Sea surface floating target detection method, device, equipment and medium

Abstract

The invention provides a sea surface floating target detection method, device and equipment and a medium, and belongs to the technical field of target detection, and the method comprises the steps: obtaining a reference signal and a to-be-detected signal of a radar in the detection process of a sea surface floating target; respectively performing feature extraction on the reference signal and the to-be-detected signal to obtain a reference signal feature corresponding to the reference signal and a to-be-detected signal feature corresponding to the to-be-detected signal; respectively determining a joint probability density value corresponding to the reference signal feature and a joint probability density value corresponding to the to-be-detected signal feature; determining a target detection threshold according to a joint probability density value corresponding to the reference signal feature and a preset false alarm rate; and comparing the target detection threshold with the joint probability density value corresponding to the to-be-detected signal feature to realize target detection. According to the method, the correlation between the features is considered, and the joint probability density value of the reference signal features is constructed, so that the accuracy of the feature space can be improved, the target and clutter can be better distinguished, and the target detection performance is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of target detection, and in particular to a method, device, equipment and medium for detecting a floating target on a sea surface. Background Art

[0002] The detection of small floating targets on the sea surface is of great significance in various fields and can provide support for small ship detection, maritime search and rescue, coastal defense security, natural target discovery, etc.

[0003] With the deepening understanding of the physical characteristics and scattering mechanism of clutter and targets, detection methods based on distinguishable features between clutter and targets have been gradually proposed. Feature-based detection methods construct feature spaces of clutter and targets and design corresponding binary classifiers for target detection. Due to the imbalance of target and clutter samples, existing binary classifiers are mainly anomaly detectors based on density and clutter space shape, which judge anomalies by constructing clutter sample spaces.

[0004] However, the existing binary classifier-based algorithms based on density and clutter space shape require prior knowledge of the feature space shape. When the prior knowledge is inaccurate, it is difficult to accurately characterize the clutter space, which will lead to a decrease in detection performance. Summary of the invention

[0005] The present invention provides a method, device, equipment and medium for detecting floating targets on the sea surface, which solves the defect that it is difficult to accurately characterize the clutter space in the prior art, thus causing a decrease in detection performance.

[0006] The present invention provides a method for detecting a floating target on a sea surface, comprising: Acquire an echo signal of the radar during the process of detecting floating targets on the sea surface, wherein the echo signal includes a reference signal corresponding to a reference unit of the radar and a signal to be detected corresponding to a detection unit of the radar, and the reference signal includes clutter; Extracting features of the reference signal and the signal to be detected respectively to obtain reference signal features corresponding to the reference signal and signal features corresponding to the signal to be detected; Respectively determining a joint probability density value corresponding to the reference signal feature and a joint probability density value corresponding to the test signal feature; Determining a target detection threshold according to a joint probability density value corresponding to the reference signal feature and a preset false alarm rate; The target detection threshold is compared with the joint probability density value corresponding to the feature of the signal to be detected to achieve target detection.

[0007] As an embodiment, determining a joint probability density value corresponding to the reference signal feature includes: Determine the edge probability density value corresponding to each of the reference signal features based on a kernel density estimation algorithm; Determine the marginal cumulative distribution function value corresponding to each of the reference signal features according to the marginal probability density value corresponding to each of the reference signal features; The joint probability density value corresponding to each of the reference signal features is determined based on the empirical Bornstein connection function and the marginal cumulative distribution function value corresponding to each of the reference signal features.

[0008] As an embodiment, determining the joint probability density value corresponding to the feature of the signal to be tested includes: The joint probability density value corresponding to each of the signal features to be measured is determined based on a kernel density estimation algorithm.

[0009] As an embodiment, the expression of the joint probability density value corresponding to all the reference signal features is as follows: in, , represents the cumulative distribution of signal feature edges, Indicates i The marginal cumulative distribution function value corresponding to the reference signal feature is is the observation vector, i =1,2, is the joint empirical cumulative distribution, m is the order of the empirical Bernstein copula function, q and l is the subscript parameter of the sum function, and are the first-order derivatives of the binomial distribution functions of the two features respectively.

