Sea clutter prediction method and device based on space-time correlation

Abstract

The invention provides a sea clutter prediction method and device based on spatio-temporal correlation, and the method comprises the steps: collecting sea clutter data corresponding to a to-be-measured distance unit and two adjacent distance units, and corresponding environment parameter data; modeling is carried out on a time correlation acquisition graph according to the collected sea clutter data, the time sequence dependence characteristic of the distance unit to be measured is obtained, the environment dependence characteristic of the distance unit to be measured is obtained according to the collected environment parameter data, and the high-dimensional prediction characteristic of the distance unit to be measured is further obtained; a left adjacent dependency feature and a right adjacent dependency feature are obtained by combining data corresponding to two adjacent distance units, and temporal correlation is also used in the two adjacent distance units and used for fully mining spatial correlation; therefore, the sea clutter prediction result of the distance unit to be measured is obtained on the basis of the temporal correlation and the spatial correlation, and the sea clutter prediction precision is fully improved.

Description

Technical Field

[0001] The present invention relates to the technical field of sea clutter prediction, and in particular to a sea clutter prediction method and device based on time-space correlation. Background Art

[0002] Sea clutter is the backscattered echo received by the radar when illuminating the sea surface. As the most complex form of radar clutter, the probability distribution of sea clutter usually deviates significantly from the Gaussian distribution, and the statistical characteristics also change over time, with non-Gaussian and non-stationary characteristics. Due to the influence of gravity waves and other factors, the sea clutter in the same distance unit has time correlation, and the sea clutter texture components of different distance units in the same time period have spatial correlation. In addition, sea clutter is dependent on sea conditions and is greatly affected by marine environmental parameters. In the detection application of sea surface targets, false alarms and misalarms occur because the amplitude of sea clutter echoes will suddenly increase. Therefore, it is necessary to conduct prediction research on sea clutter and suppress sea clutter interference based on the prediction results.

[0003] At present, the prediction methods for sea clutter are divided into traditional model prediction methods, acoustic model prediction methods and machine learning prediction methods. Traditional model prediction methods include empirical models and models based on statistical methods. Among them, empirical models are usually predicted based on sea clutter data over a period of time in the past, while models based on statistical methods predict sea clutter by establishing mathematical models. Acoustic model is a method that uses acoustic principles to predict sea clutter. Usually, a complex acoustic model is constructed to simulate the propagation of sound waves in the ocean environment, and the model is trained using known sea clutter data to predict the occurrence of sea clutter in the future. Machine learning is a method in the field of artificial intelligence that predicts the occurrence of sea clutter in the future by learning a large amount of historical sea clutter data. However, the above methods lack in-depth mining of the temporal and spatial correlation characteristics of sea clutter during modeling, resulting in limited accuracy in sea clutter prediction.

[0004] In view of this, overcoming the defects of the prior art is an urgent problem to be solved in the field of this technology. Summary of the invention

[0005] The problem to be solved by the present invention is: how to improve the prediction accuracy of sea clutter.

[0006] In a first aspect, a sea clutter prediction method based on spatiotemporal correlation is provided, comprising:

[0007] Collect sea clutter data and corresponding environmental parameter data corresponding to the distance unit to be measured and its two adjacent distance units, and pre-process the sea clutter data and environmental parameter data of different distance units respectively;

[0008] According to the pre-processed sea clutter data of the distance unit to be measured, a corresponding graph model is obtained based on time correlation, and a time-dependent feature is extracted according to the graph model corresponding to the distance unit to be measured;

[0009] Acquire the corresponding environment-dependent features according to the pre-processed environmental parameter data of the distance unit to be measured;

[0010] According to the time-dependent features and the environment-dependent features, high-dimensional prediction features of the distance unit to be measured are obtained;

[0011] Obtaining a left adjacent dependency feature corresponding to the left adjacent distance unit according to the preprocessed sea clutter data of the left adjacent distance unit and the corresponding environmental parameter data, and obtaining a right adjacent dependency feature corresponding to the right adjacent distance unit according to the preprocessed sea clutter data of the right adjacent distance unit and the corresponding environmental parameter data;

[0012] The sea clutter prediction result corresponding to the distance unit to be measured is obtained according to the high-dimensional prediction feature, the left adjacent dependency feature and the right adjacent dependency feature.

[0013] Preferably, the collecting of sea clutter data and corresponding environmental parameter data corresponding to the distance unit to be measured and its two adjacent distance units, and preprocessing the sea clutter data and environmental parameter data of different distance units respectively, specifically includes:

[0014] Query the collected sea clutter data and environmental parameter data to obtain missing values ​​and / or difference values ​​in the sea clutter data and environmental parameter data;

[0015] Taking each missing value and / or difference value obtained from the sea clutter data and the environmental parameter data as the center value, interpolating the corresponding missing value and / or difference value with the mean value of the preset span time window around the center value;

[0016] The sea clutter data and environmental parameter data after interpolation processing are subjected to maximum value normalization processing to obtain the preprocessed sea clutter data and environmental parameter data.

