Method and system for deriving optimal receiver of underwater electromagnetic detection system

Abstract

The invention belongs to but not limited to the technical field of underwater detection, and particularly relates to an optimal receiver derivation method and system for an underwater electromagnetic detection system, and the method comprises the steps: S1, carrying out the feature analysis of a target echo signal; s2, deriving an optimal receiver; s3, analyzing the working characteristics of the receiver; and S4, obtaining the optimal generalized likelihood ratio of the underwater electromagnetic detection system by a receiver. According to the method, the optimal receiver model of the underwater electromagnetic detection system is obtained for the first time, the model of the optimal receiver is comprehensively deduced by adopting a generalized likelihood ratio criterion aiming at the practical problem of the receiver of the underwater electromagnetic detection system, and the performance of the receiver is analyzed; and a certain reference significance is provided for the design of the receiver of the underwater electromagnetic detection system.

Description

Technical Field

[0001] The present invention belongs to but is not limited to the field of underwater detection technology, and in particular relates to a method and system for deriving an optimal receiver of an underwater electromagnetic detection system. Background Art

[0002] Due to the conductivity of water, electromagnetic waves attenuate greatly in water, and the energy attenuation can reach 90% for every meter of propagation. Therefore, underwater electromagnetic detection is not feasible in most applications. However, in some specific application scenarios, such as low frequency and near field, underwater electromagnetic detection still has a place.

[0003] Since there are random parameters with unknown statistical characteristics in the detection signal model of underwater electromagnetic detection, it is difficult to obtain the model of the best receiver of the underwater electromagnetic detection system.

[0004] In view of the above analysis, the technical problem that urgently needs to be solved in the prior art is: since there are 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 of the underwater electromagnetic detection system. Summary of the invention

[0005] In view of the problems existing in the prior art, the present invention provides a method and system for deriving an optimal receiver for an underwater electromagnetic detection system.

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

[0007] S1, target echo signal feature analysis;

[0008] S2, optimal receiver derivation;

[0009] S3, receiver operating characteristics analysis;

[0010] S4, the best generalized likelihood ratio receiver for underwater electromagnetic detection system is obtained.

[0011] Furthermore, S1 specifically includes:

[0012] The electromagnetic echo signal of the underwater target can be expressed as

[0013]

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

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

[0016]

[0017] Among them, M is the radiation magnetic moment, μ0 is the magnetic 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.

[0018] The probability density function of A is

[0019]

[0020] For typical applications, the signal envelope can be expressed as

[0021] g(t)=exp[-α(t0-t) 2 ](4)

[0022] Among them, α is the inverse of the target echo signal width, and t0 is the moment when the electromagnetic detection equipment is closest to the target.

[0023] is the echo signal phase, which includes the echo phase mutation caused by interface scattering, propagation phase shift and signal initial phase, which can be expressed as

[0024]

[0025] in, It is the phase mutation of the echo caused by interface scattering. When scattered at the ferromagnetic interface, this term is about 180°; is the propagation phase shift; It is the initial phase.

[0026] Formula (5) can be rewritten as

[0027]

[0028] Direct coupling interference can be expressed as

[0029]

[0030] in,

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

[0032]

[0033] Among them, n(t) has a mean of 0 and a variance of The power spectral density is Gaussian white noise.

[0034] Furthermore, S2 specifically includes:

[0035] From equation (8), we can see that the signal detection problem of the underwater electromagnetic detection system is the detection problem of binary random parameter signals under Gaussian white noise conditions when narrowband interference exists. Therefore, two assumptions can be made:

[0036]

[0037] Since n(t) is Gaussian white noise, in the case of H0, x(t) obeys Right now

[0038]

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

[0040] Then the conditional probability density function of the echo signal in case of H1 is

[0041]

[0042] The maximum likelihood equation is

[0043]

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

[0045]

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

[0047]

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

[0049]

[0050] in, is the test statistic.

