KR102100912B1 - Apparatus and method for automatic pulse classification in non-cooperative bi-static sonar - Google Patents

Abstract

The present invention relates to an apparatus and method for identifying pulses in a non-cooperative bi-static SONAR, a detector for detecting the sonar signal, a memory, and corresponding to the detected sonar signal A detection unit for generating a time-frequency domain graph and converting the generated graph to a line according to a preset algorithm, and detecting line information including the number and slope of lines detected from the line converted result, and the detected line information Based on this, the sonar signal is first identified as one of different pulse signals, and at least one dominant tone is extracted and extracted from the detected sonar signal in a manner corresponding to the first identification result. Difference between the value of the feature factors obtained from the dominant tone and the value of the feature factors for each of the plurality of pulse signals previously stored in the memory Basis to be the detection of sonar signals; and a control unit for identifying a pulse signal corresponding to any one of the plurality of pulse signals.

Description

Apparatus and method for automatically identifying pulses in a non-cooperative bi-state sonar {APPARATUS AND METHOD FOR AUTOMATIC PULSE CLASSIFICATION IN NON-COOPERATIVE BI-STATIC SONAR}

The present invention relates to a sonar (SONAR), and more particularly, to an apparatus and method for identifying pulses in a non-cooperative bi-static SONAR in which a transmitter and a receiver are spaced apart from each other.

Active sonar can be classified into a single-state sonar where a transmitter and a receiver exist at the same location and a bi-state sonar spaced apart from each other. Among these, the two-state sonar can operate the system in a non-cooperative operation that does not match the information of the transmitter and the receiver depending on the situation.

In order to detect a target in such an active sonar system, information on the active pulse is essential. Therefore, when operating the system in a non-cooperative manner, the algorithm for identifying the pulse of the signal transmitted from the transmitter determines the performance of the entire sonar system.

Meanwhile, CW (Continuous Waveform) pulse signals and LFM (Linear Frequency Modulation waveform) pulse signals have been mainly used in active sonar systems. However, recently, a comb pulse signal and a COSTAS pulse signal having a broad spectrum and excellent performance in a reverberation environment have been introduced into an active sonar system. However, since the existing pulse identification algorithm considered only CW pulse signals and LFM pulse signals represented by one line in the time-frequency domain, the Comb pulse signals and COSTAS pulse signals represented by multiple lines in the time-frequency domain are not identified. There is a problem that can not.

The present invention is to solve the above problems, in an active sonar system, a device that can automatically identify when a CW pulse signal or an LFM pulse signal, as well as a Comb pulse signal or a COSTAS pulse signal are received, and It's about how.

The pulse identification device according to an embodiment of the present invention for achieving the above object generates and generates a graph of a detector for detecting the sonar signal, a memory, and a time-frequency domain corresponding to the detected sonar signal Line transformation according to a preset algorithm, a detection unit for detecting line information including the number and slope of lines detected from the line conversion result, and the sonar signal based on the detected line information. Primary identification with any one of, and at least one dominant tone extracted from the detected sonar signal in different ways according to the type of the primary identified pulse signal, and obtained from the extracted dominant tone Based on the difference between the value of the feature factors and the value of the feature factors for each of the plurality of pulse signals previously stored in the memory, the phase Is a hydrogen or a detected signal, characterized in that it comprises a control unit for identifying a pulse signal corresponding to any one of the plurality of pulse signals.

In one embodiment, the control unit detects whether the number of detected lines is one, and when the detected number of detected lines is one, the detected sonar signal is a CW (Continuous Waveform) pulse signal and Characterized in that it is identified as a LFM (Linear Frequency Modulation waveform) pulse signal, and when the number of detected lines is not one, the detected sonar signal is identified as a Comb pulse signal and a COSTAS pulse signal.

In one embodiment, when the identification result, the detected sonar signal is identified as the CW pulse signal and the LFM pulse signal, the control unit may detect the detected sonar signal based on the slope of the detected line. When the CW pulse signal is identified as one of the LFM pulse signal and the detected sonar signal is identified as the Comb pulse signal and the COSTAS pulse signal, the line information further detected in any section of the detected sonar signal and The detected sonar signal may be identified as one of the Comb pulse signal and the COSTAS pulse signal according to a comparison result of line information detected from the entire detected sonar signal.

In one embodiment, the control unit is characterized by extracting the at least one dominant tone from the detected sonar signal in different ways according to the type of pulse identified as a result of the primary identification.

In one embodiment, the control unit acquires different characteristic factors according to the type of the pulse identified as the result of the primary identification, from the at least one dominant tone, and to a scoring function set differently for each characteristic factor. Based on this, scores corresponding to error values, which are differences between the values of the feature factors obtained from the dominant tone and the values of the feature factors for each of the plurality of pulse signals previously stored in the memory, are obtained, and scores obtained for each pulse The detected sonar signal is identified as a pulse signal corresponding to any one of the plurality of pulse signals based on the sum of the values.

In one embodiment, the scoring functions are characterized in that the smaller the error value, the higher the score is calculated.

In one embodiment, the predetermined algorithm is characterized in that the Hough transform (Hough transform) algorithm.

