to the test platform and the power module 19, the transmitting antenna 1 and the receiving antenna 2 are set on the test platform, and the power module 19 is used to power the computer 6, the Raspberry Pi 7, and the vector network analyzer 5.

[0063] like Figure 2 and Figure 3 As shown, the test platform includes a base 20, and pulleys 9 are symmetrically provided at both ends of the bottom of the base 20. The pulleys 9 are provided on the guide rails 13. The pulleys 9 located on the two guide rails 13 and symmetrically are connected by a rotating shaft. A plurality of shaft seats are symmetrically provided at both ends of the bottom of the base 20. The rotating shaft is sleeved in the shaft seat and can rotate relative to the shaft seat. The number of the shaft seat is the same as the number of the pulleys 9. The rotating shaft is connected to the servo motor 12 through a reducer 14. The servo motor 12 is connected to a motor driver 11. The motor driver 11 is connected to the Raspberry Pi 7. The Raspberry Pi 7 controls the motor driver 11 to drive the servo motor 12. The servo motor drives the reducer 14 to rotate, the reducer 14 drives the rotating shaft to rotate, and the rotating shaft drives the pulley 9 to rotate, thereby realizing the pulley 9 moving on the guide rail 13 and driving the base 20 to move. An antenna bracket 10 is provided on the upper surface of the base 20. The transmitting antenna 1 and the receiving antenna 2 are connected to the antenna bracket 10. The base 20 drives the antenna bracket 10 to move, thereby adjusting the positions of the transmitting antenna 1 and the receiving antenna 2.

[0064] The antenna bracket includes a bracket A101, one end of the bracket A101 is connected to the base 20, the other end of the bracket A101 is hinged to one end of the bracket B102, and the bracket B102 is connected to the transmitting antenna 1 and the receiving antenna 2. The hinged connection method can facilitate the adjustment of the position of the transmitting antenna 1 and the receiving antenna 2.

[0065] The Raspberry Pi 7 is connected to a temperature and humidity sensor 15, an ultrasonic sensor 16, a cooling fan 17, and an indicator light 18. The temperature and humidity sensor is used to record the state of the test environment and is only used for recording and does not participate in the experiment. The ultrasonic sensor is used to measure the distance so that the system can accurately reach the required position. The cooling fan is used to dissipate heat for the Raspberry Pi. The indicator light is used to indicate whether the system is in working condition. The temperature and humidity sensor 15, the ultrasonic sensor 16, the cooling fan 17, and the indicator light 18 are all set on the Raspberry Pi 7.

[0066] The present invention also provides an outdoor field target RCS measurement and mid-field to far-field conversion method, using the outdoor field target RCS measurement and mid-field to far-field conversion system, such as Figure 4 As shown, please follow the steps below:

[0067] Step 1: Select a calibration sphere, a receiving antenna 2 and a transmitting antenna 1 of the corresponding frequency band, a matching power amplifier 3, a low-noise power amplifier 4, and a suitable model of vector network analyzer 5 based on the size of the test target and the conditions of the test site.

[0068] Step 2: calibrate the vector network analyzer 5 and set the measurement frequency band and frequency points of the vector network analyzer 5 according to the measurement requirements;

[0069] The specific process of calibrating the vector network analyzer 5 is as follows:

[0070] Calibrate using the calibration kit that comes with the vector network analyzer 5. First, connect the two ports of the vector network analyzer 5 to the RF lines, then connect the two RF lines to the two ends of the open-circuit standard. Then, perform open-circuit calibration on the vector network analyzer 5. Then, perform short-circuit and load calibrations using the same method. Finally, complete the calibration on the vector network analyzer 5.

[0071] Step 3, assemble the system, use screws to fix the receiving antenna 2 and the transmitting antenna 1 to the antenna bracket 10 on the outdoor base 20, use one end of the RF cable to connect the output port of the vector network analyzer 5, and the other end of the RF cable to the input port of the power amplifier 3, and then use another RF cable to connect the output port of the power amplifier 3 to the transmitting antenna 1; use an RF cable to connect the receiving antenna 2 to the input port of the low-noise power amplifier, and then use another RF cable to connect the output port of the low-noise power amplifier to the receiving port of the vector network analyzer 5, connect the vector network analyzer 5 to the computer 6, connect the Raspberry Pi 7 to the motor driver 11, start the vector network analyzer 5, power amplifier 3, and low-noise power amplifier 4 to preheat, turn on the computer 6 and Raspberry Pi 7, and prepare for measurement;

[0072] Step 4: Measure the midfield data of the empty background, calibration sphere, and target. Subtract the midfield data of the empty background from the midfield data of the calibration sphere and target respectively using vector operation rules to obtain the midfield data of the calibration sphere and target without background clutter.

