A mutual detection evaluation method and device for underwater passive detection

Abstract

The present application relates to the field of underwater passive detection technology, and specifically to a mutual detection evaluation method and device for underwater passive detection. It includes: calculating the signal margin parameters of target one and target two before detecting each other based on the target detectability characteristic parameters and target detection performance of the two targets to be measured and the distribution of environmental noise; calculating the first conditional probability and the second conditional probability that target one and target two detect each other based on the signal margin parameters and the probability density function of the environmental noise before the two targets to be measured are released; calculating the mutual detection factor between target one and target two based on the first conditional probability and the second conditional probability. The present application quantifies target one and target two in the same dimension, and regards this process as conditional probability, so as to achieve the purpose of quantifying the mutual search ability of target one and target two at different spatial positions, and provides a scientific and effective quantitative evaluation criterion for the mutual search ability of two underwater targets.

Description

Technical Field

[0001] The present application relates to the technical field of underwater passive detection, and particularly relates to a mutual detection evaluation method and device for underwater passive detection. Background Art

[0002] The marine hydrographic environment is complex, and a large number of interfering targets often exist in the target data detected by an underwater platform carrying sonar equipment, making it difficult to identify the targets. In addition, active detection is likely to expose one's own position, and in an environment with "concealment" requirements, passive detection is often used. Underwater passive detection technology refers to using a sonar to passively receive the radiation noise generated by underwater targets such as ships and the signals transmitted by underwater acoustic equipment to determine the azimuth and distance of the targets. Since the passive sonar itself does not emit signals, the targets will not detect the existence and intention of the sonar. Therefore, in underwater passive detection, it is not only necessary to measure one's own detection ability, but also to hide one's own existence as much as possible, that is, to measure the ability of underwater targets to search for each other.

[0003] The ability of two underwater targets to search for each other is related to both the target detection ability and the target characteristics of the target to be searched. However, in the related art, the evaluation criteria for target detection ability and target characteristics are independent of each other, lacking a criterion for quantitatively evaluating the two in the same dimension, resulting in the inability to quantitatively evaluate the mutual search ability between targets. Summary of the Invention

[0004] In view of the fact that in the related art, the evaluation criteria for target detection ability and target characteristics are independent of each other, lacking a criterion for quantitatively evaluating the two in the same dimension, resulting in the inability to quantitatively evaluate the mutual search ability between targets.

[0005] In a first aspect, an embodiment of the present application provides a mutual detection evaluation method for underwater passive detection, and the mutual detection evaluation method includes:

[0006] Calculate the signal margin parameters of Target 1 and Target 2 before detecting each other according to the target detectability characteristic parameters, target detection performance of the two targets to be measured, and the environmental noise distribution.

[0007] Calculate the first conditional probability and the second conditional probability of Target 1 and Target 2 detecting each other according to the signal margin parameters and the environmental noise probability density function before the two targets to be measured are put into use.

[0008] Calculate the mutual detection factor between Target 1 and Target 2 according to the first conditional probability and the second conditional probability.

[0009] In combination with the first aspect, in an implementation manner, the calculating the mutual detection factor between Target 1 and Target 2 according to the first conditional probability and the second conditional probability includes:

[0010] Calculate the mutual detection factor Q of target one to target two according to the formula:

[0011]

[0012] In the formula, is the first conditional probability, is the second conditional probability.

[0013] Combined with the first aspect, in one embodiment, the calculating the mutual detection factor between target one and target two according to the first conditional probability and the second conditional probability further includes:

[0014] Taking target one as the center, place target two at different positions in the confrontation area;

[0015] Calculate the detection probability of target two with respect to target one when target two is in different areas, and draw a detection probability graph for both sides.

[0016] Combined with the first aspect, in one embodiment, the calculating the mutual detection factor between target one and target two according to the first conditional probability and the second conditional probability includes:

[0017] Taking target one as the center, place target two at different positions in the confrontation area;

[0018] Calculate the mutual detection factor of target two with respect to target one when target two is in different areas, and draw a mutual detection factor graph according to the mutual detection factors of both sides.

