Derivation method and system of optimal receiver of underwater electromagnetic detection system

Abstract

The present application belongs to but is not limited to the underwater detection technical field, especially relates to a kind of underwater electromagnetic detection system optimum receiver derivation method and system, comprising: S1, target echo signal feature analysis;S2, optimum receiver derivation;S3, receiver operating characteristic analysis;S4, underwater electromagnetic detection system optimum generalized likelihood ratio receiver obtains.The present application first obtains the optimum receiver model of underwater electromagnetic detection system, aiming at the existing reality problem of underwater electromagnetic detection system receiver, the present application uses generalized likelihood ratio criterion to comprehensively deduce the model of optimum receiver, and the performance of receiver is analyzed, which provides certain reference significance for underwater electromagnetic detection system receiver design.

Description

Technical Field

[0001] This invention belongs to, but is not limited to, the field of underwater detection technology, and particularly relates to a method and system for deriving the optimal receiver for an underwater electromagnetic detection system. Background Technology

[0002] Due to the conductivity of water, electromagnetic waves attenuate significantly in water, with an energy loss of approximately 90% for every meter of propagation. Therefore, underwater electromagnetic detection is impractical in most applications. However, in certain specific application scenarios, such as low-frequency and near-field applications, underwater electromagnetic detection still has its place.

[0003] Because the detection signal model of underwater electromagnetic detection contains random parameters with unknown statistical characteristics, it is difficult to obtain the model of the optimal receiver for the underwater electromagnetic detection system.

[0004] Based on the above analysis, the urgent technical problem that needs to be solved in the existing technology is that, due to the presence of random parameters with unknown statistical characteristics in the detection signal model of underwater electromagnetic detection, it is difficult to obtain the model of the optimal receiver for the underwater electromagnetic detection system. Summary of the Invention

[0005] To address the problems existing in the prior art, this invention provides a method and system for deriving the optimal receiver for an underwater electromagnetic detection system.

[0006] This invention is implemented as follows: a method for deriving the optimal receiver for an underwater electromagnetic detection system, comprising:

[0007] S1, Target echo signal characteristic analysis;

[0008] S2, Derivation of the optimal receiver;

[0009] S3, Receiver operating characteristics analysis;

[0010] S4, the optimal generalized likelihood ratio of the underwater electromagnetic detection system is obtained by the receiver.

[0011] Furthermore, S1 specifically includes:

[0012] The electromagnetic echo signal of an underwater target can be represented as

[0013]

[0014] Where t is time, A is the amplitude of the signal, g(t) is the envelope of the signal, and ω0 is the frequency of the signal. The phase of the signal.

[0015] For the case where the transmitting and receiving antennas are vertically configured, the signal amplitude can be obtained from the expression for the total magnetic field of the magnetic dipole.

[0016]

[0017] where M is the magnetic moment of the radiation, μ0 is the permeability of seawater, a is the distance between the transmitting and receiving antennas, and h is the distance between the transmitting antenna and the target.

[0018] The probability density function of A is

[0019]

[0020] For a typical application, the signal envelope can be represented as

[0021] g(t) = exp[-α(t0-t) 2 ](4)

[0022] where α is the inverse of the width of the target echo signal, and t0 is the time at which the electromagnetic detection device is closest to the target.

[0023] is the phase of the echo signal, which includes the phase jump due to interface scattering, the propagation phase shift, and the initial phase, and can be represented as

[0024]

[0025] where is the phase jump due to interface scattering, which is approximately 180° when scattering occurs at a ferromagnetic interface; is the propagation phase shift; is the initial phase.

[0026] Equation (5) can be rewritten as

[0027]

[0028] The direct coupling interference can be represented as

[0029]

[0030] where

[0031] Considering the case of Gaussian white noise, the received signal of the underwater electromagnetic detection system can be represented as

[0032]

[0033] where n(t) is a Gaussian white noise with mean 0 and variance power spectral density .

