Hybrid cfar-based target detection method applicable to mountainous terrain and apparatus therefor

Abstract

According to one embodiment of the present invention, by applying a hybrid CFAR-based target detection method applicable to mountainous terrain and an apparatus for the same, accurate target detection can be performed by utilizing the complementary characteristics of two different CFAR algorithms, and errors caused by clutter or noise can be minimized by comparing and combining the results of the two algorithms.

Description

Technology Field

[0001] The present invention relates to a hybrid CFAR-based target detection method and an apparatus for the same. Background Technology

[0002] The content described in this section merely provides background information regarding the present embodiment and does not constitute prior art.

[0003] Radar systems are mounted and operated on platforms (e.g., aircraft, satellites). As the platform moves, the radar system collects data, processes the collected data in real time, and provides target information to the operator.

[0004] The radar system detects moving targets by applying the CFAR (Constant False Alarm Rate) algorithm to the collected data.

[0005] CA-CFAR (Cell Averaging Constant False Alarm Rate) is one of the detection methods primarily used in radar and sonar systems to detect and track targets. CFAR processing is used to distinguish targets from signals and maintain a constant false alarm rate by dynamically adjusting thresholds. CA-CFAR is based on a method that uses the average value of reference cells to determine specific thresholds.

[0006] MCA-CFAR (Minimum selected Cell Average Constant False Alarm Rate) is a variation of the CFAR algorithm, the main feature of which is to set the detection threshold using the average of the minimum values ​​selected from the surrounding reference cells.

[0007] The MCA-CFAR algorithm collects data from several surrounding reference cells, selects a specific percentage (e.g., at least 10% or 20%, etc.) of cell values ​​from the collected data, calculates the average of the selected cell values, and uses this average value as the detection threshold.

[0008] The MCA-CFAR algorithm can improve detection performance by ignoring large outliers within the clutter and setting thresholds using minimum values ​​of a certain proportion. MCA-CFAR is designed to enhance detection performance, particularly in environments with high clutter levels. Prior art literature

[0009] Republic of Korea Published Patent Application No. 10-2023-0049207 (April 13, 2023) The problem to be solved

[0010] The objective of the present invention is to provide a hybrid CFAR-based target detection method applicable to mountainous terrain and an apparatus therefor, which can perform accurate target detection by utilizing the complementary characteristics of two different CFAR algorithms and minimize errors caused by clutter or noise by comparing and combining the results of the two algorithms.

[0011] Other unspecified objects of the present invention may be further considered to the extent that they can be easily inferred from the following detailed description and effects. means of solving the problem

[0012] A hybrid CFAR-based target detection device according to an embodiment of the present invention for achieving the above-mentioned purpose comprises: a first CFAR (Constant False Alarm Rate) algorithm processing unit that uses the average value of a plurality of first reference cells around a first target cell as a threshold value; a second CFAR algorithm processing unit that adjusts the value of another second reference cell based on the value of a second reference cell selected from a plurality of second reference cells around a second target cell, and then uses the average value of the adjusted plurality of second reference cells as a threshold value; and a data processing unit that compares the first processing result using the first algorithm with the second processing result using the second algorithm to determine which of the target detected through the first algorithm and the target detected through the second algorithm will be determined as the target.

[0013] The above data processing unit is characterized by detecting the detected target when the same target is detected in both the first processing result and the second processing result.

[0014] The data processing unit is characterized by detecting a target based on the result of detecting a target in either the first processing result or the second processing result and the target detection result of the previous scan, when a target detection result of the previous scan exists.

[0015] The data processing unit is characterized by comparing a first probability distribution based on the first algorithm and a second probability distribution based on the second algorithm when different targets are detected in the first processing result and the second processing result, or when a target is detected in only one of the first processing result and the second processing result and there is no target detection result of the previous scan, and selecting either the first processing result or the second processing result based on the comparison result.

[0016] The data processing unit is characterized by selecting either the first processing result or the second processing result based on the number of the first reference cells and the number of the second reference cells.

[0017] The data processing unit is characterized by comparing the size of a first value obtained by dividing the square of the number of the first reference cell by a first constant and a second value obtained by dividing the square of the number of the second reference cell, and if the first value is greater than the second value, selecting the second processing result, and if the second value is greater than the first value, selecting the first processing result.

[0018] Herein, it is characterized by further including a characteristic data adjustment unit that adjusts the first signal characteristic data of the received signal.

[0019] Herein, it is characterized by further including a filter processing unit that extracts second signal characteristic data of a received signal.

