Electronic apparatus and radar parameter design method thereof

Abstract

A method for designing radar parameters performed by an electronic device according to one embodiment of the present disclosure may include: a step of identifying a set of environmental conditions and a test variable for a verification test to verify the detection range performance of the radar; a step of performing a performance simulation according to the test variable and the set of environmental conditions to identify a first statistical distribution of a predicted Signal-to-Noise Ratio (SNR) that satisfies the required cumulative detection probability of the radar; a step of normalizing the first statistical distribution based on a required SNR corresponding to the required cumulative detection probability to obtain a second statistical distribution of the normalized SNR; a step of determining a margin ratio to compensate for the degradation of the detection range performance of the radar based on an upper limit of a set confidence interval of the second statistical distribution; and a step of adjusting radar parameters based on the margin ratio.

Description

Technology Field

[0001] The present disclosure relates to an electronic device and a method for designing its radar parameters. More specifically, it relates to an electronic device and a method for designing its radar parameters to compensate for the degradation of radar detection range performance due to a limited number of test repetitions. Background Technology

[0002] The detection range performance of a radar is generally defined based on detection probability, which can be expressed as the probability of a target being detected at a specific distance. In particular, for high-speed approaching air defense targets, cumulative detection probability is used as a primary performance indicator rather than single detection probability. Cumulative detection probability refers to the probability of detecting a target at least once out of multiple detection opportunities while the radar is searching for it; the Hit & Miss (HM) and First Detection (FD) methods are widely known for calculating this. The HM method calculates the cumulative detection probability after determining the single detection probability based on the success or failure of detection in each scan, while the FD method derives the detection range corresponding to the target probability by recording the distance of the first detection in each test. Theoretically, since a radar's detection probability is determined by the Signal-to-Noise Ratio (SNR), the HM or FD methods converge to the theoretical performance based on SNR if a sufficient number of repeated tests are performed.

[0003] Meanwhile, in actual flight tests, the success of detection can vary even at the same distance due to fluctuations in the target's Radar Cross Section (RCS). Accordingly, multiple flight tests must be conducted repeatedly to verify accurate detection range performance. However, there may be limitations to repeatedly performing actual flight tests due to constraints on time and cost.

[0004] When the number of flight test repetitions is limited, a problem may arise where the cumulative detection probability (detection range performance) obtained through the HM or FD method does not match the theoretical detection range performance. Consequently, a problem may occur where the radar's detection range performance fails to meet the target performance or where distorted verification results are obtained. The problem to be solved

[0005] The problem that the technical concept of the present disclosure aims to solve is to provide an electronic device and a method for designing its radar parameters that can prevent degradation of detection range performance due to a limited number of flight tests by verifying the statistical distribution of the radar's detection range performance through simulation and determining the margin of the detection range performance based on the statistical distribution.

[0006] However, the technical problem to be solved by the embodiments of the present invention is not limited to the technical problem described above, and other technical problems can be inferred from the following embodiments. means of solving the problem

[0007] A method for designing radar parameters performed by an electronic device according to one embodiment of the present disclosure may include: a step of identifying a set of environmental conditions and a test variable for a verification test to verify the detection range performance of the radar; a step of performing a performance simulation according to the test variable and the set of environmental conditions to identify a first statistical distribution of a predicted Signal-to-Noise Ratio (SNR) that satisfies the required cumulative detection probability of the radar; a step of normalizing the first statistical distribution based on a required SNR corresponding to the required cumulative detection probability to obtain a second statistical distribution of the normalized SNR; a step of determining a margin ratio to compensate for the degradation of the detection range performance of the radar based on an upper limit of a set confidence interval of the second statistical distribution; and a step of adjusting radar parameters based on the margin ratio.

[0008] According to one embodiment, the step of determining the margin ratio may include: a step of modeling the correlation between the test variable and the upper limit of the set confidence interval obtained from the result of the performance simulation into a regression equation through regression analysis; and a step of calculating the margin ratio based on the regression equation.

[0009] According to one embodiment, the regression coefficients of the regression equation can be determined based on the set of the plurality of environmental conditions.

[0010] According to one embodiment, the test variables include the number of test repetitions and the rate of change of the target approach distance, and the rate of change of the target approach distance may be a value obtained by dividing the distance at which a target approaches the radar during a scan cycle by a reference detection distance.

[0011] According to one embodiment, the step of verifying the first statistical distribution may include the step of performing the performance simulation based on the number of test repetitions corresponding to the test variable.

[0012] According to one embodiment, a specific set of environmental conditions among the plurality of sets of environmental conditions may include at least one of a condition regarding the type of target according to the Radar Cross Section (RCS) fluctuation, a condition regarding the detection declaration method of the radar, and a condition regarding the cumulative detection probability calculation method of the radar.

[0013] According to one embodiment, the cumulative detection probability calculation method of the radar may include at least one of the HM (Hit and Miss) method and the FD (First Detection) method.

[0014] According to one embodiment, the step of adjusting the radar parameters includes the step of adjusting the SNR of the radar by adjusting the radar parameters based on the margin ratio, and the radar parameters may include at least one of the transmission power of the radar, the transmit / receive antenna gain of the radar, and the frequency of the radar.

[0015] According to one embodiment, the performance simulation can be performed based on the Monte Carlo simulation method.

[0016] An electronic device according to another embodiment of the present disclosure comprises: a processor; and one or more memories for storing one or more instructions. When the one or more instructions are executed, the processor can control the processor to identify a set of test variables and a plurality of environmental conditions for a verification test to verify the detection range performance of a radar, perform a performance simulation according to the test variables and the plurality of environmental conditions, identify a first statistical distribution of a predicted Signal-to-Noise Ratio (SNR) that satisfies the required cumulative detection probability of the radar, normalize the first statistical distribution based on the required SNR corresponding to the required cumulative detection probability to obtain a second statistical distribution of the normalized SNR, determine a margin ratio to compensate for the degradation of the detection range performance of the radar based on the upper limit of a set confidence interval of the second statistical distribution, and adjust radar parameters based on the margin ratio.