[0017] Preferably, the method of acquiring corresponding graph modeling based on time correlation according to the preprocessed sea clutter data of the distance unit to be measured, and extracting the time-dependent features according to the graph modeling corresponding to the distance unit to be measured, specifically includes:

[0018] The preprocessed sea clutter data of the range unit to be measured at multiple times are used as a node feature matrix, and a weighted adjacency matrix of the sea clutter data at different times is obtained;

[0019] Obtaining graph modeling of sea clutter data based on time correlation according to the node feature matrix and the weighted adjacency matrix;

[0020] The graph modeling is input into the graph neural network for learning to obtain the timing dependency features corresponding to the distance unit to be measured.

[0021] Preferably, the step of obtaining the corresponding environment-dependent feature according to the pre-processed environmental parameter data of the distance unit to be measured specifically includes:

[0022] Obtaining multi-source input according to the pre-processed environmental parameter data of the distance unit to be measured;

[0023] The multi-source input is input into the GRU model to obtain the environment-dependent features corresponding to the distance unit to be measured.

[0024] Preferably, the obtaining of high-dimensional prediction features of the distance unit to be measured according to the time-series dependent features and the environment dependent features specifically includes:

[0025] Obtain the weight of the sea clutter data and the weight of the environmental parameter data corresponding to the distance unit to be measured;

[0026] The weight of the sea clutter data is used as the weight corresponding to the time series dependent feature, the weight of the environmental parameter data is used as the weight corresponding to the environment dependent feature, and the time series dependent feature and the environment dependent feature are weighted and added to obtain the high-dimensional prediction feature of the distance unit to be measured.

[0027] Preferably, the obtaining of the left adjacent dependency feature corresponding to the left adjacent distance unit according to the pre-processed sea clutter data of the left adjacent distance unit and the corresponding environmental parameter data specifically includes:

[0028] Acquire corresponding left adjacent graph modeling based on time correlation according to preprocessed sea clutter data of left adjacent range units, and extract left adjacent temporal dependency features according to the left adjacent graph modeling;

[0029] Obtain corresponding left adjacent environment dependent features according to the preprocessed environmental parameter data of the left adjacent distance unit;

[0030] The left adjacent dependency feature is obtained according to the left adjacent temporal dependency feature and the left adjacent environmental dependency feature.

[0031] Preferably, the obtaining of the right adjacent dependency feature corresponding to the right adjacent distance unit according to the pre-processed sea clutter data of the right adjacent distance unit and the corresponding environmental parameter data specifically includes:

[0032] Acquire corresponding right adjacent graph modeling based on time correlation according to preprocessed sea clutter data of right adjacent distance units, and extract right adjacent temporal dependency features according to the right adjacent graph modeling;

[0033] Obtaining corresponding right adjacent environment dependent features according to the preprocessed environmental parameter data of the right adjacent distance unit;

[0034] The right adjacent dependency feature is obtained according to the right adjacent timing dependency feature and the right adjacent environment dependency feature.

[0035] Preferably, obtaining the sea clutter prediction result corresponding to the range unit to be measured according to the high-dimensional prediction feature, the left adjacent dependency feature and the right adjacent dependency feature specifically includes:

[0036] Obtain the attention layer parameters corresponding to the distance unit to be measured, the left adjacent distance unit, and the right adjacent distance unit respectively;

[0037] Obtaining the unnormalized attention feature of the distance unit to be measured according to the high-dimensional prediction feature and the attention layer parameter of the distance unit to be measured, obtaining the unnormalized attention feature of the left adjacent distance unit according to the left adjacent dependency feature and the attention layer parameter of the left adjacent distance unit, and obtaining the unnormalized attention feature of the right adjacent distance unit according to the right adjacent dependency feature and the attention layer parameter of the right adjacent distance unit;

[0038] The attention weight corresponding to the distance unit to be measured, the attention weight corresponding to the left adjacent distance unit, and the attention weight corresponding to the right adjacent distance unit are obtained according to the unnormalized attention feature of the distance unit to be measured, the unnormalized attention feature of the left adjacent distance unit, and the unnormalized attention feature of the right adjacent distance unit;

[0039] The attention vector is obtained by weighting and summing the high-dimensional prediction features, the left adjacent dependent features, and the right adjacent dependent features according to their corresponding attention weights;

[0040] The attention vector is low-dimensionally mapped to obtain the sea clutter prediction result corresponding to the distance unit to be measured.

[0041] In a second aspect, a method for predicting sea clutter based on spatiotemporal correlation is provided, comprising at least one processor and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the processor to execute the method for predicting sea clutter based on spatiotemporal correlation.

[0042] In a third aspect, the present invention further provides a non-volatile computer storage medium, wherein the computer storage medium stores computer executable instructions, and the computer executable instructions are executed by one or more processors to complete the method described in the first aspect.

[0043] In a fourth aspect, a chip is provided, comprising: a processor and an interface, for calling and running a computer program stored in the memory from a memory, and executing the method of the first aspect.