[0020] The first algorithm is characterized as being a CA-CFAR (Cell Averaging Constant False Alarm Rate) algorithm, and the second algorithm is characterized as being an MCA-CFAR (Minimum Selected Cell Average Constant False Alarm Rate) algorithm.

[0021] A target detection method performed in a device comprising a memory in which one or more programs for detecting a target according to an embodiment of the present invention for achieving the above-described purpose are stored, and one or more processors that perform operations according to said one or more programs, comprises: a first CFAR (Constant False Alarm Rate) algorithm processing step in which the average value of a plurality of first reference cells around a first target cell is used as a threshold value; a second CFAR algorithm processing step in which the value of another second reference cell is adjusted based on the value of a second reference cell selected among a plurality of second reference cells around a second target cell, and then the average value of the adjusted plurality of second reference cells is used as a threshold value; and a data processing step in which a first processing result using said first algorithm and a second processing result using said second algorithm are compared to determine which of the target detected through said first algorithm and the target detected through said second algorithm is determined as the target.

[0022] The data processing step is characterized by comparing a first probability distribution based on the first algorithm and a second probability distribution based on the second algorithm when different targets are detected in the first processing result and the second processing result, or when a target is detected in only one of the first processing result and the second processing result and there is no target detection result of the previous scan, and selecting either the first processing result or the second processing result based on the comparison result.

[0023] The above data processing step is characterized by selecting either the first processing result or the second processing result based on the number of the first reference cells and the number of the second reference cells.

[0024] A computer program according to one embodiment of the present invention for achieving the above-described purpose is stored on a computer-readable recording medium and executes any one of the above-described hybrid CFAR target detection methods on a computer. Effects of the invention

[0025] As described above, according to one embodiment of the present invention, by applying a hybrid CFAR-based target detection method applicable to mountainous terrain and an apparatus for the same, accurate target detection can be performed by utilizing the complementary characteristics of two different CFAR algorithms, and errors caused by clutter or noise can be minimized by comparing and combining the results of the two algorithms.

[0026] According to one embodiment of the present invention, by applying a hybrid CFAR-based target detection method applicable to mountainous terrain and an apparatus for the same, targets can be detected even in a multi-target environment and targets can be stably detected even in a complex clutter environment.

[0027] Even if an effect is not explicitly mentioned herein, the effects and potential effects described in the following specification expected by the technical features of the present invention are treated as described in the specification of the present invention. Brief explanation of the drawing

[0028] Figure 1 is a block diagram illustrating target detection using the CA-CFAR algorithm. Figure 2(a) is a topographic analysis map of a section where strong terrain clutter occurs, and Figure 2(b) is a diagram showing the threshold value of the CA-CFAR algorithm applied to the section where strong terrain clutter occurs in Figure 2(a). FIG. 3 is a block diagram illustrating the configuration of a CFAR-based target detection device according to an embodiment of the present invention. FIG. 4 is a flowchart illustrating a CFAR-based target detection method according to an embodiment of the present invention. Figure 5 is a diagram showing the signal processing results using a CFAR algorithm when a target with a small RCS moves at a Doppler velocity adjacent to the clutter in terrain where strong terrain clutter occurs. Figure 6 is a diagram showing the signal processing results using the CFAR algorithm in general terrain. FIG. 7 is a diagram illustrating a first CFAR algorithm used by a CFAR-based target detection device according to an embodiment of the present invention. FIG. 8 is a diagram illustrating a second CFAR algorithm used by a CFAR-based target detection device according to an embodiment of the present invention. FIG. 9 is a diagram illustrating a hybrid CFAR algorithm in which a first CFAR algorithm and a second CFAR algorithm are merged and used by a CFAR-based target detection device according to an embodiment of the present invention. FIG. 10 is a diagram illustrating operations performed in the data processing unit of a CFAR-based target detection device according to an embodiment of the present invention. FIG. 11 is a block diagram illustrating the hardware configuration of a CFAR-based target detection device configured to perform a CFAR-based target detection method according to an embodiment of the present invention. FIG. 12 (a) is a diagram showing the signal processing result performed by applying only the CA-CFAR algorithm, and FIG. 12 (b) is a diagram showing the signal processing result performed by applying the hybrid CFAR algorithm according to the present invention. Specific details for implementing the invention

[0029] Hereinafter, embodiments of the present invention will be described in detail with reference to the attached drawings. 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 attached 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. Unless otherwise defined, all terms used in this specification (including technical and scientific terms) may be used in a meaning that is commonly understood by those skilled in the art to which the present invention belongs. Furthermore, terms defined in commonly used dictionaries are not to be interpreted ideally or excessively unless explicitly and specifically defined otherwise.

