KR102215062B1 - Apparatus for processing signal of multi-mode radar for detecting unmanned aerial vehicle and method thereof - Google Patents

Abstract

The present invention discloses a multi-mode radar signal processing apparatus and method for detecting a UAV. That is, the present invention supports both a PD radar signal processing processor and an FMCW radar signal processing processor in a single hardware, so that both a target located at a long distance and a target located at a short distance can be detected.

Description

TECHNICAL FIELD [0002] Apparatus for processing signal of multi-mode radar for detecting unmanned aerial vehicle and method thereof

The present invention relates to a multimode radar signal processing apparatus and method for detecting unmanned aerial vehicles, and in particular, a PD radar signal processing processor and an FMCW radar signal processing processor in a single hardware to detect both a target located at a long distance and a target located at a short distance. It relates to a multimode radar signal processing apparatus and method for detecting unmanned aerial vehicles that support all of them.

The radar system transmits an electromagnetic wave signal and then detects the reflected signal on the target to measure the distance and speed to the target. Because it is hardly affected by the environment such as day and night, the radar, camera, ultrasonic, infrared It has a great advantage over sensors such as sensors. Accordingly, recently, radar technology has been applied not only to the defense field of aircraft and ships, but also to drones, automobiles, and medical treatments, and various studies are being conducted.

The transmission waveform of radar is largely divided into PD (pulse doppler) radar and FMCW (Frequency Modulated Continuous Wave) radar, and the system operation method is different depending on the transmission waveform. The PD radar calculates target information from the received pulse signal by transmitting a constant pulse. The FMCW radar is a method of continuously transmitting and receiving a frequency modulated signal to obtain target information using a bit signal that is the difference between a transmitted signal and a received signal. The PD radar method has the advantage of being able to detect a long-distance target with high peak transmission power, but it cannot be received while transmitting a pulse, making it difficult to detect a short-range target. On the other hand, since the FMCW radar method transmits and receives continuously, there is no blind range, and it has a higher resolution than the PD radar, which is advantageous for short-range target detection, but has limitations in detecting long-range objects due to limited transmission power. . FMCW radar has a triangular waveform method that uses both an up chirp and a down chirp according to a transmission waveform, and a sawtooth wave method that transmits only using either an upward chirp or a downward chirp. The triangular wave type FMCW radar can obtain the distance and speed from the target by low-complexity calculation through the combination of the beat frequencies of the upward chirp and the downward chirp, but when there are multiple targets, numerous combinations of beat frequencies occur. It is difficult to distinguish between a target and a false target. On the other hand, the sawtooth wave type FMCW radar continuously transmits multiple chirp signals by increasing the slope of the sawtooth wave and uses a fast-chirp train method, which means that the detected beat frequency corresponds to the distance to the target. Since it is easy to detect multiple targets, it is used in many applications compared to the triangular wave FMCW radar.

In particular, since UAVs operated in various fields such as transportation, logistics, construction and aerial photography are exposed to various environments, it is necessary to detect them in advance and provide location information for safe operation of UAVs.

Korean Patent Registration No. 10-1295061 [Title: FMCW radar signal processing module and its memory management method]

An object of the present invention is a multimode radar signal processing apparatus for unmanned aerial vehicle detection that supports both a PD radar signal processing processor and an FMCW radar signal processing processor in a single hardware so as to detect both a target located at a long distance and a target located at a short distance, and the same There is a way to provide.

Another object of the present invention is to provide a multimode radar signal processing apparatus and method for detecting an unmanned aerial vehicle that share a signal processing processor and a memory commonly required in PD radar and FMCW radar.

According to an embodiment of the present invention, a multimode radar signal processing apparatus for detecting a UAV includes: an ADC unit that performs a sampling function on a received signal and outputs a sampled bit frequency signal; A preprocessor for outputting a preprocessed output value by performing a preprocessing function on the sampled bit frequency signal output by the ADC unit; An FFT processor for calculating distance information from a target in an FMCW radar by performing a multiplication operation in a frequency domain based on the preprocessed output value; In the FMCW radar calculated by the FFT processor, the distance information from the target and the transmission data stored in the reference lookup table are multiplied in the frequency domain, and then returned to the time domain. I-FFT processor for outputting information; According to the distance to the target calculated based on the received signal and the transmitted signal, the output value of the FFT processor, which is the output result of the FMCW radar, or the output value of the I-FFT processor, which is the output result of the PD radar, is selected and output as a first output value. The first mux to do; A memory controller sequentially storing a first output value output from the first mux in a 2D memory unit; A second mux for outputting data having the same distance bin index among data stored in the 2D memory unit as a second output value; And calculating the distance to the fixed target, the distance to the moving target, and the speed of the moving target based on the first output value output from the first mux and the second output value output from the second mux, and clutter and noise. It may include a post-processing unit that determines whether or not the target is an actual target and outputs the calculated distance to the fixed target, the distance to the moving target, and the speed of the moving target.

As an example related to the present invention, the preprocessor may include a buffer temporarily storing the sampled beat frequency signal in order to remove a DC offset of the received signal reflected by a target; When the number of data temporarily stored in the buffer becomes a preset number, an average is calculated for the preset number of temporarily stored data, and a difference value between the preset number of temporarily stored data and the calculated average is calculated, respectively. DC removal unit to calculate; And a multiplication operation between a preset weight and a plurality of calculated difference values in order to reduce the magnitude of the side lobe signal generated by performing the FFT function, and the result of the multiplication operation is the preprocessed output value according to the execution of the preprocessing function. It may include a Hamming window that outputs as a result.

, 상기 R1은 FMCW 레이더에 의한 표적과의 거리를 나타내고, 상기 B는 대역폭을 나타내고, 상기 T는 한 첩의 시간을 나타내고, 상기 c는 신호의 전파 속도를 나타낼 수 있다.As an example related to the present invention, the FFT processor calculates distance information from the FMCW radar to the target by the following equation,

, R1 denotes a distance to a target by an FMCW radar, B denotes a bandwidth, T denotes a time of a concubine, and c denotes a propagation speed of a signal.

, 상기 y[n]은 송신 신호와 수신 신호 사이의 상관 연산(correlation)이고, 상기 x[n]은 수신 신호이고, 상기 h[n]은 선형 주파수 변조된 송신 펄스 신호를 나타내고, 상기 n은 인덱스를 나타낼 수 있다.As an example related to the present invention, the I-FFT processor includes a process of calculating a correlation operation between a transmission signal and a reception signal by the following equation,

, Y[n] is a correlation between a transmission signal and a reception signal, x[n] is a reception signal, h[n] represents a linear frequency modulated transmission pulse signal, and n is Can indicate index.

, 상기 R2는 PD 레이더에 의한 표적과의 거리를 나타내고, 상기 △t는 송신 신호와 수신 신호의 지연 시간을 나타내고, 상기 c는 신호의 전파 속도를 나타낼 수 있다.As an example related to the present invention, the I-FFT processor multiplies a sampling time for the received signal by an index representing a peak value in the calculated correlation operation (y[n]) to determine the delay time between the transmitted signal and the received signal ( Δt) is calculated, and the distance to the target in the PD radar is calculated by the following equation,

, R2 denotes a distance to a target by a PD radar, Δt denotes a delay time between a transmission signal and a received signal, and c denotes a propagation speed of the signal.

As an example related to the present invention, the post-processing unit may include an MTI processor that calculates a component of the moving target by removing a fixed target component from a first output value output from the first mux.

As an example related to the present invention, the post-processing unit may further include an I-MTI processor that calculates a component of a fixed target by removing a moving target component from a first output value output from the first mux.

As an example related to the present invention, the MTI processor and the I-MTI processor may each calculate a component of a moving target and a component of a fixed target by applying a recursive algorithm to reduce complexity.

As an example related to the present invention, the post-processing unit may further include a Doppler FFT processor that calculates a speed of a moving target based on a second output value output from the second mux.

, 상기 f₀는 중심 주파수를 나타내고, 상기 f_d는 도플러 주파수를 나타낼 수 있다.As an example related to the present invention, the Doppler FFT processor accumulates data of a range bin of one PRI (pulse repetition interval) by a product of a distance bin and the number of pulses, and FFT the accumulated distance bin data. It is calculated by performing an operation to calculate the Doppler frequency and calculating the speed of the moving target by the following equation,

, F ₀ may represent a center frequency, and f _d may represent a Doppler frequency.

As an example related to the present invention, the post-processor calculates an average of some of the first output values by applying a CFAR algorithm based on a first output value output from the first mux to calculate an adaptive threshold value, It may further include a CFAR and a peak detector for comparing the first output value and the calculated adaptive threshold value.

As an example related to the present invention, the CFAR and peak detection unit, when the comparison result, when the first output value is greater than the calculated adaptive threshold value, determine the frequency component, and the calculated distance to the fixed target, The distance to the moving target and the speed of the moving target can be output.

As an example related to the present invention, the CFAR algorithm may include any one of a CA CFAR algorithm, an SO CFAR algorithm, and a GO CFAR algorithm.

