KR20110091386A

KR20110091386A - A method for estimating the self propelled decoy's deceiving capability of the sonar system passive mode

Info

Publication number: KR20110091386A
Application number: KR1020100011199A
Authority: KR
Inventors: 김영선; 나영남
Original assignee: 국방과학연구소
Priority date: 2010-02-05
Filing date: 2010-02-05
Publication date: 2011-08-11
Also published as: KR101136399B1

Abstract

PURPOSE: A method for estimating the deceiving capability of a decoy in a ship sonar passive mode is provided to supply a computer simulation test using a computer, thereby estimating the deceiving capability. CONSTITUTION: Simulation data is inputted. The passive sonar deceiving capability of a decoy is estimated by repeating a deceiving capability simulation test n times(S101). The simulation test ends(S105). The passive sonar and deceiving capability of the decoy are measured(S107). The measured deceiving capability is displayed(S108).

Description

A METHOD FOR ESTIMATING THE SELF PROPELLED DECOY'S DECEIVING CAPABILITY OF THE SONAR SYSTEM PASSIVE MODE}

TECHNICAL FIELD The present invention relates to a naval ship or a system, and more particularly, to securing means for predicting the deception capability of an autonomous deceptor for a naval ship or a manual mode.

A sonar system is a device that estimates the direction and distance of a target maneuvering underwater using sound waves.

In general, sound waves are used as a means to detect submarine targets maneuvering underwater. Sound waves have a slow propagation rate, but they have longer wavelengths than radio waves, allowing long-range detection. Therefore, the sonar system is used to detect underwater targets by means of sound waves for military purposes.

There are two types of sonar systems: passive and active.

That is, a passive sonar system that detects noise emitted from a target and an active sonar system that detects echoes reflected from a target by shooting sound pulses can be classified into two types. Passive sonar system has the disadvantage that it takes a long time and complicated acoustic sensor to detect the distance of the target, but can detect the target in secret and has a long detection distance compared to the active sonar system. Active sonar systems, on the other hand, are not as stealthy or have a long detection range as passive, but have the advantage of simultaneously detecting the azimuth and distance of the target in a short time.

When the sonar system is used for military purposes, the target range is an important factor in the Great Submarine War. Based on the target detection distance, it is classified into a trap for long-range detection and a trap for short-range detection and quick response. The passive sonar system is based on a trap for long-range detection and the active sonar system is based on short-range detection information. Mainly used in traps aimed at attacking the target.

Hereinafter, the necessity of the algorithm will be described through the operation concept of the trap sonar and the deception machine.

First, a passive sonar system (hereinafter referred to as a passive sonar) will be described. The most common method used to detect underwater targets is acoustically sonar. 1 is a functional block diagram of the contention or manual mode. Referring to FIG. 1, a (radiation) signal generated from a target is received through the sensor 1 in a state mixed with ambient noise. The received radiation signal is analyzed with a series of signal processing through the amplifier 2, the beam former 3, the filter 4, the signal processor 5 and the like. Then, the detection is determined in the detector 6, the energy for each direction, the frequency spectrum, the target trackinggram, etc. are selectively displayed on the indicator 7 by the operator. Advances in electronics make beams for all directions (from 0 to 180 degrees) electronically, and target wideband detection and tonal analysis by dividing the entire receive frequency range into several broad frequency bands. It is divided into narrowband detection.

Hereinafter, deception will be described.

Deception is a defense that is selected by the underwater target (submarine), which is active in the ocean, in a situation where the opponent is known to his opponent. It is induced to misunderstand the target and operated for the purpose of lowering the sonar's target detection ability. Depending on the mode of operation, it is divided into floating deception and autonomous deception:

2 is a conceptual diagram of the operation of floating deception. 3 is a floating deception block diagram.

Hereinafter, a floating decoy will be described with reference to FIGS. 2 and 3.

The floating deception machine (FIG. 2) is installed and operated at a certain position in the water, and can be classified into a jammer that simulates the broadband noise of the underwater target and a decoy that decodes the narrowband signal. Unlike moving underwater targets, multiple detractors may be used simultaneously to compensate for weaknesses operating at a certain location.

Typically have a sonar-like component (FIG. 3) but have an underwater installation at the back for installation underwater.

As shown in FIG. 2, the passive sonar performs detection and tracking on the target by analyzing the signal emitted from the underwater target through the process of FIG. 1. Underwater targets attempt to reduce the detection capability and detectability of the shipyard by operating a deception machine that generates the same type of deception signal as the radiation signal.

As shown in FIG. 3, the floating deceptor generates a deception signal generated according to a preset program through the power amplifier and the sensor unit. It generates a high power signal at a certain position in the water and sinks to the bottom of its life.