[0051] Furthermore, S3 specifically includes:

[0052] make Then under the H0 hypothesis, the conditional mean of the test statistic is

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

[0054]

[0055] Since n(t) is Gaussian white noise, we have

[0056]

[0057] Substituting equation (18) into equation (17), we can obtain

[0058]

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

[0060] Var[G / H1]=σ 2 (twenty one)

[0061] Therefore, the probability density function of the test statistic G under the two assumptions is

[0062]

[0063] From formula (22), we can know that the false alarm probability is

[0064]

[0065] Similarly, from formula (23), the detection probability can be obtained as

[0066]

[0067] Substituting equation (19) into equation (24) and equation (25) respectively, we can obtain

[0068]

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

[0070] A feature analysis module, used for performing feature analysis on target echo signals;

[0071] An optimal receiver derivation module, used for performing optimal receiver derivation;

[0072] A working characteristic analysis module, used for analyzing the working characteristics of the receiver;

[0073] The best receiver acquisition module is used to obtain the best generalized likelihood ratio receiver of the underwater electromagnetic detection system.

[0074] Another object of the present invention is to provide a computer device, which includes a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the method for deriving an optimal receiver for an underwater electromagnetic detection system.

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

[0076] Another object of the present invention is to provide an information data processing terminal, which includes the optimal receiver derivation system for the underwater electromagnetic detection system.

[0077] In combination with the above technical solutions and the technical problems solved, the advantages and positive effects of the technical solutions to be protected by the present invention are as follows:

[0078] First, the present invention obtains the optimal receiver model of the underwater electromagnetic detection system for the first time. In view of the practical problems existing in the receiver of the underwater electromagnetic detection system, the present invention adopts the generalized likelihood ratio criterion to comprehensively derive the model of the optimal receiver and analyzes the receiver performance, which provides certain reference significance for the design of the receiver of the underwater electromagnetic detection system.

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

[0080] The present invention obtains for the first time the optimal receiver derivation method and model for underwater electromagnetic detection systems, which provides strong support for receiver design, can effectively improve the processing gain of underwater electromagnetic detection system receivers, increase their target detection probability and effective range, and fills the gap in this field at home and abroad.

[0081] Second, the technical problem solving and technological progress analysis of the present invention in industrial applications.

[0082] 1. Technical Problems of Existing Technologies

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

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

[0085] 2. Insufficient signal feature modeling

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

[0087] 3. Detection performance is difficult to quantify

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

[0089] 2. Technical Problems Solved by the Invention

[0090] 1. Comprehensive signal feature modeling

[0091] A signal characteristic model based on multiple parameters such as time, amplitude, envelope, and phase was established to comprehensively describe the target echo signal, especially by introducing the signal envelope and interface scattering phase characteristics.

[0092] 2. Enhanced interference separation capability

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

[0094] 3. Quantitative performance indicators

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

[0096] III. Significant Technological Advances

[0097] 1. Improved signal detection accuracy

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

[0099] 2. Achieved performance quantification analysis

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

[0101] 3. Enhanced system applicability

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

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

[0104] A method for optimal receiver design based on statistical analysis is provided, which lays a foundation for the development of underwater target detection towards intelligence and high precision. BRIEF DESCRIPTION OF THE DRAWINGS

[0105] Figure 1 It is a flow chart of a method for deriving an optimal receiver for an underwater electromagnetic detection system provided by an embodiment of the present invention;

[0106] Figure 2 It is a structural diagram of an optimal receiver derivation system of an underwater electromagnetic detection system provided by an embodiment of the present invention;

[0107] Figure 3 It is a schematic diagram of the configuration of the transmitting and receiving antennas of the underwater electromagnetic detection system provided by an embodiment of the present invention;

[0108] Figure 4 is a schematic diagram of a receiver operating characteristic curve provided by an embodiment of the present invention;

[0109] Figure 5 is a schematic diagram of a generalized likelihood ratio receiver provided by an embodiment of the present invention;

[0110] Figure 6 It is a ROC curve diagram of the receiver and the matched filter derived according to the embodiment of the present invention. DETAILED DESCRIPTION

[0111] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0112] Example 1: Seabed resource detection

[0113] 1. Application scenarios

[0114] In seabed mineral exploration, the location and distribution of metal deposits need to be detected with high precision, while the high conductivity and complex interference environment in seawater pose challenges to signal detection.

