Cross-water-air medium communication method based on data fusion

Abstract

The invention discloses a cross-water and air medium communication method based on data fusion, a transmitting end is located underwater, a receiving end is located on water, and the cross-water and air medium communication method is a multi-sensor system composed of a millimeter wave radar, a laser radar and a terahertz radar and arranged at equal intervals; the cross-water-air medium communication method comprises the following steps: receiving a vibration signal of a water surface through a millimeter wave radar, a laser radar and a terahertz radar, and constructing a state vector and an observation model required by a Kalman filtering algorithm; then initializing the water surface displacement and the error covariance matrix, predicting the water surface displacement and the covariance matrix at the next moment through a state transition model, calculating the weight of each radar according to the signal-to-noise ratio, the error rate and the measurement error standard deviation of the radar, and calculating the water surface displacement and the error covariance matrix of the radar through a Kalman filtering algorithm. Updating a water surface displacement and error covariance matrix by using measurement data of each radar; and finally, integrating updating results of all radars to obtain a final water surface displacement and error covariance matrix, and improving the quality of cross-water-air medium communication.

Description

Technical Field

[0001] The present invention relates to the field of communication, and in particular to a cross-water-air medium communication method based on data fusion. Background Art

[0002] Direct communication across water and air has a wide range of potential applications. In the civilian field, direct communication across water and air is an important part of the air, space, land and sea observation network, which restricts the real-time, convenience and economy of data transmission from underwater to surface. In the military field, the demand for information transmission across water and air is becoming more and more urgent, which has become a key issue restricting the improvement of the combat effectiveness of underwater vehicles. An emerging cross-water-air communication provides an effective way to achieve cross-water-air communication by detecting the water surface wave vibration signal excited by the sound source to invert the information transmitted by the underwater sound source. This communication technology combines underwater acoustics with water radio technology. Its feasibility of realizing direct communication across water and air has been fully proven and has broad application prospects.

[0003] In cross-water-air medium communication combining underwater acoustics and electromagnetic waves, due to the complex ocean environment, a single sensor cannot accurately and completely capture the vibration of the water surface, resulting in a decrease in communication quality. Figure 1 As shown, the communication quality of a single sensor across water and air media drops sharply with the increase of detection altitude and the influence of the marine environment. Summary of the invention

[0004] In view of the shortcomings of the prior art, the present invention proposes a cross-water-air medium communication method based on data fusion. By adopting three different sensors, namely millimeter wave radar, lidar and terahertz radar, to detect the vibration of the water surface, data with different accuracy, integrity and noise levels are obtained. Then, the accuracy of vibration displacement measurement is improved through data fusion, thereby improving the accuracy of subsequent demodulation and achieving reliable communication in complex marine environments.

[0005] The objective of the present invention is achieved through the following technical solutions:

[0006] A cross-water-air medium communication method based on data fusion is implemented by a communication system consisting of a transmitter and a receiver; the transmitter is located underwater and consists of a plurality of high-power sound sources forming an underwater sound source array, all of which send sound wave signals, and the sound waves hit the water surface to generate micrometer-level vibrations; the receiver is located above water and is a multi-sensor system consisting of millimeter-wave radar, laser radar and terahertz radar and placed at equal intervals to form a receiving array;

[0007] The cross-water-air medium communication method comprises the following steps:

[0008] Step 1: The millimeter wave radar, the laser radar and the terahertz radar respectively obtain the measurement values ​​of the received signals;

[0009] Step 2: Construct the state vector and observation model required for the Kalman filter algorithm; the state vector is the displacement in the received signal measurement value; the observation model is the observation equation that defines the relationship between the received signal measurement value in step 1 and the state vector:

[0010] z t (1) =H t (1) x t +v t (1)

[0011] z t (2) =H t (2) x t +v t (2)

[0012] z t (3) =H t (3) x t +v t (3)

[0013] Among them, H t (1) , H t (2) and H t (3) They are the observation matrix between the water surface displacement value measured by the millimeter wave radar and the actual water surface displacement, the observation matrix between the water surface displacement value measured by the lidar and the actual water surface displacement, and the observation matrix between the water surface displacement value measured by the terahertz radar and the actual water surface displacement; v t (1) 、v t (2) and v t (3) are the measurement noise of millimeter wave radar, lidar and terahertz radar respectively; x t represents the state vector, which represents the water surface displacement at time t;