Hereinafter, self propelled decoy will be described.

4 is a conceptual diagram of the operation of self-deceptive deception. 5 is a block diagram of self-deceptive deception. Hereinafter, autonomous deception will be described with reference to FIGS. 4 and 5.

The self-propelled deception machine has a self-propelled device composed of a propulsion part and a propeller at the location of the underwater installation part of the floating deception device, unlike the floating deception device operated at a fixed position.

As shown in FIG. 4, an alternative operational concept is the same as in FIG. 2. However, unlike the floating deception machine that generates the deception signal at a fixed position, it moves at a speed similar to that of the underwater target, and generates the deception signal.

As shown in Figure 5, the self-destructing deception machine is generally the same structure as the floating except for the propulsion unit and propeller for the movement, and the receiving sensor to the outside. On the other hand, the generation of the deception signal, the moving direction and the speed, etc. are in accordance with a preset program.

The effectiveness and design of the deception machine, which simulates the target signal (radio noise) of the underwater target so that the underwater target (submarine), which is secretly active in the ocean, can be misinterpreted as the actual target by the son of the opponent trap in the presence of its own. In order to determine the adequacy of the specification, an assessment of the deception capability of the ship or the ship must be preceded.

In addition, in order to measure the deception capacity of the deception vessels and to verify the validity of the design specifications, it is necessary to operate multiple vessels and multiple deception systems at sea, but considering the unstable experimental environment and the time and cost of the sea It is easy to see the importance of computer simulation.

Accordingly, the present invention is to secure a means for estimating the deception capability for the trap or manual mode of self-destructive deception.

On the other hand, in order to effectively perform the role as an instrument for autonomous deception system analysis, in addition to sonar and deception, some of the sonar operator's roles should be algorithmized and the calculation time should be minimized.

In order to solve the problems of the present invention as described above,

The method of predicting the deception capacity of the deception machine for the shipyard or manual mode is

(a) inputting simulation data;

(b) simulating a scenario by creating a scenario and constructing an object system;

(c) initializing the sonar environment and target location and tracking the target;

(d) performing radiation signal generation and signal transduction modeling from the target, followed by signal processing and analysis;

(e) determining whether to detect the target based on the analysis result;

(f) resetting the sonar and target position;

(g) if tracking of the target location is over, checking whether the simulation is over;

(h) when the simulation is finished, measuring the deception capability and displaying the measurement result.

Preferably, in the step (g)

If the tracking of the target position is not finished, it is characterized in that it comprises the step of repeating the detection or detection simulation process.

Preferably, the inclusion or detection simulation is characterized in that it comprises the steps (d) and (e).

Preferably, in the step (h)

If the simulation is not finished, characterized in that the steps (c) to (g) are performed.

Preferably, in the step (a)

The simulation data is marine environment information,

And at least one of a sea state, a sound speed, a noise level, a scattering strength, and a sea depth.

In addition, in order to solve the problems of the present invention as described above, the method of predicting the deception capacity of the deception machine in the trap or manual mode according to the present invention,

Preparing a simulation by inputting simulation data to predict the deception capability of the deception machine for the shipyard or manual mode;

Measuring the deception capability of the deception machine against the trap sonar by performing a loop representing the target detection and tracking process and a repeated loop for the Monte Carlo simulation;

And displaying a result of the deception capability of the measured deception.

Preferably, the loop representing the target detection and tracking process is

Performing deception signal generation and signal transfer modeling simulating radiation signals;

Signal processing and analyzing;

Determining whether to detect a target;

Resetting the sonar and the target position; characterized in that it comprises a.

Preferably, the repeating loop for the Monte Carlo simulation

Initializing the sonar environment and the target position;

Target detection and tracking (repeat loop);

And determining whether to detect the target based on the tracking result.

Preferably, the simulation data is marine environment information,

In addition, in order to solve the problems of the present invention as described above, the method of predicting the deception capacity of the deception machine for the shipyard or manual mode according to the present invention,

(A) generating a scenario using input data and various keys in a data input file (passive_gui or input_interface), constructing an object system, and preparing conditions for simulation;

(B) performing target tracking for a predetermined time every time through the target tracking module (passive_track);

(C) determining whether the target is in a trap or passive mode with respect to a target each time the target tracking is performed through a passive target contact module (passive_sonar_basic);

(D) determining whether the target is detected through a target analysis module (target_analysis) on the contacted signal;

(E) resetting the sonar and the target position at the next time through the reset module (ship_target_update);

(F) the target contact determination is characterized in that it comprises a step of applying a module for simulating a plurality of axle or signal contact.