[0019] Combined with the first aspect, in one embodiment, the according to the signal margin parameter and the environmental noise probability density function before the placement of two targets to be measured includes:

[0020] Obtain the environmental noise level, the radiated noise level of the target to be measured, the propagation loss between the two targets to be measured, and the passive sonar array gain parameter of the target to be measured;

[0021] Calculate the signal margin parameters of target one and target two before detecting each other according to the environmental noise level, the radiated noise level of the target to be measured, the propagation loss between the two targets to be measured, and the passive sonar array gain parameter of the target to be measured.

[0022] Combined with the first aspect, in one embodiment, the calculating the signal margin parameters of target one and target two before detecting each other according to the environmental noise level, the radiated noise level of the target to be measured, the propagation loss between the two targets to be measured, and the passive sonar array gain parameter of the target to be measured includes: calculating the first signal margin parameter of target one according to the formula and the second signal margin parameter of target two :

[0023]

[0024]

[0025] Wherein, and are the radiated noise levels of Target 1 and Target 2 respectively, is the propagation loss from Target 2 to Target 1, is the propagation loss from Target 1 to Target 2, is the ambient noise level, and are the passive sonar array gains of Target 1 and Target 2 respectively.

[0026] Combined with the first aspect, in one implementation manner, the obtaining of the passive sonar array gain parameter of the target to be measured includes:

[0027] Obtaining the number of elements of the first passive sonar array of Target 1 and the number of elements of the second passive sonar array of Target 2;

[0028] Calculating the first passive sonar array gain of Target 1 and the second passive sonar array gain of Target 2 :

[0029]

[0030]

[0031] Wherein, is the number of elements of the first passive sonar array, is the number of elements of the second passive sonar array.

[0032] Combined with the first aspect, in one implementation manner, the obtaining of the ambient noise level includes: determining the ambient noise level and the ambient noise probability density function according to the historical data of the detection environment.

[0033] Combined with the first aspect, in one implementation manner, the calculating of the first conditional probability and the second conditional probability that Target 1 and Target 2 detect each other according to the ambient noise probability density function and the signal margin parameter when the two targets to be measured do not exist includes:

[0034] When the ambient noise probability density function conforms to Gaussian white noise of normal distribution, calculating the first conditional probability of Target 1 and the second conditional probability of Target 2 :

[0035]

[0036]

[0037] Where, is the standard deviation of ambient noise, The signal detected for target 1 contains the noise event radiated by target 2. The signal detected for target 2 contains the noise event radiated by target 1. is the detection threshold.

[0038] In a second aspect, an embodiment of the present application provides a mutual detectability evaluation device for underwater passive detection, wherein the mutual detectability factor calculation device includes:

[0039] A signal margin calculation unit is used to calculate the signal margin parameters of target one and target two before detecting each other based on the target detectability characteristic parameters and target detection performance of the two targets to be detected and the environmental noise distribution;

[0040] A probability calculation unit, which is used to calculate a first conditional probability and a second conditional probability that the first target and the second target detect each other based on the environmental noise probability density function and the signal margin parameter when the two targets to be detected do not exist;

[0041] An evaluation unit is used to calculate the mutual exploration factor between the first target and the second target according to the first conditional probability and the second conditional probability.

[0042] The beneficial effects of the technical solutions provided in the embodiments of the present application include:

[0043] This application simultaneously considers the target characteristics and detection capabilities of target one and target two, quantifies them in the same dimension, and regards this process as conditional probability. It provides a method for calculating the mutual detection factor between the two targets, thereby achieving the purpose of quantifying the mutual search capabilities of target one and target two at different spatial positions, and providing a scientific and effective quantitative evaluation criterion for the mutual search capabilities of two underwater targets. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 This is a flowchart of the mutual exploration evaluation method for this application;

[0045] Figure 2 Schematic diagram of the flow of a mutual exploration evaluation method in a specific embodiment of the present application;

[0046] Figure 3 Detection probability map of target one and target two in the embodiment of the present application;

[0047] Figure 4 Detection probability map of target 2 to target 1 in the embodiment of the present application;

[0048] Figure 5 is a mutual exploration factor graph between target one and target two in the embodiment of the present application;

[0049] Figure 6 Schematic diagram of the hardware structure of the interoperability evaluation device involved in the embodiment of the present application. DETAILED DESCRIPTION

[0050] In order to enable those skilled in the art to better understand the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0051] Regarding related technologies, the evaluation criteria for target detection capability and target characteristics are independent of each other, and there is a lack of criteria for quantitatively evaluating the two in the same dimension, resulting in the inability to quantitatively evaluate the mutual search capability between targets.