[0073] The specific process is:

[0074] Step 4.1: Measure the lateral dimension D of the target and, based on the mid-field division conditions of the radar antenna, obtain the range of the distance R between the target and the antenna:

[0075]

[0076] In formula (1), λ is the wavelength corresponding to the center frequency used in the measurement;

[0077] After obtaining R, measure the distance from transmitting antenna 1 and receiving antenna 2 to the target, so that the distance meets the range requirement of R and is a fixed value;

[0078] Step 4.2: Remove the target and use the Raspberry Pi 7 to control the motor driver 11 to drive the servo motor to rotate, thereby driving the base 20 to move along the guide rail 13 to measure the midfield data of the empty background, and then store the data on the vector network analyzer 5;

[0079] Step 4.3, placing a calibration ball and a target at the target position determined in step 4.1, repeating step 4.2 to measure the midfield data of the calibration ball and the midfield data of the target, and storing the data on the vector network analyzer 5;

[0080] Step 4.4: Using vector calculation rules, subtract the midfield data of the empty background from the midfield data of the calibration sphere and target, thus obtaining the midfield data of the calibration sphere and target without background clutter.

[0081] Step 5: According to the theory of physical optics, the far-field electric field of the calibration sphere and the target is obtained from the midfield data obtained in step 4, and the RCS of the target is obtained by the comparison method.

[0082] The specific process is:

[0083] According to the theory of physical optics, the current on the target surface can be expressed as:

[0084]

[0085] In formula (2), is the normal unit vector of the target, is the unit vector of propagation direction, η0 is the wave impedance of free space, is the incident electric field on the target surface;

[0086] in, It consists of two parts:

[0087]

[0088] In formula (3), is the electric field on the target surface under ideal plane wave illumination, g(x,y) is a correction function used to correct the deviation between actual illumination and plane wave illumination. is the polarization vector of the electric field, x and y are the horizontal and vertical coordinates respectively, j is the imaginary unit, and k is the wave number;

[0089] Through analysis, we can find that the phase difference generated during measurement mainly comes from two aspects: the first is the phase difference caused by the different incident waves, and the second is the phase difference caused by the rotation of the object. Therefore, the correction function g(x,y) can also be divided into two parts: the first part is used to correct the phase difference caused by the incident wave; the second part is used to correct the phase difference caused by the rotation, so we can get:

[0090]

[0091] Then the target's mid-field electric field can be expressed as:

[0092]

[0093] In formula (5), u = 2ktan(α), k is the wave number;

[0094] The far-field electric field of the target can be expressed as:

[0095]

[0096] According to the relationship between the two, the formula for obtaining the far-field electric field from the mid-field electric field is:

[0097]

[0098] Where * represents the convolution operation;

[0099] After obtaining the far-field electric field of the target, the RCS of the target can be obtained by using the comparison method:

[0100]

[0101] In formula (8), E t is the far-field electric field of the target, E s is the far-field electric field of the calibration sphere, σ s is the standard RCS of the calibration sphere;

[0102] According to electromagnetic scattering theory, the standard RCS of the calibration sphere is:

[0103] σ s =πa 2 (9)

[0104] In formula (9), a is the radius of the calibration sphere;

[0105] In step 6, the midfield data of the calibration sphere and the target without background clutter obtained in step 4 are substituted into formula (7) to obtain the far-field electric field of the calibration sphere and the target. Then, the far-field electric field of the calibration sphere and the target and the standard RCS of the calibration sphere are substituted into formula (8) to obtain the RCS of the target.

[0106] Example

[0107] In order to verify the reliability of the method of the present invention, the scheme of the present invention was implemented in an outdoor field.

[0108] Step 1: First, select a tank model as the target. The tank is 30 cm long, 15 cm wide, and 7 cm high. Select a calibration sphere with a diameter of 30 cm based on the target size and the conditions of the test site. At the same time, select a 12 GHz-18 GHz transmitting antenna 1, a receiving antenna 2, a 0 GHz-40 GHz power amplifier 3, a low-noise power amplifier 4, and a vector network analyzer 5 with model AV3672C according to the test requirements.

[0109] Step 2: Calibrate the vector network analyzer 5 using the 31123 calibration kit that comes with the vector network analyzer 5. First, connect the two ports of the vector network analyzer 5 to RF cables, then connect the two RF cables to the two ends of the open-circuit standard. Then, perform open-circuit calibration on the vector network analyzer 5. Subsequently, perform short-circuit and load calibrations using the same method. Finally, complete the calibration on the vector network analyzer 5. Based on the measurement task requirements, set the measurement frequency band of the vector network analyzer 5 to 12 GHz-18 GHz, the number of frequency points to 401, and the test range to 1 m on both sides of the target, with a test every 0.2 m.