Preferably, in the step (C) the passive target contact module (passive_sonar_basic) is

Perform a detection probability calculation algorithm for a given signal excess (SE),

Here, the signal excess value calculates the detection probability for the target signal in consideration of the instability of the detection environment in the ocean,

The signal excess (SE) is SE = SL-TL-(NL-DI)-DT

It is characterized by that.

Preferably, the detection probability calculation method

According to the signal excess value

It is characterized by using an equation.

Preferably, in the step (D)

Two levels of deception measures (Acoustic Counter) through the target plate logic module (target_logic_passive), which algorithmized the sonar's automatic target detection function and operator's detection ability in the target_analysis module. -Counter Measure level (ACCM level) is characterized by generating the virtual target information by determining whether to detect the target.

In the prior art, it was necessary to operate a plurality of vessels and a plurality of deception systems at sea to measure the deception capability and verify the validity of the design specifications of the deception vessels. And by providing a method for simulation using a computer in consideration of the cost, etc., there is an effect that can measure the deception capacity of the deception machine for the various modes of ships and manual mode.

1 is a functional block diagram of a ship station or a manual mode.
2 is a conceptual diagram of the operation of floating deception.
3 is a floating deception block diagram.
4 is a conceptual diagram of the operation of self-deceptive deception.
5 is a block diagram of self-deceptive deception.
FIG. 6 is a flowchart of a deception capability prediction algorithm for a trap or a manual mode of an autonomous deception machine as an embodiment of the present invention.
FIG. 7 is a graph illustrating narrowband beep levels and noise levels of targets and deceptors applied to a shipyard or a passive mode narrowband detector as an embodiment of the present invention.
8 is a graph illustrating the detection probability of a shipyard or a passive mode narrowband detector according to an embodiment of the present invention.
FIG. 9 is a graph illustrating a broad band tone level and a noise level of a target and a deception device applied to a shipyard or a passive mode wideband detector as an embodiment of the present invention.
10 is a graph illustrating the detection probability of the target and the deception of a ship station or a passive mode broadband detector according to one embodiment of the present invention.
FIG. 11 is a diagram illustrating an algorithm classified into modularity according to an embodiment of the present invention.
12 is a flowchart of a manual target detection algorithm according to one embodiment of the present invention.
13 is a flowchart illustrating a target tracking algorithm using a virtual target according to an embodiment of the present invention.
14 is a main menu screen according to an embodiment of the present invention.
FIG. 15 is an auxiliary screen of an array (array sensor) according to an embodiment of the present invention.
FIG. 16 is an auxiliary screen of an accm (Acoustic Counter-Counter Measure).
FIG. 17 shows an auxiliary screen of sea (sea / sound environment information) according to one embodiment of the present invention.
18 is an auxiliary screen of a scenario (simulation scenario and target, deception information) as an embodiment of the present invention.
19 is an embodiment of the present invention, an information input screen for the trap sonar.

The present invention is applied to a ship sonar system. However, the present invention is not limited thereto, and the technical idea of the present invention may be applied to systems and fields of other technical fields.

As the inventive concept allows for various changes and numerous embodiments, particular embodiments will be illustrated in the drawings and described in detail in the written description. However, this is not intended to limit the present invention to specific embodiments, it should be understood to include all modifications, equivalents, and substitutes included in the spirit and scope of the present invention.

Terms including ordinal numbers such as first and second may be used to describe various components, but the components are not limited by the terms. The terms are used only for the purpose of distinguishing one component from another. For example, without departing from the scope of the present invention, the first component may be referred to as the second component, and similarly, the second component may also be referred to as the first component. The term “and / or” includes any item of a plurality of related listed items or a plurality of related listed yields.

When an element is referred to as being "connected" or "connected" to another element, it may be directly connected or connected to the other element, but other elements may be present in between. On the other hand, when a component is said to be "directly connected" or "directly connected" to another component, it should be understood that there is no other component in between.

The terminology used herein is for the purpose of describing particular example embodiments only and is not intended to be limiting of the invention. Singular expressions include plural expressions unless the context clearly indicates otherwise. In this application, the terms "comprise" or "have" are intended to indicate that there is a feature, number, step, action, component, part, or combination thereof described on the specification, and one or more other features. It is to be understood that the present disclosure does not exclude the possibility of the presence or the addition of numbers, steps, operations, components, components, or a combination thereof.

Unless defined otherwise, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by one of ordinary skill in the art. Terms such as those defined in the commonly used dictionaries should be construed as having meanings consistent with the meanings in the context of the related art and shall not be construed in ideal or excessively formal meanings unless expressly defined in this application. Do not.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Reference will now be made in detail to the preferred embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like reference numerals refer to the like elements throughout. The description will be omitted.

The present invention has been conceived to secure a means for predicting the deception capability for a naval vessel or manual mode of self-destructive deception.