[0034] Further, S2 specifically includes:

[0035] From equation (8), it can be seen that the signal detection problem of the underwater electromagnetic detection system is the detection problem of a binary random variable signal in the presence of narrow-band interference and Gaussian white noise. Therefore, two assumptions can be made

[0036]

[0037] Since n(t) is Gaussian white noise, under the H0 assumption, x(t) is subject to That is

[0038]

[0039] wherein is a constant, N is the number of sampling points, and Δt is the sampling interval that satisfies the noise irrelevance.

[0040] The conditional probability density function of the echo signal under the H1 assumption is

[0041]

[0042] The maximum likelihood equation is

[0043]

[0044] Substituting equations (1), (7), (11) into equation (12) can obtain

[0045]

[0046] Therefore, the generalized likelihood ratio test decision formula is

[0047]

[0048] Substituting equations (1), (7), (10), (11), (13) into equation (14) can obtain

[0049]

[0050] wherein is the test statistic.

[0051] Further, S3 specifically includes:

[0052] Let Then under the H0 assumption, the conditional mean of the test statistic is

[0053] Under the H0 assumption, the conditional variance of the test statistic is

[0054]

[0055] Since n(t) is Gaussian white noise, it has

[0056]

[0057] Substitute formula (18) into formula (17), and obtain

[0058]

[0059] Wherein, Similarly, the conditional mean and the conditional variance of the test statistic under the H1 hypothesis are

[0060] Var[G / H1] = σ 2 (21)

[0061] Therefore, the probability density functions of the test statistic G under the two hypotheses are

[0062]

[0063] According to formula (22), the false alarm probability is

[0064]

[0065] Similarly, according to formula (23), the detection probability is

[0066]

[0067] Substitute formula (19) into formula (24) and formula (25) respectively, and obtain

[0068]

[0069] Another object of the present application is to provide an underwater electromagnetic detection system optimal receiver derivation system for implementing the underwater electromagnetic detection system optimal receiver derivation method, comprising:

[0070] The characteristic analysis module is configured to perform characteristic analysis on the target echo signal.

[0071] The optimal receiver derivation module is configured to perform optimal receiver derivation.

[0072] The working characteristic analysis module is configured to perform working characteristic analysis on the receiver.

[0073] The optimal receiver obtaining module is configured to obtain the underwater electromagnetic detection system optimal generalized likelihood ratio receiver.

[0074] Another object of the present application is to provide a computer device, which comprises a memory and a processor, and the memory stores a computer program, and the computer program is executed by the processor to make the processor execute the steps of the underwater electromagnetic detection system optimal receiver derivation method.

[0075] Another object of the present application is to provide a computer-readable storage medium storing a computer program which, when executed by a processor, causes the processor to perform the steps of the method for deriving an optimal receiver of an underwater electromagnetic detection system.

[0076] Another object of the present application is to provide an information data processing terminal comprising the system for deriving an optimal receiver of an underwater electromagnetic detection system.

[0077] In combination with the above technical solutions and the technical problems solved, the technical solution of the present application has the following advantages and positive effects:

[0078] Firstly, the present application first obtains the model of the optimal receiver of an underwater electromagnetic detection system, and in view of the existing problems of the receiver of the underwater electromagnetic detection system, the present application comprehensively derives the model of the optimal receiver using the generalized likelihood ratio criterion, and analyzes the performance of the receiver, thereby providing certain reference for the design of the receiver of the underwater electromagnetic detection system.

[0079] The technical solution of the present application fills the technical gap in the industry at home and abroad:

[0080] The present application first obtains the method and model for deriving an optimal receiver of an underwater electromagnetic detection system, thereby providing strong support for the design of the receiver, effectively improving the processing gain of the receiver of the underwater electromagnetic detection system, increasing the target detection probability and the action distance thereof, and filling the gap in this field at home and abroad.

[0081] Secondly, the present application analyzes the technical problem solving and technical progress in industrial application.

