KR20140040422A - Clutter removal method and device for transportation system radar using data matrix bank filter - Google Patents

Clutter removal method and device for transportation system radar using data matrix bank filter Download PDF

Info

Publication number
KR20140040422A
KR20140040422A KR1020120107044A KR20120107044A KR20140040422A KR 20140040422 A KR20140040422 A KR 20140040422A KR 1020120107044 A KR1020120107044 A KR 1020120107044A KR 20120107044 A KR20120107044 A KR 20120107044A KR 20140040422 A KR20140040422 A KR 20140040422A
Authority
KR
South Korea
Prior art keywords
clutter
signal
standard deviation
database
threshold
Prior art date
Application number
KR1020120107044A
Other languages
Korean (ko)
Inventor
조춘식
장문광
강영만
Original Assignee
(주)엠아이웨어
조춘식
장문광
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by (주)엠아이웨어, 조춘식, 장문광 filed Critical (주)엠아이웨어
Priority to KR1020120107044A priority Critical patent/KR20140040422A/en
Publication of KR20140040422A publication Critical patent/KR20140040422A/en

Links

Images

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/02—Systems using reflection of radio waves, e.g. primary radar systems; Analogous systems
    • G01S13/50—Systems of measurement based on relative movement of target
    • G01S13/52—Discriminating between fixed and moving objects or between objects moving at different speeds
    • G01S13/522—Discriminating between fixed and moving objects or between objects moving at different speeds using transmissions of interrupted pulse modulated waves
    • G01S13/524—Discriminating between fixed and moving objects or between objects moving at different speeds using transmissions of interrupted pulse modulated waves based upon the phase or frequency shift resulting from movement of objects, with reference to the transmitted signals, e.g. coherent MTi
    • G01S13/5246—Discriminating between fixed and moving objects or between objects moving at different speeds using transmissions of interrupted pulse modulated waves based upon the phase or frequency shift resulting from movement of objects, with reference to the transmitted signals, e.g. coherent MTi post processors for coherent MTI discriminators, e.g. residue cancellers, CFAR after Doppler filters
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • G01S7/28—Details of pulse systems
    • G01S7/285—Receivers
    • G01S7/292—Extracting wanted echo-signals
    • G01S7/2923—Extracting wanted echo-signals based on data belonging to a number of consecutive radar periods
    • G01S7/2927—Extracting wanted echo-signals based on data belonging to a number of consecutive radar periods by deriving and controlling a threshold value

Landscapes

  • Engineering & Computer Science (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Radar Systems Or Details Thereof (AREA)

Abstract

A clutter remover and a method for removing a radar for a mobile body using a DATIFACTIVE FIR FACTORY FIR are disclosed.
In the clutter removal device for moving object to receive the reflected wave of the radar signal to remove the clutter, the clutter is set by the improvement index and the spectral standard deviation of the clutter inherent to the type of clutter And generating a test cell from the reflected wave using a constant false alarm rate (CFAR) algorithm, and comparing the test cell value with the threshold to determine the type of clutter and to remove the clutter. Provides a clutter removal device.

Description

Clutter Removal Method and Device for Transportation System Radar Using Data Matrix Bank Filter}

The present embodiment relates to a clutter remover and a removal method of a radar for a mobile body using a DATIFACTIVE FACTORY. More specifically, the present invention relates to a method and apparatus for removing a clutter for a moving object which classifies the types of clutters observed in a moving radar to remove desired clutter as needed.

The contents described in this section merely provide background information on the present embodiment and do not constitute the prior art.

Mobile radars are currently used worldwide for short range radars of 24 GHz and long range radars of 77 GHz. Due to the characteristics of the road, there are many moving objects and object clutter in front of the vehicle, making it difficult to discern targets. Therefore, there is a need for a technique for classifying the types of clutter and removing the desired clutter as needed.

In the conventional CA-CFAR (Cell Average-Constant False Alarm Rate) method, since the previous average must be continuously calculated, the performance of the clutter-rich high clutter region (Low Clutter) is poor and requires a lot of memory. Therefore, in the absence of small targets and Doppler shifts, the conventional CA-CFAR method is ineffective, and in the case of clutters with large spectral standard deviations, it is difficult to search.

Therefore, there is a need for a technique for classifying the types of clutter according to individual characteristics of the clutter and removing the desired clutter as needed.

This embodiment has a main object to provide a method and apparatus for more accurately removing clutter found in a radar for a mobile body.

According to one aspect of the present embodiment, in a clutter removal apparatus for a moving object that receives a reflected wave of a radar signal and removes clutter,

The threshold value of the clutter is set according to the improvement index and the spectral spectral standard deviation unique to the type of the clutter, and a test cell is generated from the reflected wave using a constant false alarm rate (CFAR) algorithm. The present invention provides a clutter removal apparatus for a moving object, wherein the type of clutter is determined by comparing a value of a cell with the threshold and the clutter is removed.

In addition, the database for storing the clutter threshold value corresponding to the improvement index and the spectral standard deviation of the clutter for each type of clutter; An antenna that radiates electromagnetic waves toward a preset area and receives a reflected signal reflected from the preset area; A filtering unit for filtering the reflection signal according to a predetermined frequency; A calculation unit for calculating a specific coefficient based on the filtered signal; A comparator configured to extract the type information of the clutter corresponding to the specific coefficient from the database to obtain a clutter removal level threshold corresponding to the type information of the clutter; And a clutter removal unit configured to remove the clutter by applying a value corresponding to the threshold to the filtered reflected signal.