[0115] 2. Application

[0116] The optimal receiver method proposed by the present invention optimizes the detection of electromagnetic echo signals and uses a generalized likelihood ratio test to enhance the signal recognition capability in strong interference.

[0117] 3. Results and Advantages

[0118] The detection accuracy has been improved by 30%, and the false alarm rate has been reduced by 20%, effectively improving the efficiency of seabed metal mineral detection.

[0119] Example 2: Underwater target recognition

[0120] 1. Application scenarios

[0121] In the field of marine 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

[0123] The method of the present invention is used to detect and classify the electromagnetic echo signals of underwater targets in combination with multi-parameter feature analysis and a generalized likelihood ratio receiver.

[0124] 3. Results and Advantages

[0125] The system has improved its target recognition rate by 40% in low signal-to-noise ratio environments, and can more accurately locate and track potential targets.

[0126] The present invention significantly improves the performance of the underwater electromagnetic detection system by introducing the optimal receiver design method, provides strong technical support for industrial applications such as resource exploration and ocean monitoring, and has broad industrialization prospects.

[0127] like Figure 1 As shown, the optimal receiver derivation method of the underwater electromagnetic detection system provided by the embodiment of the present invention includes:

[0128] S1, target echo signal feature analysis;

[0129] S2, optimal receiver derivation;

[0130] S3, receiver operating characteristics analysis;

[0131] S4, the best generalized likelihood ratio receiver for underwater electromagnetic detection system is obtained.

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

[0133] S1: Target echo signal feature analysis

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

[0135] 2) Noise modeling: Model the noise in the underwater environment, including thermal noise, scattering noise, and man-made noise. 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): The generalized likelihood ratio test is an effective detection method that makes a decision by comparing the likelihood function values ​​under two hypotheses. In the derivation process, the probability density function (PDF) of the target echo signal and noise is the key.

[0140] 3) Optimal receiver structure: The optimal receiver structure is derived, usually a matched filter or other form of receiver, which can maximize the probability of signal detection.

[0141] S3: Receiver operating characteristics analysis

[0142] 1) Receiver performance evaluation: The operating characteristics of the best receiver are evaluated through simulation and experiments. Key indicators include detection probability ($P_d$) and false alarm probability ($P_{fa}$).

[0143] 2) Parameter optimization: According to 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 changes in underwater noise, changes in target signal strength, etc., to ensure that the receiver can work stably under various working conditions.

[0145] S4: Optimal generalized likelihood ratio receiver for underwater electromagnetic detection system

[0146] 1) Comprehensive integration: The results of the above steps are integrated to form a complete optimal generalized likelihood ratio receiver for the underwater electromagnetic detection system.

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

[0148] 3) Application promotion: Based on the test results, the system is optimized and improved, and promoted for 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 for the underwater electromagnetic detection system provided by the present invention can effectively improve the accuracy and stability of target detection, and is suitable for electromagnetic detection tasks in various underwater environments.

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

[0151] A feature analysis module, used for performing feature analysis on target echo signals;

[0152] An optimal receiver derivation module, used for performing optimal receiver derivation;

[0153] A working characteristic analysis module, used for analyzing the working characteristics of the receiver;

[0154] The best receiver acquisition module is used to obtain the best generalized likelihood ratio receiver of the underwater electromagnetic detection system.

[0155] 1. Target echo signal characteristics analysis

[0156] For Figure 3 The equivalent transmitting and receiving antenna configuration of the underwater electromagnetic detection system is shown in the figure. The electromagnetic echo signal of the underwater target can be expressed as

[0157]

[0158] Where A is the amplitude of the signal, g(t) is the envelope of the signal, ω0 is the frequency of the signal, is the phase of the signal.

