Secondary radar performance test method and device based on channel simulation

Abstract

The invention relates to the technical field of communication, and discloses a secondary radar performance test method and device based on channel simulation, and the method comprises the following steps: A, channel coefficient generation: calculating large-scale fading channel parameter information and small-scale fading channel parameter information, then superposing the large-scale fading channel parameter information and the small-scale fading channel parameter information to obtain a wireless channel coefficient; b, upper computer information configuration and parameter acquisition: upper computer information is configured, and calculation parameters are constructed; c, testing the secondary radar in the current configuration environment; wherein the execution sequence of the step A and the step B is one of the following sequences: firstly starting to execute the step A and then starting to execute the step B, firstly starting to execute the step B and then starting to execute the step A, and simultaneously executing the step A and the step B. According to the invention, the problem that the performance index of the secondary radar in a complex scene is difficult to calibrate smoothly in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the field of communication technology, and in particular to a secondary radar performance testing method and device based on channel simulation. Background Art

[0002] The difference in channel characteristics is one of the core differences between wireless communication and wired communication. The wireless channel provides a direct carrier and medium for the transmission of electromagnetic waves, and its characteristics directly affect the performance of the entire wireless communication system. Since the actual wireless channel is very sensitive, uncontrollable and non-repeatable, it is not an effective method to directly test and verify the developed wireless communication terminal and the corresponding wireless communication algorithm under real complex environmental conditions. It is necessary to develop a wireless channel simulator to meet the comprehensive assessment of system performance.

[0003] Secondary radar is a wireless electronic positioning and identification system composed of interrogation radar and response radar. The interrogation radar emits electromagnetic waves, and the response radar is triggered after receiving the electromagnetic waves of the interrogation radar and emits response electromagnetic waves. The interrogation radar works according to the received response electromagnetic waves to achieve positioning and identification functions.

[0004] Through the working principle of secondary radar, it can be concluded that secondary radar is a wireless communication terminal, which mainly communicates between devices through wireless channels to complete the main functions of the equipment. In the development process of secondary radar, it is necessary to test the performance of secondary radar.

[0005] In the past, secondary radar performance tests were usually conducted in the field. Since some signals that affect air traffic control are generated during the secondary radar test, the test needs to be reported and there are some test restrictions. Since the actual wireless channel is very sensitive, uncontrollable and non-repeatable, it is difficult to operate and reproduce problems when conducting performance tests in some complex scenarios. At the same time, the confidence level of the data obtained is relatively low, and it cannot really play a substantial role in the update and improvement of the equipment.

[0006] In the past, reliable performance data of the secondary radar in simple scenarios could usually be obtained in field tests, and the performance of the secondary radar could be calibrated based on this data. However, in complex scenarios, problems that would not occur in simple scenarios would be exposed, thereby reducing the actual performance of the secondary radar. However, when simulating complex scenarios in the field, the performance indicators of the secondary radar in complex scenarios cannot be calibrated smoothly because the actual wireless channel is very sensitive, uncontrollable and non-repeatable. Summary of the invention

[0007] In order to overcome the shortcomings of the prior art, the present invention provides a secondary radar performance testing method and device based on channel simulation, which solves the problems existing in the prior art such as difficulty in smoothly calibrating the performance indicators of the secondary radar in complex scenarios.

[0008] The technical solution adopted by the present invention to solve the above problems is:

[0009] A secondary radar performance test method based on channel simulation comprises the following steps:

[0010] A, channel coefficient generation: calculate large-scale fading channel parameter information and small-scale fading channel parameter information, and then superimpose the large-scale fading channel parameter information and the small-scale fading channel parameter information to obtain the wireless channel coefficient;

[0011] B. Host computer information configuration and parameter acquisition: configure host computer information and build calculation parameters;

[0012] C. Test the secondary radar under the current configuration environment;

[0013] The execution order of step A and step B is one of the following: first start executing step A and then start executing step B, first start executing step B and then start executing step A, or execute step A and step B at the same time.