According to an embodiment of the present invention, a multimode radar signal processing method for detecting a UAV includes the steps of: outputting a sampled bit frequency signal by performing a sampling function on a received signal by an ADC unit; Outputting a preprocessed output value by performing a preprocessing function on the sampled beat frequency signal output by the ADC unit by a preprocessor; Calculating distance information from a target in an FMCW radar by performing a multiplication operation in a frequency domain based on the preprocessed output value, by an FFT processor; In the FMCW radar calculated by the FFT processor, the I-FFT processor multiplies the distance information from the target and the transmission data stored in the reference lookup table in the frequency domain, and then returns the result back to the time domain as PD. Outputting information on a distance from a target on a radar; By the first mux, the output value of the FFT processor, which is an output result of the FMCW radar or the output value of the I-FFT processor, is selected according to the distance between the target calculated based on the received signal and the transmitted signal. Outputting a first output value; Sequentially storing, by a memory controller, a first output value output from the first mux in a 2D memory unit; Outputting, by a second mux, data having the same distance bin index among data stored in the 2D memory unit as a second output value; Calculating, by a post-processing unit, a distance to a fixed target, a distance to a moving target, and a speed of a moving target based on a first output value output from the first mux and a second output value output from the second mux. ; And outputting the calculated distance to the fixed target, the distance to the moving target, and the speed of the moving target, by the post-processing unit, by determining whether the target is an actual target from clutter and noise. I can.

As an example related to the present invention, the step of performing a preprocessing function on the sampled bit frequency signal and outputting a preprocessed output value includes, in order to remove a DC offset of the received signal reflected by a target, the sampled bit frequency Temporarily storing the signal in a buffer; Calculating an average of the preset number of temporarily stored data when the number of data temporarily stored in the buffer becomes a preset number; Calculating a difference value between the predetermined number of temporarily stored data and the calculated average; Outputting a plurality of difference values corresponding to the calculated predetermined number; And a multiplication operation between a preset weight and a plurality of calculated difference values in order to reduce the magnitude of the side lobe signal generated by performing the FFT function, and the result of the multiplication operation is the preprocessed output value according to the execution of the preprocessing function. It may include a process of outputting to.

, 상기 R1은 FMCW 레이더에 의한 표적과의 거리를 나타내고, 상기 B는 대역폭을 나타내고, 상기 T는 한 첩의 시간을 나타내고, 상기 c는 신호의 전파 속도를 나타낼 수 있다.As an example related to the present invention, the step of calculating distance information from the target in the FMCW radar is calculated by the following equation,

, R1 denotes a distance to a target by an FMCW radar, B denotes a bandwidth, T denotes a time of a concubine, and c denotes a propagation speed of a signal.

, 상기 y[n]은 송신 신호와 수신 신호 사이의 상관 연산(correlation)이고, 상기 x[n]은 수신 신호이고, 상기 h[n]은 선형 주파수 변조된 송신 펄스 신호를 나타내고, 상기 n은 인덱스를 나타낼 수 있다.As an example related to the present invention, the step of outputting information on a distance to a target in the PD radar includes a step of calculating a correlation calculation between a transmitted signal and a received signal by the following equation,

, Y[n] is a correlation between a transmission signal and a reception signal, x[n] is a reception signal, h[n] represents a linear frequency modulated transmission pulse signal, and n is Can indicate index.

, 상기 R2는 PD 레이더에 의한 표적과의 거리를 나타내고, 상기 △t는 송신 신호와 수신 신호의 지연 시간을 나타내고, 상기 c는 신호의 전파 속도를 나타낼 수 있다.As an example related to the present invention, the outputting of the distance information from the PD radar as distance information from the target may be performed by multiplying an index representing a peak value in the calculated correlation calculation (y[n]) by a sampling time for the received signal. Calculating a delay time (Δt) between the transmission signal and the reception signal; And calculating the distance to the target in the PD radar by the following equation,

, R2 denotes a distance to a target by a PD radar, Δt denotes a delay time between a transmission signal and a received signal, and c denotes a propagation speed of the signal.

As an example related to the present invention, the distance to the moving target is calculated by removing the fixed target component from the first output value output from the first mux by the MTI processor included in the post-processing unit. can do.

As an example related to the present invention, the distance to the fixed target is a component of the fixed target by removing a moving target component from the first output value output from the first mux by the I-MTI processor included in the post-processing unit. Can be calculated.

As an example related to the present invention, the MTI processor and the I-MTI processor may each calculate a component of a moving target and a component of a fixed target by applying a recursive algorithm to reduce complexity.

As an example related to the present invention, the speed of the moving target may be calculated based on a second output value output from the second mux by a Doppler FFT processor included in the post-processing unit.

, 상기 f₀는 중심 주파수를 나타내고, 상기 f_d는 도플러 주파수를 나타낼 수 있다.As an example related to the present invention, the speed of the moving target is a process of accumulating data of a distance bin of one PRI by a product of a distance bin and the number of pulses by a Doppler FFT processor included in the post-processing unit; Calculating a Doppler frequency by performing an FFT operation on the accumulated distance bin data by the Doppler FFT processor; And calculating the speed of the moving target by the following equation, by the Doppler FFT processor,

, F ₀ may represent a center frequency, and f _d may represent a Doppler frequency.

As an example related to the present invention, the step of determining whether the target is the actual target may include applying a CFAR algorithm based on a first output value output from the first mux by a CFAR and a peak detection unit included in the post-processing unit. Calculating an adaptive threshold value by calculating an average of some of the first output values; Comparing the first output value and the calculated adaptive threshold value by the CFAR and the peak detector; And as a result of the comparison, when the first output value is greater than the calculated adaptive threshold value, the CFAR and the peak detector determine it as a frequency component, and the calculated distance to the fixed target and the distance to the moving target. And outputting the speed of the moving target.

As an example related to the present invention, the CFAR algorithm may include any one of a CA CFAR algorithm, an SO CFAR algorithm, and a GO CFAR algorithm.

According to an embodiment of the present invention, a multimode radar signal processing apparatus for detecting a UAV includes: an ADC unit that performs a sampling function on a received signal and outputs a sampled bit frequency signal; When the sampled bit frequency signal is temporarily stored in a buffer, and the number of data temporarily stored in the buffer becomes a preset number, an average is calculated for the preset number of temporarily stored data, and the preset number of A DC removal unit that calculates a difference value between the temporarily stored data and the calculated average, and outputs a plurality of difference values corresponding to the calculated predetermined number; A hamming window unit that performs a multiplication operation between a preset weight and the calculated difference values, and outputs a result of the multiplication operation as a preprocessed output value according to the execution of a preprocessing function; An FFT processor for calculating distance information from a target in an FMCW radar by performing a multiplication operation in a frequency domain based on the preprocessed output value; In the FMCW radar calculated by the FFT, the distance information from the target and the transmission data stored in the reference lookup table are multiplied in the frequency domain, and the result of returning to the time domain is returned to the target distance information in the PD radar. I-FFT processor to output to; According to the distance to the target calculated based on the received signal and the transmitted signal, the output value of the FFT processor, which is the output result of the FMCW radar, or the output value of the I-FFT processor, which is the output result of the PD radar, is selected and output as a first output value. The first mux to do; A memory controller configured to control a 2D memory unit to sequentially store first output values output from the first mux; A second mux for outputting data having the same distance bin index among data stored in the 2D memory unit as a second output value; An MTI processor for calculating a distance to the moving target by removing a fixed target component from a first output value output from the first mux by applying a recursive algorithm; An I-MTI processor for calculating a distance to a fixed target by removing a moving target component from a first output value output from the first mux by applying a recursive algorithm; A Doppler FFT processor that calculates a velocity of a moving target based on a second output value output from the second mux; And calculating an average of some of the first output values by applying a CFAR algorithm based on the first output value output from the first mux, calculating an adaptive threshold value, and calculating the first output value and the calculated adaptive threshold. Values are compared, and as a result of the comparison, when the first output value is larger than the calculated adaptive threshold value, it is determined as a frequency component, and the calculated distance to the fixed target, the distance to the moving target, and the moving target It may include a CFAR outputting speed and a peak detection unit.