[0017] A computer-readable, non-transient computer-readable storage medium storing a program for executing a radar parameter design method according to one embodiment of the present disclosure, wherein the radar parameter design method comprises: a step of identifying a set of test variables and a plurality of environmental conditions for a verification test to verify the detection range performance of the radar; a step of performing a performance simulation according to the test variables and the plurality of environmental conditions to identify a first statistical distribution of a predicted Signal-to-Noise Ratio (SNR) that satisfies the required cumulative detection probability of the radar; a step of normalizing the first statistical distribution based on the required SNR corresponding to the required cumulative detection probability to obtain a second statistical distribution of the normalized SNR; a step of determining a margin ratio to compensate for the degradation of the detection range performance of the radar based on the upper limit of a set confidence interval of the second statistical distribution; and a step of adjusting the radar parameters based on the margin ratio.

[0018] The various embodiments of the present disclosure described above are merely some of the preferred embodiments of the present disclosure, and various embodiments reflecting the technical features of the various embodiments of the present disclosure can be derived and understood by those skilled in the art based on the detailed description to be described below. Effects of the invention

[0019] According to the present disclosure, by verifying the statistical distribution of the detection range performance of a radar through simulation and determining the margin of the detection range performance based on the statistical distribution, it is possible to prevent the degradation of detection range performance due to a limited number of flight tests.

[0020] The effects of the present invention are not limited to those mentioned above, and other unmentioned effects will be clearly understood by a person skilled in the art to which the present invention pertains (referred to as "person skilled in the art") from the description in the claims. Brief explanation of the drawing

[0021] FIG. 1 is a drawing showing an electronic device according to one embodiment. FIG. 2 is a flowchart illustrating a radar parameter design method according to one embodiment. FIG. 3 is a diagram showing an example of the statistical distribution of normalized SNR according to one embodiment. FIG. 4 is a flowchart illustrating the operation of an electronic device according to one embodiment to calculate a margin ratio based on test variables and a set of multiple environmental conditions. Specific details for implementing the invention

[0022] The terms used in the embodiments have been selected to be as widely used as possible, taking into account their functions in the present disclosure; however, these may vary depending on the intent of those skilled in the art, case law, the emergence of new technologies, etc. Additionally, in specific cases, terms have been arbitrarily selected by the applicant, and in such cases, their meanings will be described in detail in the relevant explanatory section. Therefore, terms used in the present disclosure should be defined not merely by their names, but based on their meanings and the overall content of the present disclosure.

[0023] When a part of a specification is described as "including" a certain component, this means that, unless specifically stated otherwise, it does not exclude other components but may include additional components. Furthermore, terms such as "~part" or "~module" as used in the specification refer to a unit that processes at least one function or operation, and this may be implemented in hardware or software, or as a combination of hardware and software.

[0024] The expression "at least one of a, b, and c" described throughout the specification may include 'a alone', 'b alone', 'c alone', 'a and b', 'a and c', 'b and c', or 'a, b, and c all'.

[0025] The "terminal" mentioned below may be implemented as a computer or portable terminal capable of connecting to a server or other terminal via a network. Here, the computer includes, for example, a notebook, desktop, or laptop equipped with a web browser, and the portable terminal may include, for example, a wireless communication device that ensures portability and mobility, and may include all types of handheld-based wireless communication devices such as communication-based terminals like IMT (International Mobile Telecommunication), CDMA (Code Division Multiple Access), W-CDMA (W-Code Division Multiple Access), and LTE (Long Term Evolution), smartphones, tablet PCs, etc.

[0026] Embodiments of the present disclosure are described below with reference to the attached drawings so that those skilled in the art can easily implement them. However, the present disclosure may be embodied in various different forms and is not limited to the embodiments described herein.

[0027] Hereinafter, embodiments of the present invention will be described in detail with reference to the attached drawings.

[0028] In describing the embodiments, technical details that are well known in the art to which the present invention belongs and are not directly related to the present invention are omitted. This is intended to convey the essence of the present invention more clearly without obscuring it by omitting unnecessary explanations.

[0029] For the same reason, some components in the attached drawings have been exaggerated, omitted, or schematically depicted. Additionally, the size of each component does not entirely reflect its actual dimensions. Identical or corresponding components in each drawing have been assigned the same reference numbers.

[0030] The advantages and features of the present invention and the methods for achieving them will become clear by referring to the embodiments described below in detail together with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below but may be implemented in various different forms. These embodiments are provided merely to ensure that the disclosure of the present invention is complete and to fully inform those skilled in the art of the scope of the invention, and the present invention is defined only by the scope of the claims. Throughout the specification, the same reference numerals refer to the same components.

[0031] At this time, it will be understood that each block of the process flow diagrams and combinations of the flow diagrams can be executed by computer program instructions. Since these computer program instructions can be loaded into the processor of a general-purpose computer, a special-purpose computer, or other programmable data processing equipment, the instructions executed through the processor of the computer or other programmable data processing equipment create means to perform the functions described in the flow diagram block(s). Since these computer program instructions can also be stored in computer-available or computer-readable memory that can be directed toward the computer or other programmable data processing equipment to implement the function in a specific way, the instructions stored in computer-available or computer-readable memory can also produce a manufactured item containing the means of instruction to perform the function described in the flow diagram block(s). Since computer program instructions can be loaded onto a computer or other programmable data processing equipment, instructions that perform a series of operation steps on the computer or other programmable data processing equipment to create a process executed by the computer can also provide steps for executing the functions described in the flowchart block(s).