In order to effectively function as an instrument for autonomous deception system analysis, in addition to sonar and deception, some of the sonar operator's roles must be algorithmized and the computation time reduced as much as possible. The present invention has the following characteristics to meet these conditions: 1) Monte Carlo method repeated experiments to simulate the deception capability of the deception machine to the naval ship, and the instability of the operating environment Probabilistic representations are applied to the traps and deception algorithms to mimic their impact on the detection performance of the traps (this will be described with reference to FIGS. 6 and 11);

2) Applying a random number to the detection probability Pd calculated in the state where the signal exceeding value SE is applied, and applying a target detection determination algorithm for the passive mode of the trap station (this will be described with reference to FIG. 12). Shall be);

3) Create virtual targets irrespective of the number of actual targets, extract the tracked target information from the maximum cumulative detection probability for each virtual target, and analyze the tracked target information to detect each target and the probability of deception. Apply a target tracking algorithm using a virtual target that calculates (this will be described with reference to FIG. 13);

4) Simultaneous exhibition of the target detection probability according to the use of the deception period, the increase and decrease of the deception detection probability bar graph is displayed in conjunction with the number of deception periods, and the target and deception detection probability values are presented (in this regard, FIGS. 7 to 7). It will be described with reference to FIG. 10);

5) Spectrum of target radiation noise, deception signal, and ambient noise are displayed separately in wideband and narrowband (this will be described with reference to FIGS. 7 to 10).

According to this feature of the present invention, the operation method of the present invention is as follows.

That is, the algorithm according to the present invention provides a graphical user interface (GUI) input method (corresponding to FIGS. 14 and 15 to 19) for user convenience, and a matlab for researchers who want to develop algorithms and solve information. The input data file (input_interface) can be edited directly in the command window of the command window or can be executed through interactive input.

On the other hand, when comparing the present invention with the prior art as follows:

1) Methods for obtaining probabilistic results

-The existing technique (product) or deception simulation algorithm places an artificial error term in the Signal Excess (SE) equation, and adds an error term according to the Markov process to the position estimation equation of the target. However,

-The present invention calculates the detection probability from the signal excess (SE) without any causal error term, and the azimuth and frequency of the target have the error in the form of a normal distribution in which the variance value is expressed as a signal-to-noise ratio. This is to maintain consistency in the application of error terms and calculation of probability of detection, and is one of the probabilistic representations used to predict sonar's target detection capability in recent years;

2) Display of simulation results

-The existing simulation algorithm is used to analyze the effects of torpedoes (or torpedoes) before the torpedo attack rate, torpedo (sonar) and the target movement trajectory. Sometimes sonar signal processing results are displayed in a time series;

-The present invention exhibits the target detection probability of Hamsona and the frequency spectrum for generating the deception signal.

Hereinafter, with reference to the accompanying drawings, it will be described an embodiment of the present invention in more detail.

6 is a flowchart of a trap or manual mode deception prediction algorithm of an autonomous deception system as an embodiment of the present invention.

6 is a flow chart of the deception capability prediction algorithm of the deception machine for the naval ship or manual mode (S10), the naval ship or deception capability simulation (S20), and the deception capacity measurement and result display (S31), etc. It is divided into three parts. In addition, in FIG. 6, the trap or the deception capability simulation portion is composed of a loop representing the target detection and tracking process (S24 ~ S28) and a repeating loop for Monte Carlo simulation (S22 ~ S30).

Referring to Figure 6 in more detail as follows.

By preparing the simulation data (S11), generating a scenario and configuring the object system (S12), the simulation is prepared. Start the simulation (S21), and initiate the target tracking by initializing the sonar environment and the target position (S22 and S23). After performing sound transfer modeling on the radiation signal from the target (S24), signal processing and analysis is performed (S25). Based on the analysis result, it is determined whether to detect the target (S26). Then, the sonar and the target position is reset (S27). Check whether the target tracking of the sonar is finished (S28). At this time, if the tracking is not finished, the contention or detection simulation process (that is, S24 to S27) is repeated from the step S24. If the target tracking is finished, the target detection information is analyzed (S29). Then, it is checked whether the simulation is completed (S30). If the simulation is not finished, the process is performed again from the step S22. When the simulation is finished, the deception capacity is measured (S32) and the results are displayed (S33).

7 to 10 are graphs related to the results of the simulation of FIG. 6. Hereinafter, the simulation results of FIG. 6 will be described with reference to FIGS. 7 to 10.

FIG. 7 is a graph illustrating narrow-band signal tones and noise levels applied to an simulation as an embodiment of the present invention. FIG. In FIG. 7, the frequency of the target signal is applied as a reference frequency to the target signal, the deception signal, the ambient noise, etc., and the number may be limited for the convenience of calculation.