According to an embodiment of the present invention, a multimode radar signal processing method for detecting a UAV includes the steps of: outputting a sampled bit frequency signal by performing a sampling function on a received signal by an ADC unit; Temporarily storing the sampled beat frequency signal in a buffer by a DC removal unit; Calculating an average of the preset number of temporarily stored data when the number of data temporarily stored in the buffer becomes a preset number, by the DC removal unit; Calculating, by the DC removing unit, a difference value between the predetermined number of temporarily stored data and the calculated average, and outputting a plurality of difference values corresponding to the calculated predetermined number; Performing, by a Hamming window unit, a multiplication operation between a preset weight and a plurality of calculated difference values, and outputting a result of the multiplication operation as a preprocessed output value according to execution of a preprocessing function; Calculating distance information from a target in an FMCW radar by performing a multiplication operation in a frequency domain based on the preprocessed output value, by an FFT processor; In the FMCW radar calculated by the FFT, the I-FFT processor performs a multiplication operation on the distance information to the target and the transmission data stored in the reference lookup table in the frequency domain, and returns the result back to the time domain in the PD radar. Outputting the distance information from the target to the target; By the first mux, the output value of the FFT processor, which is an output result of the FMCW radar or the output value of the I-FFT processor, is selected according to the distance between the target calculated based on the received signal and the transmitted signal. Outputting a first output value; Sequentially storing, by a memory controller, a first output value output from the first mux in a 2D memory unit; Outputting, by a second mux, data having the same distance bin index among data stored in the 2D memory unit as a second output value; Calculating a distance to the moving target by removing a fixed target component from the first output value output from the first mux by applying a recursive algorithm, by an MTI processor; Calculating a distance to a fixed target by removing a moving target component from the first output value output from the first mux by applying a recursive algorithm, by an I-MTI processor; Calculating, by a Doppler FFT processor, a speed of a moving target based on a second output value output from the second mux; Calculating an adaptive threshold value by calculating an average of some of the first output values by applying a CFAR algorithm based on the first output value output from the first mux by a CFAR and a peak detection unit; Comparing the first output value with the calculated adaptive threshold value by the CFAR and the peak detector; And as a result of the comparison, when the first output value is greater than the calculated adaptive threshold value, the CFAR and the peak detector determine it as a frequency component, and the calculated distance to the fixed target and the distance to the moving target. And outputting the speed of the moving target.

The present invention supports both a PD radar signal processing processor and an FMCW radar signal processing processor in a single hardware, so that it is possible to detect both a target located at a long distance and a target located at a short distance.

In addition, the present invention has the effect of improving overall system operation efficiency and enabling design with low complexity by sharing a signal processing processor and a memory commonly required in PD radar and FMCW radar.

1 is a block diagram showing the configuration of a multimode radar signal processing apparatus for detecting a UAV according to an embodiment of the present invention.
2 is a diagram illustrating an example of a transmission/reception signal of a PD radar according to an embodiment of the present invention.
3 is a diagram showing an example of a hardware structure of an FFT processor according to an embodiment of the present invention.
4 is a diagram showing an example of a hardware structure of the CFAR and peak detection unit according to an embodiment of the present invention.
5 is a diagram showing a distance-speed map in which targets moving at a constant speed are detected by a multimode radar signal processing apparatus according to an embodiment of the present invention.
6 is a diagram illustrating an example of an FPGA platform-based verification environment for a multimode radar signal processing apparatus according to an embodiment of the present invention.
7 is a flowchart illustrating a method of processing a multimode radar signal for detection of a UAV according to an embodiment of the present invention.

It should be noted that the technical terms used in the present invention are used only to describe specific embodiments, and are not intended to limit the present invention. In addition, the technical terms used in the present invention should be interpreted as generally understood by those of ordinary skill in the technical field to which the present invention belongs, unless otherwise defined in the present invention, and is excessively comprehensive. It should not be construed as a human meaning or an excessively reduced meaning. In addition, when a technical term used in the present invention is an incorrect technical term that does not accurately express the spirit of the present invention, it should be replaced with a technical term that can be correctly understood by those skilled in the art. In addition, general terms used in the present invention should be interpreted as defined in the dictionary or according to the context before and after, and should not be interpreted as an excessively reduced meaning.

In addition, the singular expression used in the present invention includes a plurality of expressions unless the context clearly indicates otherwise. In the present invention, terms such as “consisting of” or “comprising” should not be construed as necessarily including all of the various components or steps described in the invention, and some components or some steps may not be included. It should be construed that it may or may further include additional components or steps.

In addition, terms including ordinal numbers such as first and second used in the present invention may be used to describe the constituent elements, but the constituent elements should not be limited by the terms. The terms are used only for the purpose of distinguishing one component from another. For example, without departing from the scope of the present invention, a first component may be referred to as a second component, and similarly, a second component may be referred to as a first component.

Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings, but the same or similar components are assigned the same reference numerals regardless of the reference numerals, and redundant descriptions thereof will be omitted.

In addition, in describing the present invention, when it is determined that a detailed description of a related known technology may obscure the subject matter of the present invention, a detailed description thereof will be omitted. In addition, it should be noted that the accompanying drawings are only for easily understanding the spirit of the present invention and should not be construed as limiting the spirit of the present invention by the accompanying drawings.

1 is a block diagram showing the configuration of a multimode radar signal processing apparatus 10 for detecting a UAV according to an embodiment of the present invention.

As shown in FIG. 1, the multimode radar signal processing apparatus 10 for unmanned aerial vehicle detection includes an ADC unit 100, a preprocessor 200, an FFT processor 300, a reference lookup table 400, and an I-FFT. It is composed of a processor 500, a first mux 600, a 2D memory unit 700, a memory controller 800, a second mux 900, and a post-processing unit 1000. Not all components of the multimode radar signal processing apparatus 10 shown in FIG. 1 are essential components, and the multimode radar signal processing apparatus 10 is implemented by more components than those shown in FIG. 1 Alternatively, the multimode radar signal processing apparatus 10 may be implemented with fewer components.

The ADC unit (Analog-Digital Converter) 100 performs a sampling (or sampling) function on a received signal received through a receiving unit (not shown), and the sampled (or sampled) bit frequency for the received signal. Output the signal.

That is, as shown in FIG. 2, a transmission signal (or radar signal) during a phase matching processing interval (CPI: coherent processing interval) is a short pulse (not shown) through an antenna (not shown) at a constant period (PRI: pulse repetition interval). τ) is transmitted (or radiated), and the signal reflected from the target (or the received signal) is received through the receiving unit until the next pulse is transmitted according to the maximum detection distance standard, and the ADC unit 100 receives the received signal. Performs a sampling function on the signal and outputs a sampled beat frequency signal.

The preprocessing unit 200 includes a sampled bit frequency signal (or output data of the ADC unit 100) that is an output of the ADC unit 100 in order to improve the detection performance of a target included in the received signal. Performs the preprocessing function for

In addition, as shown in FIG. 1, the preprocessor 200 includes a DC removal unit 210, a buffer 220, and a hamming window unit 230.

The DC removal unit 210 removes the DC offset of the signal (or the received signal) reflected by the target, the output data of the ADC unit 100 (or the ADC unit ( The sampled beat frequency signal, which is the output of 100), is temporarily stored in the buffer 220.

In addition, the DC removal unit 210 calculates an average of the output data of the ADC unit 100 of a preset number (for example, 1024 pieces) temporarily stored in the buffer 220.

That is, the DC removal unit 210 calculates an average of the preset number of temporarily stored data when the number of data temporarily stored in the buffer 220 becomes the preset number (or a preset number). .

Further, the DC removal unit 210 calculates a difference value between the preset number of output data (or the preset number of temporarily stored data) of the ADC unit 100 and the calculated average, respectively, and the calculation A plurality of difference values corresponding to the preset number are output.

In this way, the DC removal unit 210 calculates and outputs the difference between the output data of the ADC unit 100 and the average value of one pup, and the input data of the DC removal unit 210 (or the ADC unit The output data of (100)) is stored in the buffer 220 having the size of a pup, calculates an average, and performs a subtraction operation between the input data and the average, and outputs the difference value.

The buffer 220 stores the output data of the ADC unit 100 in the preset number unit (or chirp unit).

For example, as shown in FIG. 1, the buffer 220 may be configured in a 24×1024 shape.

In addition, in order to reduce the size of a side lobe signal generated by performing the FFT function, the Hamming window 230 includes a preset weight (or preset position-specific parameter) and the calculated A multiplication operation between a plurality of difference values (or a plurality of difference values output from the DC removal unit 210) is performed, and a result of the multiplication operation is output.

That is, the Hamming window unit 230 performs a pre-set Hamming window parameter and a multiplication operation on the signal of the DC offset from which the DC offset has been removed by the DC removal unit 210, and preprocesses the result of the multiplication operation. Output as an output value of the unit 200 (or an output value of the Hamming window unit 230 / an output value according to the execution of the preprocessing function).

The FFT processor (Fast Fourier Transform Processor) 300 performs a multiplication operation in the frequency domain based on the output value of the preprocessing unit 200 (or the output value of the Hamming window unit 230) to match the target in the FMCW radar. Calculate distance information (or output result of FMCW radar).

That is, the FFT processor 300 calculates distance information from the FMCW radar to the target through the multiplication operation in the frequency domain using the following [Equation 1].

Here, R1 denotes the distance to the target by the FMCW radar, B denotes the bandwidth, T denotes the time of one lap, and c denotes the propagation speed of the signal.

In addition, the output value of the FFT processor 300 is provided as an input of the first mux 600 for selectively outputting FMCW distance information and an input of the I-FFT processor 500 for a pulse compression technique (or separated) )do. In this case, the FMCW distance information indicates distance information from the target in the FMCW radar.

3 is a diagram showing an example of a hardware structure of an FFT processor 300 according to an embodiment of the present invention.

In the FFT algorithm, radix-2 and radix-4 algorithms are generally used, and radix-4 is superior to radix-2 in terms of yield, but since the structure of the butterfly operation is relatively complicated, in order to reduce the complexity of the high radix algorithm. The radix-2 ² and radix-2 ³ algorithms have been proposed. The radix-2 ² algorithm has the same butterfly operation structure as the radix-2 algorithm and has the same number of complex multiplication as radix-4. The radix-2 ³ algorithm can reduce complex multiplication compared to the radix-2 ² algorithm by implementing non-simple multiplication as a simple multiplication using three radix-2 butterfly operators.