[0032] Additionally, each block may represent a module, segment, or part of code containing one or more executable instructions for executing a specified logical function(s). It should also be noted that in some alternative execution examples, the functions mentioned in the blocks may occur out of order. For instance, two blocks described in succession may actually be executed substantially simultaneously, or the blocks may be executed in reverse order according to their corresponding functions.

[0033] FIG. 1 is a drawing showing an electronic device according to one embodiment.

[0034] Referring to FIG. 1, the electronic device (100) may include a processor (110) and a memory (120) according to one embodiment. The electronic device (100) illustrated in FIG. 1 is illustrated only with components related to the present embodiment, and it will be understood by those skilled in the art related to the present embodiment that other general-purpose components may be included in addition to the components illustrated in FIG. 1.

[0035] The electronic device (100) of FIG. 1 may be a device that checks the statistical distribution of the detection range performance of the radar through simulation and designs radar parameters according to the margin ratio determined based on the statistical distribution. The electronic device (100) may check the test variables and multiple sets of environmental conditions for a verification test to verify the detection range performance of the radar, check the statistical distribution of the SNR obtained by performing a performance simulation according to the test variables and multiple sets of environmental conditions, and determine the margin ratio based on the statistical distribution.

[0036] In the present disclosure, "verification test" may mean a flight test for measuring the detection range performance of a radar. Hereinafter, the verification test may be referred to as a flight test.

[0037] In particular, the radar parameter design method of the present disclosure can be effectively applied to the defense sector, which requires various target detection and tracking missions. For example, in the case of radar systems operated in the defense sector, it is essential to verify accurate detection range performance through iterative flight tests. However, during the actual development phase, it is difficult to conduct iterative flight tests due to time and cost constraints; consequently, the limited number of flight test iterations may lead to inaccurate results in radar performance verification and limitations in achieving target performance. Accordingly, there is a need for technology to design radar parameters that can compensate for the degradation of detection range performance caused by the limited number of flight test iterations. In response to these characteristics of the defense sector, the radar parameter design method presented in the present disclosure below can be understood as a technical concept easily applicable to the defense sector, as it prevents the degradation of detection range performance caused by the limited number of flight test iterations by verifying the statistical distribution of the radar's detection range performance according to the number of test iterations through simulation, designing radar parameters based on the margin ratio determined from the statistical distribution, and modeling a regression equation to rapidly calculate the margin ratio.

[0038] A processor (110) is a component capable of performing operations or data processing regarding the control and / or communication of each component of an electronic device (100). For example, the processor (110) can control the electronic device (100) overall by executing programs stored in memory (120) within the electronic device (100). The processor (110) may be implemented as a CPU (central processing unit), GPU (graphics processing unit), AP (application processor), etc., provided within the electronic device (100), but is not limited thereto. Unless there are special circumstances, in this disclosure, the processor (110) may refer to a set of one or more processors (110).

[0039] The processor (110) may be implemented as a computer or a similar device according to hardware, software, or a combination thereof. In terms of hardware, the processor (110) may be implemented in the form of an electronic circuit that processes electrical signals to perform control functions, and in terms of software, it may be implemented in the form of a program that drives the hardware processor (110). Meanwhile, unless otherwise specifically mentioned in the description below, the operation of the electronic device (100) may be interpreted as being performed by the control of the processor (110). That is, when modules implemented in the electronic device (100) are executed to design radar parameters, the modules may be interpreted as controlling the processor (110) to perform the following operations of the electronic device (100).

[0040] Memory (120) is hardware that stores various data processed within an electronic device (100), and memory (120) can temporarily or semi-permanently store data processed and data to be processed in the electronic device (100). For example, data regarding an operating system (OS) for operating the electronic device (100) can be stored in the memory (120) of the electronic device (100). Memory (120) may include random access memory (RAM) such as dynamic random access memory (DRAM) and static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), CD-ROM, Blu-ray or other optical disc storage, hard disk drive (HDD), solid state drive (SSD), or flash memory. Such memory (120) may be provided as an embedded type or a removable type. Additionally, memory (120) may store instructions for the operation of the processor (110). Unless there are special circumstances, in the present disclosure, memory (120) may mean a set of one or more memories (120).

[0041] Meanwhile, the electronic device (100) may include a communication device comprising one or more transceivers, an input unit, and an output unit. The communication device is a device for performing wired / wireless communication and can communicate with an external electronic device. The external electronic device may be a terminal or a server. In addition, communication technologies used by the communication device may include GSM (Global System for Mobile communication), CDMA (Code Division Multi Access), LTE (Long Term Evolution), 5G, WLAN (Wireless LAN), Wi-Fi (Wireless-Fidelity), Bluetooth, RFID (Radio Frequency Identification), Infrared Data Association (IrDA), ZigBee, NFC (Near Field Communication), etc. The input unit may be, for example, a traditional type of keypad or keyboard, a mouse, a microphone for receiving voice signals, a camera, and various other types of input means for detecting or receiving various forms of user input. The output unit may be, for example, a display that outputs images, a speaker that outputs sound, a haptic device that generates vibrations, and various other forms of output means.

[0042] Additionally, at least some of the components within the electronic device (100) may be implemented as an integrated unit or as a single or multiple unit. At least some of the components within the electronic device (100) may be connected to each other via a bus, GPIO (general purpose input / output), SPI (serial peripheral interface) or MIPI (mobile industry processor interface), etc., to exchange data and / or signals.