8 is a graph illustrating the detection probability of a shipyard or a passive mode narrowband detector according to an embodiment of the present invention. In Figure 8, the target detection probability of the simulation results is calculated by the number of detection / the number of experiments. The red bar shows the target detection probability in the absence of deception, and the blue bar shows the detection probability when operating the deception. In FIG. 8, target no. 1 (blue bar) indicates a target detection probability when deception operation is performed, and target no. 2 and 3 (blue bar) indicate detection probabilities for deception periods 1 and 2, respectively. In addition, in FIG. 8, Pd denotes a target detection probability (= detection count / total experiment count), and DPI = 1-Pd, K denotes an effect diagram of deception.

9 is a graph illustrating a wideband signal tone and a noise level according to an embodiment of the present invention. In FIG. 9, the wideband signal sound and noise level were selected in an environment in which the sound absorption level was low (ship noise is the address sound source and the influence of the wind was excluded). The frequency of the target signal is applied as the reference frequency of the target signal and the deception signal, and the separate reference frequency and the slope are applied to the ambient noise.

10 is a graph illustrating the detection probability of the target and the deception of a ship station or a passive mode broadband detector according to one embodiment of the present invention. In Fig. 10, the detection probability of the wideband detector is configured in the same manner as in the narrowband case. The simulation result of FIG. 10 is configured to show the effect of the deception by showing the detection probability and the detection probability of the deception during the target before and after the deception operation. In other words, the red bar shows the target detection probability when operating only the target without deception, the target no.1 among the blue bars shows the probability of detecting the target (submersible), and the target no. The probability of detecting 2 is shown. In FIG. 10, Pd represents a target detection probability when the deceptor is operated, and DPI represents a probability of detecting the deception.

FIG. 11 is a diagram illustrating an algorithm classified into modularity according to an embodiment of the present invention.

11 is composed of data input and scenario and object system configuration step (S101), simulation step (S110), detection capability measurement step (S107), result display step (S108) and the like, of which is represented by the dark dotted line The experiment module (multi_run_passive) (S110) actually serves as the main program, and performs n iterative simulations.

Passive target tracking (passive_track) module (S115) represented by a thin dotted line consists of num_scan tracking steps for every simulation, and determines whether manual target detection is performed for every step where tracking is performed (S112). The sonar and the target position are reset (S113). This is because the position changes due to the movement of the sonar and the target, and thus the target detection condition is changed.

Determination of target detection at a given stage requires information such as the relative position of the target and the size of the deception signal, in addition to the position of the target and deception. The trap sonar model (S114) includes an underwater acoustic model that affects the size and shape of the target signal sound, such as an algorithm for the detection of the passive sonar and a sound wave transmission model and a noise model.

Referring to FIG. 11 in more detail, data is input and a scenario and a target system are configured (S101). Conduct a simulation of detection capability (S102). Manual target tracking is performed by repeatedly detecting a predetermined number of times (that is, num_scan times) (S103). If manual target tracking has not been completed, the process continues with S103. If manual target tracking is terminated (S104), a target vector is generated (S105) and whether the simulation is terminated (S106). If the simulation is not finished, the process returns to step S102 and repeats the subsequent steps. When the simulation is finished, the deception capacity is measured and the results are displayed (S107 and S108).

On the other hand, the manual target tracking step, that is, S103 is: passive target contact (S111); Target analysis (S112); It includes the step of performing the sonar and the target position reset (S113). In particular, the manual target contact (S111) step is performed using a plurality of ships or models (S114).

That is, as shown in FIG. 11, in the data input file (passive_gui or input_interface), a scenario is generated using input data and various keys, and an object system is configured to satisfy the conditions for simulation (S101).

And, as a trap station or a deception simulation, the deception simulation module (multi_run_passive) (S110), which actually plays the role of the main program, repeatedly performs simulations to obtain probabilistic results:

1) target tracking is performed for a certain time each time through the target tracking module (passive_track);

2) when tracking is completed, a target vector is generated based on the virtual target information generated during the tracking process;

3) at each step where tracking is performed, determining whether the ship is in the trap or in the manual mode through a passive target contact module (passive_sonar_basic);

4) determine whether the virtual target is increased or updated by determining whether the target is detected through a target analysis module (target_analysis);

5) The reset module (ship_target_update) resets the sonar and target positions at the next time;

6) Modules for simulating multiple contact or signal contacts are used to determine target contact;

7) Hamsa or model (S114) includes a module (pd_calc_passive) for calculating the target detection probability in the corresponding step when a plurality of signals are contacted;

8) The target analysis module (S112) includes a target module (target_logic_passive) for each target plate that algorithmizes the sonar's automatic target detection function and the supplementary detection capability by the operator. It plays a role of judging target detection according to (Acoustic Counter-Counter Measure level: ACCM level).