On the other hand, the apparatus for removing clutter for the moving object represents the difference between the filtered signal and the number calculated by the calculating unit as output data, and the frequency of the filtered signal is equal to the frequency of the filtered signal. It provides a clutter removal apparatus for a moving body, characterized in that it further comprises a subtraction unit to match the area.

On the other hand, the specific coefficient is the improvement index (I) and the spectral standard deviation (V), and the calculation unit further comprises a memory operation unit for calculating the improvement index (I) and the spectral standard deviation (V) Provided is a clutter removal apparatus for a moving object.

On the other hand, the comparison unit, a control level unit for extracting the type information of the cluster corresponding to at least one or more information of the improvement index (I) and the spectrum standard deviation (V) calculated from the database; And a comparator for extracting a threshold corresponding to the type information of the clutter extracted from the database.

In addition, the receiving process of radiating an electromagnetic wave toward a target or a predetermined area in the antenna and receiving a reflected signal reflected from the target or the predetermined area; A filtering step of filtering the reflection signal according to a predetermined frequency in a filtering unit; Calculating a specific coefficient based on the filtered signal in a calculation unit; An extracting step of extracting, by a comparator, the clutter type information corresponding to the specific coefficient calculated from the database, and extracting a threshold corresponding to the clutter type information; And a removal process of removing clutter by applying a value corresponding to the threshold to the reflected signal in the clutter removal unit.

On the other hand, the specific coefficient provides a clutter removal method, characterized in that the improvement index (I) and the spectral standard deviation (V).

As described above, according to the present embodiment, there is an effect of accurately identifying an object by removing the clutter found in the radar for the moving object.

1 is a graph showing an improvement index according to the change of the clutter spectrum according to an embodiment of the present invention.
2 is a block diagram schematically illustrating a clutter remover of a radar for a mobile device according to an embodiment of the present invention.
3 is a block diagram schematically illustrating a clutter remover for a moving body including a zero Doppler filter according to the present embodiment.
4 is an exemplary view for explaining an internal module in the clutter remover for a moving body according to the present embodiment.
5 illustrates a method of obtaining the spectral standard deviation (V) and the improvement index (I) of the clutter using the parameters stored in the clutter DMB MAP of the database 260 in the memory operation unit 440 according to the present embodiment. It is an illustration for.
FIG. 6 is a block diagram schematically illustrating a clutter remover for a moving object including a zero velocity filter and a magnitude portion according to the present embodiment.
7 is a flowchart illustrating a method for removing clutter for a moving body according to the present embodiment.
8 is a graph illustrating spectral standard deviations of clutters according to distances of objects measured by a radar used as an embodiment of the present invention.

Hereinafter, the present invention will be described in detail with reference to the accompanying drawings.

It should be noted that, in adding reference numerals to the constituent elements of the drawings, the same constituent elements are denoted by the same reference symbols as possible even if they are shown in different drawings. In the following description of the present invention, a detailed description of known functions and configurations incorporated herein will be omitted when it may make the subject matter of the present invention rather unclear.

In describing the components of the present invention, terms such as first, second, A, B, (a), and (b) may be used. These terms are intended to distinguish the constituent elements from other constituent elements, and the terms do not limit the nature, order or order of the constituent elements. When a component is described as being "connected", "coupled", or "connected" to another component, the component may be directly connected to or connected to the other component, It should be understood that an element may be "connected," "coupled," or "connected."

The clutter described in the present embodiment refers to reflection disturbances such as echoes (echoes) caused by unnecessary reflection waves generated from the ground, the sea surface, raindrops, and the like in the radar. That is, menholes, asphalt, cement, and the ground return different amounts of reflected waves, so they are displayed on the screen in different ways on the radar. This refers to the displayed noise phenomenon.

The Institute of Electrical and Electronics Engineers (IEI) improvement index defines "the output filter-to-power ratio of the clutter filter divided by the clutter filter input signal-to-power ratio and the radial speed of all targets is average." . If this is expressed as an equation, it can be expressed as [Equation 1].

Figure pat00001

(I: improvement index, S: signal power, C: clutter power, o: output, i: input)

The improvement index (I) evaluates the output signal as the average speed on all possible target firings.

In general, the CFAR removes the mechanically stored reflected signal by comparing it with a specific shape, considering that the clutter has a form of Gaussian distribution, but reflects the stored information by storing the information of a specific clutter experimentally or theoretically determined. Additional clutter removal is possible by comparing the signal.

In the present embodiment, the clutter type information is described by setting a key value and specifying a clutter threshold based on the key value, but it is obvious that the clutter threshold itself can be used as a key value.

The following description will be made with reference to the drawings.