[0014] Step 3: Initialize the water surface displacement and error covariance matrix; the error covariance matrix is ​​used to characterize the deviation between the estimated value and the actual value of the water surface displacement;

[0015] Step 4: Predict the water surface displacement and covariance matrix at the next moment through the state transition model;

[0016] Step 5: Calculate the weight of each radar according to its signal-to-noise ratio, bit error rate and measurement error standard deviation;

[0017] Step 6: Use the Kalman filter algorithm to update the water surface displacement and error covariance matrix using the measurement data of each radar;

[0018] Step 7: Integrate the update results of all radars to obtain the final water surface displacement and error covariance matrix.

[0019] Furthermore, in step 4, the water surface displacement and covariance matrix at the next moment are predicted by the state transition model, specifically:

[0020] Based on the estimated water surface displacement at the previous moment, predict the water surface displacement at the current moment:

[0021]

[0022] in, is the predicted value of the water surface displacement at the current moment, F is the state transfer matrix, and is the unit matrix; is the water surface displacement estimated at the previous moment;

[0023] The state estimation error covariance matrix of the current moment is predicted based on the updated state estimation error covariance matrix of the previous moment:

[0024]

[0025] Among them, P t+1|t is the state estimation error covariance matrix at the current moment, P t|t is the updated state estimation error covariance matrix of the previous moment, and Q is the process noise covariance matrix.

[0026] Furthermore, in step 5, the weight w of each radar sensor is calculated according to the signal-to-noise ratio, bit error rate and measurement error standard deviation of each radar sensor. i , the specific calculation formula is as follows:

[0027]

[0028] Among them, SNR i Represents the signal-to-noise ratio of the radar sensor, BER i represents the bit error rate, σ i Represents the standard deviation of measurement error.

[0029] Furthermore, the step six specifically includes the following sub-steps:

[0030] S6.1: Calculate the covariance matrix R of the millimeter wave radar separately1 and Kalman gain

[0031]

[0032] Among them, σ 1 represents the standard deviation of the measurement error of the millimeter wave radar, P t+1|t is the state estimation error covariance matrix of the current moment calculated in step 4, w 1 is the weight of the millimeter-wave radar calculated in step 5;

[0033] S6.2: Calculate the water surface displacement estimated by the millimeter wave radar at the current moment And update the state estimation error covariance matrix of the millimeter wave radar

[0034]

[0035] in, is the predicted value of the water surface displacement at the current moment calculated in step 4, is the water surface displacement value measured by the millimeter-wave radar at the current moment; P t+1|t is the state estimation error covariance matrix of the current moment calculated in step 4;

[0036] S6.3: Calculate the covariance matrix R of the lidar separately 2 and Kalman gain

[0037]

[0038] Among them, σ 2 represents the standard deviation of the laser radar measurement error, is the state estimation error covariance matrix of the current moment obtained in the update step of the millimeter wave radar; w 2 is the weight of the lidar calculated in step 5.

[0039] S6.4: Calculate the water surface displacement estimated by the laser radar at the current moment And update the state estimation error covariance matrix of the lidar

[0040]

[0041] in, is the predicted value of the water surface displacement at the current moment estimated from the measurement value of the millimeter wave radar, is the water surface displacement value measured by the lidar at the current moment; The state estimation error covariance matrix of the current moment finally obtained by the millimeter wave radar update step;

[0042] S6.5: Calculate the covariance matrix R of the terahertz radar separately 3 and Kalman gain

[0043]

[0044] Among them, σ 3 represents the standard deviation of the measurement error of the terahertz radar, is the state estimation error covariance matrix of the current moment obtained in the last step of the laser radar update, w 3 is the weight of the terahertz radar calculated in step 5;

[0045] S6.6: Calculate the water surface displacement estimated by the terahertz radar at the current moment And update the state estimation error covariance matrix of the terahertz radar

[0046]

[0047] in, is the predicted value of the water surface displacement at the current moment estimated from the laser radar measurement value, is the displacement of the water surface measured by the terahertz radar at the current moment; It is the state estimation error covariance matrix of the current moment finally obtained by the lidar update step.