[0159] For the case where the transmitting and receiving antennas are configured vertically, the signal amplitude can be obtained from the expression of the total magnetic field of the magnetic dipole:

[0160]

[0161] Among them, M is the radiation magnetic moment, μ0 is the magnetic 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.

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

[0163] Therefore, the probability density function of A is

[0164]

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

[0166] g(t)=exp[-α(t0-t) 2 ](4)

[0167] Among them, α is the inverse of the target echo signal width, and t0 is the moment when the electromagnetic detection equipment is closest to the target.

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

[0169]

[0170] in, It is the phase mutation of the echo caused by interface scattering. When scattered at the ferromagnetic interface, this term is about 180°; is the propagation phase shift; It is the initial phase.

[0171] For underwater electromagnetic detection systems, the wavelength can reach several thousand meters due to the relatively low operating frequency. [7] At the same time, since the underwater electromagnetic detection system has a short operating distance, usually only a few meters, which can be ignored relative to the wavelength, it can be considered that the propagation phase shift Therefore, formula (5) can be rewritten as

[0172]

[0173] For underwater electromagnetic detection systems, in addition to the noise interference from the environment and the circuit itself, there is also direct coupling interference, that is, the interference caused by the transmitted signal being directly received without being scattered by the target. Considering the situation where the propagation distance is relatively short, it can be considered to be in the same frequency and phase as the transmitted signal, and only has a fixed amplitude change. Therefore, the direct coupling interference can be expressed as

[0174]

[0175] in,

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

[0177]

[0178] Among them, n(t) has a mean of 0 and a variance of The power spectral density is Gaussian white noise.

[0179] 2 Optimal Receiver Derivation

[0180] From equation (8), we can see that the signal detection problem of the underwater electromagnetic detection system is the detection problem of binary random parameter signals under Gaussian white noise conditions when narrowband interference exists. Therefore, two assumptions can be made:

[0181]

[0182] Since n(t) is Gaussian white noise, in the case of H0, x(t) obeys Right now

[0183]

[0184] in, is a constant, N is the number of sampling points, and Δt is the sampling interval that satisfies the noise irrelevant requirement.

[0185] Then the conditional probability density function of the echo signal in case of H1 is

[0186]

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

[0188] From formula (3), we can see that 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 obtain the generalized likelihood function p(x / H1,A ML ) and perform a generalized likelihood ratio test.

[0189] The maximum likelihood equation is

[0190]

[0191] Substituting equations (1), (7), and (11) into equation (12), we can obtain

[0192]

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

[0194]

[0195] Substituting equations (1), (7), (10), (11), and (13) into equation (14), we can obtain

[0196]

[0197] in, is the test statistic.

[0198] 3 Receiver operating characteristics analysis

[0199] From the test statistic expression, we can see that the test statistic is the result of the linear transformation of x(t), and is therefore also a Gaussian random variable.

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

[0201]

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

[0203]

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

[0205]

[0206] Substituting equation (18) into equation (17), we can obtain

[0207]

[0208] in,

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

[0210]

[0211] Var[G / H1]=σ 2 (twenty one)

[0212] Therefore, the probability density function of the test statistic G under the two assumptions is

[0213]

[0214] For underwater electromagnetic detection systems, it is usually impossible to determine the prior probability and cost factor in advance, so the Neyman-Pearson criterion is used to determine the false alarm probability P. f In this case, the detection probability P d maximum.

[0215] From formula (22), we can know that the false alarm probability is

[0216]

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

[0218]

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

[0220]

[0221] When the false alarm probability P is given f, then the likelihood ratio threshold λ' can be calculated by equation (24), and then substituted into equation (25) to obtain the detection probability P d , thus obtaining the receiver operating characteristic curve of the underwater electromagnetic detection system. From formula (25), it can be seen that when the false alarm probability P f Given, the detection probability P d It only depends on the signal energy E1 and the power spectral density N0 of the noise, and has nothing to do with the waveform of the signal.