[0030] The terms used in this application are used merely to describe specific embodiments and are not intended to limit the invention. Singular expressions include plural expressions unless the context clearly indicates otherwise. In this application, terms such as “have,” “may have,” “include,” or “may include” are intended to indicate the existence of the features, numbers, steps, actions, components, parts, or combinations thereof described in the specification, and should be understood as not precluding the existence or addition of one or more other features, numbers, steps, actions, components, parts, or combinations thereof. Terms including ordinal numbers, such as “second,” “first,” etc., may be used to describe various components, but said components are not limited by said terms.

[0031] The above terms are used solely for the purpose of distinguishing one component from another. For example, without departing from the scope of the present invention, the second component may be named the first component, and similarly, the first component may be named the second component. The term "and / or" includes a combination of a plurality of related described items or any of a plurality of related described items.

[0032] In this specification, identification symbols (e.g., a, b, c, etc.) for each step are used for convenience of explanation and do not indicate the order of the steps; the steps may occur differently from the specified order unless the context clearly indicates a specific order. That is, the steps may occur in the same order as specified, may be performed substantially simultaneously, or may be performed in the reverse order.

[0033] Additionally, the term “part” as used in this specification may refer to software or hardware components such as field-programmable gate arrays (FPGAs) or ASICs, and the “part” performs certain roles. However, the “part” is not limited to software or hardware. The “part” may be configured to reside in an addressable storage medium or configured to run one or more processors. Thus, as an example, the “part” may include components such as software components, object-oriented software components, class components, and task components, as well as processes, functions, attributes, procedures, subroutines, segments of program code, drivers, firmware, microcode, circuits, data structures, and variables. The functions provided within the components and “parts” may be combined into a smaller number of components and “parts” or further separated into additional components and “parts.”

[0034] Figure 1 is a block diagram illustrating target detection using the CA-CFAR algorithm. Figure 2(a) is a terrain analysis map of a section where strong terrain clutter occurs, and Figure 2(b) is a diagram showing the threshold value of the CA-CFAR algorithm applied to the section where strong terrain clutter occurs in Figure 2(a).

[0035] Radar performs the function of detecting targets through signal processing (pulse compression, Doppler filter, CFAR) and data processing (tracking filter, target detection). It detects targets while maintaining a constant false alarm rate through CFAR (Constant False Alarm Rate) processing.

[0036] In the domestic radar operation situation, a large number of radio equipment are concentrated in specific high-altitude areas with excellent radio environments to secure line of sight in the limited territory, and systems that emit high-power transmission signals for long-range detection, such as weather radar and early warning radar, are deployed and operated in close proximity, causing radio interference frequently.

[0037] Due to radio interference and terrain clutter occurring in mountainous terrain, it is difficult to detect targets in clutter boundary environments (Doppler adjacent to clutter) penetrating at low altitudes.

[0038] Based on the actual radar data collected, a phenomenon is identified where the target detection rate decreases in some sections, and when a target with a small RCS (Radar Cross Section) passes through this section, the reflected signal of the target is small compared to the terrain clutter, so the threshold value of CA-CFAR increases, and a CFAR masking phenomenon is confirmed in which the small target signal is not detected.

[0039] Targets are detected through CA-CFAR processing, which offers superior performance in uniform environments with relatively small interference and variations in the target environment during radar signal processing.

[0040] In areas with strong terrain clutter, if a target with a relatively small RCS moves at a Doppler velocity adjacent to the clutter, the CA-CFAR threshold is formed higher than the target's signal due to the strong terrain clutter signal, increasing the likelihood of failure to detect it.

[0041] CA-CFAR experiences a degradation in target detection performance if strong clutter signals are present in adjacent sections along the distance direction.

[0042] In the present invention, a section with strong terrain clutter may refer to a section with complex terrain characteristics in a radar system. For example, a section with strong terrain clutter may refer to a mountainous area, but is not necessarily limited thereto; it may be a densely built area or an area with a high concentration of other physical obstacles, and in such areas, radar signals exhibit strong clutter due to the characteristics of the terrain.

[0043] Clutter refers to signals within a radar's received signal that are not the desired target signal, specifically signals reflected from terrain, buildings, trees, flocks of birds, etc. Such clutter interferes with the accurate detection and tracking of targets.

[0044] Various embodiments of a hybrid CFAR-based target detection method applicable to mountainous terrain and an apparatus for the same (hereinafter referred to as the CFAR-based target detection method and apparatus) according to the present invention will be described in detail below with reference to the attached drawings.