[0110] Step 3: Preheat the vector network analyzer 5, power amplifier 3, and low-noise power amplifier 4, and connect and power the computer 6 and Raspberry Pi 7 to prepare for measurement.

[0111] Step 4: Measure the midfield data of the empty background, calibration sphere, and target. Subtract the midfield data of the empty background from the midfield data of the calibration sphere and target respectively using vector operation rules to obtain the midfield data of the calibration sphere and target without background clutter.

[0112] The specific process is:

[0113] Step 4.1: Measure the horizontal dimension of the target D = 0.3m. Based on the mid-field division conditions of the radar antenna, the range of the distance R between the target and the antenna is obtained:

[0114]

[0115] In formula (1), λ is the wavelength corresponding to the center frequency used in the measurement;

[0116] After obtaining 0.72m≤R≤9m, adjust the distance between transmitting antenna 1 and receiving antenna 2 and the target to 5m, which meets this condition.

[0117] Step 4.2: Remove the target and use the Raspberry Pi 7 to control the motor driver 11 to drive the servo motor to rotate, thereby driving the base 20 to move along the guide rail 13 to measure the midfield data of the empty background, and then store the data on the vector network analyzer 5;

[0118] Step 4.3, placing a calibration ball and a target at predetermined positions respectively, repeating step 4.2 to measure the midfield data of the calibration ball and the midfield data of the target, and storing the data on the vector network analyzer 5;

[0119] Step 4.4, using the vector operation rule, the midfield data of the calibration sphere and the target are respectively subtracted from the midfield data of the empty background, that is, the midfield data of the calibration sphere and the target without background clutter are obtained, such as Figure 5 and Figure 6 The following figure shows the result when the transmitting antenna 1 and the receiving antenna 2 are facing the target.

[0120] Step 5: According to the theory of physical optics, the far-field electric field of the calibration sphere and the target is obtained from the midfield data obtained in step 4, and the RCS of the target is obtained by the comparison method.

[0121] The specific process is:

[0122] According to the theory of physical optics, the current on the target surface can be expressed as:

[0123]

[0124] In formula (2), is the normal unit vector of the target, is the unit vector of propagation direction, η0 is the wave impedance of free space, is the incident electric field on the target surface;

[0125] in, It consists of two parts:

[0126]

[0127] In formula (3), is the electric field on the target surface under ideal plane wave illumination, g(x,y) is a correction function used to correct the deviation between actual illumination and plane wave illumination. is the polarization vector of the electric field, x and y are the horizontal and vertical coordinates respectively, j is the imaginary unit, and k is the wave number;

[0128] Through analysis, we can find that the phase difference generated during measurement mainly comes from two aspects: the first is the phase difference caused by the different incident waves, and the second is the phase difference caused by the rotation of the object. Therefore, the correction function g(x,y) can also be divided into two parts: the first part is used to correct the phase difference caused by the incident wave; the second part is used to correct the phase difference caused by the rotation, so we can get:

[0129]

[0130] Then the target's mid-field electric field can be expressed as:

[0131]

[0132] In formula (5), u = 2ktan(α), k is the wave number;

[0133] The far-field electric field of the target can be expressed as:

[0134]

[0135] According to the relationship between the two, the formula for obtaining the far-field electric field from the mid-field electric field is:

[0136]

[0137] Where * represents the convolution operation;

[0138] After obtaining the far-field electric field of the target, the RCS of the target can be obtained by using the comparison method:

[0139]

[0140] In formula (8), E t is the far-field electric field of the target, E s is the far-field electric field of the calibration sphere, σ s is the standard RCS of the calibration sphere;

[0141] According to electromagnetic scattering theory, the standard RCS of the calibration sphere is:

[0142] σ s =πa 2 (9)

[0143] In formula (9), a is the radius of the calibration sphere;

[0144] In step 6, the midfield data of the calibration sphere and the target without background clutter obtained in step 4 are substituted into formula (7) to obtain the far-field electric field of the calibration sphere and the target. Then, the far-field electric field of the calibration sphere and the target and the standard RCS of the calibration sphere are substituted into formula (8) to obtain the RCS of the target, as shown in the following example: Figure 7The figure shows the result when the transmitting antenna 1 and the receiving antenna 2 are facing the target.