And, by controlling the deception capability (d_effect) module (S107) and output display (output_display) module (S108) to analyze the simulation results and display the analysis results.

12 is a flowchart of a manual target contact algorithm according to one embodiment of the present invention. 12 is a detailed description of step S111 of FIG. 12 is a detection probability calculation algorithm for the signal excess value given in the passive target contact module (passive_sonar_basic). In FIG. 12, if the detection probability Pd is calculated, it is determined whether the target is in contact with the generated random number.

As shown in FIG. 12, considering the instability of the detection environment in the ocean, a signal excess (SE) and a detection probability for the target signal are calculated as follows.

Signal Excess (SE) varies the detection threshold (DT) algorithm according to the type of detector. In other words,

SE = SL-TL- (NL-DI)-DT

At this time, the detection probability calculation method is calculated as follows according to the signal excess value.

On the other hand, the stochastic method is applied to the azimuth and frequency estimation algorithm, and the effect of the correlation with the corresponding gate is reflected in the detection probability.

13 is a flowchart illustrating a target tracking algorithm using a virtual target according to an embodiment of the present invention.

FIG. 13 shows a process of generating a target tracking using a virtual target and a target vector. After the contact information is generated in the passive target contact module (passive_sonar_basic) S111 of FIG. 11, the target analysis module (target_analysis) ( In other words, the virtual target information is generated in S112, and the target vector generation module S105 finds the actual target for the virtual target showing the maximum cumulative detection probability after the end of the target tracking, and uses the detected target in the experiment. As the experiment is repeated, the detection target information generates a real target vector.

Meanwhile, the present invention is divided into a method using graphics (GUI) and a method for researchers.

14 is a main menu screen according to an embodiment of the present invention.

15 to 19 are subscreens of the main menu screen of FIG. 14.

That is, FIG. 15 is an auxiliary screen of an array (array sensor) according to one embodiment of the present invention.

FIG. 16 is an auxiliary screen of an accm (Acoustic Counter-Counter Measure).

FIG. 17 shows an auxiliary screen of sea (sea / sound environment information) according to one embodiment of the present invention.

18 is an auxiliary screen of a scenario (simulation scenario and target, deception information) as an embodiment of the present invention.

19 shows an auxiliary screen of a trap sonar as an embodiment of the present invention.

As shown in Figure 14 to 19, the graphical user interface (GUI: Graphical User Interface) of the present invention is designed for the operator's convenience, the main screen consisting of eight main group keys (Figure 14) and for the selected group It consists of an auxiliary screen (FIGS. 15 to 19) for specific data input.

14, the main screen is frequency (frequency estimator), bearing (orientation estimator), array (array sensor), accm (deception measures), sea (marine environment), scenario (simulation and target), sonar (sonar) System) and multirun (repeated simulation), consisting of selection keys for 8 groups, each of which creates a subscreen.

-The information for the experiment is input through the sub screens. Scenarios and multiruns are used to generate scenarios for simulation and input information such as target and deception information, the number of experiments, and output pictures. It is used for inputting information of resources to be mobilized respectively.

Five sub-screens are shown in FIGS. 15 to 19 except for relatively simple cases such as frequency (frequency estimator), bearing (orientation estimator), and multirun (repetitive experiment) among the eight group keys of the main screen.

The operating method is that if passive_gui is executed in a matlab screen first, the main screen of FIG. 14 appears, and if one of eight group keys is selected, a corresponding subscreen appears. After modifying the necessary information on the subscreen, select the 'OK' key to return to the main screen. After modifying all necessary information in the same way, select the 'OK' key at the bottom of the main screen and the program will be executed.

On the other hand, the algorithm according to the above embodiment, when the user executes the input_interface in the command window of the matlab (matlab) command may be asked the following questions.

all inputs set default (0 (default): off, 1: on) =

In this case, 0 is inputted one by one through an interactive sentence, and 1 is inputted when applying all stored data in a pre-edited input file (input_interface).

Hereinafter, the drawings of FIGS. 14 to 19 will be described.

Fig. 14 is a graphical user interface (GUI) screen for controlling the operation of the present invention, in which all input data is divided into eight groups. Data is input to each group selected through this screen through the subscreens of FIGS. 15 to 19, and a default value is applied to the unselected group. Press the "OK" button at the bottom to complete the input and run the program.

Referring to FIG. 15, a type and size information of an array sensor is input as a specification input screen of the array sensor.

Array sensors include linear arrays (1), planar arrays (2), cylindrical arrays (3), etc.The horizontal length of the array size in meters is the horizontal size of the linear array and the flat array, and the vertical length is the flat type. The longitudinal length of the array and the cylindrical array, and the diameter are used for the cylindrical array, respectively.