Pulse identification method according to an embodiment of the present invention for achieving the above object,

A first step of generating a graph of the time-frequency domain for the detected sonar signal and performing a line transformation on the generated graph according to a preset algorithm, and detecting line information including the number of lines and a slope from the line conversion result The second step, the third step of first identifying the sonar signal as one of different pulse signals based on the detected line information, and the different ways according to the type of the first identified pulse signal. A fourth step of extracting at least one dominant tone from the detected sonar signal; and the value of the feature factors obtained from the extracted dominant tone and the feature factors for each of the plurality of pulse signals previously stored in the memory. A fifth step of calculating an error value that is a difference in values, and based on error values of feature factors for each of the plurality of pulse signals, And a sixth step of identifying the detected sonar signal as a pulse signal corresponding to any one of the plurality of pulse signals.

In one embodiment, the third step, the 3-1 step of detecting whether the number of lines detected from the detected line information is one, and the detected sonar signal CW (Continuous Waveform) pulse signal And one of 3-2 steps of identifying a linear frequency modulation waveform (LFM) pulse signal and 3-3 steps of identifying the detected sonar signal as a comb pulse signal and a COSTAS pulse signal. Characterized in that it is a step of further including depending on whether or not one.

In one embodiment, the 3-2 step, the a step of calculating the slope of the detected line, and based on the calculated slope of the line, the detected sonar signal is the CW pulse signal and the LFM pulse Further comprising a step b for identifying any one of the signals, the steps 3-3, step c to further detect line information for an arbitrary section of the detected sonar signal, and detection in the optional section A d step of comparing the detected line information with the line information detected in the entire detected sonar signal, and e identifying the detected sonar signal as one of the Comb pulse signal and the COSTAS pulse signal according to the comparison result Characterized in that it further comprises a step.

In one embodiment, the fourth step, according to the type of the pulse identified as a result of the primary identification, extracting a dominant tone from the entire detected sonar signal, or the detected sonar signal as a plurality of sub-signals Characterized in that it is a step of extracting dominant tones for each of the sliced and sliced sub-signals.

In an embodiment, the fifth step includes steps 5-1 of acquiring different feature factors according to the type of the pulse identified as the result of the primary identification from the at least one dominant tone, and the feature factors Based on the differently set scoring function, it is possible to obtain scores corresponding to error values that are differences between the values of the feature factors obtained from the dominant tone and the values of the feature factors for each of the plurality of pre-stored pulse signals. Step 5-2, step 5-3 for summing the scores corresponding to the error value of each characteristic factor for each of the plurality of pulse signals, and the highest value of the sum of the scores added for each of the plurality of pulse signals. It characterized in that it comprises a fifth step of identifying the pulse signal, the pulse signal corresponding to the detected sonar signal.

In one embodiment, the scoring functions are characterized in that the smaller the error value, the higher the score is calculated.

In one embodiment, the predetermined algorithm is characterized in that the Hough transform (Hough transform) algorithm.

The effects of the pulse identification device and method according to the present invention are as follows.

According to at least one of the embodiments of the present invention, the present invention provides a CW pulse signal, an LFM pulse signal, and a Comb pulse signal based on the number and slope of line components detected from time-frequency data for the detected signal. Or, it can be identified by any one of the COSTAS pulse signals. Accordingly, the present invention has an effect that, even when the receiver does not have information on the pulse signal transmitted from the transmitter in advance, the pulse signal transmitted from the transmitter can be identified and the sonar system can be operated accordingly.

According to at least one of the embodiments of the present invention, the present invention can identify and operate the sonar system according to the CW pulse signal or the LFM pulse signal, as well as when the Comb pulse signal or the COSTAS pulse signal is transmitted. It has the effect of improving the performance of the sonar system in the environment.

1 is a block diagram showing the configuration of a pulse identification device according to an embodiment of the present invention.
2 is a conceptual diagram illustrating an example of a pulse information database provided in a pulse identification device according to an embodiment of the present invention.
3 is a flowchart illustrating an operation process of identifying a pulse of a detected signal in a pulse identification device according to an embodiment of the present invention.
FIG. 4 is a conceptual diagram schematically showing a primary identification process in the operation process of FIG. 3.
5 is a flowchart illustrating the primary identification process in more detail in the operation process of FIG. 3.
6 is a conceptual diagram illustrating an example in which various types of pulses that can be identified in the pulse identification device according to an embodiment of the present invention are analyzed in a time-frequency band.
7 is a flowchart illustrating in more detail an operation process of extracting dominant tones and feature factors based on a primary identification result of the operation process of FIG. 3.
8 is a flowchart illustrating in more detail the secondary identification process of the operation process of FIG. 3.
9 illustrates an example of scoring functions that can be applied in a pulse identification device according to an embodiment of the present invention.
10 and 11 are conceptual views schematically illustrating the secondary identification process in the operation process of FIG. 3.

It should be noted that the technical terms used in this specification are only used to describe specific embodiments and are not intended to limit the present invention. In addition, the singular expression used in this specification includes the plural expression unless the context clearly indicates otherwise. In this specification, "consists." Or "includes." Terms such as should not be construed as including all the various components, or multiple steps described in the specification, some of the components or some steps may not be included, or additional components or steps It should be construed as being more inclusive.

In addition, in the description of the technology disclosed in the present specification, when it is determined that the detailed description of the related known technology may obscure the gist of the technology disclosed herein, the detailed description will be omitted.

First, Figure 1 is a block diagram showing the configuration of a pulse identification device according to an embodiment of the present invention. And Figure 2 is a conceptual diagram showing an example of a pulse information database provided in the pulse identification device according to an embodiment of the present invention.