[0010] As an embodiment, the extracting features of the reference signal and the signal to be detected respectively to obtain reference signal features corresponding to the reference signal and signal features corresponding to the signal to be detected includes: Performing arbitrary domain transformation on the reference signal and the signal to be detected respectively to obtain the reference signal and the signal to be detected after domain transformation; Corresponding domain features are extracted from the reference signal and the signal to be detected after domain transformation, respectively, to obtain reference signal features corresponding to the reference signal and signal features to be detected corresponding to the signal to be detected.

[0011] As an embodiment, determining the target detection threshold according to the joint probability density value corresponding to the reference signal feature and a preset false alarm rate includes: Determining a product between the false alarm rate and a signal length of the reference signal; The minimum value between the product and the joint probability density value is used as the target detection threshold.

[0012] As an embodiment, the comparing the target detection threshold and the joint probability density value corresponding to the feature of the signal to be detected to achieve target detection includes: If the joint probability density value corresponding to the feature of the signal to be detected is not less than the target detection threshold, the feature of the signal to be detected is determined to be clutter; If the joint probability density value corresponding to the signal feature to be detected is less than the target detection threshold, the signal feature to be detected is determined to be a target.

[0013] The present invention also provides a sea surface floating target detection device, comprising: A signal acquisition module, used to acquire an echo signal of the radar during the process of detecting floating targets on the sea surface, wherein the echo signal includes a reference signal corresponding to a radar reference unit and a signal to be detected corresponding to a radar detection unit, and the reference signal includes clutter; A feature extraction module, used to extract features from the reference signal and the signal to be detected, respectively, to obtain reference signal features corresponding to the reference signal and signal features corresponding to the signal to be detected; A first determination module is used to respectively determine the joint probability density value corresponding to the reference signal feature and the joint probability density value corresponding to the signal feature to be tested; A second determination module is used to determine a target detection threshold according to a joint probability density value corresponding to the reference signal feature and a preset false alarm rate; The target detection module is used to compare the target detection threshold with the joint probability density value corresponding to the feature of the signal to be detected to achieve target detection.

[0014] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, a method for detecting a floating target on the sea surface as described in any one of the above methods is implemented.

[0015] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, a method for detecting a floating target on the sea surface as described in any one of the above is implemented.

[0016] The present invention provides a method, device, equipment and medium for detecting floating targets on the sea surface, which obtains the echo signal of the radar in the process of performing floating target detection on the sea surface, wherein the echo signal includes a reference signal corresponding to a radar reference unit and a signal to be detected corresponding to a radar detection unit, and the reference signal includes clutter; feature extraction is performed on the reference signal and the signal to be detected respectively to obtain the reference signal feature corresponding to the reference signal and the signal to be detected feature corresponding to the signal to be detected; the joint probability density value corresponding to the reference signal feature and the joint probability density value corresponding to the signal to be detected feature are determined respectively; the target detection threshold is determined according to the joint probability density value corresponding to the reference signal feature and the preset false alarm rate; the target detection threshold is compared with the joint probability density value corresponding to the signal to be detected feature to achieve target detection. The present invention takes into account the correlation between features in the process of constructing the feature space of clutter by constructing the joint probability density value corresponding to the reference signal feature, which can improve the accuracy of the feature space, help to better distinguish targets from clutter, and improve target detection performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 This is one of the flow charts of a method for detecting floating targets on the sea surface provided by the present invention.

[0019] Figure 2 This is the second flow chart of a method for detecting floating targets on the sea surface provided by the present invention.

[0020] Figure 3a-3d 1 to 4 are schematic diagrams comparing the performance results of the present invention and the prior art on different data.

[0021] Figure 4 It is a structural schematic diagram of a sea surface floating target detection device provided by the present invention.

[0022] Figure 5 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0023] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are 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 the present invention.