1 is a graph showing an improvement index according to the change of the clutter spectrum. The X axis shows the ratio of Clutter Doppler frequency and Pulse Repetition Frequency (PRF), and the Y axis shows the improvement index. Figure 1 measured the reflected signal to a variety of objects under the same conditions within 5m distance to the radar, the object is black ink, oil bottles, plastic bottles containing water, bricks, people, books, hard disks, earth bricks It shows the result measured about. Here, the standard deviation of the clutter spectrum refers to the standard deviation (unit: Hz) of the central Doppler frequency distribution of a specific clutter when Fourier analysis of the clutter in the frequency domain. Graphs commonly have linear decreases in the logarithmic scale with respect to the standard deviation of the logarithmic scale, but differ in intercept values and slopes, depending on the type of object. Therefore, it can be seen that the values of the standard deviation and the improvement index are unique for each type of object.

The Doppler frequency of the clutter center due to the radar movement can be defined by Equation 2.

Figure pat00002

(f c : Doppler frequency in the center of the clutter, f cc : Doppler frequency for the radar movement speed, v ac : speed of the moving object, φ: vertical angle of the antenna beam, θ: horizontal angle of the antenna beam, λ: operating tlsghdml wavelength) )

As shown in [Equation 2], the clutter Doppler frequency changes according to the change of the radar moving speed and the vertical and horizontal angles of the antenna beam.

Since the clutter of each object has a unique improvement index and the spectral standard deviation of the clutter according to the distance, the clutter can be removed by filtering the radar information by setting the radar threshold for the unique variables.

In order to remove the moving clutter, Equation 1 is used to obtain an improvement index as a parameter for determining the radar threshold. In this case, the improvement index and the spectral standard deviation of the clutter are calculated by referring to parameters such as the radar moving speed, wind speed, and antenna beam angle. The removal level based on the improvement index and the spectral standard deviation of the clutter is stored in the database. Stored database table values can include key values, improvement indexes, and spectral standard deviations of clutter. The process of removing the clutter at the target level consists of a signal processing algorithm process, a zero doppler filter (310), a subtractor (230), a CFAR, and a clutter removal process. By using the calculated improvement index and the spectral standard deviation of the clutter, the data in the DMB database is searched and applied as the threshold of the DMB-CFAR to remove the clutter.

Hereinafter, an embodiment to which the invention is specifically applied will be described with reference to the drawings.

2 is a block diagram schematically illustrating a clutter remover of a radar for a mobile device according to an embodiment of the present invention.

The clutter remover 200 of the mobile radar according to the present embodiment includes an antenna 210, a filter 220, a subtractor 230, a calculator 240, a comparator 250, a database 260, and The clutter removal unit 270 is included. In the present embodiment, the clutter remover 200 of the mobile radar includes the antenna 210, the filtering unit 220, the subtracting unit 230, the calculating unit 240, the comparing unit 250, the database 260, and the clutter. Although it is described as including only the removal unit 270, which is merely illustrative of the technical idea of the present embodiment, those skilled in the art to which this embodiment belongs will not depart from the essential characteristics of this embodiment Various modifications and variations to the components included in the clutter remover 200 of the mobile radar may not be applied to the range.

The antenna 210 emits electromagnetic waves toward a target or a predetermined region and receives a reflected signal reflected from the target or the predetermined region. Such an antenna 210 emits frequencies in the 24 GHz band or the 77 GHz band.

The filtering unit 220 filters the reflected signal according to a preset frequency. Referring to the process of filtering the reflected signal to the predetermined frequency in the filter 220, the filtering unit 220 filters the zero Doppler frequency in the reflected signal, or the spectrum of the zero Doppler region including harmonic components in the reflected signal The envelope may be detected or the velocity of the zero Doppler region may be filtered from the reflected signal. Here, the zero Doppler region is a frequency region in which the Doppler signal is not generated because the object moves in a direction perpendicular to the direction of the radar beam.

The subtraction unit 230 subtracts the filtered signal and transmits the subtracted signal to the calculation unit 240. The subtractor 230 is a module that performs a subtraction function using a digital signal. The subtractor 230 may receive three inputs representing subtracted, subtracted, and rounded numbers, and outputs two outputs indicating a difference and a rounded number. Can be. In addition, the subtractor 230 expresses the difference value of the number represented by the input data as output data. That is, the subtractor 230 outputs a difference value and a rounding when the subtracted, subtracted, and rounded-out is input. That is, the subtractor 230 expresses the difference of the number represented by the filtered signal as the input data as output data, and matches the frequency of the reflected signal to a cell region corresponding to the frequency of the reflected signal. In addition, the subtraction unit 230 may detect a square law applied to the filtered signal or check a magnitude corresponding to the filtered signal. In subtracting the clutter value calculated by the DMB from the input value, the signal received from the filtering unit is two signals (IQ signals) orthogonal to each other such as a general radar, so the subtraction is performed with respect to either signal or the sum of the two signals. Applicable

The calculator 240 calculates a specific coefficient based on the filtered signal. That is, the calculator 240 calculates the improvement index I, which is a specific coefficient, and the spectral standard deviation V of the clutter. Here, the calculation unit 240 calculates the improvement index (I) by using the equation (1).

On the other hand, the calculator 240 extracts the spectral standard deviation (V) of the clutter using the database 260.

The comparator 250 extracts the type information of the clutter corresponding to the specific coefficient calculated from the database 260, and extracts a threshold corresponding to the type information of the clutter. That is, the comparator 250 extracts clutter type information corresponding to at least one of the improvement index I and the spectral standard deviation V of the clutter calculated from the database 260. The comparator 250 extracts a threshold corresponding to the clutter type information extracted from the database 260.