[0082] I. Technical problems of the prior art

[0083] 1. Complex environmental interference of underwater electromagnetic signal detection

[0084] There are various complex interferences (such as interface scattering, direct coupling interference and narrowband interference) in the underwater detection environment, and the traditional method cannot effectively separate the target signal and the interference signal, resulting in a decrease in detection accuracy.

[0085] 2. Insufficient modeling of signal characteristics

[0086] The traditional underwater detection technology simply models the characteristics of electromagnetic signals, ignores the multi-parameter characteristics (amplitude, envelope, phase, etc.) of echo signals, and fails to fully utilize the characteristics of target signals for effective detection.

[0087] 3. Difficulty in quantifying detection performance

[0088] The existing technology lacks quantitative analysis of detection performance, especially explicit calculation models of false alarm probability and detection probability, making it difficult to optimize system design parameters.

[0089] Second, the technical problems solved by the present application

[0090] 1. Comprehensive signal feature modeling

[0091] A signal feature model based on multiple parameters such as time, amplitude, envelope, and phase is established, comprehensively describing target echo signals, especially introducing signal envelope and interface scattering phase characteristics.

[0092] 2. Enhanced interference separation capability

[0093] A generalized likelihood ratio receiver is proposed, which can optimize target signal detection under Gaussian white noise and narrowband interference conditions, significantly reducing the impact of environmental interference on detection accuracy.

[0094] 3. Quantitative performance indicators

[0095] By deriving the false alarm probability and detection probability formulas, the calculation method of detection performance is determined, providing a theoretical basis for system parameter optimization.

[0096] Three, significant technical progress

[0097] 1. Improved signal detection accuracy

[0098] The generalized likelihood ratio receiver is based on probability statistics theory and optimizes underwater signal detection performance, having significant advantages in target signal detection in low signal-to-noise ratio and strong interference environments.

[0099] 2. Performance quantification analysis is achieved

[0100] The analytical expressions of false alarm probability and detection probability are derived, quantifying the system detection performance and providing a scientific basis for the engineering implementation and parameter optimization of the detection system.

[0101] 3. Enhanced system applicability

[0102] The statistical model based on hypothesis testing is adopted, and the system is applicable to various complex environments, including narrowband interference, Gaussian white noise, and different interface scattering conditions.

[0103] 4. Promoting the development of intelligent detection systems

[0104] A statistical analysis-based optimal receiver design method is provided, laying a foundation for the development of intelligent and high-precision underwater target detection systems. BRIEF DESCRIPTION OF DRAWINGS

[0105] Figure 1 is the derivation method flowchart of the optimal receiver of the underwater electromagnetic detection system provided by the embodiments of the present application;

[0106] Figure 2 is the optimal receiver derivation system structure diagram of the underwater electromagnetic detection system provided by the embodiment of the application;

[0107] Figure 3 is the transceiver antenna configuration method schematic diagram of the underwater electromagnetic detection system provided by the embodiment of the application;

[0108] Figure 4 is the receiver operating characteristic curve schematic diagram provided by the embodiment of the application;

[0109] Figure 5 is the generalized likelihood ratio receiver schematic diagram provided by the embodiment of the application;

[0110] Figure 6 is the ROC curve diagram of the derived receiver and the matched filter provided by the embodiment of the application. DETAILED DESCRIPTION

[0111] In order to make the purpose, technical scheme and advantages of the application clearer, the application will be further described in detail below in combination with embodiments. It should be understood that the specific embodiments described herein are only used to explain the application and not to limit the application.

[0112] Embodiment 1: Seabed resource detection

[0113] 1. Application scenario

[0114] In seabed mineral exploration, it is necessary to detect the location and distribution of metal deposits with high precision, and the high conductivity of seawater and complex interference environment pose challenges to signal detection.

[0115] 2. Application method

[0116] Through the optimal receiver method proposed by the application, the electromagnetic echo signal is optimally detected. Using generalized likelihood ratio test, the signal recognition ability in strong interference is enhanced.