[0014] As a preferred technical solution, in step A, an irregular ground model is used to calculate large-scale fading channel parameter information.

[0015] As a preferred technical solution, in step A, the Rice channel model is used to calculate the small-scale fading channel parameter information.

[0016] As a preferred technical solution, in step B, the configured host computer information includes a channel configuration file, a point trace file, and database information.

[0017] As a preferred technical solution, in step B, the method for configuring the channel configuration file is: bind the RF air interface connected to the secondary radar, bind each secondary radar to two air interfaces, namely the transmitting port and the receiving port, and then associate the air interface bound to the secondary radar set in the point trace file.

[0018] As a preferred technical solution, in step B, the method for configuring the trace file is: constructing a simulation scene through three-dimensional modeling software, digitally modeling the device, inputting the attribute information required for channel calculation, and generating the trace file.

[0019] As a preferred technical solution, in step B, when digitally modeling the device, the antenna information and environmental information of the device are configured.

[0020] As a preferred technical solution, in step B, the method for configuring database information is: searching the corresponding Rice factor K under current conditions from a data table.

[0021] As a preferred technical solution, the following steps are included:

[0022] A1, sort out the secondary radar field test data to obtain the parameters required to set the K value;

[0023] A2, place the secondary radar field test data in the coordinate system according to the weather conditions and operating range recorded in the test, then select the data range and calculation step size according to the arrangement of the secondary radar field test data, and calculate the multipath probability of each point within the step size;

[0024] A3, normalizing the acquired data to obtain a data set;

[0025] A4, use k-means algorithm to perform cluster analysis on the data set, and obtain the classification result by changing the number of classes and iterating.

[0026] A5, sort the K values ​​of each class by the coordinate z of the class center point, then perform two-dimensional mapping on each class, take a cross section of the three-dimensional space perpendicular to the z axis, and determine the distribution of the K values ​​of each class on the two-dimensional coordinate system composed of relative distance and environmental conditions;

[0027] A6, according to the correlation between multipath probability and K value, the K value is determined for the center point of each class;

[0028] A7, by inputting the initial conditions of the center point of the class with the highest multipath probability into the host computer, and then randomly setting the K value, multiple channel calculations are performed through the FPGA program to obtain the number of times the multipath phenomenon occurs in the secondary radar equipment, reproduce the multipath probability of this class, and determine the minimum K setting value; for each of the remaining classes, increase the K value in turn, reproduce the multipath probability, determine the K value of each class in turn, complete the establishment of the data table, and then select the K value of the wireless channel according to the input value of the point trace file.

[0029] A secondary radar performance test device based on channel simulation, used to implement a secondary radar performance test method based on channel simulation, comprising a channel coefficient generation module and a host computer electrically connected to each other;

[0030] Among them, the channel coefficient generation module is used to calculate the large-scale fading channel parameter information and the small-scale fading channel parameter information, and then superimpose the large-scale fading channel parameter information and the small-scale fading channel parameter information to obtain the wireless channel coefficient; the host computer is used to construct the calculation parameters and test the secondary radar.

[0031] Compared with the prior art, the present invention has the following beneficial effects:

[0032] The present invention generates a reliable wireless channel simulation for secondary radar equipment through channel simulation and data-driven methods. On this basis, the real performance of the secondary radar in complex scenarios can be tested, providing a test infrastructure for equipment testing and supporting equipment improvement and optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 This is a diagram of the composition of the secondary radar performance test device;

[0034] Figure 2 Flowchart for FPGA channel coefficient generation;

[0035] Figure 3 Schematic diagram of the correlation between large-scale fading and small-scale fading;

[0036] Figure 4 This is a schematic diagram of the Rice model;

[0037] Figure 5 It is a schematic diagram of the function of each parameter information of the point trace file;

[0038] Figure 6 is the multipath probability distribution diagram;

[0039] Figure 7 This is the cluster analysis result diagram;

[0040] Figure 8 Schematic diagram of the correspondence between each class, environment and distance. DETAILED DESCRIPTION

[0041] The present invention will be further described in detail below in conjunction with embodiments and drawings, but the embodiments of the present invention are not limited thereto.