[0024] It should be noted that all actions of acquiring signals, information or data in the present invention are performed in compliance with the corresponding data protection laws and policies of the location and with the authorization given by the owner of the corresponding device.

[0025] In the field of feature detection of floating targets on the sea surface, existing feature-based detection research can be mainly divided into the following two types: one focuses on extracting physically interpretable features in different domains, such as energy in the time domain and distribution of spectrum in the frequency domain; the other focuses on proposing better binary classifiers to achieve accurate classification, such as density-based anomaly detection algorithms and convex hull-based detector designs.

[0026] The algorithms used by existing binary classifiers need to assume the distribution of the feature space. When the actual distribution of the feature space deviates from the prior, it is difficult to accurately characterize the clutter space. In addition, the existing algorithms do not take into account the correlation between features, resulting in a decrease in the detection performance of the binary classifier.

[0027] In order to overcome the problem that the existing feature detection of floating targets on the sea surface does not take into account the deviation between the real distribution of features and the prior and the correlation between features, resulting in a decrease in detection performance, the present invention provides a method, device, equipment and medium for detecting floating targets on the sea surface, which combines clutter features, is conducive to establishing an accurate clutter feature distribution model, improves the quality of feature space construction in multi-feature target detection, and further improves target detection performance. The present invention is described in detail below with reference to the accompanying drawings.

[0028] Figure 1 FIG. 1 is a flow chart of a method for detecting floating targets on the sea surface provided by the present invention. Figure 1 As shown, the present invention provides a method for detecting floating targets on the sea surface, which can be applied to the field of detecting floating targets on the sea surface, and can also be applied to the field of detecting small floating targets on the sea surface. If applied to the field of detecting small floating targets on the sea surface, the target of the technical solution is adaptively changed to a small target. The method provided by the present invention includes steps S100 to S500.

[0029] Step S100, acquiring an echo signal of a radar during a process of detecting a floating target on the sea surface, wherein the echo signal includes a reference signal corresponding to a radar reference unit and a signal to be detected corresponding to a radar detection unit, and the reference signal includes clutter.

[0030] Constant False Alarm Rate Detector (CFAR) is a radar target detection algorithm. The algorithm first specifies a range Doppler unit as a detection unit, sets a circle of protection units around the detection unit, and then sets a circle of reference units around the protection unit.

[0031] The radar reference unit provided in the embodiment of the present invention refers to the reference unit in the constant false alarm detection algorithm, the radar detection unit refers to the detection unit in the constant false alarm detection algorithm, and clutter refers to other interference signals or noises other than the signal reflected from the target object during the radar detection process.

[0032] Step S200 , performing feature extraction on the reference signal and the signal to be detected respectively, to obtain a reference signal feature corresponding to the reference signal and a signal to be detected feature corresponding to the signal to be detected.

[0033] The embodiment of the present invention does not limit the feature extraction algorithm, as long as separable features can be extracted from the reference signal and the signal to be detected. The reference signal is used to construct the feature space of clutter.

[0034] Step S300, respectively determining a joint probability density value corresponding to the reference signal feature and a joint probability density value corresponding to the signal to be measured feature.

[0035] Step S400, determining a target detection threshold according to a joint probability density value corresponding to the reference signal feature and a preset false alarm rate.

[0036] Step S500, comparing the target detection threshold and the joint probability density value corresponding to the feature of the signal to be detected to achieve target detection.

[0037] It can be understood that the present invention takes into account the correlation between features in the process of constructing the feature space of clutter by constructing the joint probability density value corresponding to the reference signal feature, which can improve the accuracy of the feature space, help to better distinguish targets from clutter, and improve target detection performance. In addition, the present invention uses the probability density value corresponding to the feature for target detection instead of directly using the feature for target detection, which can further improve the accuracy of target detection.

[0038] Based on the above embodiment, as an optional embodiment, the feature extraction of the reference signal and the signal to be detected is performed respectively to obtain the reference signal feature corresponding to the reference signal and the signal to be detected feature corresponding to the signal to be detected, including the following steps.