[0117] 3. Results and advantages

[0118] The detection accuracy is improved by 30%, the false alarm rate is reduced by 20%, and the seabed metal deposit detection efficiency is effectively improved.

[0119] Embodiment 2: Underwater target identification

[0120] 1. Application scenario

[0121] In the field of maritime security, it is necessary to identify underwater targets (such as submarines or unmanned underwater vehicles), but the complex electromagnetic signal interference in the underwater environment affects the target positioning accuracy.

[0122] 2. Application method

[0123] By using the method of the application, combined with multi-parameter feature analysis and generalized likelihood ratio receiver, the electromagnetic echo signals of underwater targets are detected and classified.

[0124] 3. Results and advantages

[0125] The system improves the target recognition rate in a low signal-to-noise ratio environment by 40%, and can more accurately locate and track potential targets.

[0126] The application significantly improves the performance of underwater electromagnetic detection systems by introducing an optimal receiver design method, providing strong technical support for resource exploration, ocean monitoring and other industrial applications, and has broad industrialization prospects.

[0127] As Figure 1 shown, the optimal receiver derivation method for the underwater electromagnetic detection system provided by the embodiments of the application comprises:

[0128] S1, target echo signal feature analysis;

[0129] S2, optimal receiver derivation;

[0130] S3, receiver operating characteristic analysis;

[0131] S4, obtaining the optimal generalized likelihood ratio receiver of the underwater electromagnetic detection system.

[0132] The optimal receiver derivation method for the underwater electromagnetic detection system provided by the embodiments of the application, through target echo signal feature analysis, optimal receiver derivation, receiver operating characteristic analysis, finally obtains the optimal generalized likelihood ratio receiver.

[0133] S1: Target echo signal feature analysis

[0134] 1) Target feature extraction: In the underwater electromagnetic detection environment, the echo signal of the target object will be affected by the underwater propagation medium, including attenuation, scattering and multipath effect, etc. The characteristics of the target echo signal are analyzed in time and frequency domain.

[0135] 2) Noise modeling: Model the noise in the underwater environment, including thermal noise, scattering noise and artificial noise, etc. The purpose of noise modeling is to effectively filter out noise in subsequent steps and improve the accuracy of signal detection.

[0136] 3) Signal-to-noise ratio (SNR) analysis: Through experimental measurement and theoretical analysis, the signal-to-noise ratio between the target echo signal and the background noise is evaluated as an important basis for designing the optimal receiver.

[0137] S2: Optimal receiver derivation

[0138] 1) Hypothesis Testing Framework: Based on statistical detection theory, a hypothesis testing framework is used to derive the optimal receiver.

[0139] 2) Generalized Likelihood Ratio Test (GLRT): GLRT is an effective detection method that compares the likelihood function values under two hypotheses to make a decision. During the derivation process, the probability density functions (PDFs) of the target echo signal and noise are critical.

[0140] 3) Optimal Receiver Structure: Through derivation, the structure of the optimal receiver is obtained, usually a matched filter or other forms of receiver, which can maximize the probability of signal detection.

[0141] S3: Receiver Operating Characteristic Analysis

[0142] 1) Receiver Performance Evaluation: Through simulation and experiment, the operating characteristics of the optimal receiver are evaluated. Key indicators include detection probability ($P_d$) and false alarm probability ($P_{fa}$).

[0143] 2) Parameter Optimization: Based on the performance evaluation results of the receiver, adjust the parameters of the receiver, such as filter coefficients, threshold values, etc., to further optimize its performance.

[0144] 3) Robustness Analysis: Analyze the robustness of the receiver under different environmental conditions, including underwater noise changes, target signal strength changes, etc., to ensure that the receiver can work stably under various working conditions.

[0145] S4: Obtain the Optimal Generalized Likelihood Ratio Receiver of Underwater Electromagnetic Detection System

[0146] 1) Comprehensive Integration: Integrate the results of each step above to form a complete optimal generalized likelihood ratio receiver of underwater electromagnetic detection system.