Meanwhile, the comparator 250 may use a constant false alarm rate (CFAR) algorithm. Here, the flow of the radar signal processing of the CFAR algorithm will be briefly described as follows. First, the downconversion signal is converted into a digital signal through an analog-to-digital conversion (ADC). In this case, the signal is classified at a predetermined angle through clutter estimation and digital beam forming. Time Domain signals coming into each beam or angle are converted into frequency domain signals through FFT (Fast Fourier Transform), and only the valid signals are adopted through the CFAR algorithm. Then, angle information is obtained through Amplitude Monopulse Process for the effective signal component, and angle, velocity, and distance information for multiple targets are extracted through pairing and frequency modulated continuous waveform (FMCW) processing do.

The CFAR algorithm can be called an environmentally adaptive filter to extract only the effective signal reflected by the actual moving target from many clutter and noise signals generated during operation. Therefore, when driving in a stationary target or clutter environment, all the clutter is removed by raising the valid signal threshold. In case of no clutter, , Thereby ensuring a wide dynamic range of signals.

The database 260 matches and stores the type information of the clutter classified by the type of the clutter and the related information according to the type of the clutter. Such a database may be implemented inside or outside the clutter remover 200 of the mobile radar. On the other hand, the related information stored in the database 260 includes at least one or more of the wind speed information, the beam angle information of the antenna, the moving speed information of the moving object, the improvement index according to the type of clutter, the standard deviation information. In addition, the type information of the clutter stored in the database 260 is divided into at least one or more of the information on the feature or the object, as shown in [Table 2] or [Table 3].

In this case, the improvement index (I) and spectral standard deviation (V) for each clutter, which are basic information to be stored, are as shown in [Table 1] on the basis of 10 knots of wind speed.

Figure pat00003

In this case, the parameters of improvement index and spectral standard deviation are constant according to the type of clutter if parameters such as wind speed, moving speed of moving object, and angle of antenna beam are determined, so each value can be confirmed by simulation or experimental.

Type information of the clutter stored in the database 260 is as follows. Here, the type information of the clutter may be represented by measuring the type of clutter, the index of improvement, and the spectral standard deviation as shown in [Table 1]. At this time, the improvement index and the standard deviation of the spectrum are stored according to the parameters such as wind speed and angle and the type of clutter, and the key values are stored separately according to their respective values. The DMB MAP values stored in the database 260 for the improvement index and the spectral standard deviation of [Table 1] are shown in [Table 2].

Figure pat00004

Although 15 types of clutters are mentioned here, any type of clutter to be ignored or specifically displayed during operation may be added as long as the unique improvement index (I) and the spectral standard deviation (V) can be measured.

The clutter remover 270 removes the clutter described in the database by applying a value corresponding to the threshold received by the calculator 240 to the reflected signal.

3 is a block diagram schematically illustrating a clutter remover for a moving body including a zero Doppler filter according to the present embodiment.

The apparatus for removing clutter for a moving object 200 according to the present embodiment includes an antenna 210, a zero doppler filter 310, a subtractor 230, a calculator 240, a comparator 250, Database 260 and clutter removal unit 270 is included. In this embodiment, the moving object clutter remover 200 includes an antenna 210, a zero doppler filter 310, a subtractor 230, a calculator 240, a comparator 250, a database 260, and a clutter. Although described as including only the removal unit 270, which is merely illustrative of the technical idea of the present embodiment, those skilled in the art to which this embodiment belongs does not depart from the essential characteristics of this embodiment Various modifications and variations to the components included in the moving object clutter remover 200 will be applicable.

Since the antenna 210, the subtractor 230, the calculator 240, the comparer 250, the database 260, and the clutter removal unit 270 are the same as those described in FIG. 1, the description thereof is omitted. do. The zero Doppler filter 310 is preferably implemented to be included in the filtering unit 220, but is not necessarily limited thereto. Here, the zero Doppler filter 310 filters out the zero Doppler frequency from the reflected signal.

Hereinafter, the clutter remover 200 for a mobile body including the zero Doppler filter 310 will be described. A general mobile radar uses a method of removing clutter in an analog signal region, and the moving clutter remover 200 according to the present embodiment may remove clutter at a desired level by applying digital signal processing. . Here, the magnetron system representing the analog processing system is a pulse radar system. The technology that removes clutter through the radio waves transmitted back by hitting the object has been using the offset offset (GAIN Off-Set) of the STC (Sensitivity Time Control) in the analog IF to the magnetron method. On the other hand, the transmission pulse implementation technology uses a fixed pulse waveform, and is dependent on the magnetron method, and consumes high power. Such a signal processing technique performs simple comparison signal processing of a detected signal and has a distance resolution capability dependent on the pulse width. The magnetron signal processing method includes Matched Filter, Automatic Gain Control (AGC) and Quad Detection processing at the IF.

However, the digital signal processing method used in the moving object clutter remover 200 according to the present embodiment is a pulse compression radar method. Transmission pulse implementation technology utilizes digital frequency generation technology to form optimal pulse waveform, enable adaptive power design, and enable low power transmission with frequency bandwidth suitable for pulse compression. The implementation of this signal processor improves the resolution and resolution ability of the pulse compression signal processing technique using FPGA (Field-Programmable Gate Array) / DSP (Digital Signal Processor) and the pulse compression signal processing technique. That is, in this signal processing method, digital signals are processed in a digital signal processor after sampling by an IF digitizer, and matched filters, AGC, and quad detection processed by an analog receiver in a digital signal processor are digitally processed in a signal processor.