[0039] Step S210 , performing arbitrary domain transformation on the reference signal and the signal to be detected respectively to obtain the reference signal and the signal to be detected after domain transformation.

[0040] The reference signal and the signal to be detected may be respectively transformed in the time domain or in the frequency domain or in the time-frequency domain, which is not limited in the embodiment of the present invention and has universal applicability.

[0041] Step S220, performing corresponding domain feature extraction on the reference signal and the signal to be detected after domain transformation, respectively, to obtain reference signal features corresponding to the reference signal and signal features to be detected corresponding to the signal to be detected.

[0042] Optionally, with respect to the transformation manner of the reference signal and the signal to be detected, the embodiment of the present invention utilizes a corresponding feature extraction algorithm to perform feature extraction on the reference signal and the signal to be detected after domain transformation.

[0043] For time domain transformation, extract time domain features such as signal amplitude, correlation time, and envelope characteristics; for frequency domain transformation, use fast Fourier transform to extract spectrum features such as main frequency, spectrum width, and harmonic characteristics, or use power spectrum density to extract frequency domain energy features; for time-frequency domain transformation, use time-frequency feature extraction algorithms to extract time-frequency features, such as short-time Fourier transform and other algorithms.

[0044] It can be understood that the present invention extracts the features of the echo signal through arbitrary domain transformation and corresponding domain feature extraction algorithm to construct the feature space of clutter and target detection, which has universality and higher flexibility.

[0045] Based on the above embodiment, as an optional embodiment, determining the joint probability density value corresponding to the reference signal feature includes the following steps.

[0046] Step S310, considering the influence of noise on the estimation result, determining the edge probability density value corresponding to each of the reference signal features based on a kernel density estimation algorithm. The kernel density estimation algorithm can reduce the influence of noise on the estimation result.

[0047] Let the reference signal be x p , the reference signal characteristic is expressed as , , the reference signal feature can be understood as the feature of the clutter. Correspondingly, the expression for determining the edge probability density value corresponding to each of the reference signal features using the kernel density estimation algorithm is as follows: ; in, For the i The marginal probability density value corresponding to the reference signal feature is his the kernel density estimation bandwidth, N is the signal length of the reference signal, K is the density kernel function used for kernel density estimation, Representative i Reference signal characteristics No. n samples, is the observation vector, i =1,2.

[0048] Step S320: determining the marginal cumulative distribution function value corresponding to each of the reference signal features according to the marginal probability density value corresponding to each of the reference signal features.

[0049] The expression of the marginal cumulative distribution function value corresponding to each of the reference signal features is as follows: ; in, Indicates i The marginal cumulative distribution function value corresponding to the feature of the reference signal is i =1,2.

[0050] Step S330, determining the joint probability density value corresponding to each of the reference signal features according to the empirical Bernstein copula function and the marginal cumulative distribution function value corresponding to each of the reference signal features. The empirical Bernstein copula function is the empirical Bernstein Copula function. Compared with other Copula extraction methods, the empirical Bernstein copula function is more flexible and can better characterize the complex correlation between features.

[0051] The expression of the joint probability density value corresponding to all the reference signal features is as follows: ; in, , represents the cumulative distribution of signal feature edges, Indicates i The marginal cumulative distribution function value corresponding to the reference signal feature is is the observation vector, i =1,2, is the joint empirical cumulative distribution, m is the order of the empirical Bernstein copula function, q and l is the subscript parameter of the sum function, and are the first-order derivatives of the binomial distribution functions of the two features respectively.

[0052] After determining the marginal probability density values ​​corresponding to all the reference signal features, the joint probability density value corresponding to the reference signal features is obtained according to the marginal probability density values ​​corresponding to all the reference signal features, and the expression is as follows: in, It is k The joint probability density value of each feature of the reference signal, It is k The reference signal i The marginal probability density value of a feature.