[0048] Furthermore, the step 7 specifically includes: calculating the water surface displacement calculated in step 6 and estimated by the terahertz radar at the current moment; The final fused water surface displacement estimate The state estimation error covariance matrix of the terahertz radar is finally updated As the final covariance matrix P at the current moment t+1|t+1 .

[0049] The beneficial effects of the present invention are as follows:

[0050] The present invention makes full use of the advantages of three different sensors, namely millimeter wave radar, lidar and terahertz radar, and fuses the data measured by multiple sensors to obtain higher-precision water surface vibration displacement measurement, improve the signal-to-noise ratio and integrity of the received signal, overcome the disadvantage that a single sensor system is easily affected by sea surface waves, etc., and provides a reliable solution for the application of cross-water-air medium communication in actual marine environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 This is a schematic diagram of a cross-medium communication method combining underwater acoustics and electromagnetic waves in the prior art.

[0052] Figure 2 A schematic diagram of the communication system structure used to implement the high-precision cross-water-air medium communication method based on data fusion technology of the method of the present invention.

[0053] Figure 3 The present invention is a flowchart of a method for cross-water-air medium communication based on data fusion according to an embodiment of the present invention. DETAILED DESCRIPTION

[0054] The present invention will be described in detail below based on the accompanying drawings and preferred embodiments, and the purpose and effects of the present invention will become more clear. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0055] Millimeter-wave radar, laser radar and terahertz radar have their own advantages and disadvantages. Laser radar has high accuracy and small beam. It has high accuracy under conditions of small waves, no fog and strong light, but is easily affected by ambient light. When millimeter-wave radar detects echoes under wave conditions, it follows the principle of diffuse reflection, and the integrity of the reflected signal is better than that of laser radar. The band used by terahertz radar is between microwave millimeter waves and infrared light, and its accuracy is improved compared to millimeter-wave radar, and its stability is higher than that of laser radar. When the three sensors are used simultaneously to detect vibrations on the water surface, the three sensors can obtain data with different accuracy, integrity and noise levels.

[0056] like Figure 2 As shown, the communication system adopted by the cross-water-air medium communication method based on data fusion of the present invention includes a transmitting end and a receiving end. The transmitting end is located underwater and is composed of an underwater sound source array composed of multiple high-power sound sources. All sound sources send sound wave signals, and the sound waves hit the water surface to generate micron-level vibrations. The receiving end is located above the water and is a multi-sensor system. It simultaneously receives vibration signals from the water surface and processes these signals through the data fusion algorithm of the Kalman filter to obtain more accurate vibration displacement measurement values.

[0057] The multi-sensor system on the water consists of high-precision millimeter-wave radars, lidars, and terahertz radars, which are placed at equal intervals to form a receiving array. Due to the presence of waves on the sea surface, the direction of the reflected signal received by each radar will change with the changes in the sea surface, causing each radar to receive reflected signals with random intensity and time at different times.

[0058] like Figure 3 As shown, the cross-water-air medium communication method based on data fusion of this embodiment includes the following steps:

[0059] Step 1: The millimeter wave radar, the laser radar and the terahertz radar respectively obtain the measurement values ​​of the received signals;

[0060] Among them, the millimeter wave radar measurement value is The lidar measurements are The terahertz radar measurement value is The subscript t represents time t, that is, and They represent the water surface displacement values ​​measured by millimeter wave radar, lidar and terahertz radar at time t respectively.

[0061] Step 2: Construct the state vector and observation model required by the Kalman filter algorithm;

[0062] Define the state vector x t , is the water surface displacement x at time t t :

[0063] x t =x t

[0064] The relationship between the state vector and the measurement value in step 1 is defined by the observation equation. For millimeter wave radar, lidar and terahertz radar, there is a corresponding observation equation:

[0065] Z t (1) =H t (1) x t +v t (1)

[0066] z t (2) =H t (2) x t +v t (2)

[0067] z t (3) =H t (3) +v t (3)

[0068] Among them, H t (1) , H t (2) and H t (3) is the observation matrix, which represents the relationship between the water surface displacement values ​​measured by millimeter wave radar, lidar and terahertz radar and the actual water surface displacement; v t (1)、v t (2) and v t (3) They are the measurement noise of millimeter wave radar, lidar and terahertz radar respectively.