[0043] The radar parameter design method of the present disclosure, performed by the electronic device (100) of FIG. 1, may also be implemented by a computer-readable, non-transient computer-readable storage medium (or non-transient recording medium) for the operation. The radar parameter design method may be implemented as a software module or algorithm and may be stored on a computer-readable recording medium as computer-readable code or program instructions executable on a processor (110). Here, computer-readable recording media include magnetic storage media (e.g., ROM (read-only memory), floppy disk, hard disk, etc.) and optical reading media (e.g., CD-ROM, DVD: Digital Versatile Disc). Computer-readable recording media may be distributed across networked computer systems, so that computer-readable code may be stored and executed in a distributed manner. The medium may be computer-readable, stored in memory (120), and executed on a processor (110).

[0044] An electronic device (100) according to one embodiment may further include a display (not shown). The display may visually provide various information to the outside under the control of a processor (110). The display may include a touch circuit configured to detect a user's touch or a sensor circuit configured to measure the intensity of the force generated by the touch.

[0045] FIG. 2 is a flowchart illustrating a radar parameter design method according to one embodiment.

[0046] Referring to FIG. 2, in step S210, the electronic device (100) can identify test variables and a set of multiple environmental conditions for a verification test to verify the detection range performance of the radar. The detection range performance of the radar can be defined based on the cumulative detection probability. The cumulative detection probability may refer to the probability that detection of a target target is successful at least once before the target target reaches a reference detection range. The electronic device (100) may determine that the detection range performance of the radar satisfies the target detection range performance if the cumulative detection probability of the radar is greater than or equal to the required cumulative detection probability. At this time, the cumulative detection probability of the radar may be determined by the signal-to-noise ratio (hereinafter, the radar's SNR) of the radar's received signal, and the radar's SNR may be determined by internal design parameters of the radar, such as transmission power and antenna gain.

[0047] According to one embodiment, the test variables may include the number of test repetitions and the rate of change of the target approach distance. In this case, the rate of change of the target approach distance may be a value obtained by dividing the distance at which a target approaches the radar during a scan cycle by a reference detection distance.

[0048] According to one embodiment, the number of test repetitions may refer to the number of repetitions of flight tests (or verification tests) performed to verify the detection range performance of a radar under the same test environment conditions (e.g., type of target, method of calculating cumulative detection probability, etc.). The number of test repetitions may be a variable associated with the statistical reliability and variance of the radar's detection range performance. For example, if the number of test repetitions is sufficiently large, the radar's detection range performance may converge to the theoretical target performance of the radar according to the SNR. However, even under the same test environment conditions, if the number of test repetitions is limited (e.g., less than 20 times), there may be an error between the measured radar's detection range performance and the target performance, and the target performance may not be satisfied. An electronic device (100) according to one embodiment of the present disclosure can prevent the radar's detection range performance from failing to meet the target performance due to the limited number of test repetitions by determining the margin of the radar parameter (or SNR) by reflecting the statistical deviation of the detection range performance caused by the limitation of the number of test repetitions.

[0049] According to one embodiment, the rate of change in target approach distance may be a value obtained by dividing the distance at which a target approaches the radar during the radar's scan cycle (e.g., the amount of decrease in the target's distance during one scan cycle) by the reference detection distance. Meanwhile, the rate of change in target approach distance may be a variable that affects the statistical reliability of the radar's detection range performance, such as the number of test repetitions mentioned above. For example, if the rate of change in target approach distance is relatively large, it may not be possible to secure a sufficient number of scans for the target near the reference detection distance, which may affect the statistical characteristics of the detection range performance.

[0050] According to one embodiment, among a plurality of sets of environmental conditions, a specific set of environmental conditions may include at least one of a condition regarding the type of target based on Radar Cross Section (RCS) fluctuation, a condition regarding the method of declaring detection by the radar, and a condition regarding the method of calculating the cumulative detection probability by the radar. In the present disclosure, "set of environmental conditions" may refer to a set of initial environmental conditions (e.g., type of target, method of declaring detection, method of calculating cumulative detection probability) that are pre-set to define a test environment in which a flight test (verification test) or performance simulation is performed.

[0051] According to one embodiment, the electronic device (100) can select any one of a plurality of sets of environmental conditions and can set a test environment for flight testing or performance simulation based on the environmental conditions of the selected set of environmental conditions.

[0052] According to one embodiment, the target type based on RCS fluctuation may include at least one of a Swerling Type 1 model (Swerling #1) and a Swerling Type 2 model (Swerling #2). In this case, the Swerling model may refer to a model for statistically expressing the characteristic that the radar reflection cross-section of a target fluctuates over time.

[0053] According to one embodiment, the detection declaration method of the radar may include at least one of a 1-of-1 method and a 3-of-8 method. For example, the 1-of-1 method may mean a method of declaring final detection for a beam when detection is successful in only one observation opportunity for a specific beam direction or a specific distance interval. The 3-of-8 method may mean a method of declaring final detection for a beam only when detection is successful in at least 3 of the 8 bursts constituting a single beam.

[0054] According to one embodiment, the method for calculating the cumulative detection probability of a radar may include at least one of the HM (Hit and Miss) method and the FD (First Detection) method. For example, the HM method is a method of calculating the cumulative detection probability after calculating the single detection probability based on whether detection was successful in each scan, and the FD method may be a method of deriving the detection distance corresponding to the target cumulative detection probability by recording the distance at which the first detection occurred in each test.

[0055] In step S220, the electronic device (100) can perform a performance simulation according to a set of test variables and multiple environmental conditions to determine a first statistical distribution of the predicted Signal-to-Noise Ratio (SNR) that satisfies the required cumulative detection probability of the radar.

[0056] According to one embodiment, the electronic device (100) can perform a performance simulation based on the number of test repetitions corresponding to the test variable. The electronic device (100) can obtain a predicted SNR that satisfies the required cumulative detection probability of the radar by performing M virtual flight tests corresponding to the number of test repetitions (M) within a single performance simulation. At this time, the test environment of the performance simulation can be set based on the rate of change of target approach distance included in the test variable and a plurality of sets of environmental conditions. As described above, the cumulative detection probability is determined by the SNR of the radar, and "predicted SNR" may refer to the SNR value required to satisfy the required (target) cumulative detection probability in a simulation environment corresponding to a specific test variable (e.g., number of test repetitions, rate of change of target approach distance) and a specific set of environmental conditions (e.g., type of target, detection declaration method, cumulative detection probability calculation method).