[0052] First, as Figure 1 As shown, an embodiment of the present application provides a mutual detectability evaluation method for underwater passive detection, and the mutual detectability evaluation method includes:

[0053] Step S1: Calculate the signal margin parameters of target 1 and target 2 before detecting each other based on the target detectability characteristic parameters and target detection performance of the two targets and the environmental noise distribution.

[0054] Specifically, the ambient noise level, the radiated noise level of the target to be detected, the propagation loss between the two targets to be detected, and the passive sonar array gain parameters of the targets to be detected can be obtained. The signal margin parameters of target one and target two before detecting each other are then calculated based on the ambient noise level, the radiated noise level of the target to be detected, the propagation loss between the two targets to be detected, and the passive sonar array gain parameters of the targets to be detected.

[0055] Furthermore, if Figure 2 As shown, in some specific embodiments, the above step S1 includes:

[0056] Step S1a: Obtain environmental noise distribution.

[0057] Specifically, the environmental noise level is determined based on the historical data of the hidden environment. And the ambient noise probability density function P.

[0058] In some optional embodiments, taking normally distributed Gaussian white noise as an example, for events without target signals , the probability density function of the signal received by the sonar is:

[0059]

[0060] It is understandable that the signal probability density function is used for subsequent calculation of the detection probability.

[0061] Step S1b: Obtain the target detectable characteristic parameters.

[0062] Specifically, relevant parameters are input according to the target characteristics, such as the target radiation noise level and , the subscript represents target one, represents target two.

[0063] Step S1c: Obtain the passive detection performance parameters and calculate the passive sonar array gain parameters of the target to be measured.

[0064] Specifically, obtain the number of the first passive sonar array elements of target one and the number of the second passive sonar array elements of target two; calculate the first passive sonar array gain of target one and the second passive sonar array gain of target two :

[0065]

[0066]

[0067] In the formula, is the number of the first passive sonar array elements, is the number of the second passive sonar array elements.

[0068] Step S1d: Calculate the signal margin parameters of target one and target two before detecting each other according to the environmental noise level, the radiation noise level of the target to be measured, the propagation loss between the two targets to be measured, and the passive sonar array gain parameters of the target to be measured.

[0069] Specifically, calculate the first signal margin parameter of target one according to the formula and the second signal margin parameter of target two :

[0070]

[0071]

[0072] In the formula, and are respectively the radiation noise levels of target one and target two, is the propagation loss from target two to target one, is the propagation loss from target one to target two, is the environmental noise level, and The passive sonar array gains for Target 1 and Target 2 respectively.

[0073] Step S2: Calculate the first conditional probability and the second conditional probability that Target 1 and Target 2 detect each other according to the signal margin parameter and the environmental noise probability density function before the two targets to be measured are dropped.

[0074] It should be noted that the environmental noise probability density function needs to be determined according to the noise environment.

[0075] In some specific embodiments, when the environmental noise probability density function conforms to Gaussian white noise with a normal distribution, calculate the first conditional probability that Target 1 detects Target 2 according to the formula and the second conditional probability that Target 2 detects Target 1 :

[0076]

[0077]

[0078] In the formula, is the environmental noise standard deviation, is the event that the signal detected by Target 1 contains the radiation noise of Target 2, is the event that the signal detected by Target 2 contains the radiation noise of Target 1, is the detection threshold.

[0079] Step S3: Calculate the mutual detection factor between Target 1 and Target 2 according to the first conditional probability and the second conditional probability.