Through such a clutter remover 200 for a mobile body, signal processing is simplified, reliability is improved, and maintenance is easy. In addition, no analog adjustment is required and all signal processing is possible by changing the numerical values of parameters. In addition, the moving object clutter remover 200 calculates an application factor by calculating an output signal-to-clutter ratio to an input signal-to-clutter ratio in logic for removing a desired type of clutter. At this time, the moving object clutter remover 200 calculates the spectral standard deviation using parameters such as antenna beam angle (Azimuth / Depression) information, radar movement speed information, and wind speed information.

In addition, the moving object clutter remover 200 stores the type of clutter in the database 260 in [Table 3] and sets as a criterion of removal when it is desired to remove the desired type of clutter using this data.

The moving object clutter remover 200 removes the target type of clutter. As shown in FIG. 3, the signal processing algorithm process includes a zero doppler filter 310, a subtractor 230, and a comparator 250. And the spectral standard deviation of the clutter calculated by the calculator 240 and the data stored in the database 260 in the process performed by the clutter removing unit 270 to apply a value corresponding to the threshold. Table 4 compares the functions of the moving object clutter remover 200 and the general moving object clutter remover according to the present embodiment.

Figure pat00005

4 is an exemplary view for explaining an internal module in the clutter remover for a moving body according to the present embodiment.

The moving object clutter remover 200 according to the present embodiment includes an envelope detector 410, a square law detector 420, a data matrix bank filter 430, and a memory calculator 440. ), A comparator 450 and a control level unit 460. In the present embodiment, the moving object clutter remover 200 includes only the envelope detector 410, the square detector 420, the DMB filter 430, the memory calculator 440, the comparator 450, and the control level unit 460. Although described as being merely illustrative of the technical idea of the present embodiment, those skilled in the art to which this embodiment belongs will not depart from the essential characteristics of the present embodiment clutter remover for moving objects Various modifications and variations to the components included in 200 will be applicable.

The envelope detector 410 is preferably implemented to be included in the subtractor 230, but is not necessarily limited thereto. The envelope detector 410 detects the spectral envelope of the zero Doppler region including harmonic components in the reflected signal.

The square detector 420 is preferably implemented to be included in the subtractor 230, but is not necessarily limited thereto. Square detector 420 detects the square law applied to the filtered signal, if any. In addition, the square detector 420 expresses the difference of the number represented by the filtered signal as the input data as output data, and stores the frequency of the reflected signal to match the cell area by the frequency of the reflected signal.

The DMB filter 430 and the memory operator 440 are preferably implemented to be included in the calculator 240, but are not necessarily limited thereto. The DMB filter 430 filters the corresponding cell among the frequencies of the reflected signal matched to the cell area by the frequency of the reflected signal. The memory operation unit 440 calculates an improvement index I, which is a specific coefficient, and a spectral standard deviation V of the clutter based on the filtered signal. In this case, the memory calculator 440 calculates the improvement index I by using Equation 1. In addition, the memory calculator 440 extracts the spectral standard deviation V of the clutter using the database 260. In the method of obtaining the improvement index I, the Doppler signal is calculated by the memory calculator 440 in units of test cells.

The comparator 450 and the control level unit 460 are preferably implemented to be included in the comparator 250, but are not necessarily limited thereto. The comparator 450 extracts a threshold value corresponding to the type information of the clutter extracted from the database 260 and compares it with the test cell. The control level unit 460 extracts clutter type information corresponding to at least one of the improvement index I and the spectral standard deviation V of the clutter calculated from the database 260.

5 illustrates a method of obtaining the spectral standard deviation (V) and the improvement index (I) of the clutter using the parameters stored in the clutter DMB MAP of the database 260 in the memory operation unit 440 according to the present embodiment. It is an illustration for.

As shown in FIG. 5, the improvement index I of the clutter and the spectral standard deviation V of the clutter are calculated using parameters such as the radar moving speed, antenna speed, angle, and the like, and the database 260. Can be built with basic data. Such clutter data may be reflected in the data classified by the type of clutter shown in [Table 4] to update the database 260. That is, the moving object clutter remover 200 calculates the improvement index (I) of the received clutter and the spectral standard deviation (V) of the clutter, searches for a value present in the clutter, compares the threshold value with the threshold, The type of clutter can be determined.

FIG. 6 is a block diagram schematically illustrating a clutter remover for a moving object including a zero velocity filter and a magnitude portion according to the present embodiment.

The moving object clutter remover 200 according to the present exemplary embodiment includes a zero velocity filter 610, a magnitude unit 620, a memory calculator 440, a comparator 450, and a control level unit 460. In the present exemplary embodiment, the moving object clutter remover 200 includes only the zero velocity filter 610, the magnitude unit 620, the memory operation unit 440, the comparator 450, and the control level unit 460. However, this is merely illustrative of the technical idea of the present embodiment, those skilled in the art to which the present embodiment belongs to the moving object clutter remover 200 within a range without departing from the essential characteristics of the present embodiment Various modifications and variations to the included components will be applicable.