[0042] Example 1

[0043] like Figures 1 to 8 As shown, the present invention designs a set of relatively reasonable channel parameters by means of wireless channel simulation combined with reliable test data in simple outdoor scenes. Channel settings are performed through corresponding channel parameters to obtain reliable simulations in simple scenes. On the basis of the reliable simulations of simple scenes that have been obtained, complex scenes are constructed and wireless channel simulations are simulated. In this scenario, the performance of the secondary radar in complex scenes is tested to obtain relatively reliable and repeatable test data. These data are used to provide support for the performance improvement of the secondary radar system. Through this technical means, reproducible problem data in complex scenes that were not easy to obtain in previous outdoor tests can be obtained. The present invention mainly solves two problems: on the one hand, it can simplify the complexity of outdoor tests; on the other hand, it can obtain relatively reliable performance data of secondary radars in complex scenes, providing data support for equipment improvements.

[0044] The present invention is a secondary radar performance test device, which mainly generates a reusable complex test environment by simulating wireless channels to test the real performance of the secondary radar in a complex environment. The device is mainly divided into two parts. One part is a multi-channel channel parameter calculation module constructed by four groups of FPGAs, which is mainly used for parallel calculation and real-time generation of channel parameters. The other part is a host computer, which mainly sets the position information, speed information, and environmental parameters according to the point trace information file, and at the same time, binds the point traces for the test equipment and configures the test channel. The composition of the present invention is as follows: Figure 1 In the process of introducing the device, a bottom-up order is adopted, starting from the bottom to describe the device composition and design principle.

[0045] (1) FPGA channel coefficient generation module

[0046] Because the generation of channel coefficients requires a lot of calculations, the computing system built on FPGA can effectively reduce the computing time through hardware acceleration and parallel computing. The entire channel calculation module has 32 sub-modules that can perform channel coefficient calculations in parallel. The calculation process of each sub-module is as follows: Figure 2 shown.

[0047] The FPGA channel coefficient calculation module allocates independent computing resources to the transmission and reception of each interrogator and transponder, and calculates the wireless channel coefficients in the relevant transmission and reception process. Environmental parameters, relative position, multipath delay and Doppler shift are input by the host computer. Based on these parameters, the FPGA performs corresponding phase and frequency operations through the relevant channel model and generates the corresponding channel coefficients.

[0048] There are two main effects of large-scale fading and small-scale fading in wireless channels. Large-scale fading occurs in a relatively macroscopic environment and is a relatively slow-changing fading, which describes the change of wireless signals over a fairly long distance; while small-scale fading occurs in a relatively microscopic environment and is a relatively fast-changing fading, which describes the change of wireless signals within a few to dozens of carrier wavelengths. In the same communication scenario, large-scale fading and small-scale fading do not exist in isolation. It can only be said that in a certain space or time scale, large-scale fading or small-scale fading dominates. The relationship between large-scale fading and small-scale fading is as follows: Figure 3 shown.

[0049] Secondary radar mainly works in the L-band, with a working distance much greater than the wavelength, and the working space is usually in the suburbs or relatively open areas. Based on such working scenarios, the Longley-Rice model (also known as the irregular ground model) is selected in large-scale fading. The Longley-Rice model is based on a computer statistical model. The model is applicable to a frequency range of 20 to 40000MHz, an antenna height of 0.5 to 3000m, a coverage radius of 1 to 2000km, and a surface refractive index of 250 to 400Ns. In the Longley-Rice model calculation, the relative distance and environmental parameters input by the host computer are mainly used as calculation parameters to obtain large-scale fading channel parameter information under a specific environment.