[0069] The state transfer matrix F describes how the system transfers from one moment to the next without considering external input. In the system described in this embodiment, the system state variable is the water surface displacement. Therefore, without considering external input, the system state will maintain the value of the previous moment. Therefore, the state transfer matrix is ​​a unit matrix, that is:

[0070] F=1

[0071] The observation matrix describes the relationship between the sensor and the state vector, that is, the relationship between the water surface displacement measured by the millimeter wave radar, lidar and terahertz radar and the actual water surface displacement. For all sensors, in an ideal state, when noise is not considered, the sensor should reflect the actual water surface vibration without scaling. Therefore, the observation matrix is ​​also fixed as a unit matrix, that is:

[0072]

[0073] Step 3: Initialize the water surface displacement and error covariance matrix; the error covariance matrix is ​​used to characterize the deviation between the estimated value and the actual value of the water surface displacement.

[0074] The state estimate is an estimate of the displacement of the water surface. Initialization is to manually set an initial water surface displacement value in the absence of millimeter wave radar, lidar and terahertz radar measurement values.

[0075] The error covariance reflects the degree of deviation between the estimated value and the true value of the state, that is, the deviation between the estimated value and the actual value of the water surface displacement, expressed as the state vector x t and estimated values The expected value of the difference between is calculated. In the initialization stage of data fusion, the initial error covariance P 0 , that is, manually set an initial error covariance value.

[0076] Step 4: Predict the water surface displacement and covariance matrix at the next moment through the state transition model.

[0077] (1) State prediction: In the state prediction stage, the water surface displacement at the current moment is predicted based on the water surface displacement estimated at the previous moment. The calculation method is to input the water surface displacement estimated at the previous moment Multiplying with the state transfer matrix F, the predicted value of the water surface displacement at the current moment is

[0078]

[0079] in, is the predicted value of the water surface displacement at the current moment, F is the state transfer matrix, and is the unit matrix; is the water surface displacement estimated at the previous moment.

[0080] (2) Covariance prediction: Estimate the error covariance matrix P based on the updated state at the previous moment t|t Predict the state estimation error covariance matrix P at the current moment t+1|t The calculation method is to input the updated state estimation error covariance matrix P at the previous moment t|t , perform the following operations with the state transfer matrix F and the process noise covariance matrix Q to obtain the state estimation error covariance matrix P at the current moment t+1|t :

[0081]

[0082] Where Q is the process noise covariance matrix, which is obtained through theoretical modeling or empirical estimation.

[0083] Step 5: Calculate the weight of each radar based on its signal-to-noise ratio, bit error rate, and measurement error standard deviation.

[0084] The weight w of each sensor is calculated based on the signal-to-noise ratio (SNR), bit error rate (BER) and standard deviation of measurement error (σ) of each sensor. i :

[0085]

[0086] Step 6: Use the Kalman filter algorithm to update the water surface displacement and error covariance matrix using the measurement data of each radar. Specifically, for each radar sensor, update the state estimate and covariance.

[0087] Step 6 specifically includes the following sub-steps:

[0088] S6.1: Calculate the covariance matrix R of the millimeter wave radar separately 1 and Kalman gain

[0089]

[0090] Among them, σ 1 represents the standard deviation of the measurement error of the millimeter wave radar, P t+1|t is the state estimation error covariance matrix of the current moment calculated in step 4, w 1 is the weight of the millimeter-wave radar calculated in step 5;

[0091] S6.2: Calculate the water surface displacement estimated by the millimeter wave radar at the current moment And update the state estimation error covariance matrix of the millimeter wave radar

[0092]

[0093] in, is the predicted value of the water surface displacement at the current moment calculated in step 4, is the water surface displacement value measured by the millimeter-wave radar at the current moment; P t+1|t is the state estimation error covariance matrix of the current moment calculated in step 4;

[0094] S6.3: Calculate the covariance matrix R of the lidar separately 2 and Kalman gain

[0095]

[0096] Among them, σ 2 represents the standard deviation of the laser radar measurement error, is the state estimation error covariance matrix of the current moment obtained in the update step of the millimeter wave radar; w 2 is the weight of the lidar calculated in step 5.