Referring to FIG. 1, the pulse identification device 100 according to an embodiment of the present invention includes a control unit 110 and a detection unit 120 connected to the control unit 110 and controlled by the control unit 110 , It may be configured to include the line information detection unit 130, the modeling unit 140, the memory 150 and the output unit 160.

First, the detector 120 may detect a sonar signal transmitted from a transmitter. Here, the transmitter may be a transmitter according to a non-cooperative two-state sonar system. That is, the transmitter may be spaced apart from the receiver, that is, the detector 120, and as the system is operated non-cooperatively, the receiver, i.e., the detector 120 and the pulse information for the signals transmitted to each other in advance It may be transmitters that are not synchronized with each other.

Then, the line information detector 130 analyzes the signal detected by the detector 120 in time-frequency, and the time-frequency of the detected signal from the time-frequency analysis result of the detected signal based on a preset algorithm. Line information corresponding to the component can be detected. To this end, the line information detection unit 130 includes a time-frequency analysis unit (not shown) for analyzing the detected signal according to a time-frequency component and a conversion unit (not shown) according to a preset algorithm. You can.

Here, the preset algorithm may be a Hough transform algorithm. In this case, the line information detection unit 130 converts the analysis result of the detected signal analyzed in the time-frequency domain through the conversion unit, based on the Huff transform algorithm, to the time-frequency component of the detected signal. The corresponding at least one line information may be detected.

The modeling unit 140 auto-regressive modeling (hereinafter AR modeling) for acquiring information of a dominant tone from each of the detected signals or a plurality of sub signals that slice the detected signals. ).

In addition, the memory 150 may store data for supporting the function of the pulse identification device 100 according to an embodiment of the present invention. The memory 150 may store a number of application programs or applications driven by the pulse identification device 100 and data and instructions for the operation of the pulse identification device 100. Also, information input / output from each component connected to the controller 110 may be temporarily or permanently stored.

Also, the memory 150 may include a database (hereinafter, pulse information database 152) including information on a plurality of different pulse signals. The pulse information database 152 may include information on a value of at least one characteristic factor for each pulse signal.

2 shows examples of information stored in the pulse information database 152.

As shown in FIG. 2, the pulse information database 152 may include information about preset feature factors for each different pulse signal. In addition, the pulse signals may be classified according to the type of pulse, for example, CW pulse group 200, LFM pulse group 210, Comb pulse group 220, or COSTAS pulse group 230. .

Meanwhile, the pulse signal information stored in the pulse information database 152 may be information of pulse signals that can be transmitted from a transmitter. That is, the pulse information database 152 may be a database including information about characteristic factors for each of various types of pulse signals that can be transmitted from a transmitter.

In addition, the memory 150 may further include information about predetermined scoring functions. The scoring function corresponds to an error between the feature factors for each pulse stored in the pulse information database 152 and the value of the feature factors obtained from the dominant tone obtained from the signal detected by the detector 120. It may be a function for calculating a score.

The scoring function may be different for each feature factor so that different scores are calculated according to the error for each feature factor. Hereinafter, some regions of the memory 150 in which different scoring functions are stored for each of the feature factors will be referred to as a scoring function unit 154.

In addition, the output unit 160 may output various information under the control of the control unit 110. The output unit 160 may include at least one of a display unit capable of displaying image information or an audio unit capable of outputting sound information, and identified from a signal detected under the control of the controller 110 It outputs pulses and various information (eg, two-dimensional or three-dimensional graphs formed by each axis of at least two components of the detected signal's frequency, amplitude, and time) in the process performed for the pulse identification. You can.

Meanwhile, the control unit 110 may control the overall operation of the pulse identification device 100 according to an embodiment of the present invention. The control unit 110 processes the input or output signals, data, information, etc. through the above-described components, or drives an application program stored in the memory 150 to generate a pulse of the signal detected by the detection unit 120. It is possible to identify and output the identification result through the output unit 160.

To this end, the controller 110 may first control the line information detector 130 to detect at least one line information corresponding to the detected signal. In addition, the controller 110 may primarily identify the detected signal pulse based on the detected line information. Here, the controller 110 may use the detected signal based on the number of detected lines and the slope in the time-frequency domain of the detected line, either a CW (Continuous Wave) pulse signal or an LFM (Linear Frequency Modulation) pulse signal. , Or Comb pulse signal or COSTAS pulse signal. Hereinafter, the primary identification process will be described in more detail with reference to FIGS. 4 to 5.

In addition, when the type of the pulse is firstly identified, the controller 110 may extract the dominant tone in different ways according to the identified pulse type. The dominant tone may be extracted through an AR modeling process. In addition, the AR modeling may be performed through the modeling unit 140.

Here, the control unit 110 may slice the detected signal into a plurality of sub signals based on the type of the identified pulse. Also, AR modeling may be performed through the modeling unit 140 for each of the detected signals or a plurality of sub-signals generated through the slicing process based on the identified pulse type. In addition, as a result of the AR modeling, a two-dimensional graph having frequency components and amplitude components as respective axes can be obtained.

Here, the controller 110 may acquire the dominant tone through a frequency component having a peak value of the 2D graph. That is, the frequency having the peak value of the 2D graph may be the center frequency of the dominant tone, and the bandwidth of the dominant tone may be obtained based on the amplitude according to the frequency having the peak value of the 2D graph.