[0044] According to a fifth aspect, a computer program product comprising instructions is provided. When the instructions are executed on a computer or a processor, the computer or the processor executes the method according to the first aspect.

[0045] In a sixth aspect, a sea clutter prediction system based on spatiotemporal correlation is provided, comprising the sea clutter prediction based on spatiotemporal correlation of the second aspect and using the sea clutter prediction method based on spatiotemporal correlation of the first aspect.

[0046] The present invention provides a sea clutter prediction method and device based on time-space correlation, which collects sea clutter data corresponding to a distance unit to be measured and two adjacent distance units and corresponding environmental parameter data, and gives a time correlation acquisition graph modeling according to the collected sea clutter data to obtain the time series dependency feature of the distance unit to be measured, obtains the environmental dependency feature of the distance unit to be measured according to the collected environmental parameter data, further obtains the high-dimensional prediction feature of the distance unit to be measured, and then combines the data corresponding to the two adjacent distance units to obtain the left adjacent dependency feature and the right adjacent dependency feature, which are used to fully mine the spatial correlation, thereby obtaining the sea clutter prediction result of the distance unit to be measured on the basis of the time correlation and the spatial correlation, and fully improving the prediction accuracy of the sea clutter. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only 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.

[0048] Figure 1 A method flow chart of a sea clutter prediction method based on spatiotemporal correlation provided by an embodiment of the present invention;

[0049] Figure 2 A flow chart of a method for preprocessing collected data in a sea clutter prediction method based on spatiotemporal correlation provided by an embodiment of the present invention;

[0050] Figure 3 A flow chart of a method for acquiring time-dependent features in a sea clutter prediction method based on spatiotemporal correlation provided by an embodiment of the present invention;

[0051] Figure 4A flow chart of a method for obtaining environment-dependent features in a sea clutter prediction method based on spatiotemporal correlation provided by an embodiment of the present invention;

[0052] Figure 5 A flow chart of a method for obtaining high-dimensional prediction features in a sea clutter prediction method based on spatiotemporal correlation provided by an embodiment of the present invention;

[0053] Figure 6 A flow chart of a method for obtaining left adjacent dependent features in a sea clutter prediction method based on spatiotemporal correlation provided by an embodiment of the present invention;

[0054] Figure 7 A flow chart of a method for obtaining right adjacent dependent features in a sea clutter prediction method based on spatiotemporal correlation provided by an embodiment of the present invention;

[0055] Figure 8 A flow chart of a method for obtaining sea clutter prediction results in a sea clutter prediction method based on spatiotemporal correlation provided by an embodiment of the present invention;

[0056] Fig. 9 A flow chart of a method for predicting sea clutter based on spatiotemporal correlation provided by an embodiment of the present invention;

[0057] Fig.10 A flowchart of another method for predicting sea clutter based on spatiotemporal correlation provided by an embodiment of the present invention;

[0058] Fig.11 A schematic diagram of a sea clutter prediction device based on spatiotemporal correlation provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0059] 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 accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0060] In the description of the present invention, it is necessary to understand that the terms "center", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present disclosure and simplifying the description, and do not indicate or imply that the referred device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present disclosure.

[0061] In the description of the present invention, the terms "first" and "second" are used only for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the embodiments of the present disclosure, unless otherwise specified, the meaning of "multiple" is two or more. In addition, for example, the same type of nouns may be described as two independent individuals by adding "A" and "B" at the end. In this case, the corresponding features defined as "A" and "B" are only used to distinguish the same type of individuals for description purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of indicated technical features.

[0062] As used herein, "about," "substantially," or "approximately" includes the stated value and an average value that is within an acceptable range of deviation from the particular value as determined by one of ordinary skill in the art taking into account the measurements in question and the errors associated with the measurement of the particular quantity (i.e., the limitations of the measurement system).

[0063] Unless the context requires otherwise, throughout the specification and claims, the term "including" is to be interpreted as open inclusion, that is, "including, but not limited to". In the description of the specification, the terms "one embodiment", "some embodiments", "exemplary embodiments", "examples", "specific examples" or "some examples" and the like are intended to indicate that specific features, structures, materials or characteristics associated with the embodiment or example are included in at least one embodiment or example of the present disclosure. The schematic representation of the above terms does not necessarily refer to the same embodiment or example. In addition, the specific features, structures, materials or characteristics may be included in any one or more embodiments or examples in any appropriate manner, that is, although they may be carried in the embodiments or examples of the above terms due to reasons such as the order and position of appearance, it is not limited to that they can be carried in combination by one embodiment or example.

[0064] In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0065] Embodiment 1:

[0066] This embodiment provides a sea clutter prediction method based on time-space correlation, such as Figure 1 As shown, including:

[0067] In step 101, sea clutter data and corresponding environmental parameter data corresponding to the distance unit to be measured and its two adjacent distance units are collected, and the sea clutter data and environmental parameter data of different distance units are preprocessed respectively.