In addition, as the hardware structure of the FFT processor 300, a pipeline method that satisfies the exchange relationship between complexity and yield is mainly used, and the pipeline structure of the FFT processor 300 is largely SDF (Single-path Delay Feedback). ) And MDC (Multi-path Delay Commutator) structure.

Particularly, the SDF pipeline structure is widely used due to its feature of minimizing non-simple multiplication, which has the greatest complexity in a single path.

Accordingly, the FFT processor 300, the IFFT processor 500, and the Doppler FFT processor 1030 used in the multimode radar signal processing apparatus 10 according to the embodiment of the present invention all have the same SDF pipeline structure. Can be designed as

The reference lookup table 400 stores a transmission signal transmitted (or radiated) through the antenna. In this case, the transmission signal stored in the reference lookup table 400 may be signal information converted from a frequency domain to a time domain.

The I-FFT processor (Inverse-Fast Fourier Transform Processor) 500 multiplies the output value of the FFT processor 300 and the transmission data (or transmission signal information) stored in the reference lookup table 400 in a frequency domain. After execution, the result of returning to the time domain (or the output value of the I-FFT processor 500/distance information from the target in the PD radar/PD distance information) is output.

That is, the I-FFT processor 500 must perform a pulse compression method to obtain distance information from a target with improved distance resolution in a PD radar. At this time, the pulse compression technique is a convolution-based operation in the following [Equation 2]. Since the multiplication operation is applied to all range bins as much as the pulse size and then the addition operation is performed, a considerable amount of operation is required. Will have. The I-FFT processor 500 uses the same characteristics as the multiplication operation in the frequency domain for the convolution operation in the time domain to reduce the amount of computation, and the FFT processor 300 as shown in [Equation 3] below. ), the multiplication operation is performed in the frequency domain, and then returned to the time domain.

Here, y[n] is a correlation between a transmission signal and a reception signal, x[n] is a reception signal, h[n] represents a linear frequency modulated transmission pulse signal, and n Represents an index corresponding to a preset number (for example, 1024).

In this way, if the pulse compression technique is performed by an operation such as [Equation 3], as shown in FIG. 1, the FFT processor 300 for the received signal (or the received signal) is FMCW It can be used in common with the radar's range FFT (range FFT) processor, reducing complexity.

In addition, the I-FFT processor 500 multiplies a level value (or index) representing a peak value among the calculated correlation calculations (y[n]) by a sampling time for the received signal to delay the transmission signal and the reception signal. Calculate the time (Δt).

In addition, the I-FFT processor 500 calculates the distance to the target by the PD radar according to the following [Equation 4].

Here, R2 represents the distance to the target by the PD radar, Δt represents the delay time between the transmission signal and the reception signal, and c represents the propagation speed of the signal.

The first MUX 600 transmits (or receives) the output value of the FFT processor 300, which is an output result of the FMCW radar, and the output value of the I-FFT processor 500, which is an output result of the PD radar. ) Receive.

In addition, the first mux 600 is the output value of the FFT processor 300, which is an output result of the FMCW radar, or the I, which is an output result of the PD radar, according to the distance between the target calculated based on the received signal and the transmission signal. -Select the output value of the FFT processor 500.

In addition, the first mux 600 outputs the selected output value of the FFT processor 300 or the output value of the I-FFT processor 500 as a first output value (or an output value of the first mux 600). .

At this time, the distance to the target by the FMCW radar may be a state calculated by [Equation 1], and the distance to the target by the PD radar may be a state calculated by [Equation 4].

The 2D memory unit 700 is configured in a matrix form corresponding to a preset number (or preset chirps) (for example, 1024 pieces).

For example, as shown in FIG. 1, the 2D memory unit 700 may be formed in an N×M shape. Here, N and M may be natural numbers.

In addition, the 2D memory unit 700 is a first output value (for example, an output value of the FFT processor 300 or the I-FFT processor 500) output from the first mux 600 under the control of the memory controller 800. ) Output values) are stored sequentially. In this case, the horizontal axis of the 2D memory unit 700 corresponds to PRI, and the vertical axis may correspond to the number of pulses.

The memory controller 800 manages a storage location of data stored in the 2D memory unit 700 and data to be loaded (or read data) from among data stored in the 2D memory unit 700.

That is, the memory controller 800 performs a read function for data stored in the 2D memory unit 700 and/or a management function for a write function.

The second mux 900 receives data having the same distance bin index among data (or output values) sequentially stored in the 2D memory unit 700 under the control of the memory controller 800 as a second output value (or the second output value). Output value of the mux 900).

The post processing unit 1000 is based on a first output value output from the first mux 600 and a second output value output from the second mux 900, the distance between the fixed target and the moving target It calculates the distance to the target and the speed of a moving target.

In addition, the post-processing unit 1000 determines whether the target is an actual target from clutter and noise, and outputs the calculated distance to the fixed target, the distance to the moving target, and the speed of the moving target in the case of a target.

In addition, as shown in FIG. 1, the post-processing unit 1000 includes an MTI processor 1010, an I-MTI processor 1020, a Doppler FFT processor 1030, and a CFAR and peak detection unit 1040.

The MTI processor (moving target indicator processor) 1010 calculates a distance to a moving target based on a first output value output from the first mux 600.

That is, the MTI processor 1010 removes (or filters) a fixed target component from the first output value output from the first mux 600 to calculate a component of the moving target (eg, including distance).

The I-MTI processor (inverse-moving target indicator processor) 1020 calculates a distance to a fixed target based on a first output value output from the first mux 600.

That is, the I-MTI processor 1020 calculates a component of a fixed target (eg, including distance) by removing a moving target component from the first output value output from the first mux 600.

At this time, the MTI processor 1010 and the I-MTI processor 1020 each calculate a component of a moving target and a component of a fixed target by applying a recursive algorithm to reduce complexity.

The Doppler FFT processor 1030 calculates the speed of the moving target based on a second output value output from the second mux 900.

In addition, the Doppler FFT processor 1030 uses Doppler filtering, which is a signal processing method to obtain a Doppler frequency component, and the Doppler frequency component is reflected from the target during CPI and the same distance bins in which the received signal exists. Since it is expressed as a phase component of the data, the Doppler frequency is calculated through FFT operation.

That is, the Doppler frequency can be obtained by accumulating the data of the distance bin of one PRI by the product of the distance bin and the number of pulses, and then obtaining the accumulated distance bin data through FFT operation. Is required.

In addition, after obtaining the Doppler component, the Doppler FFT processor 1030 can calculate the velocity (V) of the moving target from the center frequency (f ₀ ) and the Doppler frequency (f _d ) using the following [Equation 5]. I can.

In this way, the Doppler FFT processor 1030 calculates the Doppler frequency component by performing FFT processing on the same index of the FFT processing result of each chirp in the same manner as the PD radar, and the FMCW radar also corresponds to the number of FFT points in a chirp. Since the memory required is as much as the number of chirps transmitted, it is possible to obtain the Doppler component of the target only by using 2D memory.

The CFAR and peak detection unit (CFAR & Peak Detection) 1040 applies a CFAR algorithm (constant false alarm rate algorithm) based on the first output value output from the first mux 600 to determine whether or not the target is the final target. Thus, an average of some of the first output values is calculated to calculate an adaptive threshold. In this case, the CFAR algorithm may use a CA cell averaging CFAR (CFAR) algorithm, a smallest of CFAR (SO CFAR) algorithm, and a GO greater of CFAR (CFAR) algorithm.

In addition, the CFAR and peak detection unit 1040 compares the first output value with the calculated adaptive threshold value, and when the first output value is greater than the calculated adaptive threshold value, determines that it is a frequency component. MTI processor 1010, the I-MTI processor 1020, and the moving target component (Rmov, distance) calculated by the Doppler FFT processor 1030, respectively, the component of the fixed target (Rsta) and the speed of the moving target Each (Vmov) is output.

Further, as a result of the comparison, when the first output value is less than or equal to the calculated adaptive threshold value, the CFAR and peak detection unit 1040 outputs error information.

4 is a diagram showing an example of a hardware structure of the CFAR and peak detection unit 1040 according to an embodiment of the present invention.

The CFAR and peak detection unit 1040 sets a threshold level using an average value of a reference cell around a test cell (T), and compares the test cell with the threshold value. to be. Here, the register in the center of the center is called a test cell, and each of the left and right registers is a guard cell (G), and the index for the peak value of the target after the FFT processor 300 is determined. As a standard, the left and right values have a size similar to the peak value, and are excluded from the average when calculating the threshold value. In addition, the remaining left and right registers are reference cells and are used to obtain a threshold value from the average.

In addition, after the average operation, the CFAR and peak detection unit 1040 determines a final threshold value by performing shift and adder operations to adjust the size of the threshold value.