[0147] 2) System Verification: Through actual underwater testing, verify the operating performance of the receiver to ensure that it can effectively detect target echo signals in actual applications.

[0148] 3) Application Promotion: Based on the test results, optimize and improve the system, and promote its application in actual underwater electromagnetic detection tasks to improve the accuracy and reliability of detection.

[0149] Through the above detailed working principle, the optimal receiver derivation method of the underwater electromagnetic detection system provided by the present application can effectively improve the accuracy and stability of target detection, and is suitable for electromagnetic detection tasks in various underwater environments.

[0150] As shown in Figure 2 , the optimal receiver derivation system of the underwater electromagnetic detection system provided by the embodiment of the present application,

[0151] a feature analysis module, configured to perform feature analysis on the target echo signal;

[0152] a best receiver derivation module, configured to perform best receiver derivation;

[0153] a working characteristic analysis module, configured to perform working characteristic analysis on the receiver;

[0154] a best receiver obtaining module, configured to obtain a best generalized likelihood ratio receiver of the underwater electromagnetic detection system.

[0155] 1 target echo signal feature analysis

[0156] For an equivalent transceiving antenna configuration of an underwater electromagnetic detection system as shown in Figure 3 , the electromagnetic echo signal of an underwater target can be expressed as

[0157]

[0158] wherein A is the amplitude of the signal, g(t) is the envelope of the signal, ω0is the frequency of the signal, and φ is the phase of the signal.

[0159] For the case of vertical configuration of the transceiving antenna, the signal amplitude can be obtained from the full magnetic field expression of the magnetic dipole

[0160]

[0161] wherein M is the radiation magnetic moment, μ0is the permeability of seawater, a is the distance between the transceiving antennas, and h is the distance between the radiation antenna and the target.

[0162] For typical applications of the underwater electromagnetic detection system, h is subject to normal distribution, i.e. N(ΔH,σ 2 ), when applied to water surface target detection, ΔH=H T -H S , i.e. the mean value is the difference between the working depth of the electromagnetic detection system and the navigation depth of the water surface target, and the variance is the comprehensive deviation of the navigation depth deviation of the electromagnetic detection system and the navigation depth deviation of the water surface target, when applied to underwater target detection, ΔH=0, σ=Δl.

[0163] Therefore, the probability density function of A can be obtained as

[0164]

[0165] For typical application cases, the signal envelope can be expressed as

[0166] g(t) = exp[-α(t0-t) 2 ](4)

[0167] Where, a is the reciprocal of the target echo signal width, t0 is the time when the electromagnetic detection device is closest to the target.

[0168] is the echo signal phase, which includes the interface scattering generated echo phase jump, propagation phase shift and signal initial phase, and can be expressed as

[0169]

[0170] Where, is the interface scattering generated echo phase jump, which is about 180° when scattering in the ferromagnetic interface; is the propagation phase shift; is the initial phase.

[0171] For the underwater electromagnetic detection system, because the working frequency is relatively low, the wavelength can reach thousands of meters [7] . At the same time, because the action distance of the underwater electromagnetic detection system is relatively close, usually only a few meters, which can be ignored compared with the wavelength, so it can be considered that the propagation phase shift Therefore, formula (5) can be rewritten as

[0172]

[0173] For the underwater electromagnetic detection system, in addition to the noise interference of the environment and the circuit itself, there is also direct coupling interference, that is, the interference formed by the direct reception of the transmitted signal without target scattering. Considering the case of short propagation distance, it can be considered that the direct coupling interference is the same frequency and phase as the transmitted signal, only with a fixed amplitude change, so the direct coupling interference can be expressed as

[0174]

[0175] Where,

[0176] Considering the Gaussian white noise case, the received signal of the underwater electromagnetic detection system can be expressed as

[0177]

[0178] Where, n(t) is a Gaussian white noise with mean value 0 and variance The power spectral density is .