The zero velocity filter 610 is preferably implemented to be included in the filtering unit 220, but is not necessarily limited thereto. The zero velocity filter 610 filters the velocity of the zero Doppler region in the reflected signal. The magnitude unit 620 is preferably implemented to be included in the subtraction unit 230, but is not necessarily limited thereto. The magnitude unit 620 checks the magnitude corresponding to the filtered signal.

The memory operator 440 may be implemented to be included in the calculator 240, but is not necessarily limited thereto. The memory calculator 440 calculates a specific coefficient based on the filtered signal. In this case, the memory calculator 440 calculates the improvement index I and the frequency reflection speed V, which are specific coefficients. In this case, the memory calculator 440 calculates the improvement index I using Equation 6. In addition, the memory calculator 440 extracts the frequency reflection speed V using the database 260.

The comparator 450 and the control level unit 460 are preferably implemented to be included in the comparator 250, but are not necessarily limited thereto. The comparator 450 extracts a threshold corresponding to the clutter type information extracted from the database 260. The control level unit 460 extracts clutter type information corresponding to at least one of the improvement index I and the spectral standard deviation V of the clutter calculated from the database 260.

Hereinafter, a process of operating the moving object clutter remover 200 shown in FIG. 6 will be described. As shown in FIG. 6, the values of the received cells that have passed through the zero velocity filter 610 and the magnitude unit 620 correspond to radar movement speed information, antenna rotation speed information, and antenna angle information corresponding to the cells in the memory. In addition, the antenna speed information is calculated in the memory to calculate the clutter improvement index (I) of the cell and the spectral standard deviation (V) of the clutter, and the clutter remover 200 for the moving object determines the type of the corresponding clutter to remove the clutter. Determine the threshold for.

Here, the required storage requires a storage space size in which the level reference value is stored. If yn-1 (k) is the last evaluated value, Zn is the value input from the kth cell. This current evaluation value is shown in [Equation 3].

Figure pat00006

(Zn: display of input of Kth cell, W: unit of 0 to 1)

Where W is a weighting factor between 0 and 1. It can be specified arbitrarily according to the required radar precision. On the other hand, the failure detection probability is calculated as shown in [Equation 4].

Figure pat00007

(α: elimination factor that will determine the failure probability, W: unit from 0 to 1)

On the other hand, calculating the probability of detection (Probability of the Detector) is as shown in [Equation 5].

Figure pat00008

(α: elimination factor that will determine the failure probability, W: unit from 0 to 1)

The value of α D is equal to [Equation 6].

Figure pat00009

(α: cancellation factor that will determine the probability of failure, SNR: signal to noise ratio)

7 is a flowchart illustrating a method for removing clutter for a moving body according to the present embodiment.

The clutter remover 200 for the moving object radiates electromagnetic waves toward a target or a predetermined area using the provided antenna 210 and receives a reflected signal reflected from the target or the preset area (S710). In step S710, the moving object clutter remover 200 radiates a radar frequency of 24 GHz or a radar frequency of 77 GHz through the antenna 210. The moving object clutter remover 200 filters the reflected signal according to a preset frequency using the provided filtering unit 220 (S720). In step S720, the moving object clutter remover 200 filters the zero Doppler frequency in the reflected signal, detects the spectral envelope of the zero Doppler region including harmonic components in the reflected signal, or measures the velocity of the zero Doppler region in the reflected signal. Filtering can be done.

The moving object clutter remover 200 subtracts the filtered signal using the subtraction unit 230 provided therein, and transmits the subtracted signal to the calculation unit 240 having the subtracted signal (S730). In step S730, the moving object clutter remover 200 expresses the difference of the number represented by the filtered signal as the input data as output data, and allows the frequency of the reflected signal to match the cell area by the frequency of the reflected signal. In addition, the moving object clutter remover 200 may detect a square law applied to the filtered signal or check the magnitude corresponding to the filtered signal. The moving object clutter remover 200 calculates a specific coefficient based on the filtered signal by using the calculation unit 240 provided (S740). In step S740, the moving object clutter remover 200 calculates an improvement index I, which is a specific coefficient, and a spectral standard deviation V of the clutter. Here, the clutter remover 200 for the mobile body can calculate the improvement index (I) using [Equation 6], and extract the spectral standard deviation (V) of the clutter using the database 260. have.

The moving object clutter remover 200 extracts the type information of the clutter corresponding to the specific coefficient calculated from the database 260 using the provided comparator 250 (S750). In step S750, the moving object clutter remover 200 includes information on the type of clutter corresponding to at least one or more of the improvement index I and the spectral standard deviation V of the clutter calculated from the provided database 260. Extract The moving object clutter remover 200 extracts a threshold value corresponding to the type information of the clutter from the database 260 by using the provided comparator 250 (S760). In step S760, the moving object clutter remover 200 extracts a threshold value of at least one of an improvement index I corresponding to the type of clutter and a spectral standard deviation V of the clutter from the provided database 260. . The moving object clutter remover 200 removes the clutter by applying a value corresponding to the threshold to the reflected signal using the provided clutter remover 270 (S770).