[0053] Optionally, determining a joint probability density value corresponding to the feature of the signal to be tested includes: Step S340, determining the joint probability density value corresponding to each of the test signal features based on a kernel density estimation algorithm. The calculation method can refer to step S310. It should be noted that the embodiment of the present invention does not limit the execution order of step S310 and step S340.

[0054] It can be understood that the present invention combines the marginal distributions of various clutter features using the empirical Bernstein Copula function to form a multi-feature joint probability density, which can improve the accuracy of the clutter feature distribution model, help to better distinguish targets from clutter, and improve detection performance.

[0055] Based on the above embodiment, as an optional embodiment, determining the target detection threshold according to the joint probability density value corresponding to the reference signal feature and a preset false alarm rate includes the following steps.

[0056] Step S410: determine the product of the false alarm rate and the signal length of the reference signal. False alarm rate (FAR) refers to the probability of mistakenly determining a noise or background signal as a target signal in signal detection theory.

[0057] Step S420: taking the minimum value between the product and the joint probability density value as the target detection threshold.

[0058] The expression of target detection threshold is as follows: in, is the target detection threshold, is the joint probability density value corresponding to all reference signal features, is the false alarm rate.

[0059] Optionally, comparing the target detection threshold and the joint probability density value corresponding to the feature of the signal to be detected to achieve target detection includes the following steps.

[0060] Step S510: If the joint probability density value corresponding to the signal feature to be detected is not less than the target detection threshold, the signal feature to be detected is determined to be clutter.

[0061] Step S520: If the joint probability density value corresponding to the signal feature to be detected is less than the target detection threshold, the signal feature to be detected is determined to be a target.

[0062] It can be understood that the present invention uses the probability density function value as the detection statistic for detection, which can effectively improve the detection accuracy.

[0063] The present invention is described in detail below with reference to a preferred embodiment.

[0064] Figure 2 FIG. 2 is a flow chart of a method for detecting floating targets on the sea surface provided by the present invention. Figure 2 As shown, the feature detection method for small floating targets on the sea surface based on the empirical Bernstein Copula function of the present invention includes a training process and an application process. The training process is used to determine the joint probability density function of each clutter feature, so as to more accurately describe the clutter space and determine the detection threshold. The training process includes steps 1 to 7, and the application process includes steps 8 and 9.

[0065] The input parameters of the present invention include the reference signal x corresponding to the radar reference unit p , the signal to be detected x corresponding to the radar detection unit.

[0066] Step 1: For the reference unit signal x p The present invention is not limited to a specific domain transformation method and has universal applicability.

[0067] Step 2: Extract features from the transform domain reference unit signal , likewise, the invention is not limited to any particular pair of features.

[0068] Step 3: Obtain the edge probability density function of each clutter feature using kernel density estimation. For the specific expression, see step S310.

[0069] Step 4: Obtain the edge cumulative distribution function of each clutter feature using kernel density estimation. For the expression, see step S320.

[0070] Step 5: Use the empirical Bernstein Copula function to obtain the joint probability density function. For the expression, see step S330.

[0071] Step 6: Obtain the joint probability density function value of each reference sample. For the expression, see step S330.

[0072] Step 7: According to The quantile determines the constant false alarm threshold, and the expression is shown in step S420.

[0073] Step 8: Calculate the feature vector composed of all the features of the signal to be detected corresponding to the signal to be detected x , the probability density function value corresponding to the eigenvector is used as the detection statistic .

[0074] Step 9: If Less than If it is a target, it is a clutter.

[0075] Figure 3a It is a schematic diagram comparing the performance results of the present invention and the prior art on HH polarization data. Figure 3b It is a schematic diagram comparing the performance results of the present invention and the prior art on HV polarization data. Figure 3c It is a schematic diagram comparing the performance results of the present invention and the prior art on VH polarization data, Figure 3d is a schematic diagram comparing the performance results of the present invention and the prior art on VV polarization data. The prior art refers to a detection method for small targets in ocean clutter based on three features, wherein "-o-" is the detection result obtained by using the prior detection method for different features, and "- -" are the detection results obtained by using the method of the present invention for different features. It can be seen from the figure that the present invention has better performance.