[0068] In this embodiment, the sea clutter data can be collected by radar, and the environmental parameter data can be obtained by a buoy, an anemometer or an ocean observation station. The environmental parameter data may include one or more of a significant wave height, a maximum wave height, a wave direction, a wind speed, a wind direction and a sea condition. It should be noted that the sea clutter data and the environmental parameter data are both historical data of corresponding distance units. In the process of collecting the above data, it is inevitable that missing values ​​and / or outliers will appear. Since missing values ​​and outliers will cause errors in subsequent training and related calculations of prediction, in this embodiment, the missing values ​​and / or outliers in the data are processed by the preprocessing, thereby reducing the impact of the missing values ​​and / or outliers in subsequent calculations.

[0069] In this embodiment, due to the influence of gravity waves, the sea clutter in different distance units in the same time period has spatial correlation. In this embodiment, by simultaneously acquiring two adjacent distance units (i.e., the left adjacent distance unit and the right adjacent distance unit), and introducing the sea clutter data and environmental parameter data at the positions of the two adjacent distance units into subsequent training and prediction related calculations, it is ensured that the prediction results can have spatial correlation, thereby improving the accuracy of the prediction results.

[0070] In step 102, a corresponding graph model is obtained based on the time correlation according to the preprocessed sea clutter data of the range unit to be measured, and a time-dependent feature is extracted according to the graph model corresponding to the range unit to be measured.

[0071] In this embodiment, due to the influence of gravity waves, the sea clutter data at different time nodes in the same distance unit have time correlation. When collecting sea clutter data, the sea clutter data at different time nodes are collected for the corresponding distance unit position to be measured. Therefore, when obtaining the corresponding graph modeling, weighting can be performed based on the time correlation between different time nodes, so as to mine the influence between the sea clutter data at different time nodes, and then the corresponding graph neural network is used to learn the graph modeling, so as to output the corresponding high-order feature vector as the time series dependency feature. The time series dependency feature is applied to subsequent prediction-related calculations to ensure that the prediction result can have time correlation.

[0072] In step 103, the corresponding environment-dependent features are acquired according to the pre-processed environmental parameter data of the distance unit to be measured.

[0073] In this embodiment, since the sea clutter data is dependent on the sea conditions and is greatly affected by the environmental parameter data, the relevant features of the environmental parameter data are indispensable when predicting the sea clutter. Therefore, by inputting the environmental parameter data into the corresponding neural network, the outputted environmental dependence features are used to assist in predicting the sea clutter at the position of the distance unit to be measured.

[0074] In step 104, high-dimensional prediction features of the distance unit to be measured are obtained according to the time-dependent features and the environment-dependent features.

[0075] In this embodiment, the high-dimensional prediction feature is a prediction feature obtained based on the historical environmental parameter data and the historical sea clutter data of the predicted distance unit position. Since the historical environmental parameter data and the historical sea clutter data are both from multiple time nodes, the high-level prediction feature is a prediction feature that mines time-related characteristics.

[0076] In step 105, the left adjacent dependent feature corresponding to the left adjacent distance unit is obtained according to the preprocessed sea clutter data of the left adjacent distance unit and the corresponding environmental parameter data, and the right adjacent dependent feature corresponding to the right adjacent distance unit is obtained according to the preprocessed sea clutter data of the right adjacent distance unit and the corresponding environmental parameter data.

[0077] In this embodiment, the calculation and acquisition process of the left adjacent dependent feature and the right adjacent dependent feature is consistent with the high-order prediction feature, thereby ensuring that the left adjacent dependent feature and the right adjacent dependent feature are both obtained by relying on corresponding environmental parameter data and mining corresponding time-related features.

[0078] In step 106, the sea clutter prediction result corresponding to the range unit to be measured is obtained according to the high-dimensional prediction feature, the left adjacent dependent feature and the right adjacent dependent feature.

[0079] In this embodiment, after obtaining the high-dimensional prediction features of the distance unit to be measured, the left adjacent dependency features of the left adjacent distance unit, and the right adjacent dependency features of the right adjacent distance unit, the attention mechanism is used to assign weights to the above features, and low-dimensional mapping is performed through the fully connected layer. By introducing the left adjacent dependency features and the right adjacent dependency features, the spatial correlation is fully mined in the prediction process, thereby obtaining the prediction result of the sea clutter of the distance unit to be measured. At this time, the prediction result obtained fully mines the time correlation and spatial correlation, thereby improving the prediction accuracy of the sea clutter.

[0080] The sea clutter data and the corresponding environmental parameter data corresponding to the distance unit to be measured and its two adjacent distance units are collected, and the sea clutter data and the environmental parameter data of different distance units are pre-processed respectively, such as Figure 2 As shown, including:

[0081] In step 201, the collected sea clutter data and environmental parameter data are queried to obtain missing values ​​and / or difference values ​​in the sea clutter data and environmental parameter data.