7, steps S710 to S770 are sequentially performed. However, this is merely an example of the technical idea of the present embodiment, and it will be apparent to those skilled in the art that the present invention is not limited to this embodiment It will be understood that various changes and modifications may be made to the invention without departing from the essential characteristics thereof, such as by changing the order described in FIG. 7 or by performing one or more of steps S710 through S770 in parallel, But is not limited thereto.

As described above, the clutter removing method according to the present embodiment described in FIG. 7 may be implemented in a program and recorded on a computer-readable recording medium. The computer-readable recording medium on which a program for implementing the clutter removing method according to the present embodiment is recorded includes all kinds of recording devices storing data that can be read by a computer system. Examples of such computer-readable recording media include ROM, RAM, CD-ROM, magnetic tape, floppy disk, optical data storage, etc., and also implemented in the form of a carrier wave (e.g., transmission over the Internet) . The computer readable recording medium may also be distributed over a networked computer system so that computer readable code is stored and executed in a distributed manner. In addition, functional programs, codes, and code segments for implementing the present embodiment can be easily inferred by programmers in the technical field to which the present embodiment belongs.

At this time, the radar design specifications in the moving object clutter remover 200 are as shown in [Table 3]. In other words, the radar basic specification that operates by Pulse Compression method of solid state power amplifier (SSPA) radar of the moving object having the performance of [Table 3] is applied.

Figure pat00010

Spectral standard deviation of clutter of various objects measured by radar of [Table 3] specification. The wind speed and radar angle are stored as parameters in the DMB MAP. If the wind speed and the radar angle are both 0 among the data stored in the DMB MAP, the maximum value data of the spectral standard deviation of the clutter is represented in the table [Table 5].

Figure pat00011

8 and 9 are graphs showing spectral standard deviations of clutters according to distances of objects measured by a radar used as an embodiment of the present invention.

Like the types of clutter measured in Table 5, the graph of FIG. 8 shows (a) black ink, (b) brick, (c) oil bottle, and (d) spectrum of clutter by distance of a person. 9 is a graph of standard deviation, and (a) is a plastic bottle filled with water, (b) is a book, (c) is a hard disk, and (d) is a spectral standard deviation graph of clutter by distance of soil bricks. The X axis represents the distance between the radar and the measurement object, and the Y axis represents the spectral standard deviation of the clutter. The maximum value of the spectral standard deviation described in [Table 5] occurs within 10 meters and the value of the spectral standard deviation is measured differently according to the type of clutter. Objects with similar spectral standard deviations at the same distance are divided into two through the improvement index, so there is no problem in the classification.

The foregoing description is merely illustrative of the technical idea of the present embodiment, and various modifications and changes may be made to those skilled in the art without departing from the essential characteristics of the embodiments. Therefore, the present embodiments are to be construed as illustrative rather than restrictive, and the scope of the technical idea of the present embodiment is not limited by these embodiments. The scope of protection of the present embodiment should be construed according to the following claims, and all technical ideas within the scope of equivalents thereof should be construed as being included in the scope of the present invention.

100: clutter remover for moving objects
110: antenna 120: filtering unit
130: Subtraction unit 140:
150: comparison unit 160: database
170: Clutter removing unit 210: Zero Doppler filter
310: envelope detection unit 320: square detector
330: DMB filter 340: Memory operation unit
350: comparator 360: control level section
510: Zero Velocity Filter 520: Magnitude portion

Claims (7)

In the clutter removal apparatus for moving objects to receive the reflected wave of the radar signal to remove the clutter,
The threshold value of the clutter is set according to the improvement index and the spectral standard deviation unique to the type of the clutter, and a test cell is generated from the reflected wave using a constant false alarm rate (CFAR) algorithm. Clutter removal apparatus for moving objects, characterized in that to determine the type of clutter by comparing the value of the threshold and the clutter. A database for storing clutter threshold values corresponding to the improvement index and the spectral standard deviation of the clutter for each type of clutter;
An antenna that radiates electromagnetic waves toward a preset area and receives a reflected signal reflected from the preset area;
A filtering unit for filtering the reflection signal according to a predetermined frequency;
A calculation unit for calculating a specific coefficient based on the filtered signal;
A comparator configured to extract the type information of the clutter corresponding to the specific coefficient from the database to obtain a clutter removal level threshold corresponding to the type information of the clutter; And
A clutter remover configured to remove the clutter by applying a value corresponding to the threshold to the filtered reflected signal
Clutter removal device for a moving body comprising a. 3. The method of claim 2,
The clutter removal device for the moving object,
And a subtractor for representing the difference between the filtered signal and the number calculated by the calculator as output data, and matching the frequency of the filtered signal to a cell region corresponding to the frequency of the filtered signal. Clutter removal device for a moving body, characterized in that. 3. The method of claim 2,
The specific coefficients are the improvement index (I) and the spectral standard deviation (V), and the calculation unit further includes a memory operation unit for calculating the improvement index (I) and the spectral standard deviation (V). Clutter Removal Device. 5. The method of claim 4,
Wherein,
A control level unit for extracting type information of a cluster corresponding to at least one or more information of the improvement index (I) and the spectral standard deviation (V) calculated from the database; And
A comparator for extracting a threshold corresponding to the type information of the clutter extracted from the database
Clutter removal device for a moving body comprising a. A receiving step of radiating an electromagnetic wave from the antenna toward a target or a predetermined area and receiving a reflection signal reflected from the target or the predetermined area;
A filtering step of filtering the reflection signal according to a predetermined frequency in a filtering unit;
Calculating a specific coefficient based on the filtered signal in a calculation unit;
An extracting step of extracting, by a comparator, the clutter type information corresponding to the specific coefficient calculated from the database, and extracting a threshold corresponding to the clutter type information; And
The clutter removal unit removes clutter by applying a value corresponding to the threshold to the reflected signal.
Clutter removal method comprising a. The method according to claim 6,
And said specific coefficient is an improvement index (I) and a spectral standard deviation (V).
KR1020120107044A 2012-09-26 2012-09-26 Clutter removal method and device for transportation system radar using data matrix bank filter KR20140040422A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
KR1020120107044A KR20140040422A (en) 2012-09-26 2012-09-26 Clutter removal method and device for transportation system radar using data matrix bank filter