[0076] In summary, the present invention relates to the field of feature detection of small floating targets on the sea surface. Aiming at the problem that the traditional feature detection method assumes that the features are independent, resulting in inaccurate feature space construction and low detection performance, a feature detection method for small floating targets on the sea surface based on the empirical Bernstein Copula function is proposed. Under given features, the edge distribution of each feature can be obtained by kernel density estimation, and the joint probability density distribution can be accurately constructed by using the empirical Bernstein Copula function. The feature space of clutter can be accurately constructed, and the probability density function value is used as the detection statistic for detection. The accuracy of the present invention is higher than that of existing similar methods. It is a very potential feature detection method for small floating targets on the sea surface, which solves the defect that the existing feature detection technology for small floating targets on the sea surface directly designs a classifier in the feature space after extracting distinguishable features of target clutter, and does not design an algorithm for the correlation between features.

[0077] A sea surface floating target detection device provided by the present invention is described below. The sea surface floating target detection device described below and the sea surface floating target detection method described above can correspond to each other.

[0078] Figure 4 Schematic diagram of the structure of a sea surface floating target detection device provided by the present invention. Figure 4 As shown, the present invention also provides a sea surface floating target detection device, including the following modules.

[0079] The signal acquisition module 410 is used to acquire the echo signal of the radar during the process of detecting floating targets on the sea surface, wherein the echo signal includes a reference signal corresponding to the radar reference unit and a signal to be detected corresponding to the radar detection unit, and the reference signal includes clutter; A feature extraction module 420 is used to extract features from the reference signal and the signal to be detected, respectively, to obtain reference signal features corresponding to the reference signal and signal features corresponding to the signal to be detected; A first determination module 430, configured to respectively determine a joint probability density value corresponding to the reference signal feature and a joint probability density value corresponding to the test signal feature; A second determination module 440 is used to determine a target detection threshold according to a joint probability density value corresponding to the reference signal feature and a preset false alarm rate; The target detection module 450 is used to compare the target detection threshold with the joint probability density value corresponding to the feature of the signal to be detected to achieve target detection.

[0080] As an embodiment, the first determining module 430 is further configured to: Determine the edge probability density value corresponding to each of the reference signal features based on a kernel density estimation algorithm; Determine the marginal cumulative distribution function value corresponding to each of the reference signal features according to the marginal probability density value corresponding to each of the reference signal features; The joint probability density value corresponding to each of the reference signal features is determined based on the empirical Bornstein connection function and the marginal cumulative distribution function value corresponding to each of the reference signal features.

[0081] As an embodiment, the first determining module 430 is further configured to: The joint probability density value corresponding to each of the signal features to be measured is determined based on a kernel density estimation algorithm.

[0082] As an embodiment, the expression of the joint probability density value corresponding to all the reference signal features is as follows: in, , represents the feature marginal cumulative distribution, Indicates i The marginal cumulative distribution function value corresponding to the reference signal feature is is the observation vector, i =1,2, is the joint empirical cumulative distribution, m is the order of the empirical Bernstein copula function, q and l is the subscript parameter of the sum function, and are the first-order derivatives of the binomial distribution functions of the two features respectively.

[0083] As an embodiment, the feature extraction module 420 is further used for: Performing arbitrary domain transformation on the reference signal and the signal to be detected respectively to obtain the reference signal and the signal to be detected after domain transformation; Corresponding domain features are extracted from the reference signal and the signal to be detected after domain transformation, respectively, to obtain reference signal features corresponding to the reference signal and signal features to be detected corresponding to the signal to be detected.

[0084] As an embodiment, the second determining module 440 is further configured to: Determining a product between the false alarm rate and a signal length of the reference signal; The minimum value between the product and the joint probability density value is used as the target detection threshold.