[0057] According to one embodiment, performance simulation may be performed based on a Monte Carlo simulation method. A Monte Carlo simulation method may refer to a simulation method that estimates the statistical characteristics of radar detection range performance (e.g., predicted SNR) by generating random samples from a predefined probability distribution for probabilistic elements such as target RCS fluctuations and noise.

[0058] According to one embodiment, the electronic device (100) can repeatedly perform performance simulations according to a Monte Carlo simulation method, and by calculating a predicted SNR value that satisfies the required cumulative detection probability for each execution, a first statistical distribution of predicted SNR values ​​can be obtained.

[0059] In step S230, the electronic device (100) can obtain a second statistical distribution of the normalized SNR by normalizing the first statistical distribution based on the required SNR corresponding to the required cumulative detection probability. At this time, the normalized SNR may be the ratio of the predicted SNR to the required SNR. In the present disclosure, "required SNR" may refer to an ideal SNR value capable of satisfying the required cumulative detection probability when assuming that the number of test repetitions is infinite under the same test environment conditions. That is, unlike the aforementioned predicted SNR, the required SNR may refer to an ideal SNR without statistical uncertainty.

[0060] In step S240, the electronic device (100) can determine a margin ratio to compensate for the degradation of the radar's detection range performance based on the upper limit of the set confidence interval of the second statistical distribution. The second statistical distribution can be defined as the mean and standard deviation of the normalized SNR, and the electronic device (100) can calculate a confidence interval corresponding to a predetermined confidence level (e.g., 95%, 99%, etc.) for the second statistical distribution of the normalized SNR. At this time, the confidence interval may refer to an interval corresponding to an upper and lower range of a certain multiple of the standard deviation (e.g., 1.96 times corresponding to the 95% confidence level) centered on the mean of the normalized SNR.

[0061] According to one embodiment, the electronic device (100) may determine a margin ratio to compensate for the degradation of the radar's detection range performance based on the upper limit of the confidence interval. As described above, when the number of test repetitions is limited, the predicted SNR obtained through simulation may differ from the required SNR, and there may be cases where the predicted SNR becomes greater than the required SNR (i.e., when the normalized SNR value is greater than 1). In this case, if the predicted SNR is greater than the required SNR, a larger SNR may be required to achieve the same required cumulative detection probability, and additional SNR may be required to achieve the required cumulative detection probability. Therefore, if radar parameters are designed based only on the ideal SNR (required SNR), the radar's cumulative detection probability may be lower than the required cumulative detection probability, and a degradation of the radar's detection range performance may occur. That is, the case where a predicted SNR corresponding to the upper limit of the confidence interval is required may mean a case where the degradation of the radar's detection range performance is maximized.

[0062] The electronic device (100) can additionally provide an SNR margin that accounts for statistical variation to the ideal required SNR by applying a margin ratio based on the upper limit of the confidence interval to the radar parameters. Through this, the detection range performance of the radar can be made to meet the target performance even when the number of test repetitions is limited.

[0063] According to one embodiment, the electronic device (100) can model the correlation between the upper limit of a set confidence interval obtained from the test variable and the result of a performance simulation as a regression equation through regression analysis. Subsequently, the electronic device (100) can calculate a margin ratio based on the regression equation.

[0064] According to one embodiment, the electronic device (100) can model the correlation between the upper limit of the confidence interval of the distribution of normalized SNR and the test variable in the form of a fixed regression equation. The regression equation can be defined by the following mathematical equation 1.

[0065]

[0066] In the above mathematical formula 1, is the margin ratio, M is the number of test repetitions, inside It can represent the regression coefficients of the regression equation.

[0067] In this case, the independent variable of the regression equation may be the number of test repetitions included in the test variable, and the dependent variable of the regression equation may be the upper limit of the confidence interval (e.g., margin ratio) of the second statistical distribution obtained from the performance simulation corresponding to the test variable and a set of multiple environmental conditions.

[0068] According to one embodiment, the regression coefficient of the regression equation ( inside ) can be determined based on multiple sets of environmental conditions. Additionally, the regression coefficients can be determined based on the rate of change of the target approach distance. Specifically, the electronic device (100) can perform performance simulations for each set of environmental conditions with respect to multiple rates of change of the target approach distance. The electronic device (100) can calculate a set of regression coefficients corresponding to each set of environmental conditions by performing regression analysis on the upper limit of the confidence interval of the statistical distribution according to the simulation. At this time, the regression coefficients can be expressed as a function of the rate of change of the target approach distance. The following mathematical formula 2 is a set of regression coefficients ( inside ...represents ). In this case, the simulation environment corresponding to the first environmental condition set may have a target type of swirling type 1 model, a detection declaration method of 1-of-1, and a cumulative detection probability calculation method of HM.

[0069]

[0070] In the above mathematical formula 2, is the regression coefficient (k=1 to 6), represents the rate of change of the target approach distance. That is, each regression coefficient can be defined as a function of the rate of change of the target approach distance, and the electronic device (100) has a set of regression coefficients calculated according to a combination of each set of environmental conditions and the value of the rate of change of the target approach distance ( inside ) can be stored in advance in the form of a lookup table.