[0045] FIG. 3 is a block diagram illustrating the configuration of a CFAR-based target detection device according to an embodiment of the present invention.

[0046] Referring to FIG. 3, a CFAR-based target detection device may include a signal processing unit (110) and a data processing unit (120).

[0047] The signal processing unit (110) may include a characteristic data adjustment unit (112), a filter processing unit (114), a first CFAR algorithm processing unit (116), and a second CFAR algorithm processing unit (118).

[0048] The signal processing unit (110) can adjust the characteristics of the received radar signal, filter it, detect targets through a plurality of CFAR algorithms, and finally select which target to determine as the target.

[0049] The characteristic data adjustment unit (112) can adjust the first signal characteristic data of the received signal. The characteristic data adjustment unit (112) can perform pulse compression on the received signal after radar radio wave transmission to increase the SNR (Signal / Noise Rate).

[0050] The filter processing unit (114) can extract second signal characteristic data of the received signal. The filter processing unit can perform an operation to extract the Doppler component of the signal.

[0051] The first CFAR algorithm processing unit (116) can perform operations according to the first CFAR (Constant False Alarm Rate) algorithm using the average value of a plurality of first reference cells around the first target cell (Cell) as a threshold value.

[0052] The first algorithm processing unit and the second algorithm processing unit can each perform CFAR processing for target detection.

[0053] The first algorithm may be the CA-CFAR (Cell Averaging Constant False Alarm Rate) algorithm.

[0054] The second CFAR algorithm processing unit (118) can perform an operation according to the second CFAR algorithm that uses the average value of the adjusted multiple second reference cells as a threshold value after adjusting the value of another second reference cell based on the value of the second reference cell selected among the multiple second reference cells around the second target cell.

[0055] The second algorithm may be the MCA-CFAR (Minimum selected Cell Average Constant False Alarm Rate) algorithm.

[0056] The data processing unit (120) can determine which of the targets detected through the first algorithm and the targets detected through the second algorithm is to be determined as the target by comparing the first processing result using the first algorithm and the second processing result using the second algorithm.

[0057] The data processing unit can detect the detected target when the same target is detected in both the first processing result and the second processing result.

[0058] If the data processing unit detects a target in only one of the first processing result and the second processing result, and a target detection result from a previous scan exists, it can detect a target based on the result of detecting a target in either the first processing result or the second processing result and the target detection result from the previous scan.

[0059] If different targets are detected in the first processing result and the second processing result, or if a target is detected in only one of the first processing result and the second processing result and there is no target detection result from the previous scan, the data processing unit compares the first probability distribution based on the first algorithm and the second probability distribution based on the second algorithm, and can select either the first processing result or the second processing result based on the comparison result.

[0060] The data processing unit can select either the first processing result or the second processing result based on the number of the first reference cells and the number of the second reference cells.

[0061] The data processing unit compares the size of a first value obtained by dividing the square of the number of a first reference cell by a first constant with a second value obtained by dividing the square of the number of a second reference cell, and if the first value is greater than the second value, selects the second processing result, and if the second value is greater than the first value, selects the first processing result.

[0062] The data processing unit can display information about the determined target through a display (not shown).

[0063] Not all blocks shown in FIG. 3 are essential components, and in other embodiments, some blocks connected to the CFAR-based target detection device may be added, changed, or deleted.

[0064] FIG. 4 is a flowchart illustrating a CFAR-based target detection method according to an embodiment of the present invention.

[0065] A CFAR-based target detection method may be performed in a CFAR-based target detection device. A CFAR-based target detection device may be a device comprising a memory in which one or more programs for detecting targets are stored, and one or more processors that perform operations according to one or more programs.

[0066] In step S410, the processor can perform processing of a first CFAR (Constant False Alarm Rate) algorithm using the average value of a plurality of first reference cells around a first target cell as a threshold value.

[0067] In step S420, the processor can perform a second CFAR algorithm process using the average value of the adjusted multiple second reference cells as a threshold value after adjusting the value of another second reference cell based on the value of the second reference cell selected from among multiple second reference cells around the second target cell.

[0068] In step S430, the processor can perform data processing to determine which of the targets detected through the first algorithm and the targets detected through the second algorithm is to be determined as the target by comparing the first processing result using the first algorithm and the second processing result using the second algorithm.

[0069] Although FIG. 4 describes each process as being executed sequentially, this is merely an illustrative description, and a person skilled in the art may modify and adapt it in various ways without departing from the essential characteristics of the embodiment of the present invention, such as changing the order described in FIG. 4, executing one or more processes in parallel, or adding other processes.