[0085] As an embodiment, the target detection module 450 is further used for: If the joint probability density value corresponding to the feature of the signal to be detected is not less than the target detection threshold, the feature of the signal to be detected is determined to be clutter; If the joint probability density value corresponding to the signal feature to be detected is less than the target detection threshold, the signal feature to be detected is determined to be a target.

[0086] It should be noted that the sea surface floating target detection device provided by the present invention can execute a sea surface floating target detection method described in any of the above embodiments during specific operation, and has the technical effect corresponding to the method, which will not be elaborated in this embodiment.

[0087] Figure 5 An example of a physical structure diagram of an electronic device is shown in FIG. Figure 5As shown, the electronic device may include: a processor 510, a communication interface 520, a memory 530 and a communication bus 540, wherein the processor 510, the communication interface 520 and the memory 530 communicate with each other through the communication bus 540. The processor 510 may call the logic instructions in the memory 530 to execute a method for detecting floating targets on the sea surface, the method comprising: obtaining an echo signal of a radar in the process of detecting floating targets on the sea surface, the echo signal comprising a reference signal corresponding to a radar reference unit and a signal to be detected corresponding to a radar detection unit, the reference signal comprising clutter; performing feature extraction on the reference signal and the signal to be detected respectively to obtain a reference signal feature corresponding to the reference signal and a signal to be detected feature corresponding to the signal to be detected; determining a joint probability density value corresponding to the reference signal feature and a joint probability density value corresponding to the signal to be detected feature respectively; determining a target detection threshold according to the joint probability density value corresponding to the reference signal feature and a preset false alarm rate; and comparing the target detection threshold with the joint probability density value corresponding to the signal to be detected feature to achieve target detection.

[0088] In addition, the logic instructions in the above-mentioned memory 530 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0089] On the other hand, the present invention also provides a computer program product, which includes a computer program. The computer program can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute a method for detecting floating targets on the sea surface provided by the above methods. The method includes: obtaining an echo signal of a radar during the process of detecting floating targets on the sea surface, wherein the echo signal includes a reference signal corresponding to a radar reference unit and a signal to be detected corresponding to a radar detection unit, and the reference signal includes clutter; performing feature extraction on the reference signal and the signal to be detected respectively to obtain a reference signal feature corresponding to the reference signal and a signal to be detected feature corresponding to the signal to be detected; determining a joint probability density value corresponding to the reference signal feature and a joint probability density value corresponding to the signal to be detected feature respectively; determining a target detection threshold according to the joint probability density value corresponding to the reference signal feature and a preset false alarm rate; and comparing the target detection threshold with the joint probability density value corresponding to the signal to be detected feature to achieve target detection.

[0090] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which is implemented by a processor to execute a method for detecting floating targets on the sea surface provided by the above methods when the computer program is executed, the method comprising: obtaining an echo signal of a radar during the process of detecting floating targets on the sea surface, the echo signal comprising a reference signal corresponding to a radar reference unit and a signal to be detected corresponding to a radar detection unit, the reference signal comprising clutter; performing feature extraction on the reference signal and the signal to be detected respectively to obtain a reference signal feature corresponding to the reference signal and a signal to be detected feature corresponding to the signal to be detected; determining a joint probability density value corresponding to the reference signal feature and a joint probability density value corresponding to the signal to be detected feature respectively; determining a target detection threshold according to the joint probability density value corresponding to the reference signal feature and a preset false alarm rate; and comparing the target detection threshold with the joint probability density value corresponding to the signal to be detected feature to achieve target detection.

[0091] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0092] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0093] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for detecting floating targets on the sea surface, characterized in that: include: Acquire an echo signal of the radar during the process of detecting floating targets on the sea surface, wherein the echo signal includes a reference signal corresponding to a reference unit of the radar and a signal to be detected corresponding to a detection unit of the radar, and the reference signal includes clutter; Extracting features of the reference signal and the signal to be detected respectively to obtain reference signal features corresponding to the reference signal and signal features corresponding to the signal to be detected; Respectively determining a joint probability density value corresponding to the reference signal feature and a joint probability density value corresponding to the test signal feature; Determining a target detection threshold according to a joint probability density value corresponding to the reference signal feature and a preset false alarm rate; The target detection threshold is compared with the joint probability density value corresponding to the feature of the signal to be detected to achieve target detection.