In addition, the controller 110 may arrange the two-dimensional graphs in chronological order to obtain at least one three-dimensional graph including frequency characteristics for the entire signal or each of the sub-signals. In addition, information on a pulse length of the dominant tone may be obtained based on the obtained 3D graph.

Meanwhile, the control unit 110 may acquire preset feature factors from the obtained dominant tone. Here, the acquired characteristic factors may be different depending on the type of the primary identified pulse. Table 1 below shows examples of characteristic factors obtained differently according to the type of the identified pulse.

[Table 1]

Meanwhile, the controller 110 may select any one of the pulse groups included in the pulse information database 152 according to the type of the primary identified pulse. In addition, an error between the feature factors stored for each pulse of the selected group and the feature factors obtained through the dominant tone may be calculated. In addition, the calculated errors can be stored for each characteristic factor of each pulse.

In addition, the controller 110 may select a different scoring function for each feature factor. Here, as shown in [Table 1], since different characteristic factors can be obtained according to the type of the pulse identified as the result of the primary identification, the functions selected by the controller 110 are the pulses identified as the result of the primary identification It may be different depending on the type of.

In addition, the control unit 110 may calculate scores corresponding to each of the calculated errors through a scoring function corresponding to each characteristic factor for each pulse of the currently selected group. In addition, scores corresponding to each feature factor may be summed for each pulse, and sum points corresponding to each pulse of the currently selected group may be calculated. Also, one of the pulses of the currently selected group may be selected based on the summation point.

For example, the controller 110 may select any one pulse corresponding to the highest summation point, and may output information of the selected pulse as a result of secondary identification of the pulse for the detected signal.

3 is a flowchart illustrating an operation process of identifying a pulse of a detected signal in the pulse identification device 100 according to an embodiment of the present invention. And FIG. 4 is a conceptual diagram schematically showing a primary identification process of the operation process of FIG. 3.

Referring to Figure 3, the control unit 110 of the pulse identification device 100 according to an embodiment of the present invention, when a signal is detected through the detection unit 120, the detected signal based on the time component and frequency component Can be analyzed (hereinafter time-frequency analysis) (S300). As a result of the analysis in step S300, the detected signal may be analyzed in a two-dimensional graph form in which time components and frequency components are formed on respective axes.

Meanwhile, when time-frequency analysis is performed on the detected signal, the control unit 110 converts the two-dimensional graph formed as a result of the time-frequency analysis based on a preset algorithm and line information based on the conversion result. It can be detected (S302).

In addition, the control unit 110 may primarily identify the pulse type for the currently detected signal based on the line information detected in step S302 (S304).

4 is a conceptual diagram schematically showing such a primary identification process.

For example, the controller 110 may detect the number of detected lines and identify whether the detected signal is a CW pulse or an LFM pulse, or a Comb pulse and a COSTAS pulse according to the number of detected lines.

For example, if there is one line detected from the detected signal, the controller 110 may determine that the detected signal is a CW pulse and an LFM pulse. In addition, if the determination result is a CW pulse or an LFM pulse, the detected signal may be identified as a signal according to any one of the CW pulse and the LFM pulse based on the slope of the detected line.

On the other hand, if there are a plurality of lines detected from the detected signal, the controller 110 may determine that the detected signal is a Comb pulse and a COSTAS pulse. Then, if the determination result is a Comb pulse and a COSTAS pulse, the control unit 110 detects line information again for an arbitrary section, and the information of the line detected from the arbitrary section and the line detected from the entire section of the detected signal. You can compare whether the information is the same. In addition, the detected signal may be identified as a signal according to any one of a Comb pulse and a COSTAS pulse according to the comparison result.

When the pulse type is first identified in step S304, the controller 110 may extract the dominant tone according to the identified pulse type, and obtain at least one characteristic factor based on the extracted dominant tone (S306). Here, the feature factor may mean feature information that can be obtained from the extracted dominant tone, and different feature factors may be extracted according to the type of the primary identified pulse.

Meanwhile, in step S306, the method of extracting the dominant tone may be different from each other according to the primary identified pulse type. For example, if the primary identification result is a CW pulse or an LFM pulse with the number of detected lines being one, the controller 110 may extract a dominant tone based on the entire signal.

However, if the primary identification result is a Comb pulse or a COSTAS pulse having a plurality of detected lines, the controller 110 slices the detected signal into a plurality of signals based on the detected line information. The dominant tone may be extracted for each of the plurality of sliced signals. In this case, the plurality of signals may be sliced based on the detected line and line.

Accordingly, at step S306, at least one dominant tone may be extracted. In addition, the controller 110 may extract values of feature factors according to the type of the primary identified pulse, as shown in [Table 1], from the extracted at least one dominant tone.

For example, the controller 110 may perform AR modeling on each of the entire signal or a plurality of sub-signals generated by the slice. In addition, a center frequency, a pulse length, and a bandwidth of the dominant tone may be obtained through a 2D graph or a 3D graph obtained as a result of AR modeling. In addition, if AR modeling is performed on each of a plurality of sub-signals, the number of sub-signals may be obtained.

Here, the 2D graph may be a 2D graph having frequency components and an amplitude component as each axis, and the 3D graph lists the 2D graphs in chronological order, that is, a time component as one axis. It may be a graph further including.

If the values of the feature factors for each of the dominant tone or the plurality of dominant tones are obtained in step S306, the controller 110 may perform a secondary identification process based on the values of the acquired feature factors (S308).