[0071] For example, if there is a specific set of environmental conditions and a specific rate of change of target approach distance for verifying detection range performance, the electronic device (100) can select a set of regression coefficients corresponding to the specific set of environmental conditions. Subsequently, the electronic device (100) [can select] a given specific rate of change of target approach distance ( Using ) regression coefficients( inside ) can be determined. The electronic device (100) can quickly calculate the margin ratio required in the test environment by substituting a specific number of test repetitions (M) into the aforementioned mathematical formula 1, which consists of the determined regression coefficients.

[0072] In step S250, the electronic device (100) can adjust radar parameters based on a margin ratio. The electronic device (100) can design the margin of the radar parameters so that the required detection range performance can be achieved even in an actual flight test environment where the number of test repetitions is limited. In this case, the margin ratio may be a value representing the additional SNR margin that must be secured relative to the required SNR. For example, if the margin ratio is 1.2, the electronic device (100) can design the radar parameters so that the radar's SNR is at least 1.2 times the required SNR, thereby compensating for the degradation of detection range performance caused by the limited number of test repetitions.

[0073] According to one embodiment, the electronic device (100) can adjust the SNR of the radar by adjusting radar parameters based on a margin ratio. Radar parameters may refer to design variables of the radar internal system that determine the SNR of the radar's received signal. For example, radar parameters may include at least one of the radar's transmit power, the radar's transmit / receive antenna gain, and the radar's frequency, but are not necessarily limited thereto. That is, the electronic device (100) can set the radar's SNR margin by adjusting the radar parameters that affect the SNR upward according to the margin ratio.

[0074] FIG. 3 is a diagram showing an example of the statistical distribution of normalized SNR according to one embodiment. In the following, content corresponding to or overlapping with the above-mentioned content may be briefly explained or omitted.

[0075] Referring to FIG. 3, a graph (300) showing a second statistical distribution of normalized SNR according to the number of test repetitions (M) can be seen. At this time, the normalized SNR may be the ratio of the predicted SNR to the required SNR.

[0076] According to one embodiment, the electronic device (100) can perform a performance simulation according to a set of test variables and a plurality of environmental conditions to confirm a first statistical distribution of predicted SNR that satisfies the required cumulative detection probability of the radar. At this time, the SNR of the radar can be calculated based on the following mathematical formulas 3 and 4.

[0077]

[0078]

[0079] In the above mathematical formula 3, is the SNR at the nth scan of the radar, is the radar detection threshold, n is the number of scans performed, is an offset for modeling the SNR at the first scan (n=1), can represent the rate of change of target approach distance. At this time, It can be determined that the larger the value, the greater the opportunity for detection at a longer distance.

[0080] In the above mathematical formula 4, is the target approach distance per scan, can represent the reference detection range. In this case, the target approach distance per scan may refer to the distance the target approaches the radar per scan, and may be defined as the product of the target approach speed set during flight testing and the time of one scan cycle. Is In the case of or It can refer to the detectable threshold distance between the target and the radar in such cases.

[0081] According to one embodiment, the electronic device (100) performs M virtual flight tests corresponding to the number of test repetitions (M) through performance simulation, and can obtain a predicted SNR that satisfies the required cumulative detection probability based on the simulation results and the above mathematical formulas 3 and 4. Subsequently, the electronic device (100) can calculate a normalized SNR using the following mathematical formula 5.

[0082]

[0083] In the above mathematical formula 5, is the normalized SNR, is a predicted SNR that satisfies the required cumulative detection probability when the number of test iterations (M) is finite, can represent the required SNR that satisfies the required cumulative detection probability when the number of test iterations is infinite. The required SNR can mean an ideal SNR where no statistical uncertainty exists.

[0084] Referring further to the graph (300) of FIG. 3, the portion drawn with a solid line represents a statistical distribution corresponding to the HM method, and the portion drawn with a dotted line represents a statistical distribution corresponding to the FD method. At this time, both statistical distributions can be obtained by Monte Carlo simulation, and the simulation environment can be the same. For example, the simulation environment is a target approach distance change rate ( ) is 0.01, detection threshold( ) is 11.4dB, required cumulative detection probability( ) can be set to 90%.

[0085] Looking at an example of a confidence interval (310) of a statistical distribution illustrated in FIG. 3, the confidence interval may refer to an interval corresponding to an upper and lower range of a certain multiple of the standard deviation centered on the mean of the normalized SNR. According to one embodiment, the electronic device (100) can check the upper limit (311) of the confidence interval (310) and determine a margin ratio to compensate for the degradation of the radar's detection range performance based on the upper limit (311) of the confidence interval.

[0086] FIG. 4 is a flowchart illustrating the operation of an electronic device according to one embodiment to calculate a margin ratio based on test variables and a set of multiple environmental conditions. In the following, content corresponding to or overlapping with the above-mentioned content may be briefly explained or omitted.

[0087] Referring to FIG. 4, in step S410, the electronic device (100) can determine the number of test repetitions and the rate of change of the target approach distance corresponding to the test variable. The number of test repetitions may refer to the number of repetitions of a flight test (or verification test) performed to verify the detection range performance of the radar, and the rate of change of the target approach distance may be a value obtained by dividing the distance at which a target approaches the radar during a scan cycle by the reference detection distance.

[0088] In step S420, the electronic device (100) can determine the type of target based on the RCS fluctuation. For example, the electronic device (100) can determine whether the type of target is a Swerling Type 1 model (Swerling #1) or a Swerling Type 2 model (Swerling #2).

[0089] In step S430, the electronic device (100) can check the radar's detection declaration method. For example, the electronic device (100) can check whether the radar's detection declaration method is a 1-of-1 method or a 3-of-8 method.

[0090] In step S440, the electronic device (100) can check the method of calculating the cumulative detection probability of the radar. For example, the electronic device (100) can check whether the method of calculating the cumulative detection probability of the radar is the HM method or the FD method.