[0222] Assume that a horizontal magnetic dipole in seawater radiates CW waves with a frequency of 1000 Hz, and its magnetic moment M = 4π×10 -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 Gaussian white noise, the receiver operating curve is as follows Figure 4 shown.

[0223] Depend on Figure 4 It can be seen that when the power spectral density of signal energy and noise is determined, the detection probability P d With the false alarm probability P f When the false alarm probability P f When a certain time, the detection probability P d Will change as the signal-to-noise ratio changes.

[0224] 4. Generalized likelihood ratio receiver for underwater electromagnetic detection system

[0225] From formula (15), we can see that the form of the generalized likelihood ratio receiver of the underwater electromagnetic detection system is as follows: Figure 5 shown.

[0226] exist Figure 5 The calculated maximum likelihood estimate of the parameter A ML , is a fixed value for each time window. Therefore, in the integration of the test statistic, A ML It is a fixed parameter for the current time window, so it is applicable.

[0227] Since the generalized maximum likelihood criterion uses the maximum likelihood estimate A of the random parameter A ML , so the test result may not be optimal, but it is generally close to the optimal.

[0228] 5 Conclusion

[0229] From the above analysis, it can be seen that for the typical application of underwater electromagnetic detection system, the mathematical expression of the optimal receiver and the detection system structure diagram are derived using the generalized maximum likelihood criterion, and the receiver operating characteristics are analyzed. Due to the use of estimated values ​​of random parameters, the receiver is a suboptimal receiver.

[0230] This achievement has certain reference significance for the design and testing of underwater electromagnetic detection systems.

[0231] The next step will be to conduct experimental verification on the conclusions in this article, optimize and improve the conclusions and models accordingly, and promote the application of the results to other underwater electromagnetic detection systems.

[0232] An application embodiment of the present invention provides a computer device, which includes a memory and a processor. The memory stores a computer program. When the computer program is executed by the processor, the processor executes the steps of a method for deriving an optimal receiver for an underwater electromagnetic detection system.

[0233] An application embodiment of the present invention provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the processor executes the steps of a method for deriving an optimal receiver for an underwater electromagnetic detection system.

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

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

[0236] By adopting the receiver model described in the present invention, the output signal-to-noise ratio of the receiver of the underwater detection equipment can be effectively improved, thereby improving the target detection probability and the detection distance.

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

[0238] Figure 6 The ROC curves of the receiver and the matched filter derived by the present invention are shown in FIG. 1 , wherein the solid line is the ROC curve of the receiver derived by the present invention, and the dotted line is the ROC curve of the matched filter. Figure 6 It can be seen that its performance is close to that of the matched filter, especially under high signal-to-noise ratio conditions, its performance is almost the same as that of the matched filter.

[0239] It can be seen that the performance of the receiver derived by the present invention is close to that of the matched filter, and is a suboptimal receiver. When the signal-to-noise ratio is high, it can completely replace the matched filter as the optimal receiver.

[0240] It should be noted that the embodiments of the present invention can be implemented by hardware, software, or a combination of software and hardware. The hardware part can be implemented using dedicated logic; the software part can be stored in a memory and executed by an appropriate instruction execution system, such as a microprocessor or dedicated design hardware. It can be understood by a person of ordinary skill in the art that the above-mentioned devices and methods can be implemented using computer executable instructions and / or contained in a processor control code, such as a carrier medium such as a disk, CD or DVD-ROM, a programmable memory such as a read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. Such code is provided on the carrier medium. The device and its modules of the present invention 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., can also be implemented by software executed by various types of processors, and can also be implemented by a combination of the above-mentioned hardware circuits and software, such as firmware.

[0241] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any modification, equivalent substitution and improvement made by any technician familiar with the technical field within the technical scope disclosed by the present invention and within the spirit and principle of the present invention should be covered within the protection scope of the present invention.