[0082] In this embodiment, the sea clutter data can be obtained by radar, the effective wave height, maximum wave height and wave direction in the environmental parameter data can be obtained by buoys deployed around the radar, the wind speed and wind direction in the environmental parameter data can be obtained by an anemometer set around the radar, and other data in the environmental parameter data can be obtained through an ocean observation station. In the above process, due to errors caused by environmental or equipment factors, the missing values ​​and / or difference values ​​may appear in the acquired data.

[0083] In step 202, each missing value and / or difference value obtained from the sea clutter data and the environmental parameter data is taken as a center value, and the corresponding missing value and / or difference value is interpolated with the mean value of a preset span time window around the center value.

[0084] The preset span time window is set by those skilled in the art according to actual conditions. In this embodiment, the preset span time window can be set to 10.

[0085] In step 203, maximum value normalization processing is performed on the interpolated sea clutter data and environmental parameter data to obtain pre-processed sea clutter data and environmental parameter data.

[0086] In this embodiment, the maximum value normalization is calculated as follows:

[0087]

[0088] Among them, x is the original data of the collected sea clutter data and environmental parameter data, x min is the minimum value corresponding to the original data, x max is the maximum value corresponding to the original data.

[0089] The method obtains the corresponding graph modeling based on the time correlation according to the preprocessed sea clutter data of the distance unit to be measured, and extracts the time-dependent features according to the graph modeling corresponding to the distance unit to be measured, such as Figure 3 As shown, the method flow includes:

[0090] In step 301, the preprocessed sea clutter data of the range unit to be measured at multiple times are used as a node feature matrix, and a weighted adjacency matrix of the sea clutter data at different times is obtained.

[0091] In this embodiment, the node feature matrix is:

[0092]

[0093] in, is the sea clutter data of the range unit to be measured at the Nth moment, is the node feature matrix.

[0094] The weighted adjacency matrix is ​​A mid , A mid The relative weights of the elements at different time nodes are A ij , where A ij The expression is:

[0095]

[0096] Among them, t i is the i-th time node, t j is the j-th time node, exp(*) is the natural exponential function, and σ is the scale factor.

[0097] In step 302, a graph modeling of sea clutter data based on time correlation is obtained according to the node feature matrix and the weighted adjacency matrix.

[0098] In this embodiment, the graph is modeled as

[0099] In step 303, the graph modeling is input into a graph neural network for learning to obtain the timing dependency features corresponding to the distance unit to be measured.

[0100] In this embodiment, the graph modeling can be learned using an M-layer ChebNet network, and the calculation method is as follows:

[0101]

[0102] Among them, ChebNet M (*) is the M-layer ChebNet network, is the timing dependency characteristic corresponding to the distance unit to be measured.

[0103] The corresponding environment-dependent features are obtained according to the pre-processed environmental parameter data of the distance unit to be measured, such as Figure 4 As shown, the method flow includes:

[0104] In step 401, a multi-source input is obtained according to the pre-processed environmental parameter data of the distance unit to be measured.

[0105] In this embodiment, the multi-source input is:

[0106] in, and They are the effective wave height, maximum wave height, wave direction, wind speed, wind direction and sea conditions corresponding to the distance unit to be measured at time k.

[0107] In step 402, the multi-source input is input into the GRU model to obtain the environment-dependent features corresponding to the distance unit to be measured.

[0108] In this embodiment, the calculation method corresponding to the GRU model is as follows:

[0109]

[0110] Among them, GRU M (*) is an M-layer GRU model, is the environment dependent feature.

[0111] The high-dimensional prediction features of the distance unit to be measured are obtained according to the time-dependent features and the environment-dependent features, such as Figure 5 As shown, the method flow includes:

[0112] In step 501, the weight of the sea clutter data and the weight of the environmental parameter data corresponding to the range unit to be measured are obtained.

[0113] In step 502, the weight of the sea clutter data is used as the weight corresponding to the time series dependent feature, and the weight of the environmental parameter data is used as the weight corresponding to the environment dependent feature. The time series dependent feature and the environment dependent feature are weighted and added to obtain the high-dimensional prediction feature of the distance unit to be measured.

[0114] The weighted sum is calculated as follows:

[0115]

[0116] in, is the weight of the sea clutter data, is the weight of environmental parameter data, is the high-dimensional prediction feature.

[0117] The left adjacent dependent feature corresponding to the left adjacent distance unit is obtained according to the pre-processed sea clutter data of the left adjacent distance unit and the corresponding environmental parameter data, such as Figure 6 As shown, the method flow includes:

[0118] In step 601, a corresponding left adjacent graph model is obtained based on time correlation according to the preprocessed sea clutter data of the left adjacent range unit, and a left adjacent temporal dependency feature is extracted according to the left adjacent graph model.

[0119] In this embodiment, the left adjacent graph is modeled as The corresponding left adjacent temporal dependency feature is calculated as follows:

[0120]

[0121] The left adjacent graph is modeled The definition method of and the calculation method of the left adjacent time series dependency feature can refer to the graph modeling and time series dependency features of the distance unit to be predicted mentioned above, which will not be elaborated here.