[0080] It should be noted that on the premise that Target 1 and Target 2 are mutually detectable, divide the detection probability of Target 1 for Target 2 , by the detection probability of Target 2 for Target 1 , to obtain the mutual detection factor of Target 1 for Target 2 :

[0081]

[0082] In the formula, is the first conditional probability, is the second conditional probability.

[0083] It should be noted that in this application, by obtaining the mutual detection factor Q of Target 1 for Target 2 at the current empty position, the purpose of quantifying the mutual search capabilities of Target 1 and Target 2 is achieved.

[0084] In some preferred embodiments, such as Figure 3 , Figure 4 and Figure 5As shown, in order to clearly display the mutual detection ability between the targets to be measured, a mutual detection probability graph and a mutual detection ability factor graph of both parties can be drawn.

[0085] Specifically, with Target 1 as the center, place Target 2 at a point in the environment, traverse the confrontation area with Target 2, and use the above-mentioned mutual detection ability evaluation method to calculate the mutual detection probability and mutual detection ability factor of both parties in different areas where Target 2 is located. , thereby obtaining the mutual detection probability graph and the mutual detection ability factor graph (as shown in Figure 2 , 3 , and Figure 4. The abscissa in the figure represents the horizontal distance, and the ordinate represents the vertical depth). In this embodiment, the area where the mutual detection probability of both parties is less than a certain value is regarded as the area where mutual detection cannot be formed, and this area is marked in black.

[0086] In the second aspect, the present application provides a mutual detection ability evaluation device for underwater passive detection. The mutual detection ability evaluation includes: a signal margin calculation unit, a probability calculation unit, and an evaluation unit; wherein,

[0087] The signal margin calculation unit is used to calculate the signal margin parameters of Target 1 and Target 2 before detecting each other according to the target detectability characteristic parameters, target detection performance, and environmental noise distribution of the two targets to be measured; the probability calculation unit is used to calculate the first conditional probability and the second conditional probability of Target 1 and Target 2 detecting each other according to the environmental noise probability density function and signal margin parameters when the two targets to be measured do not exist; the evaluation unit is used to calculate the mutual detection ability factor between Target 1 and Target 2 according to the first conditional probability and the second conditional probability.

[0088] Among them, the function implementation of each module in the above-mentioned mutual detection ability evaluation device corresponds to each step in the embodiment of the above-mentioned mutual detection ability evaluation device, and its function and implementation process will not be elaborated here one by one.

[0089] In the third aspect, the embodiment of the present application provides a mutual detection ability evaluation device. The mutual detection ability evaluation device can be a device with data processing functions such as a personal computer (PC), a notebook computer, or a server.

[0090] Referring to Figure 6 , Figure 6 is a schematic hardware structure diagram of the mutual detection ability evaluation device involved in the solution of the embodiment of the present application. In the embodiment of the present application, the mutual detection ability evaluation device may include a processor, a memory, a communication interface, and a communication bus.

[0091] Among them, the communication bus can be of any type and is used to interconnect the processor, the memory, and the communication interface.

[0092] The communication interface includes interfaces such as input / output (I / O) interfaces, physical interfaces, and logical interfaces for implementing the interconnection of components inside the interoperability evaluation device, as well as interfaces for implementing the interconnection between the interoperability evaluation device and other devices (such as other computing devices or user devices). The physical interface can be an Ethernet interface, a fiber optic interface, an ATM interface, etc.; the user device can be a display, a keyboard, etc.

[0093] The memory can be various types of storage media, such as random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), flash memory, optical memory, hard disk, programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), etc.

[0094] The processor can be a general-purpose processor, which can call the interoperability evaluation program stored in the memory and execute the interoperability evaluation method provided in the embodiments of the present application. For example, the general-purpose processor can be a central processing unit (CPU). Among them, the method executed when the interoperability evaluation program is called can refer to the various embodiments of the interoperability evaluation method of the present application, which will not be elaborated here.

[0095] Those skilled in the art can understand that Figure 6 the hardware structure shown in does not constitute a limitation on the present application, and may include more or fewer components than shown, or combine some components, or have different component arrangements.