[0050] In small-scale fading, the Rice channel model is used for calculation. The Rice channel model assumes that there is a line-of-sight (LOS) communication path between the transmitter and the receiver. In this case, multiple multipath signals are received together with a strong line-of-sight path, such as Figure 4 As shown. The Rice model is a combination of the classic Rayleigh model and the pure Doppler model. The baglo r of the Rice model follows the Rice distribution:

[0051]

[0052] Among them, I 0 () represents the first kind of zero-order modified Bessel function. The Rice factor K is defined as the ratio of the direct component power to the sum of all scattered component powers. The expression of the Rice factor is as follows:

[0053]

[0054] The parameters required by the Rice model are the maximum Doppler shift, mobile speed, carrier frequency, mobile speed and line-of-sight angle, and K parameter. The maximum Doppler shift, mobile speed, carrier frequency, mobile speed and line-of-sight angle can be directly solved through the point trace file and antenna parameters. Since the K value is randomly distributed in actual complex scenarios, the K parameter can be obtained by looking up the table in the host computer database during wireless channel simulation.

[0055] By calculating large-scale fading and small-scale fading and superimposing the two waves, the wireless channel coefficients are obtained. At this time, the wireless channel receives the transmitter signal, and the receiver can obtain the received signal under the conditions of the simulated real wireless channel.

[0056] (2) Host computer information configuration and parameter acquisition

[0057] The upper computer information configuration and parameter acquisition are mainly divided into three parts, namely channel configuration file, point trace file and database information acquisition file.

[0058] The channel configuration file mainly binds the RF air interface connected to the secondary radar interrogator (or transponder). Each device is bound to two air interfaces, namely the transmit port and the receive port. Then the interrogator model (or transponder model) of the device under test set in the dot trace file is associated with the air interface bound to the interrogator (or transponder) of the corresponding secondary radar. In this way, the wireless channel coefficients between related devices can be changed by the movement of the devices on the dot trace.

[0059] Point trace files are generally generated by 3D modeling software. Through 3D modeling software, simulation scenarios are conceived and constructed. By digitally modeling the device and inputting the attribute information required for channel calculation, point trace files are generated, which can be used to calculate wireless channel coefficients. 3D modeling software can use some general software, such as STK satellite modeling software (Satellite Tool Kit), ray tracing modeling software WI (Wireless Insite), etc., or it can be designed and generated by yourself according to the format and configuration requirements of the point trace file. In the process of digital modeling of the device, it is necessary to configure the antenna information of the device, including size, operating frequency, etc. At the same time, it is necessary to set environmental information, such as weather, temperature, humidity and other information. The relative distance information of the two devices can be obtained by the position information of the point in the configuration file. The maximum Doppler shift can be calculated through the velocity information of the point. The function of each parameter information in the point trace file is as follows: Figure 5 shown.

[0060] Database information acquisition mainly involves searching the data table for the corresponding Rice factor K under current conditions. The search items in the data table are relative distance and environmental parameters. The Rice factor K mainly affects multipath delay. Both relative distance and environmental conditions have a relatively large impact on multipath delay. K is the ratio of the direct component power at the receiving end to the sum of all scattered component powers, and vice versa. However, it is impossible to determine the specific impact of these two factors. For example, the K value is different in two situations: when the weather is clear but the distance is far, and when it rains but the distance is close. Therefore, we need to use the data from field tests to support the setting of our K value.

[0061] In the process of setting the K value, it is necessary to conduct a comprehensive analysis through the data of the secondary radar field test. First, we need to organize the data to obtain the parameters we need to set the K value. We need to classify the test data of similar environmental conditions and similar effective distances, calculate the probability of multipath occurrence based on these quantities, and perform normalization. Different environmental conditions have different parameters such as ionosphere and rain attenuation. We sort different weather conditions according to the differences in parameters such as ionosphere and rain attenuation. For the same weather, we also sort them according to the differences between day and night, so as to obtain the coordinate axis of the environmental conditions. The effective distance is arranged logarithmically on the coordinate axis in kilometers. In this plane coordinate system, one coordinate axis is the environment and the other coordinate axis is the effective distance. We place the test data of the field in the coordinate system according to the weather conditions and effective distance recorded in the test. Then, according to the arrangement of the data, select the appropriate data range and calculation step length, and calculate the multipath probability of each point within the step length. Assuming that point A is the center, the data range is set to 0.5×0.5. The number of multipaths in this range divided by the total amount of data is the multipath probability of point A. After calculating the data of point A, the data of the next point is calculated according to the set step size. Then the obtained data is normalized to obtain a correct and usable data set, such as Figure 6 As shown, it provides a data basis for subsequent analysis.