In addition, the CFAR and peak detection unit 1040 compares the test cell with the threshold value, determines that the test cell is a target only when it is greater than the threshold value, stores the corresponding index, and stores the MTI in relation to the corresponding index. The component of the moving target (Rmov, distance), the component of the fixed target (Rsta) and the speed of the moving target calculated by the processor 1010, the I-MTI processor 1020, and the Doppler FFT processor 1030, respectively ( Vmov) respectively.

In the embodiment of the present invention, the description is focused on a single target, but the present invention is not limited thereto, and the multimode radar signal processing apparatus 10 can be applied to multiple targets.

Recently, UAVs have been applied to the private sector beyond the military sector and are being operated in various environments. The PD radar has the advantage of being easy to detect targets located at a long distance, and the FMCW radar has characteristics suitable for detecting targets located at a short distance, and in order to improve the detection performance of UAVs operating in various environments, the multimode radar The signal processing device 10 is configured (for example, MATLAB) to support both the PD radar waveform and the FMCW radar waveform, and the signal processing method of each radar.

[Table 1] shows an example of parameters of the multimode radar signal processing apparatus 10 according to an embodiment of the present invention.

parameter FMCW PD Center frequency(GHz) 24 Bandwidth(MHz) 250 64 Sampling Frequency(MHz) 16 256 △R(m) 0.6 2.34 Rmax(m) 154 600

That is, in the 24 GHz band, the PD radar used a bandwidth of 64 MHz and a sampling frequency of 256 MHz, and accordingly, the distance resolution was 2.34 m and the maximum detection distance was 600 m.

On the other hand, the FMCW radar used a bandwidth of 250MHz and a sampling frequency of 16MHz in the 24GHZ band, and accordingly, the distance resolution was set to 0.6m and the maximum detection distance was set to 154m.

The multi-mode radar signal processing apparatus 10 determines an operation mode based on the maximum detection distance of the PD radar and the FMCW radar.

In other words, if the target is more than 154m to less than 600m, which is the maximum detection distance of PD radar, based on the maximum detection distance of 154m of the FMCW radar, the PD radar operates, and if the target is within 154m, the FMCW radar operates. So that the target can be detected.

5 is a diagram showing a distance-speed map in which targets moving at a constant speed are detected by the multimode radar signal processing apparatus 10 according to an embodiment of the present invention.

Target 1 shown in FIG. 5 is assumed to move at a speed of 40 m/s at a position of 200 m from the radar, and target 2 is assumed to move at a speed of 15 m/s at a position of 170 m. In addition, target 3 was assumed to move at a speed of -10m/s at a position of 80m.

As shown in FIG. 5, target 1 and target 2 detected from the distance-speed map are measured by PD radar because they are located at 154m or more from the radar, whereas target 3 within 154m is measured by FMCW radar. I can confirm.

6 is a diagram illustrating an example of a verification environment based on a field programmable gate array (FPGA) platform for the multimode radar signal processing apparatus 10 according to an embodiment of the present invention.

That is, the multimode radar signal processing apparatus 10 was designed and implemented using an Altera Cyclone-IV FPGA device after designing a register-transfer level (RTL) using verilog-HDL.

[Table 2] shows the results of the Cyclone IV FPGA-based implementation of the multimode radar signal processing apparatus 10.

Blocks Logic elements Register Memory(bits) DC Removal 103 72 24,576 Hamming Window 2,039 49 0 FFT 4,947 2,618 0 IFFT 4,947 2,618 0 Doppler FFT 4,947 2,618 0 CFAR 2,640 1,784 0 2D Memory 0 0 25,165,824 Top Block 19,623 9,759 25,190,400

As shown in [Table 2], as a result of the synthesis, it was confirmed that 19.623 logic elements, 9,759 registers, and 25,190,400 memories were implemented.

[Table 3] shows the results implemented when a conventional PD radar signal processing processor and an FMCW radar signal processing processor are designed using the same device, and a multimode radar signal processing apparatus 10 according to an embodiment of the present invention Found that about 43% and 39% reduction, respectively, compared to logic elements and registers that combine the existing PD radar signal processing processor and FMCW radar signal processing processor.

Logic elements Register Memory(bits) FMCW+PD RSP Processor 34,299 19,623 50,380,800 Multi-mode RSP processor 19,623 9,759 25,190,400

As such, it is possible to support both the PD radar signal processing processor and the FMCW radar signal processing processor in a single hardware.

In addition, in this way, a signal processing processor and a memory commonly required in PD radar and FMCW radar can be shared with each other.

Hereinafter, a method of processing a multimode radar signal for detecting an unmanned aerial vehicle according to the present invention will be described in detail with reference to FIGS. 1 to 7.

7 is a flowchart illustrating a method of processing a multimode radar signal for detection of a UAV according to an embodiment of the present invention.

First, the ADC unit 100 performs a sampling (or sampling) function on a received signal received through a receiving unit (not shown), and outputs a sampled (or sampled) bit frequency signal for the received signal.

That is, during the phase matching processing period (CPI), the transmission signal (or radar signal) is transmitted (or radiated) through an antenna (not shown) at a constant period (PRI), and a signal reflected from the target (or the reception Signal) is received through the receiving unit until the next pulse is transmitted according to the maximum detection distance criterion, and the ADC unit 100 performs a sampling function on the received received signal and outputs a sampled beat frequency signal.

For example, the ADC unit 100 performs a sampling function on a signal reflected from a target received through the reception unit, and outputs a plurality of sampled bit frequency signals (S710).

Thereafter, the preprocessor 200 preprocesses the sampled bit frequency signal (or the output data of the ADC unit 100) that is the output of the ADC unit 100 in order to improve the detection performance of the target included in the received signal. Functions.

That is, the DC removal unit 210 included in the preprocessor 200 removes the DC offset of the signal (or the received signal) reflected by the target, the output data of the ADC unit 100 (or the The sampled bit frequency signal, which is the output of the ADC unit 100, is temporarily stored in the buffer 220.

In addition, the DC removal unit 210 calculates an average of the output data of the ADC unit 100 of a preset number (for example, 1024 pieces) temporarily stored in the buffer 220.

That is, the DC removal unit 210 calculates an average of the preset number of temporarily stored data when the number of data temporarily stored in the buffer 220 becomes the preset number (or a preset number). .

Further, the DC removal unit 210 calculates a difference value between the preset number of output data (or the preset number of temporarily stored data) of the ADC unit 100 and the calculated average, respectively, and the calculation A plurality of difference values corresponding to the preset number are output.

In this way, the DC removal unit 210 calculates and outputs the difference between the output data of the ADC unit 100 and the average value of one pup, and the input data of the DC removal unit 210 (or the ADC unit The output data of (100)) is stored in the buffer 220 having the size of a pup, calculates an average, and performs a subtraction operation between the input data and the average, and outputs the difference value.

In addition, in order to reduce the size of a side lobe signal generated by performing the FFT function, the Hamming window unit 230 included in the preprocessor 200 has a preset weight (or a preset position-specific parameter). And a multiplication operation between the calculated difference values (or a plurality of difference values output from the DC removal unit 210) is performed, and a result of the multiplication operation is output.

That is, the Hamming window unit 230 performs a pre-set Hamming window parameter and a multiplication operation on the signal of the DC offset from which the DC offset has been removed by the DC removal unit 210, and preprocesses the result of the multiplication operation. Output as an output value of the unit 200 (or an output value of the Hamming window unit 230 / an output value according to the execution of the preprocessing function).

For example, in order to remove the DC offset included in the signal reflected from the target received through the receiving unit, the DC removal unit 210 may generate the plurality of sampled bit frequency signals output from the ADC unit 100. Temporarily stored in the buffer 220.

In addition, when the number of sampled bit frequency signals temporarily stored in the buffer 220 corresponds to a preset number (for example, 1024), the DC removing unit 210 performs the 1024 sampled bits. Calculate the average for the frequency signal.

In addition, the DC removal unit 210 calculates a difference value between the 1024 sampled bit frequency signals and the calculated average, and outputs the calculated 1024 difference values.

In addition, the Hamming window unit 230 performs a multiplication operation between 1024 difference values output from the DC removal unit 210 and 1024 preset Hamming window parameters, and outputs 1024 multiplication operations ( S720).

Thereafter, the FFT processor 300 performs a multiplication operation in the frequency domain based on the output value of the preprocessor 200 (or the output value of the Hamming window unit 230) to determine the distance information (or FMCW) from the FMCW radar. Radar output result) is calculated.

That is, the FFT processor 300 calculates distance information from the FMCW radar to the target through the multiplication operation in the frequency domain using [Equation 1].

In addition, the output value of the FFT processor 300 is provided (or separated) as an input of the first mux 600 selectively outputting FMCW distance information and an input of the I-FFT processor 500 for a pulse compression technique. . In this case, the FMCW distance information indicates distance information from the target in the FMCW radar.

As an example, the FFT processor 300 applies the output values of 1024 multiplication operations output from the Hamming window unit 230 to the [Equation 1] to obtain distance information from the FMCW radar in the frequency domain. Calculate (S730).

Thereafter, the I-FFT processor 500 performs a multiplication operation of the output value of the FFT processor 300 and the transmission data (or transmission signal information) stored in the reference lookup table 400 in the frequency domain, and then again in the time domain. (Or the output value of the I-FFT processor 500 / distance information from the target in the PD radar / PD distance information) is output.