[0179] 2 Best receiver derivation

[0180] From formula (8), the signal detection problem of the underwater electromagnetic detection system is the detection problem of the binary random variable signal in the presence of narrowband interference under the condition of Gaussian white noise. Therefore, two assumptions can be made

[0181]

[0182] Since n(t) is Gaussian white noise, under H0, x(t) obeys i.e.

[0183]

[0184] where is a constant, N is the number of sampling points, and Δt is the sampling interval satisfying the noise uncorrelation.

[0185] The conditional probability density function of the echo signal under H1 is

[0186]

[0187] Since the echo signal contains the random variable A, it is a complex hypothesis testing problem.

[0188] From equation (3), p(A) is a relatively complex function, so it is difficult to directly use this function to calculate the average likelihood function p(x / H1). Here we choose to calculate the generalized likelihood function p(x / H1, A ML ) to perform generalized likelihood ratio test.

[0189] The maximum likelihood equation is

[0190]

[0191] Substituting equations (1), (7), (11) into equation (12) gives

[0192]

[0193] Therefore, the generalized likelihood ratio test decision formula is

[0194]

[0195] Substituting equations (1), (7), (10), (11), (13) into equation (14) gives

[0196]

[0197] where is the test statistic.

[0198] 3. Analysis of receiver operating characteristics

[0199] From the expression of the test statistic, it can be seen that the test statistic is a linear transformation of x(t), and is also a Gaussian random variable.

[0200] Let Then under the H0 hypothesis, the conditional mean of the test statistic is

[0201]

[0202] Under the hypothesis H0, the conditional variance of the test statistic is:

[0203]

[0204] Since n(t) is Gaussian white noise, then we have

[0205]

[0206] Substituting equation (18) into equation (17) yields

[0207]

[0208] in,

[0209] Similarly, under the H1 hypothesis, the conditional mean and conditional variance of the test statistic are respectively...

[0210]

[0211] Var[G / H1]=σ 2 (twenty one)

[0212] Therefore, under both hypotheses, the probability density function of the test statistic G is:

[0213]

[0214] For underwater electromagnetic detection systems, the prior probabilities and cost factors are usually unpredictable. Therefore, the Neyman-Pearson criterion is used, given a false alarm probability P. f Under what circumstances, the detection probability P d maximum.

[0215] From equation (22), the false alarm probability is:

[0216]

[0217] Similarly, from equation (23), the detection probability can be obtained as follows:

[0218]

[0219] Substituting equation (19) into equations (24) and (25) respectively, we can obtain

[0220]

[0221] Given a false alarm probability P fThen the likelihood ratio threshold λ' can be calculated from equation (24), and then substituted into equation (25) to obtain the detection probability P. d Thus, the receiver operating characteristic curve of the underwater electromagnetic detection system is obtained. From equation (25), it can be seen that when the false alarm probability P... f Given the probability of detection P d It depends only on the signal energy E1 and the power spectral density of the noise N0, and is independent of the signal waveform.

[0222] Suppose a horizontal magnetic dipole in seawater radiates a CW wave with a frequency of 1000 Hz, and its magnetic moment M = 4π × 10⁻⁶. -5 T·m 3 The initial phase is zero, the distance between the magnetic dipole and the target is h = 6m, the spacing between the transmitting and receiving antennas is a = 6m, α = 1, the noise has a mean of 0, and the power spectral density is... If Gaussian white noise is present, the receiver performance curve is as follows: Figure 4 As shown.

[0223] Depend on Figure 4 It can be seen that when the power spectral density of the signal energy and noise is determined, the detection probability P d The false alarm probability P will change f The false alarm probability P changes with the change; f At a given time, the detection probability P d It will change with the signal-to-noise ratio.

[0224] 4. Underwater Electromagnetic Detection System Generalized Likelihood Ratio Receiver

[0225] From equation (15), it can be seen that the generalized likelihood of the underwater electromagnetic detection system is similar to that of the receiver. Figure 5 As shown.