The selection result of the array sensor affects the noise calculation due to the beam's direction index (DI) and reverberation when calculating the signal excess (SE). That is, the signal excess is calculated as follows, and each variable used in the calculation is as follows.

SE = SL-TL + DI -DT-NL

SL: source level,

TL: transmission loss,

DT: detection threshold

NL: noise level,

Referring to FIG. 16, two levels of deception measures, such as azimuth gate 1 or azimuth and frequency gate 2, may be selected according to the level, and the gate value means half width. Use units of degrees (orientation) and Hz (frequency).

The sub-screen of the sea (sea / sound detection environment information) of FIG. 17 is a screen for inputting marine environment information that constitutes a target detection condition of a sonar and includes the following elements. (However, the units used are m / s (speed), Hz (frequency), dB (signal level), dB / octave (noise level slope), m (depth), etc.):

-Sea state: sea level is divided into 8 stages from 1 to 8 according to the wave height or wind speed, and the larger the number, the greater the roughness of the ocean;

Sound speed: indicates the reference speed of sound waves traveling underwater;

-Ambient noise level: means the ambient noise level (AN) of the sonar operating sea area;

Reference frequency: reference frequency for wideband ambient noise input;

Broadband noise level: caused by sea level fluctuations caused by rain or wind, expressed in slopes per octave, i.e. in dB / octave, from the reference frequency;

Narrowband noise level: information for narrowband detection, inputted at the same frequency as the target noise;

-Spectral slope, which indicates the slope of the ambient noise level, which is the degree of change in the noise level when the frequency increases by 2 times;

-Sea depth: the depth of the sonar operating area.

18 is an exemplary embodiment of the present invention, which is an auxiliary screen displaying information on a simulation scenario, a target, a deception device, and the like according to the present invention.

As shown in FIG. 18, FIG. 18 is a screen for inputting target information including a scenario, that is, a scenario for simulation and deception, respectively (seconds), degrees (directions), dBs (signal levels), and m ( Size and location), Hz (frequency), etc. are used.

-Scenario: Information for simulation test start time (start_time), number of scans (number of scan), time difference between tracking steps (time_interval), criterion for determining target detection (final_decision_Pc), after deception firing It consists of a turn of the underwater target.

Jammer: It consists of the number of jammers, the start_time of the jammer, the life_time, and the signal_level. The maximum number of jammers available is five, each operating independently. Jammer assumes a broadband noise source.

-Target: means the underwater target (submersible), the position (start_x, y, z_location) and velocity (submarine_speed) in the three-dimensional coordinate system, the horizontal and vertical traveling angle (heading_theta, phi), the radius of the target And length (target_radius, length) and the like are input. Three tonal frequencies are selected and a wideband or narrowband signal level is input according to the rental width.

The location information is used to calculate the relative position with sonar, the velocity and the angle of travel are used for the next step calculation, and the radius and length of the target are applied to the shape and size of the target radiation signal.

The reference frequency is a reference point for narrowband and wideband beeper input, and only three frequencies are included for convenience. The same applies to ambient noise and deception signals.

Decay: It consists of the number of deception, start_time, decoy_direction, velocity_ratio, and delay time (decoy_process_delay). The maximum number of deceptions that can be used is five, each operating independently of the jammer. The delay time of the deception period is the time required to generate and transmit the deception signal after receiving the sonar transmission signal, and the speed means the ratio with the target speed.

19 is an embodiment of the present invention, an information input screen for a sonar (sonar). As shown in Fig. 19, Hz (frequency and bandwidth), seconds (time), m (size and position), m / s (speed), degrees (orientation and beamwidth), dB (signal level), and the like, respectively. Has units.

-The sonar specifications include detectors for narrowband power detection (1), wideband power detection (2), narrowband amplitude detection (3), wideband amplitude detection (4), and wideband correlator (5). Beam resolution (beam_quantizer), probability of false detection (P_fa), probability of detection (P_d), detection index (detection_index), standard deviation of P_d, integral time, number of samples (no. Of time samples) ), And reception bandwidth (BW).

-Operation information: It consists of location (x, y, z-location) and velocity (x, y, z-velocity) in 3D coordinates.

While the present invention has been particularly shown and described with reference to exemplary embodiments thereof, it is to be understood that the invention is not limited to the disclosed embodiments, but, on the contrary, is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims. will be. Therefore, the true technical protection scope of the present invention will be defined by the technical spirit of the appended claims.