[0122] In step 602, the corresponding left adjacent environment dependent feature is obtained according to the pre-processed environment parameter data of the left adjacent distance unit.

[0123] The multi-source input of the preprocessed environmental parameter data of the left adjacent distance unit is defined as The definition method refers to the multi-source input of the pre-processed environmental parameter data of the above-mentioned distance unit to be measured, and the calculation method of the left adjacent environment dependent feature is:

[0124]

[0125] in, It is the left adjacent environment dependent feature.

[0126] In step 603, the left adjacent dependency feature is obtained according to the left adjacent temporal dependency feature and the left adjacent environmental dependency feature.

[0127] Get the weight of the sea clutter data of the left adjacent range unit and weights of environmental parameter data The left adjacent dependency feature is obtained by weighted summation, and the calculation method is as follows:

[0128]

[0129] in, is the left adjacent dependent feature.

[0130] The right adjacent dependent feature corresponding to the right adjacent distance unit is obtained according to the pre-processed sea clutter data of the right adjacent distance unit and the corresponding environmental parameter data, such as Figure 7 As shown, the method flow includes:

[0131] In step 701, the corresponding right adjacent graph modeling is acquired based on the time correlation according to the preprocessed sea clutter data of the right adjacent distance unit, and the right adjacent temporal dependency feature is extracted according to the right adjacent graph modeling.

[0132] In this embodiment, the right adjacent graph is modeled as The corresponding right adjacent temporal dependency feature is calculated as follows:

[0133]

[0134] The right adjacent graph model The definition method of and the calculation method of the right adjacent time series dependency feature can refer to the graph modeling and time series dependency features of the distance unit to be predicted mentioned above, which will not be elaborated here.

[0135] In step 702, the corresponding right adjacent environment dependent feature is obtained according to the preprocessed environment parameter data of the right adjacent distance unit.

[0136] The multi-source input of the preprocessed environmental parameter data of the right adjacent distance unit is defined as The definition method refers to the multi-source input of the pre-processed environmental parameter data of the above-mentioned distance unit to be measured, and the calculation method of the right adjacent environment dependent feature is:

[0137]

[0138] in, It is the right adjacent environment dependent feature.

[0139] In step 703, the right adjacent dependency feature is obtained according to the right adjacent timing dependency feature and the right adjacent environment dependency feature.

[0140] Get the weight of the sea clutter data of the right adjacent range unit and weights of environmental parameter data The right adjacent dependency feature is obtained by weighted summation, and the calculation method is as follows:

[0141]

[0142] in, is the right adjacent dependent feature.

[0143] The sea clutter prediction result corresponding to the distance unit to be measured is obtained according to the high-dimensional prediction feature, the left adjacent dependent feature and the right adjacent dependent feature, such as Figure 8 As shown, the method flow includes:

[0144] In step 801, the attention layer parameters corresponding to the distance unit to be measured, the left adjacent distance unit and the right adjacent distance unit are obtained respectively.

[0145] In step 802, the unnormalized attention feature of the distance unit to be measured is obtained according to the high-dimensional prediction feature and the attention layer parameters of the distance unit to be measured, the unnormalized attention feature of the left adjacent distance unit is obtained according to the left adjacent dependent feature and the attention layer parameters of the left adjacent distance unit, and the unnormalized attention feature of the right adjacent distance unit is obtained according to the right adjacent dependent feature and the attention layer parameters of the right adjacent distance unit.

[0146] Get the unnormalized attention feature corresponding to the distance unit to be measured, calculated as:

[0147]

[0148] Among them, P mid is the unnormalized attention feature corresponding to the distance unit to be measured, w mid and b mid These are the attention layer parameters corresponding to the distance unit to be tested.

[0149] Get the unnormalized attention feature of the left adjacent distance unit, calculated as:

[0150]

[0151] Among them, P left is the unnormalized attention feature corresponding to the left adjacent distance unit, w left and b left They are the attention layer parameters corresponding to the left adjacent distance unit.

[0152] Get the unnormalized attention feature of the right adjacent distance unit, calculated as:

[0153]

[0154] Among them, P right is the unnormalized attention feature corresponding to the right adjacent distance unit, w right and b right They are all the attention layer parameters corresponding to the right adjacent distance units.

[0155] In step 803, the attention weight corresponding to the distance unit to be measured, the attention weight corresponding to the left adjacent distance unit, and the attention weight corresponding to the right adjacent distance unit are obtained based on the unnormalized attention features of the distance unit to be measured, the unnormalized attention features of the left adjacent distance unit, and the unnormalized attention features of the right adjacent distance unit.

[0156] Get the attention weight a corresponding to the distance unit to be measured mid , calculated as:

[0157]

[0158] Get the attention weight a corresponding to the left adjacent distance unit left , calculated as:

[0159]

[0160] Get the attention weight a corresponding to the left adjacent distance unit right , calculated as:

[0161]

[0162] In step 804, the high-dimensional prediction features, the left-adjacent dependent features, and the right-adjacent dependent features are weighted and summed according to their respective corresponding attention weights to obtain an attention vector.