[0096] In a fourth aspect, the embodiments of the present application further provide a computer-readable storage medium.

[0097] The interoperability evaluation program is stored on the computer-readable storage medium of the present application. When the interoperability evaluation program is executed by a processor, the steps of the interoperability evaluation method as described above are implemented.

[0098] Among them, the method implemented when the interoperability evaluation program is executed can refer to the various embodiments of the interoperability evaluation method of the present application, which will not be elaborated here.

[0099] In summary, the present application simultaneously considers the target characteristics and detection capabilities of Target 1 and Target 2, quantifies them in the same dimension, regards this process as a conditional probability, and gives a calculation method for the mutual detection factor between the two targets, so as to achieve the purpose of quantifying the mutual search capabilities of Target 1 and Target to different spatial positions, and provides a scientific and effective quantitative evaluation criterion for the mutual search capabilities of two underwater targets.

[0100] It should be noted that the serial numbers of the embodiments of the present application above are only for description and do not represent the advantages or disadvantages of the embodiments.

[0101] The terms "including" and "having" in the description of the specification, claims and drawings of the present application and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally further include steps or units not listed, or may optionally further include other steps or units inherent to these processes, methods, products or devices. The descriptions of "Target 1", "Target 2" and "Target 3" are used to distinguish different objects, etc., which do not represent a sequence, nor do they limit that "Target 1", "Target 2" and "Target 3" are different types.

[0102] In the description of the embodiments of the present application, terms such as "exemplary", "for example" or "for instance" are used to indicate examples, illustrations or explanations. Any embodiment or design solution described as "exemplary", "for example" or "for instance" in the embodiments of the present application should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Rather, the use of words such as "exemplary", "for example" or "for instance" is intended to present related concepts in a specific manner.

[0103] In the description of the embodiments of the present application, unless otherwise specified, " / " means "or". For example, A / B may mean A or B; "and / or" in the text is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B may mean: A exists alone, A and B exist simultaneously, and B exists alone. In addition, in the description of the embodiments of the present application, "a plurality of" means two or more than two.

[0104] In some processes described in the embodiments of the present application, there are multiple operations or steps that appear in a specific order. However, it should be understood that these operations or steps may not be executed in the order in which they appear in the embodiments of the present application or may be executed in parallel. The serial numbers of the operations are only used to distinguish different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations or steps may be executed in sequence or in parallel, and these operations or steps may be combined.

[0105] Through the description of the above embodiments, those skilled in the art can clearly understand that the above-described example methods can be implemented by means of software plus a necessary general hardware platform. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium as described above (such as ROM / RAM, magnetic disk, optical disc), and includes several instructions for causing a terminal device to execute the methods described in various embodiments of the present application.

[0106] The above are only the preferred embodiments of the present application, and do not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made by using the content of the specification and drawings of the present application, or directly or indirectly applied in other related technical fields, shall be equally included in the patent protection scope of the present application.

Claims

1. An evaluation method for mutual detection orientation to underwater passive detection, characterized in that, The mutual detection evaluation method includes: Calculating the signal margin parameters of Target 1 and Target 2 before detecting each other according to the target detectability characteristic parameters, target detection performance, and environmental noise distribution of the two targets to be measured; Calculating the first conditional probability and the second conditional probability of Target 1 and Target 2 detecting each other according to the signal margin parameters and the environmental noise probability density function before the two targets to be measured are placed; Calculating the mutual detection factor between Target 1 and Target 2 according to the first conditional probability and the second conditional probability.

2. The mutual detection evaluation method for underwater passive detection according to claim 1, characterized in that The calculating the mutual detection factor between Target 1 and Target 2 according to the first conditional probability and the second conditional probability includes: Calculating the mutual detection factor Q of Target 1 for Target 2 according to the formula: Wherein, is the first conditional probability, is the second conditional probability, is the event that the signal detected by target one contains the radiated noise of target two, is the event that the signal detected by target two contains the radiated noise of target one.