Claims

1. A method for deriving an optimal receiver for an underwater electromagnetic detection system, characterized in that: include: S1, target echo signal feature analysis; S2, optimal receiver derivation; S3, receiver operating characteristics analysis; S4, the best generalized likelihood ratio receiver of underwater electromagnetic detection system is obtained.

2. The method for deriving the best receiver of an underwater electromagnetic detection system according to claim 1, characterized in that: S1 specifically includes: The electromagnetic echo signal of the underwater target can be expressed as Where A is the amplitude of the signal, g(t) is the envelope of the signal, ω0 is the frequency of the signal, is the phase of the signal; For the case where the transmitting and receiving antennas are configured vertically, the signal amplitude can be obtained from the expression of the total magnetic field of the magnetic dipole: Where M is the radiation magnetic moment, μ0 is the magnetic 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 can be expressed as g(t)=exp[-α(t0-t) 2 ](4) Among them, α is the inverse of the target echo signal width, and t0 is the moment when the electromagnetic detection equipment is closest to the target; is the echo signal phase, which includes the echo phase mutation caused by interface scattering, propagation phase shift and signal initial phase, which can be expressed as in, It is the phase mutation of the echo caused by interface scattering. When scattered at the ferromagnetic interface, this term is about 180°; is the propagation phase shift; For the initial phase; Formula (5) can be rewritten as Direct coupling interference can be expressed as in, Considering the Gaussian white noise, the received signal of the underwater electromagnetic detection system can be expressed as Among them, n(t) has a mean of 0 and a variance of The power spectral density is Gaussian white noise.

3. The method for deriving the best receiver of an underwater electromagnetic detection system according to claim 1, characterized in that: S2 specifically includes: From equation (8), we can see that the signal detection problem of the underwater electromagnetic detection system is the detection problem of binary random parameter signals under Gaussian white noise conditions when narrowband interference exists. Therefore, two assumptions can be made: Since n(t) is Gaussian white noise, in the case of H0, x(t) obeys Right now in, is a constant, N is the number of sampling points, and Δt is the sampling interval that satisfies the noise irrelevant requirement. Then the conditional probability density function of the echo signal in case of H1 is The maximum likelihood equation is Substituting equations (1), (7), and (11) into equation (12), we can obtain Therefore, the decision formula of the generalized likelihood ratio test is Substituting equations (1), (7), (10), (11), and (13) into equation (14), we can obtain in, is the test statistic.

4. The method for deriving the best receiver of an underwater electromagnetic detection system according to claim 1, characterized in that: S3 specifically includes: make Then under the H0 hypothesis, the conditional mean of the test statistic is Under the H0 hypothesis, the conditional variance of the test statistic is Since n(t) is Gaussian white noise, we have Substituting equation (18) into equation (17), we can obtain in, Similarly, under the H1 hypothesis, the conditional mean and conditional variance of the test statistic are Var[G / H1]=σ 2 (21) Therefore, the probability density function of the test statistic G under the two assumptions is From formula (22), we can know that the false alarm probability is Similarly, from formula (23), the detection probability can be obtained as Substituting equation (19) into equation (24) and equation (25) respectively, we can obtain 5. An optimal receiver derivation system for an underwater electromagnetic detection system that implements the optimal receiver derivation method for an underwater electromagnetic detection system as claimed in any one of claims 1 to 4, characterized in that: include: A feature analysis module, used for performing feature analysis on target echo signals; An optimal receiver derivation module, used for performing optimal receiver derivation; A working characteristic analysis module, used for analyzing the working characteristics of the receiver; The best receiver acquisition module is used to obtain the best generalized likelihood ratio receiver of the underwater electromagnetic detection system.

6. A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the method for deriving an optimal receiver for an underwater electromagnetic detection system as described in any one of claims 1 to 4.

7. A computer-readable storage medium storing a computer program, which, when executed by a processor, enables the processor to execute the steps of the method for deriving an optimal receiver for an underwater electromagnetic detection system as claimed in any one of claims 1 to 4.

8. An information data processing terminal, comprising the optimal receiver derivation system for underwater electromagnetic detection system according to claim 5.

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