For the secondary identification process, the controller 110 may calculate errors between values of the acquired feature factors and feature values corresponding to each of the pre-stored pulse signals. Here, the values of the characteristic factors of the pre-stored pulse signals may be information stored in the pulse information database 152 of the memory 150.

In addition, the control unit 110 may calculate scores corresponding to the calculated errors. The scores may be calculated by scoring functions set to calculate the maximum value as the error is less. In addition, the scoring functions may be set differently for each characteristic factor, and may be stored in the scoring function unit 154 of the memory 150.

In step S308, the control unit 110, based on a scoring function corresponding to each feature, is an error between feature values corresponding to each of the pre-stored pulse signals and values of feature factors obtained from the dominant tone. Scores can be calculated according to the field.

That is, in step S308, the controller 110 may obtain scores corresponding to each of the characteristic factors of the pre-stored pulse signals. Then, the controller 110 may sum each calculated score for each pre-stored pulse signal. Then, any one pulse signal having the highest sum can be selected as the pulse signal according to the secondary identification result.

In addition, the controller 110 may output the information of any one pulse signal selected according to the secondary identification result of step S308 as information of the pulse signal corresponding to the signal detected by the current detector 120 (S310). ).

Meanwhile, FIG. 5 is a flowchart illustrating the primary identification process in more detail in the operation process of FIG. 3. And FIG. 6 is a conceptual diagram illustrating an example in which various types of pulses that can be identified in the pulse identification device according to an embodiment of the present invention are analyzed in a time-frequency band.

Referring to FIG. 5, when the primary identification process (step S304) is performed, the controller 110 may first determine whether the number of lines detected in step S302 is one (S500).

Then, as a result of the determination in step S500, if the number of detected lines is one, the controller 110 may determine that the currently detected signal is one of a CW pulse signal and an LFM pulse signal (S510). Then, the control unit 110 may detect the slope of the detected line (S512). In addition, the detected signal may be determined as either a CW pulse signal or an LFM pulse signal based on the detected slope of the line.

For example, if the currently detected signal is analyzed as a two-dimensional graph in which the X-axis is a time component and the Y-axis is a frequency component, the controller 110 has a slope of the detected line, that is, a chip rate ' It may be determined whether or not a certain level is approached to 0 '(eg, when a slope has a value within a predetermined threshold range formed based on' 0 ').

For example, as can be seen from the 'CW pulse' of FIG. 6, since the CW pulse signal has a constant frequency, when the line information is detected in step S302, the detected line is on the X axis, that is, the time axis. It is because it is formed in parallel. Therefore, when the inclination of the detected line, that is, when the chip rate is '0', that is, horizontal to the X axis, the controller 110 may first identify that the currently detected signal is a CW pulse signal (S514).

On the other hand, as can be seen from the 'LFM pulse' of FIG. 6, since the LFM pulse signal has a characteristic in which the frequency is constantly changed with time, if the line information is detected in step S302, the detected line is This is because it has a constant slope. Therefore, when the slope of the detected line, i.e., the chip rate has a constant slope that is not close to the '0', that is, a slope of a certain level or more (for example, a slope exceeding the predetermined threshold range), the controller 110 May primaryly identify that the currently detected signal is an LFM pulse signal (S516).

On the other hand, as a result of the determination in step S500, if the number of detected lines is not one, the controller 110 may determine that the currently detected signal is one of a Comb pulse signal and a COSTAS pulse signal (S520). Then, the control unit 110 may detect line information again for an arbitrary section of the currently detected signal (S522). In addition, it may be determined whether the detected line information coincides with the detected line information for the entire detection signal (S524). In addition, the detected signal may be determined as one of a Comb pulse signal and a COSTAS pulse signal according to the determination result.

For example, in the case of the Comb pulse signal, as shown in the example of the signal of the Comb pulse in FIG. 6, a plurality of frequency components having a constant value with time may be detected. Accordingly, if the detected signal is analyzed as a two-dimensional graph in which the X-axis is a time component and the Y-axis is a frequency component, the controller 110 can detect a plurality of lines having the same different frequencies regardless of time. Information can be detected.

Therefore, as in the step S522, when line information is detected for an arbitrary section and the detected result is compared with the detected line information for the entire section, the same line information may be detected. Accordingly, if the line information detected in an arbitrary section in step S524 coincides with the line information detected for the entire detection signal, the control unit 110 may first identify the currently detected signal as a comb pulse (S526). .

On the other hand, in the case of the COSTAS pulse signal, as shown in the signal example of the COSTAS pulse in FIG. 6, a plurality of line information having different frequency values can be detected according to time. Accordingly, if the detected signal is analyzed as a two-dimensional graph in which the X-axis is a time component and the Y-axis is a frequency component, the controller 110 can detect a plurality of line information whose detected frequencies are different from each other over time. have.

Therefore, as in the step S522, when line information is detected for an arbitrary section and the detected result is compared with the detected line information for the entire section, different line information may be detected. Therefore, if the line information detected in an arbitrary section in step S524 does not match the line information detected for the entire detection signal, the control unit 110 may first identify the currently detected signal as a COSTAS pulse (S528). ).

7 is a flowchart illustrating in more detail an operation process of extracting dominant tones and feature factors based on a primary identification result of the operation process of FIG. 3.