[0070] Figure 5 is a diagram showing the signal processing results using a CFAR algorithm when a target with a small RCS moves at a Doppler velocity adjacent to the clutter in terrain where strong terrain clutter occurs.

[0071] Looking at the CA-CFAR processing results, the threshold is formed higher than the target signal due to the large signal of adjacent terrain clutter, so the target cannot be detected.

[0072] Looking at the MCA-CFAR processing results, targets are detected without being affected by large signals from terrain clutter by calculating a threshold value that prioritizes selecting the minimum value from comparisons with surrounding signals.

[0073] Figure 6 is a diagram showing the signal processing results using the CFAR algorithm in general terrain.

[0074] Since CFAR is an algorithm that detects targets while maintaining a constant false alarm rate through threshold adjustment, it includes a probabilistic element, and there are CFARs with different advantages and disadvantages.

[0075] In general areas, targets are intermittently not detected by MCA-CFAR but are detected by CA-CFAR.

[0076] This result is due to the fact that CA-CFAR has the advantage of excellent performance in a uniform environment.

[0077] The present invention improves the detection performance of moving targets in terrain clutter areas and maintains the detection performance of moving targets in general areas by processing with a hybrid CFAR during the radar signal processing process. The hybrid CFAR combines the advantages of CA-CFAR and MCA-CFAR, processing both methods and integrating the results.

[0078] FIG. 7 is a diagram illustrating a first CFAR algorithm used by a CFAR-based target detection device according to an embodiment of the present invention.

[0079] To obtain predictable and consistent performance in radar systems, CFAR algorithms are generally used that can adjust thresholds to maintain a constant false alarm rate from real-time data.

[0080] CA-CFAR is relatively simple in that it calculates a threshold based on the average of reference cells surrounding the target cell, and performs well in environments with relatively little interference and variation; however, its performance may decrease in cases with significant variation, such as multi-target or clutter environments.

[0081] In the CA-CFAR structure, there are three types of cells. Target cells evaluate the presence of a target; guard cells are placed around target cells to secure an area unaffected by the target; and reference cells are used to calculate thresholds and are the surrounding cells excluding target and guard cells.

[0082] In the present invention, the number of first reference cells and the number of second reference cells used by the first CFAR algorithm processing unit and the second CFAR algorithm processing unit, respectively, can be varied.

[0083] For example, the number of first reference cells and the number of second reference cells may be changed depending on factors such as the clutter environment, the size or nature of the target, and the analysis bandwidth.

[0084] The first CFAR algorithm processing unit and the second CFAR algorithm processing unit can use more reference cells as the environment has more clutter.

[0085] The first CFAR algorithm processing unit and the second CFAR algorithm processing unit can utilize more reference cells in a wide bandwidth.

[0086] The first CFAR algorithm processing unit and the second CFAR algorithm processing unit can use more reference cells as the size of the target increases.

[0087] FIG. 8 is a diagram illustrating a second CFAR algorithm used by a CFAR-based target detection device according to an embodiment of the present invention.

[0088] MCA-CFAR operates by comparing the target cell with cells spaced a specific distance from a reference cell to replace the minimum value, and then calculating the threshold value using the average value of the reference cell.

[0089] It has the advantage of being able to reliably detect targets in multi-target or clutter environments.

[0090] FIG. 9 is a diagram illustrating a hybrid CFAR algorithm in which a first CFAR algorithm and a second CFAR algorithm are merged and used by a CFAR-based target detection device according to an embodiment of the present invention.

[0091] Hybrid CFAR combines CA-CFAR and MCA-CFAR to detect targets.

[0092] By performing target detection while maintaining the false alarm rate through secondary data processing in the data processing unit, the target detection performance of the radar operating in mountainous terrain can be significantly improved.

[0093] However, since signal processing computation time increases, hardware performance and computational structure optimization for real-time processing must be considered.

[0094] According to another embodiment of the present invention, the signal processing unit may determine the number of first reference cells used in the first algorithm processing unit or the number of second reference cells used in the second algorithm processing unit based on the amount of clutter.

[0095] The signal processing unit can control the first CFAR algorithm processing unit and the second CFAR algorithm processing unit so that the number of second reference cells is greater than the number of first reference cells when the amount of clutter is greater than or equal to a preset threshold (for example, when a hybrid CFAR-based target detection device is used in mountainous terrain).

[0096] The signal processing unit can control the first CFAR algorithm processing unit and the second CFAR algorithm processing unit so that when the amount of clutter is smaller than a preset threshold, the number of the first reference cell is greater than the number of the second reference cell.