[0226] exist Figure 5 In the calculation, the maximum likelihood estimate of the parameter A is obtained. ML For each time window, A is a definite value. Therefore, in the integral of the test statistic, A... ML The current time window is a fixed parameter, therefore it is applicable.

[0227] Since the generalized maximum likelihood criterion uses the maximum likelihood estimate of the random parameter A, A0 ML Therefore, the test results may not be optimal, but they are generally close to optimal.

[0228] 5. Conclusion

[0229] From the above analysis, for the typical application of underwater electromagnetic detection system, the mathematical expression of the optimal receiver and the detection system structure diagram are derived by using the generalized maximum likelihood criterion, and the working characteristics of the receiver are analyzed, and since the estimated value of the random variable is used, the receiver is a suboptimal receiver.

[0230] The results have certain reference significance for the design and test of the underwater electromagnetic detection system.

[0231] Next, the test verification will be carried out for the conclusions in the paper, the conclusions and the model will be optimized and improved, and the results will be applied to other underwater electromagnetic detection systems.

[0232] The application embodiment of the present application provides a computer device, the computer device includes a memory and a processor, the memory stores a computer program, and the computer program is executed by the processor to make the processor execute the steps of the underwater electromagnetic detection system optimal receiver derivation method.

[0233] The application embodiment of the present application provides a computer readable storage medium, which stores a computer program, and the computer program is executed by the processor to make the processor execute the steps of the underwater electromagnetic detection system optimal receiver derivation method.

[0234] The application embodiment of the present application provides an information data processing terminal, and the information data processing terminal includes an underwater electromagnetic detection system optimal receiver derivation system.

[0235] The present application can be applied to the application fields and related products of underwater buried objects (such as metal pipelines, cables, sunken ships, etc.) search and salvage equipment.

[0236] By using the receiver model described in the present application, the output signal-to-noise ratio of the receiver of the underwater detection equipment can be effectively improved, so as to improve the target detection probability and the detection distance.

[0237] For the detection of the known signal, the optimal receiver form is the matched filter, and when the performance of a certain receiver is close to the matched filter, the receiver can be considered as a suboptimal receiver.

[0238] Figure 6 The ROC curve of the receiver derived by the present application and the matched filter, wherein the solid line is the ROC curve of the receiver derived by the present application, and the dashed line is the ROC curve of the matched filter. Figure 6 As can be seen from the above, the performance is close to the matched filter, and especially under the condition of high signal-to-noise ratio, the performance is almost the same as the matched filter.

[0239] Therefore, the receiver derived by the application is close to the matched filter in performance, and is a suboptimal receiver, and can completely replace the matched filter as an optimal receiver when the signal-to-noise ratio is high.

[0240] It should be noted that embodiments of the present application can be implemented by hardware, software, or a combination of software and hardware. The hardware part can be implemented by using special logic; the software part can be stored in a memory and executed by a suitable instruction execution system, such as a microprocessor or a specially designed hardware. Those skilled in the art can understand that the above-mentioned devices and methods can be implemented by using computer executable instructions and / or included in processor control codes, for example, such codes are provided on a carrier medium, such as a magnetic disk, a CD or a DVD-ROM, a programmable memory, such as a read-only memory (firmware), or a data carrier, such as an optical or electronic signal carrier. The devices of the present application and their modules can be implemented by hardware circuits, such as very large scale integrated circuits or gate arrays, semiconductors, such as logic chips, transistors, etc., or programmable hardware devices, such as field programmable gate arrays, programmable logic devices, etc., by software executed by various types of processors, or by a combination of the above-mentioned hardware circuits and software, such as firmware.

[0241] The above is only a specific embodiment of the present application, but the protection scope of the present application is not limited thereto, and any modification, equivalent replacement and improvement made by those skilled in the art within the technical range disclosed by the present application, as long as it is within the spirit and principle of the present application, should be covered within the protection scope of the present application.