2. A method for detecting floating targets on the sea surface according to claim 1, characterized in that: Determining a joint probability density value corresponding to the reference signal feature includes: Determine the edge probability density value corresponding to each of the reference signal features based on a kernel density estimation algorithm; Determine the marginal cumulative distribution function value corresponding to each of the reference signal features according to the marginal probability density value corresponding to each of the reference signal features; The joint probability density value corresponding to each of the reference signal features is determined based on the empirical Bornstein connection function and the marginal cumulative distribution function value corresponding to each of the reference signal features.

3. A method for detecting floating targets on the sea surface according to claim 2, characterized in that: Determining a joint probability density value corresponding to the feature of the signal to be tested includes: The joint probability density value corresponding to each of the signal features to be measured is determined based on a kernel density estimation algorithm and an empirical Bornstein link function.

4. A method for detecting floating targets on the sea surface according to claim 2 or 3, characterized in that: The marginal probability density values ​​corresponding to all the reference signal features are expressed as follows: in, , represents the cumulative distribution of signal feature edges, Indicates i The marginal cumulative distribution function value corresponding to the reference signal feature is is the observation vector, i =1,2, is the joint empirical cumulative distribution, m is the order of the empirical Bernstein copula function, q and l is the subscript parameter of the sum function, and are the first-order derivatives of the binomial distribution functions of the two features respectively.

5. A method for detecting floating targets on the sea surface according to claim 1, characterized in that: The extracting features of the reference signal and the signal to be detected respectively to obtain reference signal features corresponding to the reference signal and signal features corresponding to the signal to be detected includes: Performing arbitrary domain transformation on the reference signal and the signal to be detected respectively to obtain the reference signal and the signal to be detected after domain transformation; Corresponding domain features are extracted from the reference signal and the signal to be detected after domain transformation, respectively, to obtain reference signal features corresponding to the reference signal and signal features to be detected corresponding to the signal to be detected.

6. A method for detecting floating targets on the sea surface according to claim 1, characterized in that: The determining the target detection threshold according to the joint probability density value corresponding to the reference signal feature and a preset false alarm rate includes: Determining a product between the false alarm rate and a signal length of the reference signal; The minimum value between the product and the joint probability density value is used as the target detection threshold.

7. A method for detecting floating targets on the sea surface according to claim 1 or 6, characterized in that: The comparing the target detection threshold with the joint probability density value corresponding to the feature of the signal to be detected to achieve target detection includes: If the joint probability density value corresponding to the feature of the signal to be detected is not less than the target detection threshold, the feature of the signal to be detected is determined to be clutter; If the joint probability density value corresponding to the signal feature to be detected is less than the target detection threshold, the signal feature to be detected is determined to be a target.

8. A sea surface floating target detection device, characterized in that: include: A signal acquisition module, used to acquire an echo signal of the radar during the process of detecting floating targets on the sea surface, wherein the echo signal includes a reference signal corresponding to a radar reference unit and a signal to be detected corresponding to a radar detection unit, and the reference signal includes clutter; A feature extraction module, used to extract features from the reference signal and the signal to be detected, respectively, to obtain reference signal features corresponding to the reference signal and signal features corresponding to the signal to be detected; A first determination module is used to respectively determine the joint probability density value corresponding to the reference signal feature and the joint probability density value corresponding to the signal feature to be tested; A second determination module is used to determine a target detection threshold according to a joint probability density value corresponding to the reference signal feature and a preset false alarm rate; The target detection module is used to compare the target detection threshold with the joint probability density value corresponding to the feature of the signal to be detected to achieve target detection.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the method for detecting floating targets on the sea surface as described in any one of claims 1 to 7 is implemented.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, a method for detecting floating targets on the sea surface as described in any one of claims 1 to 7 is implemented.

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