That is, the I-FFT processor 500 must perform a pulse compression method to obtain distance information from a target with improved distance resolution in a PD radar. At this time, the pulse compression technique is a convolution-based operation in [Equation 2] above, and a multiplication operation is applied to all range bins as much as the pulse size, and then the addition operation is performed. do. The I-FFT processor 500 uses the same characteristics as the multiplication operation in the frequency domain in the convolution operation in the time domain in order to reduce the amount of calculation, and the FFT processor 300 as shown in [Equation 3] above. The multiplication operation is performed in the frequency domain from, and then returned to the time domain.

In this way, if the pulse compression technique is performed by an operation such as [Equation 3], as shown in FIG. 1, the FFT processor 300 for the received signal (or the received signal) is FMCW It can be used in common with the radar's range FFT (range FFT) processor, reducing complexity.

In addition, the I-FFT processor 500 multiplies a level value (or index) representing a peak value among the calculated correlation calculations (y[n]) by a sampling time for the received signal to delay the transmission signal and the reception signal. Calculate the time (Δt).

In addition, the I-FFT processor 500 calculates the distance to the target by the PD radar according to [Equation 4].

For example, the I-FFT processor 500 applies the output value output from the FFT processor 300 and the transmission data stored in the reference lookup table 400 to the [Equation 3] to determine the target in the PD radar. Distance information is calculated (S740).

Thereafter, the first mux 600 is the output value of the FFT processor 300 that is the output result of the FMCW radar or the I that is the output result of the PD radar according to the distance between the target and the received signal and the transmission signal. -Select the output value of the FFT processor 500, the selected output value of the FFT processor 300 or the output value of the I-FFT processor 500 as a first output value (or the output value of the first mux 600) Print.

At this time, the distance to the target by the FMCW radar may be a state calculated by [Equation 1], and the distance to the target by the PD radar may be a state calculated by [Equation 4].

For example, the first mux 600 is based on the calculated distance to the target (for example, the distance to the target calculated by the FMCW radar), the output value of the FFT processor 300 and the I-FFT The output value of the FFT processor 300 is selected from the output values of the processor 500, and the output value of the selected FFT processor 300 is output as a first output value (for example, the output value of the FFT processor 300) ( S750).

Thereafter, the 2D memory unit 700 controls a first output value (for example, the output value of the FFT processor 300 or the I-FFT processor 500) output from the first mux 600 under the control of the memory controller 800. The output value of) is stored sequentially. In this case, the horizontal axis of the 2D memory unit 700 corresponds to PRI, and the vertical axis may correspond to the number of pulses.

For example, the 2D memory unit 700 sequentially receives a first output value (for example, an output value of the FFT processor 300) output from the first mux 600 under the control of the memory controller 800. Save (S760).

Thereafter, the second mux 900 receives data having the same distance bin index among data (or output values) sequentially stored in the 2D memory unit 700 under the control of the memory controller 800 as a second output value (or the second output value). 2 Output value of the mux 900).

For example, the second mux 900 is the preset number (for example, 1024 pieces) having the same distance bin index among data sequentially stored in the 2D memory unit 700 under the control of the memory controller 800 The data of is output as a second output value (S770).

Thereafter, the post-processing unit 1000 is based on the first output value output from the first mux 600 and the second output value output from the second mux 900, the distance between the fixed target and the moving target. , Calculate the speed of a moving target, etc.

In addition, the post-processing unit 1000 determines whether the target is an actual target from clutter and noise, and outputs the calculated distance to the fixed target, the distance to the moving target, and the speed of the moving target in the case of a target.

That is, the MTI processor 1010 included in the post-processing unit 1000 calculates the distance to the moving target based on the first output value output from the first mux 600.

In this case, the MTI processor 1010 removes (or filters) a fixed target component from the first output value output from the first mux 600 to calculate a component (eg, including distance) of a moving target.

In addition, the I-MTI processor 1020 included in the post-processing unit 1000 removes the moving target component from the first output value output from the first mux 600 to provide a fixed target component (for example, distance, etc.). Inclusive).

In this case, the MTI processor 1010 and the I-MTI processor 1020 calculate a component of a moving target and a component of a fixed target by applying a recursive algorithm to reduce complexity.

In addition, the Doppler FFT processor 1030 included in the post-processing unit 1000 calculates the speed of the moving target based on the second output value output from the second mux 900.

In addition, the Doppler FFT processor 1030 uses Doppler filtering, which is a signal processing method to obtain a Doppler frequency component, and the Doppler frequency component is a phase component of data of the same distance bins in which the received signal is reflected from the target during CPI. Since it is expressed as, the Doppler frequency is calculated through the FFT operation.

That is, the Doppler frequency can be obtained by accumulating the data of the distance bin of one PRI by the product of the distance bin and the number of pulses, and then obtaining the accumulated distance bin data through FFT operation. Is required.

In addition, after obtaining the Doppler component, the Doppler FFT processor 1030 can calculate the velocity (V) of the moving target from the center frequency (f ₀ ) and the Doppler frequency (f _d ) using the above [Equation 5]. have.

In addition, the CFAR and peak detection unit 1040 included in the post-processing unit 1000 apply a CFAR algorithm based on the first output value output from the first mux 600 to determine whether the target is a final target. 1 An adaptive threshold is calculated by calculating an average of some of the output values. In this case, the CFAR algorithm may be a CA CFAR algorithm, an SO CFAR algorithm, a GO CFAR algorithm, or the like.

In addition, the CFAR and peak detection unit 1040 compares the first output value with the calculated adaptive threshold value, and when the first output value is greater than the calculated adaptive threshold value, determines that it is a frequency component. MTI processor 1010, the I-MTI processor 1020, and the moving target component (Rmov, distance) calculated by the Doppler FFT processor 1030, respectively, the component of the fixed target (Rsta) and the speed of the moving target Each (Vmov) is output.

For example, the MTI processor 1010 calculates a distance to a moving target by removing a fixed target component from a first output value output from the first mux 600.

In addition, the I-MTI processor 1020 calculates a distance to a fixed target by removing a moving target component from the first output value output from the first mux 600.

In addition, the Doppler FFT processor 1030 calculates the speed of the moving target based on the second output value output from the second mux 900.

In addition, the CFAR and peak detection unit 1040 applies a CFAR algorithm based on the first output value output from the first mux 600 to determine whether the target is a final target, and calculates an average of some of the first output values. Calculate to calculate an adaptive threshold value, compare the first output value with the calculated adaptive threshold value, and when the first output value is greater than the calculated adaptive threshold value, it is determined as a frequency component, and the MTI A component of a moving target (Rmov), a component of a fixed target (Rsta), and a velocity of a moving target (Vmov) calculated by the processor 1010, the I-MTI processor 1020, and the Doppler FFT processor 1030, respectively. Are respectively output (S780).

As described above, the embodiment of the present invention supports both a PD radar signal processing processor and an FMCW radar signal processing processor in a single hardware, so that both a target located at a long distance and a target located at a short distance can be detected.

In addition, as described above, the embodiments of the present invention share a signal processing processor and memory commonly required in PD radar and FMCW radar to improve overall system operation efficiency and design with low complexity. .

The above contents may be modified and modified without departing from the essential characteristics of the present invention by those of ordinary skill in the technical field to which the present invention pertains. Accordingly, the embodiments disclosed in the present invention are not intended to limit the technical idea of the present invention, but to explain the technical idea, and the scope of the technical idea of the present invention is not limited by these embodiments. The scope of protection of the present invention should be interpreted by the following claims, and all technical ideas within the scope equivalent thereto should be interpreted as being included in the scope of the present invention.

10: multimode radar signal processing device 100: ADC unit
200: preprocessor 300: FFT processor
400: reference lookup table 500: I-FFT processor
600: first mux 700: 2D memory unit
800: memory controller 900: second mux
1000: post-treatment unit 210: DC removal unit
220: buffer 230: hamming window part
1010: MTI processor 1020: I-MTI processor
1030: Doppler FFT processor 1040: CFAR and peak detection unit

Claims (37)