[0091] In step S450, the electronic device (100) can determine the regression coefficients of the regression equation based on the identified target type, the detection declaration method, and the cumulative detection probability calculation method. The electronic device (100) can identify a set of regression coefficients corresponding to a specific set of environmental conditions composed of the target type, the detection declaration method, and the cumulative detection probability calculation method. The electronic device (100) can store in advance the set of regression coefficients calculated according to the combination of each set of environmental conditions and the target approach distance change rate value in the form of a lookup table. Below, examples of mathematical formulas for the set of regression coefficients corresponding to each of the multiple sets of environmental conditions are described. However, an example of the set of regression coefficients corresponding to the first set of environmental conditions among the multiple sets of environmental conditions is omitted as it has been previously described in the detailed explanation of Equation 2 of FIG. 2. Below, mathematical formulas for the second to eighth sets of environmental conditions among the multiple sets of environmental conditions are described.

[0092] The following mathematical formula 6 is a set of regression coefficients corresponding to the second environmental condition set among multiple environmental condition sets ( inside It represents ).

[0093]

[0094] At this time, the simulation environment corresponding to the second environmental condition set may have a target type of swirling type 1 model, a detection declaration method of 1-of-1, and a cumulative detection probability calculation method of FD. The following mathematical formula 7 is a set of regression coefficients corresponding to the third environmental condition set among a plurality of environmental condition sets ( inside It represents ).

[0095]

[0096] At this time, the simulation environment corresponding to the third environmental condition set may have a target type of Swirling Type 1 model, a detection declaration method of the 3-of-8 method, and a cumulative detection probability calculation method of the HM method. The following mathematical formula 8 is a set of regression coefficients corresponding to the fourth environmental condition set among a plurality of environmental condition sets ( inside It represents ).

[0097]

[0098] At this time, the simulation environment corresponding to the fourth environmental condition set may have a target type of swirling type 1 model, a detection declaration method of 3-of-8, and a cumulative detection probability calculation method of FD. The following mathematical formula 9 is a set of regression coefficients corresponding to the fifth environmental condition set among a plurality of environmental condition sets ( inside It represents ).

[0099]

[0100] At this time, the simulation environment corresponding to the fifth environmental condition set may have a target type of swirling type 2 model, a detection declaration method of 1-of-1, and a cumulative detection probability calculation method of HM. The following mathematical formula 10 is a set of regression coefficients corresponding to the sixth environmental condition set among a plurality of environmental condition sets ( inside It represents ).

[0101]

[0102] At this time, the simulation environment corresponding to the 6th environmental condition set may have a target type of swirling type 2 model, a detection declaration method of 1-of-1, and a cumulative detection probability calculation method of FD. The following mathematical formula 11 is a set of regression coefficients corresponding to the 7th environmental condition set among a plurality of environmental condition sets ( inside It represents ).

[0103]

[0104] At this time, the simulation environment corresponding to the 7th environmental condition set may have a target type of swirling type 2 model, a detection declaration method of 3-of-8, and a cumulative detection probability calculation method of HM. The following mathematical formula 12 is a set of regression coefficients corresponding to the 8th environmental condition set among a plurality of environmental condition sets ( inside It represents ).

[0105]

[0106] At this time, the simulation environment corresponding to the 8th environmental condition set may have a target type of swirling type 2 model, a detection declaration method of 3-of-8, and a cumulative detection probability calculation method of FD.

[0107] In step S460, the electronic device (100) can calculate a margin ratio from a regression equation based on the number of test repetitions and the rate of change of the target approach distance. According to one embodiment, the electronic device (100) can select a set of regression coefficients corresponding to a specific set of environmental conditions. Subsequently, the electronic device (100) [calculates] the identified specific rate of change of the target approach distance ( Using ) regression coefficients( inside ) can be determined. The electronic device (100) can quickly calculate the margin ratio required in the test environment by substituting a specific number of test repetitions (M) into the mathematical formula 1 of FIG. 2 described above, which consists of the determined regression coefficients.

[0108] Various embodiments may be implemented through various means. For example, various embodiments may be implemented by hardware, firmware, software, or a combination thereof.

[0109] In the case of implementation by hardware, the method according to various embodiments may be implemented by one or more ASICs (application specific integrated circuits), DSPs (digital signal processors), DSPDs (digital signal processing devices), PLDs (programmable logic devices), FPGAs (field programmable gate arrays), processors, controllers, microcontrollers, microprocessors, etc.

[0110] In the case of implementation by firmware or software, the method according to various embodiments may be implemented in the form of modules, procedures, or functions that perform the functions or operations described above. For example, software code may be stored in memory and executed by a processor. The memory may be located inside or outside the processor and may exchange data with the processor by various known means.

[0111] The electronic device (100) according to the embodiments described above may include a processor, memory for storing and executing program data, permanent storage such as a disk drive, a communication port for communicating with an external device, and user interface devices such as a touch panel, a key, a button, etc. Methods implemented as software modules or algorithms may be stored on a computer-readable recording medium as computer-readable code or program instructions executable on the processor. Here, computer-readable recording media include magnetic storage media (e.g., ROM (read-only memory), floppy disk, hard disk, etc.) and optical reading media (e.g., CD-ROM, DVD: Digital Versatile Disc). Computer-readable recording media may be distributed across networked computer systems, allowing computer-readable code to be stored and executed in a distributed manner. The medium may be readable by a computer, stored in memory, and executed by a processor.