Claims

(a) inputting simulation data;
(b) performing a function of predicting passive deception or deception ability of deception by repeating n deception capability simulations by creating a scenario and constructing an object system;
(c) measuring the passive sonar capacity of the deception machine after the simulation is finished;
(d) displaying the result of the measured deception capability; method of predicting the deception capability of the deception machine for a naval ship or manual mode.

The method of claim 1, wherein step (b)
(b-1) manually repeating target tracking during the simulation;
(b-2) generating a target vector after the target tracking is finished;
(b-3) Determining whether the simulation is complete; Deception capacity prediction method of the deception machine for a ship station or manual mode comprising a.

The method of claim 2, wherein step (b-1)
A method of predicting the deception capability of a deception machine for a naval ship or manual mode, comprising the steps of repeatedly detecting and tracking a target by a predetermined number of times (num_scan).

The method of claim 2, wherein in the step (b-3)
If the simulation is not finished, the method of predicting the deception capacity of the deception machine for the naval ship or manual mode, characterized in that performing the steps (b-1) to (b-2).

The method of claim 2, wherein step (b-1)
Manually detecting a target using a trap or a model;
Tracking the target;
Resetting the trap station and the target position; method for predicting the deception capability of the deception machine for a naval ship or manual mode.

The method of claim 1, wherein in step (a)
The simulation data is marine environment information,
A ship station or manual comprising at least one of a sea state, sound speed, ambient noise level, scattering strength, and sea depth. Method of predicting the deception capacity of the deception machine for the mode.

Preparing a simulation by inputting the deception capability simulation data of the deception machine in order to predict the deception capability of the deception machine in the shipyard or the manual mode;
Performing a loop representing a target detection and tracking process in a repeating loop for Monte Carlo simulation;
Measuring the deception capability of the deception machine against the trap or using the information generated during the detection and tracking process;
And displaying the result of the deceptive capacity of the measured deceptive device.

The method of claim 7, wherein the loop representing the target detection and tracking process
Performing signal transduction modeling;
Signal processing and analyzing;
A method of predicting the deception capability of the deception machine for a naval ship or manual mode, comprising the step of determining whether or not to detect the target.

The method of claim 7, wherein the loop for Monte Carlo simulation
Performing sonar environment and target position initialization;
Simulating target detection and tracking of a traphouse;
Analyzing target detection information obtained through the target tracking simulation;
Determining whether the simulation is finished; Deception capability prediction method of the deception machine for a trap or manual mode comprising a.

The method of claim 7, wherein the input data for the simulation is
As marine environment information,
A ship station or manual comprising at least one of a sea state, sound speed, ambient noise level, scattering strength, and sea depth. Method of predicting the deception capacity of the deception machine for the mode.

(A) generating a scenario using input data and various keys in a data input file (passive_gui or input_interface), constructing an object system, and preparing conditions for simulation;
(B) performing target tracking for a predetermined time every time through the target tracking module (passive_track);
(C) determining whether the target is in a trap or passive mode with respect to a target each time the target tracking is performed through a passive target contact module (passive_sonar_basic);
(D) applying a target detection determination criterion calculated in the detection probability calculation module of multiple targets (pd_calc_passive) to the target contact determination, and mobilizing a module for simulating signal contact of a plurality of shipyards;
(E) resetting the sonar and the target position at the next time in the reset module (ship_target_update);
(F) A method of predicting the deception capability of the deception machine in a naval ship or manual mode by generating a target vector by analyzing the detection information generated in the tracking process.

The method of claim 11, wherein the passive target detection (passive_sonar_basic) module in the step (C)
Perform a detection probability calculation algorithm for a given signal excess (SE),
Here, the signal excess value calculates the detection probability for the target signal in consideration of the instability of the detection environment in the ocean,
The signal excess (SE) is,
A method of predicting the deception capability of a deception machine for a shipyard or a manual mode, characterized in that.

The method of claim 12, wherein the detection probability calculation method
According to the signal excess value

A method of predicting the deception capability of a deception machine for a shipyard or a manual mode, characterized by using an equation.

The method of claim 11, wherein in the target analysis module (target_analysis) of the target tracking module (passive_track) of the step (B)
Sonar's automatic target detection function and operator's detection ability complementary function analyzes the signal contacted by the target plate logic module (target_logic_passive), which is a two-step Acoustic Counter-Counter Measure level (ACCM level) To determine whether to detect the target,
A method of predicting the deception capability of the deception machine in a naval ship or manual mode, characterized by generating or updating the virtual target information according to the determination result.

The method of claim 10, wherein in step (H)
After the target tracking is completed, the actual target corresponding to the virtual target showing the maximum cumulative detection probability in the target vector generation part is regarded as the actual target detected in the experiment.
A method of predicting the deception capability of the deception machine in a naval ship or manual mode, characterized by generating detection target information corresponding to a real target vector.