[0097] S6.4: Calculate the water surface displacement estimated by the laser radar at the current moment And update the state estimation error covariance matrix of the lidar

[0098]

[0099] in, is the predicted value of the water surface displacement at the current moment estimated from the measurement value of the millimeter wave radar, is the water surface displacement value measured by the lidar at the current moment; The state estimation error covariance matrix of the current moment finally obtained by the millimeter wave radar update step;

[0100] S6.5: Calculate the covariance matrix R of the terahertz radar separately 3 and Kalman gain

[0101]

[0102] Among them, σ 3represents the standard deviation of the measurement error of the terahertz radar, is the state estimation error covariance matrix of the current moment obtained in the last step of the laser radar update, w 3 is the weight of the terahertz radar calculated in step 5;

[0103] S6.6: Calculate the water surface displacement estimated by the terahertz radar at the current moment And update the state estimation error covariance matrix of the terahertz radar

[0104]

[0105] in, is the predicted value of the water surface displacement at the current moment estimated from the laser radar measurement value, is the displacement of the water surface measured by the terahertz radar at the current moment; It is the state estimation error covariance matrix of the current moment finally obtained by the lidar update step.

[0106] Step 7: Integrate the update results of all radars to obtain the final water surface displacement and error covariance matrix.

[0107] The final fusion result is obtained through the update result of the third sensor, specifically:

[0108] Final state estimation: The final fused estimated displacement of the water surface The water surface displacement calculated in step 6 is the water surface displacement estimated by the terahertz radar at the current moment

[0109]

[0110] Final covariance matrix: The final covariance matrix P at the current moment t+1|t+1 The final updated state estimation error covariance matrix for the terahertz radar is

[0111]

[0112] Through the above method, the measurement data of millimeter-wave radar, lidar and terahertz radar are fused using an improved Kalman filter, which can effectively improve the measurement accuracy of water surface vibration displacement, thereby improving the accuracy and efficiency of cross-water-air medium communication.

[0113] Those skilled in the art can understand that the above are only preferred examples of the invention and are not intended to limit the invention. Although the invention is described in detail with reference to the above examples, those skilled in the art can still modify the technical solutions recorded in the above examples or replace some of the technical features therein with equivalents. Any modification, equivalent replacement, etc. made within the spirit and principle of the invention shall be included in the protection scope of the invention.

Claims

1. A cross-water and air medium communication method based on data fusion, characterized in that: The method is implemented by using a communication system consisting of a transmitting end and a receiving end; the transmitting end is located underwater and is composed of a plurality of high-power sound sources forming an underwater sound source array, all of which send sound wave signals, and the sound waves hit the water surface to generate micrometer-level vibrations; the receiving end is located above water and is a multi-sensor system composed of millimeter-wave radar, laser radar and terahertz radar and placed at equal intervals to form a receiving array; The cross-water-air medium communication method comprises the following steps: Step 1: The millimeter wave radar, the laser radar and the terahertz radar respectively obtain the measurement values ​​of the received signals; Step 2: Construct the state vector and observation model required for the Kalman filter algorithm; the state vector is the displacement in the received signal measurement value; the observation model is the observation equation that defines the relationship between the received signal measurement value in step 1 and the state vector: Z t (1) =H t (1) x t +υ t (1) z t (2) =H t (2) x t +υ t (2) z t (3) =H t (3) x t +υ t (3) Among them, H t (1) , H t (2) and H t (3) They are the observation matrix between the water surface displacement value measured by the millimeter wave radar and the actual water surface displacement, the observation matrix between the water surface displacement value measured by the lidar and the actual water surface displacement, and the observation matrix between the water surface displacement value measured by the terahertz radar and the actual water surface displacement;υ t (1) ,υ t (2) and t (3) are the measurement noise of millimeter wave radar, lidar and terahertz radar respectively; x t represents the state vector, which represents the water surface displacement at time t; Step 3: Initialize the water surface displacement and error covariance matrix; the error covariance matrix is ​​used to characterize the deviation between the estimated value and the actual value of the water surface displacement; Step 4: Predict the water surface displacement and covariance matrix at the next moment through the state transition model; Step 5: Calculate the weight of each radar according to its signal-to-noise ratio, bit error rate and standard deviation of measurement error; Step 6: Use the Kalman filter algorithm to update the water surface displacement and error covariance matrix using the measurement data of each radar; Step 7: Integrate the update results of all radars to obtain the final water surface displacement and error covariance matrix.