Referring to FIG. 7, when the process of extracting a dominant tone and obtaining a feature factor from the extracted dominant tone (step S306) is performed, the control unit 110 first determines the pulse type identified in step S304 as CW. Whether it is a pulse or an LFM pulse or a Comb pulse or a COSTAS pulse may be determined (S700).

Further, as a result of the determination in step S700, if the primary identified pulse type is a CW pulse or an LFM pulse in which one line is detected as a result of detecting the line information, the controller 110 adjusts the dominant tone for the entire detected signal. It can be extracted (S710). And based on the extracted dominant tone, it is possible to obtain a value of feature factors corresponding to the primary identification result (S712).

For example, if the primary identification result is a CW pulse, the controller 110 may obtain the length and center frequency of the pulse from the dominant tone as values of the feature factors. On the other hand, if the primary identification result is an LFM pulse, the control unit 110 may acquire the length, center frequency, and bandwidth of the pulse from the dominant tone as values of the characteristic factors.

Meanwhile, as a result of the determination in step S700, if the primary identified pulse type is a Comb pulse or a COSTAS pulse in which a plurality of lines are detected as a result of detecting the line information, the controller 110 converts the detected signals into a plurality of signals. You can slice.

Here, the control unit 110 may slice the detected signals based on each of the plurality of detected lines (S720). For example, if the pulse type detected as a result of the primary identification is a Comb pulse, the controller 110 may generate a plurality of sub-signals by slicing the detected signals based on different frequencies. On the other hand, if the pulse type detected as a result of the primary identification is a COSTAS pulse, the controller 110 may generate a plurality of sub-signals by slicing the detected signals based on different time intervals.

In addition, the controller 110 may extract a dominant tone for each of the sliced sub-signals (S722). Accordingly, a plurality of dominant tones may be extracted in the step S722. Then, the controller 110 may obtain values of feature factors corresponding to the primary identification result based on the extracted plurality of dominant tones (S724).

For example, if the primary identification result is a Comb pulse, the controller 110 sets the pulse length of each sub signal, the center frequency of each sub signal, and the number of sub signals from the plurality of dominant tones as values of the feature factors. Can be obtained. In addition, if the primary identification result is a COSTAS pulse, the controller 110 obtains the pulse length of each sub signal, the center frequency of each sub signal, and the number of sub signals from the plurality of dominant tones as values of the feature factors. It can be done (see [Table 1] above).

8 is a flowchart illustrating in more detail the secondary identification process of the operation process of FIG. 3. And Figure 9 shows an example of the scoring function that can be applied in the pulse identification device according to an embodiment of the present invention.

Referring first to FIG. 8, when the second identification process (step S308) is performed, the controller 110 first selects any one of the pulse information databases 152 according to the type of the first identified pulse in step S304. The pulse group of can be selected (S800).

For example, when the primary identification result is a CW pulse or an LFM pulse, the control unit 110 selects the CW pulse group 200 or the LFM pulse group 210 among the pulse groups of the pulse information database 152, or Both the CW pulse group 200 and the LFM pulse group 210 may be selected. Alternatively, when the primary identification result is a Comb pulse or a COSTAS pulse, the controller 110 selects a Comb pulse group 220 or a COSTAS pulse group 230 among the pulse groups of the pulse information database 152, or Both the comb pulse group 220 and the COSTAS pulse group 230 can be selected.

On the other hand, when the pulse group is selected in step S800, the controller 110 compares the values of the characteristic factors of each pulse included in the selected pulse group and the values of the characteristic factors obtained from the dominant tone to calculate the difference, that is, the error. It can be done (S802). For example, the error (X) may be calculated through [Equation 1] below.

[Equation 1]

는 추출된 도미넌트 톤으로부터 획득된 특징인자의 값이며, 상기

는 펄스 정보 데이터베이스(152)에 기 저장된 특정 펄스 신호의 특징인자의 값임. In [Equation 1], the above

Is the value of the feature factor obtained from the extracted dominant tone, and

Is a characteristic parameter value of a specific pulse signal previously stored in the pulse information database 152.

Meanwhile, when the value of the feature factor is the number of sliced sub-signals, the error X _L for the number of sub-signals may be calculated through Equation 2 below.

[Equation 2]

는 추출된 도미넌트 톤으로부터 획득된 서브신호 개수의 값이며, 상기

는 펄스 정보 데이터베이스(152)에 기 저장된 특정 펄스 신호의 서브신호 개수의 값임. In [Equation 2] above, the

Is a value of the number of sub-signals obtained from the extracted dominant tone, and

Is a value of the number of sub-signals of a specific pulse signal previously stored in the pulse information database 152.

As a result of the step S802, the control unit 110 may obtain, for each pulse of the currently selected pulse group, difference values between feature factor values included in each pulse and each feature factor value obtained from the dominant tone, that is, error values. have. In addition, the error values may be stored for each characteristic factor of each pulse.

Then, the controller 110 may calculate scores corresponding to each error based on a scoring function corresponding to each characteristic factor (S804). Meanwhile, as shown in FIG. 9, scoring functions may be set differently for each characteristic factor. Accordingly, the control unit 110 may select a different scoring function for each feature factor in step S804, and calculate scores corresponding to the error value of each feature factor through the selected scoring function.

Here, since the error value may be stored for each characteristic factor of each pulse as described above, scores corresponding to the error value may also be calculated for each characteristic factor of each pulse.