Claims

1. Outdoor field target RCS measurement and mid-field to far-field conversion method, characterized by: An outdoor field target RCS measurement and mid-field to far-field conversion system is used, comprising a vector network analyzer (5), wherein the output port of the vector network analyzer (5) is connected to the input port of a power amplifier (3), the output port of the power amplifier (3) is connected to a transmitting antenna (1), the receiving port of the vector network analyzer (5) is connected to the output port of a low-noise power amplifier (4), the input port of the low-noise power amplifier (4) is connected to a receiving antenna (2), the vector network analyzer (5) is respectively connected to a computer (6) and a power module (19), the computer (6) is respectively connected to a Raspberry Pi (7) and a power module (19), the Raspberry Pi (7) is respectively connected to a test platform and a power module (19), and the transmitting antenna (1) and the receiving antenna (2) are arranged on the test platform; The test platform includes a base (20), a plurality of pulleys (9) are symmetrically arranged on both sides of the bottom of the base (20), each pulley (9) is arranged on a guide rail (13), the pulleys (9) located on the two guide rails (13) and symmetrical to each other are connected through a rotating shaft, the rotating shaft is connected to a servo motor (12) through a reducer (14), the servo motor (12) is connected to a motor driver (11), and the motor driver (11) is connected to a Raspberry Pi (7), an antenna bracket (10) is arranged on the upper surface of the base (20), and a transmitting antenna (1) and a receiving antenna (2) are connected to the antenna bracket (10); The Raspberry Pi (7) is respectively connected to a temperature and humidity sensor (15), an ultrasonic sensor (16), a cooling fan (17), and an indicator light (18); Please follow the steps below to implement it: Step 1: Select a calibration sphere, a receiving antenna (2) and a transmitting antenna (1) of the corresponding frequency band, a power amplifier (3), a low-noise power amplifier (4), and a vector network analyzer (5) according to the size of the test target and the conditions of the test site; Step 2, calibrating the vector network analyzer (5), setting the measurement frequency band and frequency points of the vector network analyzer (5) according to the measurement requirements; Step 3: Turn on the vector network analyzer (5), the power amplifier (3), and the low-noise power amplifier (4) to preheat, and turn on the computer (6) and the Raspberry Pi (7); Step 4: Measure the midfield data of the empty background, calibration sphere, and target. Subtract the midfield data of the empty background from the midfield data of the calibration sphere and target respectively using vector operation rules to obtain the midfield data of the calibration sphere and target without background clutter. Step 5: According to the theory of physical optics, the far-field electric field of the calibration sphere and the target is obtained from the midfield data obtained in step 4, and the RCS of the target is obtained by the comparison method. The specific process of step 5 is: According to the theory of physical optics, the current on the target surface can be expressed as （2） In formula (2), is the normal unit vector of the target, is the propagation direction unit vector, is the wave impedance of free space, is the incident electric field on the target surface; in, （3） In formula (3), is the electric field on the target surface under ideal plane wave illumination, is a correction function used to correct the deviation between actual irradiation and plane wave irradiation, is the polarization vector of the electric field, and are the horizontal and vertical axes, j is the imaginary unit, k is the wave number; （4） Then the target's mid-field electric field can be expressed as: （5） In formula (5), u =2 k tan( ), k is the wave number; The far-field electric field of the target can be expressed as （6） According to the relationship between the two, the formula for obtaining the far-field electric field from the mid-field electric field is: （7） in Represents the convolution operation; After obtaining the far-field electric field of the target, the RCS of the target can be obtained by using the comparison method: （8） In formula (8), is the far-field electric field of the target, is the far-field electric field of the calibration sphere, is the standard RCS of the calibration sphere.

2. The outdoor field target RCS measurement and mid-field to far-field conversion method according to claim 1 is characterized in that: The specific process of step 4 is: Step 4.1: Measure the lateral dimension D of the target and, based on the mid-field division conditions of the radar antenna, obtain the range of the distance R between the target and the antenna: （1） In formula (1), The wavelength corresponding to the center frequency used in the measurement; get R Then, measure the distance from the transmitting antenna (1) and the receiving antenna (2) to the target so that the distance satisfies R The range requirement is fixed; Step 4.2, remove the target, use the Raspberry Pi (7) to control the motor driver (11) to drive the servo motor (12) to rotate, thereby driving the base (20) to move along the guide rail (13) to measure the midfield data of the empty background, and then store the data on the vector network analyzer (5); Step 4.3, placing a calibration ball and a target at the position of the target determined in step 4.1, repeating step 4.2 to measure the midfield data of the calibration ball and the midfield data of the target, and storing the data on the vector network analyzer (5); In step 4.4, the midfield data of the calibration sphere and the target are respectively subtracted from the midfield data of the empty background using the vector operation rule, so as to obtain the midfield data of the calibration sphere and the target without background clutter.

3. The outdoor field target RCS measurement and mid-field to far-field conversion method according to claim 1 is characterized in that: The standard RCS of the calibration sphere is: （9） In formula (9), a is the radius of the calibration sphere.

Images