2. The method for cross-water-air medium communication based on data fusion according to claim 1 is characterized in that: In step 4, the water surface displacement and covariance matrix at the next moment are predicted by the state transition model, specifically: Based on the estimated water surface displacement at the previous moment, predict the water surface displacement at the current moment: in, is the predicted value of the water surface displacement at the current moment, F is the state transfer matrix, and is the unit matrix; is the water surface displacement estimated at the previous moment; The state estimation error covariance matrix of the current moment is predicted based on the updated state estimation error covariance matrix of the previous moment: Among them, P t+1|t is the state estimation error covariance matrix at the current moment, P t|t is the updated state estimation error covariance matrix of the previous moment, and Q is the process noise covariance matrix.

3. The cross-water-air medium communication method based on data fusion according to claim 2 is characterized in that: In step 5, the weight w of each radar sensor is calculated according to the signal-to-noise ratio, bit error rate and measurement error standard deviation of each radar sensor. i , the specific calculation formula is as follows: Among them, SNR i Represents the signal-to-noise ratio of the radar sensor, BER i represents the bit error rate, σ i Represents the standard deviation of measurement error.

4. The method for cross-water-air medium communication based on data fusion according to claim 3 is characterized in that: The step six specifically includes the following sub-steps: S6.1: Calculate the covariance matrix R1 and Kalman gain of the millimeter wave radar respectively Among them, σ1 represents the standard deviation of the measurement error of the millimeter wave radar, P t+1|t is the state estimation error covariance matrix of the current moment calculated by step 4, and w1 is the weight of the millimeter-wave radar calculated by step 5; S6.2: Calculate the water surface displacement estimated by the millimeter wave radar at the current moment And update the state estimation error covariance matrix of the millimeter wave radar in, is the predicted value of the water surface displacement at the current moment calculated in step 4, is the water surface displacement value measured by the millimeter-wave radar at the current moment; P t+1|t is the state estimation error covariance matrix of the current moment calculated in step 4; S6.3: Calculate the covariance matrix R2 and Kalman gain of the lidar respectively Among them, σ2 represents the standard deviation of the laser radar measurement error, It is the state estimation error covariance matrix of the current moment finally obtained in the update step of the millimeter wave radar; w2 is the weight of the laser radar calculated in step five. S6.4: Calculate the water surface displacement estimated by the laser radar at the current moment And update the state estimation error covariance matrix of the lidar in, is the predicted value of the water surface displacement at the current moment estimated from the measurement value of the millimeter wave radar, is the water surface displacement value measured by the lidar at the current moment; The state estimation error covariance matrix of the current moment finally obtained by the millimeter wave radar update step; S6.5: Calculate the covariance matrix R3 and Kalman gain of the terahertz radar respectively Where σ3 represents the standard deviation of the measurement error of the terahertz radar, is the state estimation error covariance matrix of the current moment obtained in the last step of the laser radar update, and w3 is the weight of the terahertz radar calculated in step 5; S6.6: Calculate the water surface displacement estimated by the terahertz radar at the current moment And update the state estimation error covariance matrix of the terahertz radar in, is the predicted value of the water surface displacement at the current moment estimated from the laser radar measurement value, is the displacement of the water surface measured by the terahertz radar at the current moment; It is the state estimation error covariance matrix of the current moment finally obtained by the lidar update step.

5. The method for cross-water-air medium communication based on data fusion according to claim 4 is characterized in that: The step 7 specifically includes: calculating the water surface displacement calculated in step 6 and estimating the water surface displacement measured by the terahertz radar at the current moment The final fused water surface displacement estimate The state estimation error covariance matrix of the terahertz radar is finally updated As the final covariance matrix P at the current moment t+1|t+1 .

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