[0163] The attention vector V all The calculation method is:

[0164]

[0165] In step 805, the attention vector is subjected to low-dimensional mapping to obtain a sea clutter prediction result corresponding to the range unit to be measured.

[0166] In this embodiment, the attention vector V is processed by L fully connected layers. all For low-dimensional mapping, the corresponding calculation method is:

[0167]

[0168] Among them, FC L (*) is the L-layer fully connected layer, is the sea clutter prediction result corresponding to the distance unit to be measured.

[0169] Fig. 9 and Fig.10 All of them are flowcharts of the method for predicting sea clutter based on spatiotemporal correlation provided by this embodiment.

[0170] This embodiment can achieve the following beneficial effects through the above method:

[0171] (1) Considering the temporal correlation between sea clutter data in the same distance unit, graph modeling is performed on the sea clutter data, and the sea clutter graph data is learned to deeply explore the temporal correlation characteristics between sea clutter data. In addition, the corresponding environmental parameter data is extracted and weightedly fused with the sea clutter data to make the feature extraction richer and more conducive to obtaining accurate sea clutter prediction results.

[0172] (2) Considering that the sea clutter texture components of different distance units in the same time period have spatial correlation, and the closer the distance, the stronger the correlation, the sea clutter data of the left and right adjacent distance units are introduced for auxiliary prediction. At the same time, the attention mechanism is used to assign weights to the influence degree to further improve the prediction accuracy of sea clutter.

[0173] Embodiment 2:

[0174] like Fig.11, which is a schematic diagram of a sea clutter prediction device based on spatiotemporal correlation according to an embodiment of the present invention. The sea clutter prediction device based on spatiotemporal correlation according to this embodiment includes one or more processors 41 and a memory 42 .

[0175] The processor 41 and the memory 42 may be connected via a bus or other means. Fig.11 The example of connecting through bus is taken in the following.

[0176] The memory 42 is a non-volatile computer-readable storage medium that can be used to store non-volatile software programs and non-volatile computer executable programs, such as the sea clutter prediction method based on spatiotemporal correlation in the above embodiment. The processor 41 executes the sea clutter prediction method based on spatiotemporal correlation by running the non-volatile software programs and instructions stored in the memory 42.

[0177] The memory 42 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage devices. In some embodiments, the memory 42 may optionally include a memory remotely arranged relative to the processor 41, and these remote memories may be connected to the processor 41 via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0178] The program instructions / modules are stored in the memory 42, and when executed by the one or more processors 41, the sea clutter prediction method based on spatiotemporal correlation in the above embodiment is executed, for example, the above described Figure 1-Figure 5 The steps shown.

[0179] The embodiment of the present invention further provides a computer storage medium having computer program instructions stored thereon; when the computer program instructions are executed by a processor, the sea clutter prediction method based on spatiotemporal correlation provided by the embodiment of the present invention is implemented.

[0180] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A sea clutter prediction method based on spatiotemporal correlation, characterized in that: include: Collect sea clutter data and corresponding environmental parameter data corresponding to the distance unit to be measured and its two adjacent distance units, and pre-process the sea clutter data and environmental parameter data of different distance units respectively; According to the pre-processed sea clutter data of the distance unit to be measured, a corresponding graph model is obtained based on time correlation, and a time-dependent feature is extracted according to the graph model corresponding to the distance unit to be measured; Acquire the corresponding environment-dependent features according to the pre-processed environmental parameter data of the distance unit to be measured; Acquire high-dimensional prediction features of the distance unit to be measured according to the time-series dependent features and the environment dependent features; Obtaining a left adjacent dependency feature corresponding to the left adjacent distance unit according to the preprocessed sea clutter data of the left adjacent distance unit and the corresponding environmental parameter data, and obtaining a right adjacent dependency feature corresponding to the right adjacent distance unit according to the preprocessed sea clutter data of the right adjacent distance unit and the corresponding environmental parameter data; The sea clutter prediction result corresponding to the distance unit to be measured is obtained according to the high-dimensional prediction feature, the left adjacent dependency feature and the right adjacent dependency feature.

2. The sea clutter prediction method based on spatiotemporal correlation according to claim 1, characterized in that: The collecting of sea clutter data and corresponding environmental parameter data corresponding to the distance unit to be measured and its two adjacent distance units, and preprocessing the sea clutter data and environmental parameter data of different distance units respectively, specifically includes: Query the collected sea clutter data and environmental parameter data to obtain missing values ​​and / or difference values ​​in the sea clutter data and environmental parameter data; Taking each missing value and / or difference value obtained from the sea clutter data and the environmental parameter data as the center value, interpolating the corresponding missing value and / or difference value with the mean value of the preset span time window around the center value; The sea clutter data and environmental parameter data after interpolation processing are subjected to maximum value normalization processing to obtain the preprocessed sea clutter data and environmental parameter data.