[0062] After obtaining the required data set, the k-means algorithm is used to perform cluster analysis on the data set. By changing the number of classes and iterative attempts, appropriate classification results are obtained, such as Figure 7 As shown. The class with high multipath probability has small K value, and the class with low multipath probability has large K value. The K value of each class is sorted by the coordinate z (multipath probability) of the class center point, and then each class is mapped two-dimensionally, and the three-dimensional space is cross-sectioned perpendicular to the z axis to determine the distribution of the K value of each class in the two-dimensional coordinate system composed of relative distance and environmental conditions, as shown in Figure 8 shown.

[0063] According to the correlation between multipath probability and K value, the K value of the center point of each class can be determined. By inputting the initial conditions of the center point of the class with the highest multipath probability into the host computer, and then randomly setting the K value, the channel calculation is performed through the FPGA program. After multiple calculations, the number of times the secondary radar equipment has multipath phenomenon is obtained, and the multipath probability is calculated. The multipath probability obtained through the experiment can be consistent with the field test data of this class. Reproduce its multipath probability and determine the minimum setting value of K. For each of the remaining classes, increase the K value in turn, reproduce its probability, and determine the K value of each class in turn. Because the multipath probability of each class is a statistical reproduction, it avoids the randomness of a single time, so it has a higher reliability. After determining each K value, our data table is established, and the K value of the wireless channel can be selected according to the input value of the point trace file.

[0064] The host computer completes the construction of all calculation parameters through channel configuration files, point trace files and database information.

[0065] The specific implementation steps of the present invention are:

[0066] The present invention is a secondary radar performance test device. In the process of introducing the implementation and operation of the device, a top-down sequence is adopted, from the complex scene assumption of the top layer to the configuration of the bottom layer.

[0067] (1) Test scenario construction

[0068] The test scene is constructed through 3D modeling software. This device can support the construction of 1×16, 2×8, 3×5 and other scenes. In the 3D modeling software, build the device model, input the device parameters required by the point trace file, and set the test environment. Then the point trace configuration file can be output.

[0069] (2) Startup and configuration of secondary radar performance test equipment

[0070] First, power on the device and initialize the FPGA. Connect the secondary radar device to the RF air interface. The number and type of devices correspond to the objects in the trace configuration file.

[0071] Open the host computer and import the trace file. Through channel configuration settings, associate the air interface, device and objects in the trace file.

[0072] Set the time interval (if not set, it will stay at a single point and will not run according to the point trace). Click Run. Start the FPGA to calculate the configured channel coefficients. After the coefficient calculation is completed, you can test the device under the current configuration environment and record the status of the device under test.

[0073] Based on the test records, the actual performance of the secondary radar equipment is calibrated. At the same time, the test samples with problems are tested repeatedly. By reproducing the problems, system vulnerabilities are found to provide support for enhancing system performance.

[0074] The present invention proposes a method for testing the performance of a secondary radar based on wireless channel simulation;

[0075] The present invention makes reasonable selection of empirical formula and algorithm based on the characteristics of the secondary radar equipment itself;

[0076] The present invention proposes a method for setting the Rice factor K value based on data analysis;

[0077] The invention proposes a method for simulating complex scenes for a secondary radar.

[0078] As described above, the present invention can be preferably implemented.

[0079] All features disclosed in all embodiments in this specification, or steps in all methods or processes implicitly disclosed, except for mutually exclusive features and / or steps, can be combined and / or expanded or replaced in any manner.

[0080] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. According to the technical essence of the present invention, within the spirit and principles of the present invention, any simple modification, equivalent replacement and improvement made to the above embodiment still falls within the protection scope of the technical solution of the present invention.