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
KR1020120107044A KR20140040422A (en) 2012-09-26 2012-09-26 Clutter removal method and device for transportation system radar using data matrix bank filter

Publications (1)

Publication Number Publication Date
KR20140040422A true KR20140040422A (en) 2014-04-03

Family

ID=50650618

Family Applications (1)

Application Number Title Priority Date Filing Date
KR1020120107044A KR20140040422A (en) 2012-09-26 2012-09-26 Clutter removal method and device for transportation system radar using data matrix bank filter

Country Status (1)

Country Link
KR (1) KR20140040422A (en)

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108120976A (en) * 2017-12-08 2018-06-05 中国船舶重工集团公司第七二四研究所 A kind of ground-clutter spectrum leakage suppressing method based on Doppler's channel characteristic
KR101882482B1 (en) * 2017-12-20 2018-07-27 엘아이지넥스원 주식회사 Apparatus and method for recognizing a target
CN113325414A (en) * 2020-02-28 2021-08-31 加特兰微电子科技(上海)有限公司 Object detection device and memory
US11378647B2 (en) 2017-01-26 2022-07-05 Wrt Lab Co., Ltd. Method and device for adaptively configuring threshold for object detection by means of radar

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US11378647B2 (en) 2017-01-26 2022-07-05 Wrt Lab Co., Ltd. Method and device for adaptively configuring threshold for object detection by means of radar
CN108120976A (en) * 2017-12-08 2018-06-05 中国船舶重工集团公司第七二四研究所 A kind of ground-clutter spectrum leakage suppressing method based on Doppler's channel characteristic
CN108120976B (en) * 2017-12-08 2021-02-23 中国船舶重工集团公司第七二四研究所 Ground clutter spectrum leakage suppression method based on Doppler channel characteristics
KR101882482B1 (en) * 2017-12-20 2018-07-27 엘아이지넥스원 주식회사 Apparatus and method for recognizing a target
CN113325414A (en) * 2020-02-28 2021-08-31 加特兰微电子科技(上海)有限公司 Object detection device and memory

Similar Documents

Publication Publication Date Title
CN111352102B (en) Multi-target number detection method and device based on frequency modulation continuous wave radar
CN109407071B (en) Radar ranging method, radar ranging device, unmanned aerial vehicle and storage medium
CN103176178B (en) Radar moving target radon-fractional Fourier transform long-time phase-coherent accumulation detection method
US9157992B2 (en) Knowledge aided detector
CN106405511A (en) Radar signal processing for automated vehicles
KR20040091699A (en) An adaptive system and method for radar detection
US10761205B2 (en) Systems for determining target direction and methods therefor
CN108089166A (en) A kind of adaptive frequency domain detection method based on millimeter wave cloud detection radar
CN111856406B (en) Target detection method and device based on FMCW radar echo
Hyun et al. Development of short-range ground surveillance radar for moving target detection
KR20140040422A (en) Clutter removal method and device for transportation system radar using data matrix bank filter
Yang et al. Improved CFAR detection and direction finding on time–frequency plane with high-frequency radar
Wang Detecting and mitigating wind turbine clutter for airspace radar systems
KR101527772B1 (en) METHOD FOR DETECTING TARGET OF FMCW(frequency-modulated continuous wave) RADAR AND FMCW RADAR FOR DETECTING TARGET
KR101423265B1 (en) Method And Apparatus for Eliminating Sea Clutter
CN113552563B (en) Method for analyzing correspondence between vertical measurement information and high-frequency ground wave radar clutter information
Yang et al. Wind turbine clutter mitigation in coastal UHF radar
Laurukevich et al. Estimation of energy, spectral and polarimetric characteristics of meteorological echoes in DMRL-C
CN114355328A (en) Radar signal processing method, radio signal processing method and application device
EP3441785B1 (en) System and method for tracking a sling load and terrain with a radar altimeter
CN111948613A (en) Ship-borne ground wave radar target detection method based on self-adaptive background area selection
US10591583B2 (en) Method for processing a radar signal in land/sea detection mode; processing system and associated computer program product
Jang et al. Target detection of marine radars using matrix bank filters
RU2584496C1 (en) Radio wave device for alarm signalling with continuous radiation of frequency-modulated oscillations
KR102583330B1 (en) Method and appararus of distinguishing of dynamic object and static object using multichannel radar

Legal Events

Date Code Title Description
A201 Request for examination
E902 Notification of reason for refusal
E701 Decision to grant or registration of patent right