An ADC unit that performs a sampling function on the received signal and outputs a sampled bit frequency signal;
A preprocessor for outputting a preprocessed output value by performing a preprocessing function on the sampled bit frequency signal output by the ADC unit;
An FFT processor for calculating distance information from a target in an FMCW radar by performing a multiplication operation in a frequency domain based on the preprocessed output value;
In the FMCW radar calculated by the FFT processor, the distance information from the target and the transmission data stored in the reference lookup table are multiplied in the frequency domain, and then returned to the time domain. I-FFT processor for outputting information;
According to the distance to the target calculated based on the received signal and the transmitted signal, the output value of the FFT processor, which is the output result of the FMCW radar, or the output value of the I-FFT processor, which is the output result of the PD radar, is selected and output as a first output value. The first mux to do;
A memory controller sequentially storing a first output value output from the first mux in a 2D memory unit;
A second mux for outputting data having the same distance bin index among data stored in the 2D memory unit as a second output value; And
Based on the first output value output from the first mux and the second output value output from the second mux, the distance to the fixed target, the distance to the moving target, and the speed of the moving target are calculated, and from clutter and noise A multi-mode radar signal processing apparatus for detecting a UAV including a post-processing unit that determines whether it is an actual target and outputs the calculated distance to the fixed target, the distance to the moving target, and the speed of the moving target if the target is a target. The method of claim 1,
The pretreatment unit,
A buffer temporarily storing the sampled beat frequency signal to remove the DC offset of the received signal reflected by the target;
When the number of data temporarily stored in the buffer becomes a preset number, an average is calculated for the preset number of temporarily stored data, and a difference value between the preset number of temporarily stored data and the calculated average is calculated, respectively. DC removal unit to calculate; And
In order to reduce the magnitude of the side lobe signal generated by performing the FFT function, a multiplication operation between a preset weight and the calculated plurality of difference values is performed, and the result of the multiplication operation is converted to the preprocessed output value according to the preprocessing function. A multi-mode radar signal processing apparatus for detecting unmanned aerial vehicles, comprising: a Hamming window to output. The method of claim 1,
The FFT processor,
Calculate distance information from the target in the FMCW radar by the following equation,

The R1 represents the distance to the target by the FMCW radar, the B represents the bandwidth, the T represents the time of one concubine, and the c represents the propagation speed of the signal. Radar signal processing device. The method of claim 1,
The I-FFT processor,
It includes a process of calculating a correlation operation between the transmission signal and the reception signal by the following equation,

Wherein y[n] is a correlation between a transmission signal and a reception signal, x[n] is a reception signal, h[n] represents a linear frequency modulated transmission pulse signal, and n is an index A multi-mode radar signal processing apparatus for unmanned aerial vehicle detection, comprising: The method of claim 4,
The I-FFT processor,
Among the calculated correlation calculations (y[n]), the delay time (Δt) of the transmission signal and the reception signal is calculated by multiplying the sampling time for the received signal by the index representing the peak value, and the following equation Calculate the distance to the target on the PD radar,

The R2 represents the distance to the target by the PD radar, the Δt represents the delay time between the transmitted signal and the received signal, and the c represents the propagation speed of the signal, a multi-mode radar signal for unmanned aerial vehicle detection, characterized in that Processing device. The method of claim 1,
The post-processing unit,
And an MTI processor that calculates a component of the moving target by removing a fixed target component from a first output value output from the first mux. The method of claim 6,
The post-processing unit,
And an I-MTI processor for calculating a component of a fixed target by removing a moving target component from a first output value output from the first mux. The method of claim 7,
The MTI processor and the I-MTI processor,
A multimode radar signal processing apparatus for unmanned aerial vehicle detection, characterized in that a component of a moving target and a component of a fixed target are respectively calculated by applying a recursive algorithm to reduce complexity. The method of claim 7,
The post-processing unit,
And a Doppler FFT processor that calculates a speed of a moving target based on a second output value output from the second mux. The multimode radar signal processing apparatus for detecting a UAV. The method of claim 9,
The Doppler FFT processor,
The data of the range bin of one PRI (pulse repetition interval) is accumulated by the product of the distance bin and the number of pulses, and the accumulated distance bin data is subjected to FFT operation to calculate the Doppler frequency, and the following math It is calculated by the process of calculating the speed of the moving target by an equation,

Wherein f ₀ represents a center frequency, and f _d represents a Doppler frequency. The multimode radar signal processing apparatus for UAV detection, characterized in that. The method of claim 9,
The post-processing unit,
Based on the first output value output from the first mux, a CFAR algorithm is applied to calculate an average of some of the first output values to calculate an adaptive threshold value, and the first output value and the calculated adaptive threshold value A multi-mode radar signal processing apparatus for unmanned aerial vehicle detection, characterized in that it further comprises a CFAR and a peak detector for comparing. The method of claim 11,
The CFAR and peak detection unit,
As a result of the comparison, when the first output value is greater than the calculated adaptive threshold value, it is determined as a frequency component, and the calculated distance to the fixed target, the distance to the moving target, and the speed of the moving target are output. A multimode radar signal processing device for unmanned aerial vehicle detection, characterized in that. The method of claim 11,
The CFAR algorithm,
A multimode radar signal processing apparatus for UAV detection, comprising any one of CA CFAR algorithm, SO CFAR algorithm, and GO CFAR algorithm. Outputting a sampled bit frequency signal by performing a sampling function on the received signal by the ADC unit;
Outputting a preprocessed output value by performing a preprocessing function on the sampled beat frequency signal output by the ADC unit by a preprocessor;
Calculating distance information from a target in an FMCW radar by performing a multiplication operation in a frequency domain based on the preprocessed output value, by an FFT processor;
In the FMCW radar calculated by the FFT processor, the I-FFT processor multiplies the distance information from the target and the transmission data stored in the reference lookup table in the frequency domain, and then returns the result back to the time domain as PD. Outputting information on a distance from a target on a radar;
By the first mux, the output value of the FFT processor, which is an output result of the FMCW radar or the output value of the I-FFT processor, is selected according to the distance between the target calculated based on the received signal and the transmitted signal. Outputting a first output value;
Sequentially storing, by a memory controller, a first output value output from the first mux in a 2D memory unit;
Outputting, by a second mux, data having the same distance bin index among data stored in the 2D memory unit as a second output value;
Calculating, by a post-processing unit, a distance to a fixed target, a distance to a moving target, and a speed of a moving target based on a first output value output from the first mux and a second output value output from the second mux. ; And
And outputting the calculated distance to the fixed target, the distance to the moving target, and the speed of the moving target in the case of a target by determining whether the target is an actual target from clutter and noise by the post-processing unit Multimode radar signal processing method for detection. The method of claim 14,
The step of outputting a preprocessed output value by performing a preprocessing function on the sampled beat frequency signal,
Temporarily storing the sampled beat frequency signal in a buffer to remove the DC offset of the received signal reflected by the target;
Calculating an average of the preset number of temporarily stored data when the number of data temporarily stored in the buffer becomes a preset number;
Calculating a difference value between the predetermined number of temporarily stored data and the calculated average;
Outputting a plurality of difference values corresponding to the calculated predetermined number; And
In order to reduce the magnitude of the side lobe signal generated by performing the FFT function, a multiplication operation between a preset weight and the calculated plurality of difference values is performed, and the result of the multiplication operation is converted to the preprocessed output value according to the preprocessing function. A multimode radar signal processing method for unmanned aerial vehicle detection, comprising the step of outputting. The method of claim 14,
The step of calculating distance information from the target in the FMCW radar,
It is calculated by the following equation,

The R1 represents the distance to the target by the FMCW radar, the B represents the bandwidth, the T represents the time of one concubine, and the c represents the propagation speed of the signal. How to process radar signals. The method of claim 14,
Outputting distance information from the target in the PD radar,
It includes a process of calculating a correlation operation between the transmission signal and the reception signal by the following equation,

Wherein y[n] is a correlation between a transmission signal and a reception signal, x[n] is a reception signal, h[n] represents a linear frequency modulated transmission pulse signal, and n is an index Multimode radar signal processing method for unmanned aerial vehicle detection, characterized in that it represents the. The method of claim 17,
The step of outputting the distance information from the target in the PD radar,
Calculating a delay time (Δt) between the transmission signal and the reception signal by multiplying the sampling time for the received signal by an index representing a peak value among the calculated correlation calculations (y[n]); And
Further comprising the process of calculating the distance to the target in the PD radar by the following equation,

The R2 represents the distance to the target by the PD radar, the Δt represents the delay time between the transmitted signal and the received signal, and the c represents the propagation speed of the signal, a multi-mode radar signal for unmanned aerial vehicle detection, characterized in that Processing method. The method of claim 14,
The distance to the moving target is,
Multimode radar signal processing for unmanned aerial vehicle detection, characterized in that the MTI processor included in the post-processor removes a fixed target component from the first output value output from the first mux to calculate the component of the moving target Way. The method of claim 19,
The distance from the fixed target is,
A multimode radar signal for unmanned aerial vehicle detection, characterized in that the I-MTI processor included in the post-processor removes a moving target component from the first output value output from the first mux to calculate a component of a fixed target. Processing method. The method of claim 20,
The MTI processor and the I-MTI processor,
A multimode radar signal processing method for unmanned aerial vehicle detection, characterized in that a component of a moving target and a component of a fixed target are respectively calculated by applying a recursive algorithm to reduce complexity. The method of claim 20,
The speed of the moving target is,
A method of processing a multimode radar signal for unmanned aerial vehicle detection, characterized in that the Doppler FFT processor included in the post-processing unit calculates the speed of a moving target based on a second output value output from the second mux. The method of claim 14,
The speed of the moving target is,
Accumulating data of a distance bin of one PRI by a product of a distance bin and a number of pulses by a Doppler FFT processor included in the post-processing unit;
Calculating a Doppler frequency by performing an FFT operation on the accumulated distance bin data by the Doppler FFT processor; And
It is calculated by the Doppler FFT processor by a process of calculating the speed of the moving target by the following equation,