Then, the control unit 110 may sum the scores calculated for each characteristic factor for each pulse (S806). For example, if the currently detected signal is identified as a CW pulse signal as a result of the primary identification, and in step S800, the controller 110 selects only one CW pulse group 200 as a pulse group corresponding to the primary identification result. Ramen, the control unit 110 may sum the scores calculated for each characteristic factor of the A1 pulse. In addition, scores calculated for each characteristic factor of the A2 pulse may be summed, and this process may be performed for each pulse of the currently selected pulse group.

Accordingly, when the step S806 is completed, the controller 110 may obtain a summed score obtained by adding a score corresponding to an error per feature factor for each pulse signal of the currently selected group. For example, if the CW pulse group 200 is composed of three pulse signals, A1 pulse, A2 pulse, and A3 pulse, the controller 110 is the result of the step S806, and the first sum corresponding to the A1 pulse signal A score, a second summation score corresponding to the A2 pulse signal, and a third summation score corresponding to the A3 pulse signal may be obtained.

 Meanwhile, when the summation score for each pulse is calculated, the controller 110 may select one pulse signal as the pulse signal corresponding to the second identification result based on the calculated summation score (S808). For example, the controller 110 may select the pulse signal having the highest sum of the sums as the second identification result.

That is, as a result of the step S806, if the first sum score, the second sum score, and the third sum score are obtained, and if the first sum score is the highest, the control unit 110 is added to the first sum score The corresponding pulse, that is, the A1 pulse signal, may be identified as a pulse signal corresponding to the currently detected signal through the second identification process. Then, the information on the A1 pulse may be output as the final identified pulse signal in step S310 of FIG. 3.

Meanwhile, FIGS. 10 and 11 are conceptual views schematically illustrating the secondary identification process in the operation process of FIG. 3.

First, referring to FIG. 10, FIG. 10 shows an example of a case in which the pulse identified as a result of the first identification is a CW pulse or an LFM pulse.

Here, databases # 1 to #M may be pulses stored in the pulse information database 152. That is, information on characteristics of different pulse signals may be stored in each of the databases # 1 to #M.

Then, the controller 110 may calculate and store an error by comparing the value of each feature factor and the value of feature factors obtained from the extracted dominant tone for each database. Then, each feature is based on the extracted factors for the currently identified pulse, that is, CW pulse or LFM pulse, that is, scoring functions corresponding to pulse length, center frequency, and bandwidth. Scores can be calculated for each factor.

In addition, the calculated errors may be summed for each database and summed scores (Score # 1 to Score #M) may be calculated.

In addition, the controller 110 may select any one database, that is, any one pulse signal, based on the summation score having the maximum value among the calculated summation scores. In addition, the selected pulse signal may be a pulse signal identified through a secondary identification process.

Meanwhile, FIG. 11 shows an example in which the pulse identified as the result of the first identification is a Comb pulse or a COSTAS pulse.

Similarly, databases # 1 to #M may be pulses stored in the pulse information database 152. That is, information on characteristics of different pulse signals may be stored in each of the databases # 1 to #M.

Then, the controller 110 may calculate and store an error by comparing the value of each feature factor and the value of feature factors obtained from the extracted dominant tone for each database. And it corresponds to the extracted characteristic factors for the currently identified pulse, that is, the Comb pulse or COSTAS pulse, that is, the pulse length, the sub-frequency of each sub-signal, and the number of sub-signals (Line Number). Scores may be calculated for each characteristic factor based on the scoring functions.

In addition, the calculated errors may be summed for each database and summed scores (Score # 1 to Score #M) may be calculated. In addition, the controller 110 may select any one database, that is, any one pulse signal, based on the summation score having the maximum value among the calculated summation scores. In addition, the selected pulse signal may be a pulse signal identified through a secondary identification process.

Meanwhile, in the above description of the present invention, specific embodiments have been described, but various modifications can be made without departing from the scope of the present invention. Particularly, in the exemplary embodiment of the present invention, a configuration for performing secondary identification after primary identification is disclosed, but it is needless to say that a pulse corresponding to a signal detected through primary identification may be immediately identified as necessary. In this case, the accuracy may be slightly lowered, but as the second identification process is not performed, the pulse identification result can be confirmed in a shorter time.

In addition, in the above-described embodiment of the present invention, the use of the Huff transform algorithm as an algorithm for linear transformation is described as an example, but it is a matter of course that the present invention is not limited thereto. That is, the Huff transform algorithm may be replaced with other algorithms that convert a two-dimensional graph of a signal analyzed in a time-frequency domain into other line information.

Those skilled in the art to which the present invention pertains will be able to make various modifications and variations without departing from the essential characteristics of the present invention. Therefore, the embodiments disclosed in the present invention are not intended to limit the technical spirit of the present invention, but to explain, and the scope of the technical spirit 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 spirits within the scope equivalent thereto should be interpreted as being included in the scope of the present invention.