[0097] The amount of clutter can be calculated through various methods, such as clutter density based on the number of reflected signals generated by clutter within a specific area, clutter intensity based on the strength or magnitude of reflected signals from clutter, spectral analysis analyzing the Toffler characteristics of clutter, and spatial distribution based on the location or spatial distribution of clutter.

[0098] The preset threshold value may be determined differently depending on the characteristics of the target to be detected. For example, the preset threshold value may be determined as a first threshold value if the target to be detected is an aircraft, as a second threshold value if the target to be detected is an underwater vehicle, and as a third threshold value if the target to be detected is a ground vehicle.

[0099] In the case of an aircraft, interference from ground clutter may be minimal because it moves in the atmosphere, but interference may occur due to other aircraft, birds, or changes in the atmosphere. Therefore, the first threshold value may be set relatively low.

[0100] In the case of underwater vehicles, since water has the characteristic of attenuating signals, a high threshold may need to be set to overcome signal attenuation. Therefore, the second threshold may be set higher than the first threshold for detecting aircraft.

[0101] In the case of ground vehicles, there may be various clutter and interferences on the ground, such as buildings, trees, and terrain. However, these interferences are generally less complex than those in underwater environments. Therefore, the third threshold for detecting ground vehicles can be set lower than that for underwater vehicles but higher than that for aircraft.

[0102] Therefore, the second threshold can be set to be greater than the third threshold, and the third threshold can be set to be greater than the first threshold.

[0103] In addition, the preset threshold can be adjusted in the event of consecutive target detection failures or missing targets, based on the target's movement speed, or if new electronic equipment in the vicinity begins to operate or the level of noise changes.

[0104] For example, a preset threshold may be set to decrease whenever target detection fails consecutively or a target is missed, and the preset threshold may also be set to temporarily increase when new electronic equipment is operating around the CFAR-based target detection device or when the level of noise increases.

[0105] FIG. 10 is a diagram illustrating operations performed in the data processing unit of a CFAR-based target detection device according to an embodiment of the present invention.

[0106] After receiving each CFAR processing result from the data processing unit, secondary data processing is performed.

[0107] The data processing unit generates a hit map of the CFAR results and detects the target if both are detected, and if only one of the two is detected, it checks whether the previous scan's location-based target was detected and then detects the target.

[0108] In addition, if there are no previous scan targets, the Exponential noise probability distribution is calculated, and whether to detect a target is determined based on the CFAR result with the lowest probability distribution.

[0109] For example, the data processing unit can detect a target based on the processing result using the second algorithm in the case of mathematical formula 1, and can detect a target based on the processing result using the first algorithm in the case of mathematical formula 2.

[0110]

[0111]

[0112] Here, n cawa n mca can represent the number of reference cells used in the CA and MCA methods, respectively.

[0113] |n ca | and | n mca | can also represent the log-normal distributions of the CA and MCA methods, respectively.

[0114] In addition, although it is stated that the constant 2 is included in mathematical formulas 1 and 2, it is obvious that the present invention is not necessarily limited to the constant 2 and can be implemented with other constants.

[0115] The false alarm rate for false targets can be maintained through selective result processing using probability distributions.

[0116] Results of simulating the noise probability distribution for CA-CFAR and MCA-CFAR processing with system thermal noise (|n ca | 2 ) / 2≒|n mca | 2 It derives results such as the above and can be utilized in the data processing unit when comparing the Exponential noise probability distribution.

[0117] FIG. 11 is a block diagram illustrating the hardware configuration of a CFAR-based target detection device configured to perform a CFAR-based target detection method according to an embodiment of the present invention.

[0118] A CFAR-based target detection device (100) includes at least one processor (130), a computer-readable storage medium (140), and a communication bus (170).

[0119] The processor (130) can be controlled to operate as a CFAR-based target detection device (100). For example, the processor (130) can execute one or more programs (141) stored in a computer-readable storage medium (140). One or more programs (141) may include one or more computer-executable instructions, and the computer-executable instructions may be configured to cause the CFAR-based target detection device (100) to perform operations according to exemplary embodiments when executed by the processor (130). The processor can perform operations for the CFAR-based target detection method described through FIG. 4.

[0120] A computer-readable storage medium (140) is configured to store computer-executable instructions or program code, program data and / or other suitable forms of information. Computer-executable instructions or program code, program data and / or other suitable forms of information may also be provided through an input / output interface (150) or a communication interface (160). A program (140) stored in a computer-readable storage medium (140) comprises a set of instructions executable by a processor (130). In one embodiment, the computer-readable storage medium (140) may be memory (volatile memory such as random access memory, non-volatile memory, or a suitable combination thereof), one or more magnetic disk storage devices, optical disk storage devices, flash memory devices, or other forms of storage media that can be accessed by a CFAR-based target detection device (100) and store desired information, or a suitable combination thereof.