Wherein f ₀ represents a center frequency, and f _d represents a Doppler frequency. A multimode radar signal processing method for UAV detection, characterized in that. The method of claim 14,
The step of determining whether the actual target is,
By applying a CFAR algorithm based on the first output value output from the first mux by the CFAR included in the post-processing unit and the peak detection unit, calculating an average of some of the first output values to calculate an adaptive threshold value. process;
Comparing the first output value and the calculated adaptive threshold value by the CFAR and the peak detector; And
As a result of the comparison, when the first output value is greater than the calculated adaptive threshold value, the CFAR and the peak detector determine it as a frequency component, and the calculated distance to the fixed target, the distance to the moving target, and A method of processing a multimode radar signal for unmanned aerial vehicle detection, comprising the step of outputting the speed of a moving target. The method of claim 24,
The CFAR algorithm,
A multimode radar signal processing method for UAV detection, comprising any one of CA CFAR algorithm, SO CFAR algorithm, and GO CFAR algorithm. An ADC unit that performs a sampling function on the received signal and outputs a sampled bit frequency signal;
When the sampled bit frequency signal is temporarily stored in a buffer, and the number of data temporarily stored in the buffer becomes a preset number, an average is calculated for the preset number of temporarily stored data, and the preset number of A DC removal unit that calculates a difference value between the temporarily stored data and the calculated average, and outputs a plurality of difference values corresponding to the calculated predetermined number;
A hamming window unit that performs a multiplication operation between a preset weight and the calculated difference values, and outputs a result of the multiplication operation as a preprocessed output value according to the execution of a preprocessing function;
An FFT processor for calculating distance information from a target in an FMCW radar by performing a multiplication operation in a frequency domain based on the preprocessed output value;
In the FMCW radar calculated by the FFT processor, the distance information from the target and the transmission data stored in the reference lookup table are multiplied in the frequency domain, and then returned to the time domain. I-FFT processor for outputting information;
According to the distance to the target calculated based on the received signal and the transmitted signal, the output value of the FFT processor, which is the output result of the FMCW radar, or the output value of the I-FFT processor, which is the output result of the PD radar, is selected and output as a first output value. The first mux to do;
A memory controller configured to control a 2D memory unit to sequentially store first output values output from the first mux;
A second mux for outputting data having the same distance bin index among data stored in the 2D memory unit as a second output value;
An MTI processor for calculating a distance to a moving target by applying a recursive algorithm to remove a fixed target component from a first output value output from the first mux;
An I-MTI processor for calculating a distance to a fixed target by removing a moving target component from a first output value output from the first mux by applying a recursive algorithm;
A Doppler FFT processor that calculates a velocity of a moving target based on a second output value output from the second mux; And
Based on the first output value output from the first mux, a CFAR algorithm is applied to calculate an average of some of the first output values to calculate an adaptive threshold value, and the first output value and the calculated adaptive threshold value And compare
As a result of the comparison, when the first output value is greater than the calculated adaptive threshold, the CFAR determines as a frequency component and outputs the calculated distance to the fixed target, the distance to the moving target, and the speed of the moving target. And a peak detection unit. The method of claim 26,
The FFT processor,
Calculate distance information from the target in the FMCW radar by the following equation,

The R1 represents the distance to the target by the FMCW radar, the B represents the bandwidth, the T represents the time of one concubine, and the c represents the propagation speed of the signal. Radar signal processing device. The method of claim 26,
The I-FFT processor,
It includes a process of calculating a correlation operation between the transmission signal and the reception signal by the following equation,

Wherein y[n] is a correlation between a transmission signal and a reception signal, x[n] is a reception signal, h[n] represents a linear frequency modulated transmission pulse signal, and n is an index A multi-mode radar signal processing apparatus for unmanned aerial vehicle detection, comprising: The method of claim 28,
The I-FFT processor,
Among the calculated correlation calculations (y[n]), the delay time (Δt) of the transmission signal and the reception signal is calculated by multiplying the sampling time for the received signal by the index representing the peak value, and the following equation Calculate the distance to the target on the PD radar,

The R2 represents the distance to the target by the PD radar, the Δt represents the delay time between the transmitted signal and the received signal, and the c represents the propagation speed of the signal, a multi-mode radar signal for unmanned aerial vehicle detection, characterized in that Processing device. The method of claim 26,
The Doppler FFT processor,
The data of the distance bin of one PRI is accumulated by the product of the distance bin and the number of pulses, and the Doppler frequency is calculated by performing FFT operation on the accumulated distance bin data, and the speed of the moving target is calculated by the following equation. It is calculated by the process of calculating,

Wherein f ₀ represents a center frequency, and f _d represents a Doppler frequency. The multimode radar signal processing apparatus for UAV detection, characterized in that. The method of claim 26,
The CFAR algorithm,
A multimode radar signal processing apparatus for UAV detection, comprising any one of CA CFAR algorithm, SO CFAR algorithm, and GO CFAR algorithm. Outputting a sampled bit frequency signal by performing a sampling function on the received signal by the ADC unit;
Temporarily storing the sampled beat frequency signal in a buffer by a DC removal unit;
Calculating an average of the preset number of temporarily stored data when the number of data temporarily stored in the buffer becomes a preset number, by the DC removal unit;
Calculating, by the DC removing unit, a difference value between the predetermined number of temporarily stored data and the calculated average, and outputting a plurality of difference values corresponding to the calculated predetermined number;
Performing, by a Hamming window unit, a multiplication operation between a preset weight and a plurality of calculated difference values, and outputting a result of the multiplication operation as a preprocessed output value according to execution of a preprocessing function;
Calculating distance information from a target in an FMCW radar by performing a multiplication operation in a frequency domain based on the preprocessed output value, by an FFT processor;
In the FMCW radar calculated by the FFT processor, the I-FFT processor multiplies the distance information from the target and the transmission data stored in the reference lookup table in the frequency domain, and then returns the result back to the time domain as PD. Outputting information on a distance from a target on a radar;
By the first mux, the output value of the FFT processor, which is an output result of the FMCW radar or the output value of the I-FFT processor, is selected according to the distance between the target calculated based on the received signal and the transmitted signal. Outputting a first output value;
Sequentially storing, by a memory controller, a first output value output from the first mux in a 2D memory unit;
Outputting, by a second mux, data having the same distance bin index among data stored in the 2D memory unit as a second output value;
Calculating a distance to a moving target by removing a fixed target component from a first output value output from the first mux by applying a recursive algorithm, by an MTI processor;
Calculating a distance to a fixed target by removing a moving target component from the first output value output from the first mux by applying a recursive algorithm, by an I-MTI processor;
Calculating, by a Doppler FFT processor, a speed of a moving target based on a second output value output from the second mux;
Calculating an adaptive threshold value by calculating an average of some of the first output values by applying a CFAR algorithm based on the first output value output from the first mux by a CFAR and a peak detection unit;
Comparing the first output value with the calculated adaptive threshold value by the CFAR and the peak detector; And
As a result of the comparison, when the first output value is greater than the calculated adaptive threshold value, the CFAR and the peak detector determine it as a frequency component, and the calculated distance to the fixed target, the distance to the moving target, and Multimode radar signal processing method for unmanned aerial vehicle detection comprising the step of outputting the speed of a moving target. The method of claim 32,
The step of calculating distance information from the target in the FMCW radar,
It is calculated by the following equation,

The R1 represents the distance to the target by the FMCW radar, the B represents the bandwidth, the T represents the time of one concubine, and the c represents the propagation speed of the signal. How to process radar signals. The method of claim 32,
Outputting distance information from the target in the PD radar,
It includes a process of calculating a correlation operation between the transmission signal and the reception signal by the following equation,

Wherein y[n] is a correlation between a transmission signal and a reception signal, x[n] is a reception signal, h[n] represents a linear frequency modulated transmission pulse signal, and n is an index Multimode radar signal processing method for unmanned aerial vehicle detection, characterized in that it represents the. The method of claim 34,
The step of outputting the distance information from the target in the PD radar,
Calculating a delay time (Δt) between the transmission signal and the reception signal by multiplying the sampling time for the received signal by an index representing a peak value among the calculated correlation calculations (y[n]); And
Further comprising the process of calculating the distance to the target in the PD radar by the following equation,

The R2 represents the distance to the target by the PD radar, the Δt represents the delay time between the transmitted signal and the received signal, and the c represents the propagation speed of the signal, a multi-mode radar signal for unmanned aerial vehicle detection, characterized in that Processing method. The method of claim 32,
The step of calculating the speed of the moving target,
The process of accumulating the data of the distance bin of one PRI by the product of the distance bin and the number of pulses;
Calculating a Doppler frequency by performing an FFT operation on the accumulated distance bin data by the Doppler FFT processor; And
It is calculated by the Doppler FFT processor by a process of calculating the speed of the moving target by the following equation,

Wherein f ₀ represents a center frequency, and f _d represents a Doppler frequency. A multimode radar signal processing method for UAV detection, characterized in that. The method of claim 32,
The CFAR algorithm,
A multimode radar signal processing method for UAV detection, comprising any one of CA CFAR algorithm, SO CFAR algorithm, and GO CFAR algorithm.

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