3. The mutual detection evaluation method for underwater passive detection according to claim 2, wherein, The calculating the mutual detection factor between Target 1 and Target 2 according to the first conditional probability and the second conditional probability further includes: Taking Target 1 as the center and placing Target 2 at different positions in the confrontation area; Calculating the detection probabilities of Target 2 in different areas with respect to Target 1 and drawing the detection probability graph of both sides.

4. The mutual detection evaluation method for underwater passive detection according to claim 2, wherein The calculating the mutual detection factor between Target 1 and Target 2 according to the first conditional probability and the second conditional probability includes: Taking Target 1 as the center and placing Target 2 at different positions in the confrontation area; Calculating the mutual detection factor between Target 2 in different areas and Target 1 and drawing the mutual detection factor graph according to the mutual detection factors of both sides.

5. The mutual detection evaluation method for underwater passive detection according to claim 1, wherein The calculating according to the signal margin parameters and the environmental noise probability density function before the two targets to be measured are placed includes: Obtaining the environmental noise level, the radiated noise level of the target to be measured, the propagation loss between the two targets to be measured, and the passive sonar array gain parameter of the target to be measured; Calculating the signal margin parameters of Target 1 and Target 2 before detecting each other according to the environmental noise level, the radiated noise level of the target to be measured, the propagation loss between the two targets to be measured, and the passive sonar array gain parameter of the target to be measured.

6. The mutual detection evaluation method for underwater passive detection according to claim 5, characterized in that, Calculating the signal margin parameters of Target 1 and Target 2 before detecting each other according to the ambient noise level, the radiated noise level of the target to be measured, the propagation loss between two targets to be measured, and the passive sonar array gain parameter of the target to be measured, including: calculating the first signal margin parameter of Target 1 according to the formula and the second signal margin parameter of Target 2 : Wherein, and are the radiated noise levels of Target 1 and Target 2 respectively, is the propagation loss from Target 2 to Target 1, is the propagation loss from Target 1 to Target 2, is the ambient noise level, and are the passive sonar array gains of Target 1 and Target 2 respectively.

7. The mutual detection evaluation method for underwater passive detection according to claim 5, characterized in that The obtaining the passive sonar array gain parameter of the target to be measured includes: Obtaining the number of the first passive sonar array elements of Target 1 and the number of the second passive sonar array elements of Target 2; Calculate the first passive sonar array gain of target one based on the number of elements of the first passive sonar array and the number of elements of the second passive sonar array and the second passive sonar array gain of target two : In the formula, is the number of elements of the first passive sonar array, is the number of elements of the second passive sonar array.

8. The mutual detection evaluation method for underwater passive detection according to claim 5, wherein The obtaining the environmental noise level includes: determining the environmental noise level and the environmental noise probability density function according to the historical data of the detection concealment environment.

9. The mutual detection evaluation method for underwater passive detection according to claim 1, wherein The calculating the first conditional probability and the second conditional probability of Target 1 and Target 2 detecting each other according to the environmental noise probability density function and the signal margin parameters when the two targets to be measured do not exist includes: When the environmental noise probability density function conforms to Gaussian white noise with a normal distribution, calculate the first conditional probability of target one according to the formula and the second conditional probability of target two : Wherein, is the standard deviation of environmental noise, is that the signal detected by Target 1 contains the radiation noise event of Target 2, is that the signal detected by Target 2 contains the radiation noise event of Target 1, is the detection threshold, is the first signal margin parameter of Target 1, is the second signal margin parameter of Target 2.

10. An inter-detection evaluation device for underwater passive detection, characterized in that, The mutual detection evaluation device includes: A signal margin calculation unit, which is used to calculate the signal margin parameters of Target 1 and Target 2 before detecting each other according to the target detectability characteristic parameters, target detection performance, and environmental noise distribution of the two targets to be measured; A probability calculation unit, which is used to calculate the first conditional probability and the second conditional probability of Target 1 and Target 2 detecting each other according to the environmental noise probability density function and the signal margin parameters when the two targets to be measured do not exist; An evaluation unit, which is used to calculate the mutual detection factor between Target 1 and Target 2 according to the first conditional probability and the second conditional probability.

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