[0121] The communication bus (170) interconnects various other components of the CFAR-based target detection device (100), including the processor (130) and the computer-readable storage medium (140).

[0122] The CFAR-based target detection device (100) may also include one or more input / output interfaces (150) and one or more communication interfaces (160) that provide interfaces for one or more input / output devices. The input / output interface (150) and the communication interface (160) are connected to a communication bus (170). An input / output device (not shown) may be connected to other components of the CFAR-based target detection device (100) through the input / output interface (150).

[0123] FIG. 12 (a) is a diagram showing the signal processing result performed by applying only the CA-CFAR algorithm, and FIG. 12 (b) is a diagram showing the signal processing result performed by applying the hybrid CFAR algorithm according to the present invention.

[0124] According to the present invention, target detection performance can be improved through MCA-CFAR signal processing to detect small targets adjacent to radio interference or terrain clutter.

[0125] In addition, target detection performance is maintained in general areas by parallel processing of CA-CFAR signals, which perform well in uniform environments rather than terrain clutter areas.

[0126] In addition, through hybrid CFAR signal processing incorporating these advantages, not only is existing detection performance maintained in general areas, but detection performance for low-altitude infiltrating targets in terrain clutter areas can also be improved.

[0127] The present application also provides a computer storage medium. Program instructions are stored in the computer storage medium, and when the program instructions are executed by a processor, the above-described hybrid CFAR-based target detection method is realized.

[0128] A computer storage medium according to one embodiment of the present invention may be a U disk, SD card, PD optical drive, mobile hard disk, large capacity floppy drive, flash memory, multimedia memory card, server, etc., but is not necessarily limited thereto.

[0129] The present invention proposes a composite CFAR application method considering mountainous terrain conditions. The present invention proposes a CFAR algorithm to secure excellent detection performance of radar in harsh conditions where homogeneous and heterogeneous environments coexist, due to the characteristics of mountainous terrain and peninsulas surrounded by sea on three sides, such as South Korea.

[0130] The present invention is a technology capable of improving detection performance in clutter-boundary environments, such as mountainous terrain where clutter is large and interference occurs, by applying a hybrid-CFAR that combines the advantages of CA (Cell Average)-CFAR, which performs well in uniform environments, and MCA (Minimum Selected Cell Average)-CFAR, which enhances detection performance in non-uniform environments.

[0131] The present invention can be utilized in civilian or military radars that need to detect targets in areas within the radar detection zone where terrain clutter signals are frequently generated.

[0132] The present invention proposes a technology that can improve the detection performance of targets infiltrating at low altitudes in mountainous terrain by applying composite-CFAR technology in radar signal processing.

[0133] Although it is described that all components constituting the embodiments of the present invention described above are combined or operate in combination, the present invention is not necessarily limited to such embodiments. That is, within the scope of the purpose of the present invention, all such components may be selectively combined in one or more ways to operate. Furthermore, while all such components may each be implemented as a single independent piece of hardware, they may also be implemented as a computer program having a program module that performs some or all of the combined functions in one or more pieces of hardware by selectively combining some or all of the components. Additionally, such a computer program may be stored on a computer-readable media such as a USB memory, CD disk, or flash memory, and read and executed by a computer to implement the embodiments of the present invention. Magnetic recording media, optical recording media, etc., may be included as recording media for the computer program.

[0134] The foregoing description is merely an illustrative explanation of the technical concept of the present invention, and those skilled in the art to which the present invention pertains will be able to make various modifications, changes, and substitutions within the scope of the essential characteristics of the present invention. Accordingly, the embodiments disclosed in the present invention and the accompanying drawings are intended to explain, not limit, the technical concept of the present invention, and the scope of the technical concept of the present invention is not limited by such embodiments and accompanying drawings. The scope of protection of the present invention shall be interpreted by the claims below, and all technical concepts within an equivalent scope shall be interpreted as being included within the scope of rights of the present invention. Explanation of the symbols

[0135] 100: Hybrid CFAR-based target detection device 110: Signal processing unit 112: Feature Data Adjustment Section 114: Filter processing unit 116: 1st CFAR Algorithm Processing Unit 118: Second CFAR Algorithm Processing Unit 120: Data processing unit