100: pulse identification device
110: control unit 120: detection unit
130: line information detection unit 140: modeling unit
150: memory 152: pulse information database
154: scoring function unit 160: output unit

Claims (14)

In a non-cooperative two-state sonar system, a device for identifying a pulse of a detected sonar signal,
A detector for detecting the sonar signal;
Memory;
A detection unit for generating a graph of a time-frequency domain corresponding to the detected sonar signal, converting the generated graph into lines according to a preset algorithm, and detecting line information including the number and slope of lines detected from the line converted results ; And,
Based on the detected line information, the sonar signal is firstly identified as one of different pulse signals, and at least one dominant from the sonar signal is detected in different ways according to the type of the first identified pulse signal. Based on the difference between the values of the characteristic factors obtained from the extracted dominant tone and the values of the characteristic factors for each of the plurality of pulse signals pre-stored in the memory, the detected sonar signal is recalled. And a control unit for identifying a pulse signal corresponding to any one of the plurality of pulse signals. According to claim 1, The control unit,
It is detected whether the number of the detected lines is one,
When the number of lines detected as a result of the detection is 1, the detected sonar signal is identified as a CW (Continuous Waveform) pulse signal and an LFM (Linear Frequency Modulation waveform) pulse signal,
When the number of the detected lines is not one, the pulse identification device, characterized in that for identifying the detected sonar signal as a Comb pulse signal and a COSTAS pulse signal. According to claim 2, The control unit,
As a result of the identification,
When the detected sonar signal is identified as the CW pulse signal and the LFM pulse signal, the detected sonar signal is identified as one of the CW pulse signal and the LFM pulse signal based on the slope of the detected line,
When the detected sonar signal is identified as the Comb pulse signal and the COSTAS pulse signal, a comparison result of line information further detected in any section of the detected sonar signal and line information detected in the entire detected sonar signal Pulse identification device, characterized in that for identifying the detected sonar signal as one of the comb pulse signal and the COSTAS pulse signal. delete According to claim 1, The control unit,
From the at least one dominant tone, different characteristic factors are acquired according to the type of pulse identified as a result of the primary identification,
Based on the scoring function set differently for each of the feature factors, the value of the feature factors obtained from the dominant tone and the error values that are the difference between the feature factor values for each of the plurality of pulse signals previously stored in the memory A pulse identification device comprising obtaining corresponding scores and identifying the detected sonar signal as a pulse signal corresponding to any one of the plurality of pulse signals based on a sum of scores obtained for each pulse. The method of claim 5, wherein the scoring functions,
The smaller the error value, the pulse identification device characterized in that the higher the score is calculated functions. The method of claim 1, wherein the predetermined algorithm,
Pulse identification device characterized in that the Hough transform (Hough transform) algorithm. In a non-cooperative bi-state sonar system, a method of identifying a pulse of a detected sonar signal,
A first step of generating a graph of the time-frequency domain for the detected sonar signal and converting the generated graph to a line according to a preset algorithm;
A second step of detecting line information including the number of lines and a slope from the line conversion result;
A third step of firstly identifying the sonar signal as one of different pulse signals based on the detected line information;
A fourth step of extracting at least one dominant tone from the detected sonar signal in different ways according to the type of the primary identified pulse signal;
A fifth step of calculating an error value that is a difference between the value of the feature factors obtained from the extracted dominant tone and the value of the feature factors for each of a plurality of pre-stored pulse signals; And,
And a sixth step of identifying the detected sonar signal as a pulse signal corresponding to any one of the plurality of pulse signals based on error values of characteristic factors for each of the plurality of pulse signals. Pulse identification method. The method of claim 8, wherein the third step,
A 3-1 step of detecting whether the number of detected lines is one from the detected line information; And,
Step 3-2 identifying the detected sonar signal as a CW (Continuous Waveform) pulse signal and LFM (Linear Frequency Modulation waveform) pulse signal, and a method for identifying the detected sonar signal as a Comb pulse signal and a COSTAS pulse signal. Pulse identification method characterized in that it further comprises a step of any one of 3-3, depending on whether the number of detected lines is one. The method of claim 9,
Step 3-2,
A step of calculating a slope of the detected line; And,
Further comprising a step b for identifying the detected sonar signal as one of the CW pulse signal and the LFM pulse signal based on the calculated slope of the line,
Step 3-3,
C) further detecting line information for an arbitrary section of the detected sonar signal;
A step d of comparing line information detected in the arbitrary section and line information detected in the entire detected sonar signal; And,
And an e step of identifying the detected sonar signal as one of the comb pulse signal and the COSTAS pulse signal according to the comparison result. The method of claim 8, wherein the fourth step,
Depending on the type of the pulse identified as the result of the primary identification, a dominant tone is extracted from the entire detected sonar signal, or the detected sonar signal is sliced into a plurality of sub-signals and each of a plurality of sliced sub-signals, respectively. And extracting dominant tones for the pulse. The method of claim 8, wherein the fifth step,
A 5-1 step of acquiring different characteristic factors according to the type of the pulse identified as the result of the primary identification from the at least one dominant tone;
Scores corresponding to error values, which are differences between values of feature factors obtained from the dominant tone and values of feature factors for each of the plurality of pre-stored pulse signals, based on a scoring function set differently for each feature factor. A 5-2 step of acquiring them;
A step 5-3 of adding up scores corresponding to the error value of each feature factor for each of the plurality of pulse signals; And,
And a fifth to fourth step of identifying the pulse signal having the highest value among the sum values of the scores summed for each of the plurality of pulse signals as a pulse signal corresponding to the detected sonar signal. . The method of claim 12, wherein the scoring functions,
The smaller the error value, the pulse identification method characterized in that the higher score is calculated functions. The method of claim 8, wherein the predetermined algorithm,
Pulse identification method characterized in that the Hough transform (Hough transform) algorithm.

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