[0112] The present embodiment may be represented by functional block configurations and various processing steps. These functional blocks may be implemented by various numbers of hardware and / or software configurations that execute specific functions. For example, the embodiment may employ integrated circuit configurations such as memory, processing, logic, look-up tables, etc., capable of executing various functions by the control of one or more microprocessors or other control devices. Similar to how components may be implemented as software programming or software elements, the present embodiment may be implemented in programming or scripting languages ​​such as C, C++, Java, assembler, etc., including various algorithms implemented as combinations of data structures, processes, routines, or other programming configurations. Functional aspects may be implemented as algorithms executed on one or more processors. Additionally, the present embodiment may employ prior art for electronic configuration, signal processing, and / or data processing. Terms such as "mechanism," "element," "means," and "configuration" may be used broadly and are not limited to mechanical and physical configurations. The above terms may include the meaning of a series of software processes (routines) in conjunction with processors, etc.

[0113] The aforementioned embodiments are merely examples, and other embodiments may be implemented within the scope of the claims set forth below.

Claims

Claim 1 A method for designing radar parameters performed by an electronic device comprises: a step of identifying a set of environmental conditions and a test variable including the number of test repetitions of a verification test to verify the detection range performance of the radar; a step of performing a performance simulation according to the test variable and the set of environmental conditions to identify a first statistical distribution of a predicted Signal-to-Noise Ratio (SNR) that corresponds to the case where the number of test repetitions is a first number and satisfies the required cumulative detection probability of the radar; a step of normalizing the first statistical distribution based on the required SNR corresponding to the required cumulative detection probability to obtain a second statistical distribution of the normalized SNR; and a step of determining a margin ratio to compensate for the degradation of the detection range performance of the radar when the number of test repetitions is the first number, based on the upper limit of a set confidence interval of the second statistical distribution. A method for designing radar parameters, comprising the step of adjusting radar parameters based on the margin ratio, wherein the step of determining the margin ratio includes: the step of modeling the correlation between the upper limit of the set confidence interval obtained from the test variable and the result of the performance simulation into a regression equation through regression analysis; and the step of calculating the margin ratio when the number of test repetitions is the first number based on substituting the first number corresponding to the number of test repetitions included in the test variable into the regression equation. Claim 2 delete Claim 3 A radar parameter design method according to claim 1, wherein the regression coefficients of the regression equation are determined based on the plurality of sets of environmental conditions. Claim 4 A radar parameter design method according to claim 1, wherein the test variable includes a rate of change in target approach distance, and the rate of change in target approach distance is a value obtained by dividing the distance at which a target approaches the radar during a scan cycle by a reference detection distance. Claim 5 A radar parameter design method according to claim 1, wherein the step of verifying the first statistical distribution includes the step of performing the performance simulation based on the number of test repetitions corresponding to the test variable. Claim 6 A radar parameter design method according to claim 1, wherein a specific set of environmental conditions among the plurality of sets of environmental conditions includes at least one of a condition regarding the type of target according to the Radar Cross Section (RCS) fluctuation, a condition regarding the detection declaration method of the radar, and a condition regarding the cumulative detection probability calculation method of the radar. Claim 7 In claim 6, the method for calculating the cumulative detection probability of the radar comprises at least one of the HM (Hit and Miss) method and the FD (First Detection) method, a radar parameter design method. Claim 8 A method for designing radar parameters according to claim 1, wherein the step of adjusting the radar parameters includes the step of adjusting the SNR of the radar by adjusting the radar parameters based on the margin ratio, and the radar parameters include at least one of the transmission power of the radar, the transmission and reception antenna gain of the radar, and the frequency of the radar. Claim 9 In claim 1, the performance simulation is a radar parameter design method performed based on the Monte Carlo simulation method. Claim 10 In electronic devices, a processor; The system includes one or more memories for storing one or more instructions, wherein, when the one or more instructions are executed, the processor identifies a set of environmental conditions and a test variable including the number of test repetitions of a verification test to verify the detection range performance of the radar, performs a performance simulation according to the test variable and the set of environmental conditions, identifies a first statistical distribution of a predicted Signal-to-Noise Ratio (SNR) corresponding to the case where the number of test repetitions is a first time and satisfies the required cumulative detection probability of the radar, normalizes the first statistical distribution based on the required SNR corresponding to the required cumulative detection probability to obtain a second statistical distribution of the normalized SNR, determines a margin ratio to compensate for the degradation of the detection range performance of the radar when the number of test repetitions is the first time based on the upper limit of a set confidence interval of the second statistical distribution, controls the processor to adjust radar parameters based on the margin ratio, and determines the margin ratio An electronic device comprising: modeling the correlation between the upper limit of the set confidence interval obtained from the above test variable and the result of the above performance simulation into a regression equation through regression analysis; and calculating the margin ratio when the number of test repetitions is the first number based on substituting the first number corresponding to the number of test repetitions included in the above test variable into the regression equation. Claim 11 A computer-readable, non-transient computer-readable storage medium storing a program for executing a radar parameter design method on a computer, wherein the radar parameter design method comprises: a step of identifying a test variable including the number of test repetitions of a verification test to verify the detection range performance of the radar and a set of multiple environmental conditions; a step of performing a performance simulation according to the test variable and the set of multiple environmental conditions to identify a first statistical distribution of a predicted Signal-to-Noise Ratio (SNR) that corresponds to the case where the number of test repetitions is a first number and satisfies the required cumulative detection probability of the radar; a step of normalizing the first statistical distribution based on the required SNR corresponding to the required cumulative detection probability to obtain a second statistical distribution of the normalized SNR; and a step of determining a margin ratio to compensate for the degradation of the detection range performance of the radar when the number of test repetitions is the first number, based on the upper limit of a set confidence interval of the second statistical distribution. A non-transient computer-readable storage medium comprising: a step of adjusting radar parameters based on the margin ratio, wherein the step of determining the margin ratio includes: a step of modeling the correlation between the upper limit of the set confidence interval obtained from the test variable and the result of the performance simulation into a regression equation through regression analysis; and a step of calculating the margin ratio when the number of test repetitions is the first number, based on substituting the first number corresponding to the number of test repetitions included in the test variable into the regression equation.

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