3. The sea clutter prediction method based on spatiotemporal correlation according to claim 1, characterized in that: The method of obtaining corresponding graph modeling based on time correlation according to the preprocessed sea clutter data of the distance unit to be measured, and extracting the time-dependent features according to the graph modeling corresponding to the distance unit to be measured, specifically includes: The preprocessed sea clutter data of the range unit to be measured at multiple times are used as the node feature matrix, and the weighted adjacency matrix of the sea clutter data at different times is obtained; Obtaining graph modeling of sea clutter data based on time correlation according to the node feature matrix and the weighted adjacency matrix; The graph modeling is input into the graph neural network for learning to obtain the timing dependency features corresponding to the distance unit to be measured.

4. The sea clutter prediction method based on spatiotemporal correlation according to claim 1, characterized in that: The step of obtaining the corresponding environment-dependent features according to the pre-processed environmental parameter data of the distance unit to be measured specifically includes: Obtaining multi-source input according to the pre-processed environmental parameter data of the distance unit to be measured; The multi-source input is input into the GRU model to obtain the environment-dependent features corresponding to the distance unit to be measured.

5. The sea clutter prediction method based on spatiotemporal correlation according to claim 1, characterized in that: The step of obtaining the high-dimensional prediction features of the distance unit to be measured according to the time-series dependent features and the environment dependent features specifically includes: Obtain the weight of the sea clutter data and the weight of the environmental parameter data corresponding to the distance unit to be measured; The weight of the sea clutter data is used as the weight corresponding to the time series dependent feature, the weight of the environmental parameter data is used as the weight corresponding to the environment dependent feature, and the time series dependent feature and the environment dependent feature are weighted and added to obtain the high-dimensional prediction feature of the distance unit to be measured.

6. The sea clutter prediction method based on spatiotemporal correlation according to claim 1, characterized in that: The step of obtaining the left adjacent dependency feature corresponding to the left adjacent distance unit according to the preprocessed sea clutter data of the left adjacent distance unit and the corresponding environmental parameter data specifically includes: Acquire corresponding left adjacent graph modeling based on time correlation according to preprocessed sea clutter data of left adjacent range units, and extract left adjacent temporal dependency features according to the left adjacent graph modeling; Obtain corresponding left adjacent environment dependent features according to the preprocessed environmental parameter data of the left adjacent distance unit; The left adjacent dependency feature is obtained according to the left adjacent temporal dependency feature and the left adjacent environmental dependency feature.

7. The sea clutter prediction method based on spatiotemporal correlation according to claim 1, characterized in that: The step of obtaining the right adjacent dependency feature corresponding to the right adjacent distance unit according to the pre-processed sea clutter data of the right adjacent distance unit and the corresponding environmental parameter data specifically includes: Acquire corresponding right adjacent graph modeling based on time correlation according to preprocessed sea clutter data of right adjacent distance units, and extract right adjacent temporal dependency features according to the right adjacent graph modeling; Obtaining corresponding right adjacent environment dependent features according to the preprocessed environmental parameter data of the right adjacent distance unit; The right adjacent dependency feature is obtained according to the right adjacent timing dependency feature and the right adjacent environment dependency feature.

8. The sea clutter prediction method based on spatiotemporal correlation according to claim 1, characterized in that: The obtaining the sea clutter prediction result corresponding to the range unit to be measured according to the high-dimensional prediction feature, the left adjacent dependency feature and the right adjacent dependency feature specifically includes: Obtain the attention layer parameters corresponding to the distance unit to be measured, the left adjacent distance unit, and the right adjacent distance unit respectively; Obtaining the unnormalized attention feature of the distance unit to be measured according to the high-dimensional prediction feature and the attention layer parameter of the distance unit to be measured, obtaining the unnormalized attention feature of the left adjacent distance unit according to the left adjacent dependency feature and the attention layer parameter of the left adjacent distance unit, and obtaining the unnormalized attention feature of the right adjacent distance unit according to the right adjacent dependency feature and the attention layer parameter of the right adjacent distance unit; The attention weight corresponding to the distance unit to be measured, the attention weight corresponding to the left adjacent distance unit, and the attention weight corresponding to the right adjacent distance unit are obtained according to the unnormalized attention feature of the distance unit to be measured, the unnormalized attention feature of the left adjacent distance unit, and the unnormalized attention feature of the right adjacent distance unit; The attention vector is obtained by weighting and summing the high-dimensional prediction features, the left adjacent dependent features, and the right adjacent dependent features according to their corresponding attention weights; The attention vector is low-dimensionally mapped to obtain the sea clutter prediction result corresponding to the distance unit to be measured.

9. A sea clutter prediction device based on time-space correlation, characterized in that: The invention comprises at least one processor and a memory in communication with the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the processor to execute the sea clutter prediction method based on spatiotemporal correlation according to any one of claims 1 to 8.

10. A non-volatile computer storage medium, characterized in that: The computer storage medium stores computer program instructions, which, when executed by one or more processors, implement the sea clutter prediction method based on spatiotemporal correlation according to any one of claims 1 to 8.

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