Claims

Claim 1 A first CFAR (Constant False Alarm Rate) algorithm processing unit that uses the average value of a plurality of first reference cells surrounding a first target cell as a threshold value; a second CFAR algorithm processing unit that adjusts the value of another second reference cell based on the value of a second reference cell selected from a plurality of second reference cells surrounding a second target cell, and then uses the average value of the adjusted plurality of second reference cells as a threshold value; a data processing unit that compares the first processing result using the first algorithm with the second processing result using the second algorithm to determine which of the target detected through the first algorithm and the target detected through the second algorithm is to be determined as the target; and a signal processing unit that determines the number of first reference cells used in the first algorithm processing unit or the number of second reference cells used in the second algorithm processing unit based on the amount of clutter.The signal processing unit controls the first algorithm processing unit and the second algorithm processing unit such that the number of the second reference cells is greater than the number of the first reference cells when the amount of clutter is greater than or equal to a preset threshold, and controls the first algorithm processing unit and the second algorithm processing unit such that the number of the first reference cells is greater than the number of the second reference cells when the amount of clutter is less than the preset threshold; the data processing unit receives the first processing result and the second processing result as input, performs secondary data processing, generates a heat map for the first processing result and the second processing result, and when different targets are detected in the first processing result and the second processing result, or when a target is detected in only one of the first processing result and the second processing result and there is no target detection result of the previous scan, compares a first value obtained by dividing the square of the number of the first reference cells by a first constant with a second value obtained by dividing the square of the number of the second reference cells, and if the first value is greater than the second value, the second processing A hybrid CFAR-based target detection device characterized by selecting a result and selecting the first processing result when the second value is greater than the first value. Claim 2 A hybrid CFAR-based target detection device according to claim 1, wherein the data processing unit detects the detected target when the same target is detected in both the first processing result and the second processing result. Claim 3 A hybrid CFAR-based target detection device according to claim 1, wherein the data processing unit detects a target in only one of the first processing result and the second processing result, and if a target detection result of a previous scan exists, the data processing unit detects a target based on the result of detecting a target in the first processing result or the second processing result and the target detection result of the previous scan. Claim 4 delete Claim 5 delete Claim 6 delete Claim 7 A hybrid CFAR-based target detection device according to claim 1, further comprising a characteristic data adjustment unit that adjusts the first signal characteristic data of a received signal. Claim 8 A hybrid CFAR-based target detection device according to claim 7, further comprising a filter processing unit for extracting second signal characteristic data of a received signal. Claim 9 A hybrid CFAR-based target detection device according to claim 1, characterized in that the first algorithm is a CA-CFAR (Cell Averaging Constant False Alarm Rate) algorithm and the second algorithm is an MCA-CFAR (Minimum selected Cell Average Constant False Alarm Rate) algorithm. Claim 10 A target detection method performed in a device comprising a memory storing one or more programs for detecting a target and one or more processors performing operations according to said one or more programs, the method comprising: a first CFAR (Constant False Alarm Rate) algorithm processing step using the average value of a plurality of first reference cells surrounding a first target cell as a threshold value; a second CFAR algorithm processing step using the average value of the adjusted plurality of second reference cells as a threshold value after adjusting the value of another second reference cell based on the value of a second reference cell selected from a plurality of second reference cells surrounding a second target cell; a data processing step determining which of the target detected through the first algorithm and the target detected through the second algorithm is determined as the target by comparing the first processing result using the first algorithm and the second processing result using the second algorithm; and a signal processing step determining the number of first reference cells used in the first CFAR algorithm processing step or the number of second reference cells used in the second CFAR algorithm processing step based on the amount of clutter.A hybrid type comprising, wherein the signal processing step controls the number of the second reference cells to be greater than the number of the first reference cells when the amount of clutter is greater than or equal to a preset threshold, and controls the number of the first reference cells to be greater than the number of the second reference cells when the amount of clutter is less than the preset threshold; and the data processing step receives the first processing result and the second processing result as input, performs secondary data processing, generates a heat map for the first processing result and the second processing result, and, in the case where different targets are detected in the first processing result and the second processing result, or where a target is detected in only one of the first processing result and the second processing result and there is no target detection result of the previous scan, compares a first value obtained by dividing the square of the number of the first reference cells by a first constant with a second value obtained by dividing the square of the number of the second reference cells, and selects the second processing result if the first value is greater than the second value, and selects the first processing result if the second value is greater than the first value. CFAR-based target detection method. Claim 11 delete Claim 12 delete Claim 13 A computer program stored on a computer-readable recording medium to execute the hybrid CFAR-based target detection method described in paragraph 10 on a computer.

Images