Claims

1. A secondary radar performance test method based on channel simulation, characterized in that: The following steps are involved: A, channel coefficient generation: calculate large-scale fading channel parameter information and small-scale fading channel parameter information, and then superimpose the large-scale fading channel parameter information and the small-scale fading channel parameter information to obtain the wireless channel coefficient; B. Host computer information configuration and parameter acquisition: configure host computer information and build calculation parameters; C. Test the secondary radar under the current configuration environment; The execution order of step A and step B is one of the following: first start executing step A and then start executing step B, first start executing step B and then start executing step A, or execute step A and step B at the same time.

2. A secondary radar performance test method based on channel simulation according to claim 1, characterized in that: In step A, an irregular ground model is used to calculate large-scale fading channel parameter information.

3. The secondary radar performance test method based on channel simulation according to claim 1 is characterized in that: In step A, the Rice channel model is used to calculate the small-scale fading channel parameter information.

4. A secondary radar performance test method based on channel simulation according to any one of claims 1 to 3, characterized in that: In step B, the configured host computer information includes channel configuration files, point trace files, and database information.

5. A secondary radar performance test method based on channel simulation according to claim 4, characterized in that: In step B, the method for configuring the channel profile is as follows: bind the RF air interface connected to the secondary radar, bind each secondary radar to two air interfaces, namely the transmitting port and the receiving port, and then associate the air interfaces bound to the secondary radars set in the trace file.

6. A secondary radar performance test method based on channel simulation according to claim 4, characterized in that: In step B, the method for configuring the trace file is: constructing a simulation scene through three-dimensional modeling software, digitally modeling the device, inputting the attribute information required for channel calculation, and generating the trace file.

7. A secondary radar performance test method based on channel simulation according to claim 6, characterized in that: In step B, when digital modeling is performed on the device, antenna information and environment information of the device are configured.

8. The secondary radar performance test method based on channel simulation according to claim 4 is characterized in that: In step B, the method for configuring the database information is: searching the corresponding Rice factor K under the current conditions from the data table.

9. A secondary radar performance test method based on channel simulation according to claim 8, characterized in that: The following steps are involved: A1, sort out the secondary radar field test data to obtain the parameters required to set the K value; A2, place the secondary radar field test data in the coordinate system according to the weather conditions and operating range recorded in the test, then select the data range and calculation step size according to the arrangement of the secondary radar field test data, and calculate the multipath probability of each point within the step size; A3, normalizing the acquired data to obtain a data set; A4, use k-means algorithm to perform cluster analysis on the data set, and obtain the classification result by changing the number of classes and iterating. A5, sort the K values ​​of each class by the coordinate z of the class center point, then perform two-dimensional mapping on each class, take a cross section of the three-dimensional space perpendicular to the z axis, and determine the distribution of the K values ​​of each class on the two-dimensional coordinate system composed of relative distance and environmental conditions; A6, according to the correlation between multipath probability and K value, the K value is determined for the center point of each class; A7, by inputting the initial conditions of the center point of the class with the highest multipath probability into the host computer, and then randomly setting the K value, multiple channel calculations are performed through the FPGA program to obtain the number of times the multipath phenomenon occurs in the secondary radar equipment, reproduce the multipath probability of this class, and determine the minimum K setting value; for each of the remaining classes, increase the K value in turn, reproduce the multipath probability, determine the K value of each class in turn, complete the establishment of the data table, and then select the K value of the wireless channel according to the input value of the point trace file.

10. A secondary radar performance test device based on channel simulation, characterized in that: A secondary radar performance test method based on channel simulation for implementing any one of claims 1 to 9, comprising a channel coefficient generation module and a host computer electrically connected to each other; Among them, the channel coefficient generation module is used to calculate the large-scale fading channel parameter information and the small-scale fading channel parameter information, and then superimpose the large-scale fading channel parameter information and the small-scale fading channel parameter information to obtain the wireless channel coefficient; the host computer is used to construct the calculation parameters and test the secondary radar.

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