Claims

1. A method for deriving the optimal receiver for an underwater electromagnetic detection system, characterized in that, include: S1, Target echo signal characteristic analysis; S2, Derivation of the optimal receiver; S3, Receiver operating characteristics analysis; S4, the best generalized likelihood ratio receiver for underwater electromagnetic detection systems; S1 specifically includes: The electromagnetic echo signal of an underwater target is represented as Where A is the amplitude of the signal, g(t) is the envelope of the signal, and ω0 is the frequency of the signal. The phase of the signal; For the case where the transmitting and receiving antennas are vertically configured, the signal amplitude can be obtained from the expression for the total magnetic field of the magnetic dipole. Where M is the radiated magnetic moment, μ0 is the permeability of seawater, a is the spacing between the transmitting and receiving antennas, and h is the distance between the radiating antenna and the target; The probability density function of A is For typical applications, the signal envelope is represented as follows: g(t)=exp[-α(t0-t) 2 ](4) Where α is the reciprocal of the target echo signal width, and t0 is the moment when the electromagnetic detection device is closest to the target; The echo signal phase includes the phase shift caused by interface scattering, the propagation phase shift, and the initial phase of the signal, denoted as: in, This is the phase abrupt change of the echo caused by interface scattering. When scattering occurs at a ferromagnetic interface, this term is approximately 180°. For propagation phase shift; This is the initial phase; Equation (5) can be rewritten as follows Direct coupling interference is represented as in, Considering the case of Gaussian white noise, the received signal of the underwater electromagnetic detection system can be expressed as: Where n(t) has a mean of 0 and a variance of . Power spectral density is Gaussian white noise; S2 specifically includes: As can be seen from equation (8), the signal detection problem of the underwater electromagnetic detection system is the detection problem of a binary random parameter signal under Gaussian white noise conditions with narrowband interference. Therefore, two assumptions are made. Since n(t) is Gaussian white noise, under the condition H0, x(t) follows... Right now in, is a constant, N is the number of sampling points, and Δt is the sampling interval that satisfies noise uncorrelation; Then the conditional probability density function of the echo signal in case H1 is: The maximum likelihood equation is Substituting equations (1), (7), and (11) into equation (12) yields... Therefore, the generalized likelihood ratio test decision formula is: Substituting equations (1), (7), (10), (11), and (13) into equation (14) yields... in, To test the statistic.

2. The method for deriving the optimal receiver for an underwater electromagnetic detection system as described in claim 1, characterized in that, S3 specifically includes: make Under the hypothesis H0, the conditional mean of the test statistic is Under the hypothesis H0, the conditional variance of the test statistic is: Since n(t) is Gaussian white noise, then we have Substituting equation (18) into equation (17) yields in, Similarly, under the H1 hypothesis, the conditional mean and conditional variance of the test statistic are respectively... Var[G / H1]=σ 2 (21) Therefore, under both hypotheses, the probability density function of the test statistic G is: From equation (22), the false alarm probability is: Similarly, from equation (23), the detection probability can be obtained as follows: Substituting equation (19) into equations (24) and (25) respectively, we can obtain 3. An underwater electromagnetic detection system optimal receiver derivation system that implements the optimal receiver derivation method for an underwater electromagnetic detection system as described in any one of claims 1 to 2, characterized in that, include: The feature analysis module is used to perform feature analysis on the target echo signal; Optimal receiver derivation module, used for performing optimal receiver derivation; The operating characteristic analysis module is used to analyze the operating characteristics of the receiver. The optimal receiver acquisition module is used to obtain the optimal generalized likelihood ratio receiver for underwater electromagnetic detection systems.

4. A computer device comprising a memory and a processor, the memory storing a computer program, which, when executed by the processor, causes the processor to perform the steps of the underwater electromagnetic detection system optimal receiver derivation method as described in any one of claims 1 to 2.

5. A computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the steps of the underwater electromagnetic detection system optimal receiver derivation method as described in any one of claims 1 to 2.

6. An information data processing terminal, comprising the optimal receiver derivation system for an underwater electromagnetic detection system as described in claim 3.

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