Environment-adaptive underwater acoustic network topology optimization method

Abstract

The invention relates to an environment-adaptive underwater acoustic network topology optimization method, which comprises the following steps of: 1, acquiring marine environment parameters; 2, the computer module simulates an underwater acoustic communication channel according to the marine environment parameters; step 3, a signal detection threshold; step 4, calculating an error rate; and 5, estimating a local optimal solution of the topological structure. According to the method, the ray acoustic model and the marine environment parameters are utilized to simulate and predict the marine propagation performance and the underwater acoustic communication system, the node positions are selected in a local optimal solution mode so as to cope with the complex and changeable marine environment, and the effectiveness and reliability of underwater transmission can be improved while the underwater acoustic communication coverage range can be guaranteed.

Description

Technical Field:

[0001] The present invention belongs to the technical field of underwater acoustic engineering, and particularly relates to an environment-adaptive underwater acoustic network topology optimization method. Background Art:

[0002] With the development of ocean development and national defense construction, higher requirements are put forward for underwater information transmission in ocean environment exploration and ocean engineering technology. It is required that underwater communication nodes accurately and real-time transmit data to surface relay nodes and finally to the data processing center. When performing long-distance information transmission in the ocean, restricted by the propagation loss, absorption loss and scattering characteristics in seawater, electromagnetic waves and light waves cannot perform long-distance information transmission, and only sound waves can transmit long distances in the ocean environment. Therefore, sound waves play an important role in application scenarios such as ocean environment monitoring, target detection, underwater communication and countermeasure.

[0003] However, the ocean environment is complex and changeable. The sound speed in seawater will change with environmental factors such as temperature, salinity and depth, and will also be affected by environmental factors such as wind speed, surface waves and tides, resulting in serious time-varying multipath, high environmental noise, limited bandwidth, large transmission delay and large spatial fluctuations in the underwater acoustic communication channel, seriously affecting the reliability and effectiveness of the underwater acoustic communication channel.

[0004] In the traditional buoy placement method, in the shallow sea area, it is mostly grid-based layout. Each network node is arranged at the center of the grid or at the four corner nodes of the grid, and the depth position is also fixed. Since the change of the ocean sound speed field is not considered, it may lead to the interruption of the link communication, loss of transmission information, and the reduction of the placement distance due to the decrease of communication quality will lead to the reduction of the communication coverage area. Therefore, when placing underwater nodes, there is an urgent need for a topology design method that can ensure the link quality of underwater communication and optimize the communication coverage area. Summary of the Invention:

[0005] The technical problem to be solved by the present invention is to provide an environment-adaptive underwater acoustic network topology optimization method for the situation that the existing placement method fails to make full use of ocean environment parameters and cannot match the complex and changeable ocean environment. This method is based on the simulation of the underwater acoustic channel by the ocean environment, can improve the reliability of the communication link, and can ensure the communication coverage range through local optimal solutions.

[0006] The technical solution of the present invention is to provide an environment-adaptive underwater acoustic network topology optimization method, including the following steps,

[0007] Step 1, obtain ocean environment parameters;

[0008] Step 2: The computer module simulates the underwater acoustic communication channel according to the ocean environmental parameters; the simulation of the underwater acoustic communication channel is mainly based on the ray acoustics model and the ocean environmental parameters to simulate and predict the propagation loss.

[0009] Step 3: Signal detection threshold.

[0010] Step 4: Bit error rate calculation.

[0011] Step 5: Estimation of the local optimal solution of the topological structure.

[0012] In view of the situation that the existing deployment method fails to make full use of the ocean environmental parameters and cannot match the complex and changeable ocean environment, the present invention can improve the reliability of the communication link based on the simulation of the underwater acoustic channel by the ocean environment, and can ensure the communication coverage range through the local optimal solution. It mainly obtains the real-time parameters of the ocean environmental parameters, including temperature, salinity, pressure, ocean environmental noise, etc. through the environmental parameter measurement sensor; and through the computer processing module, for the obtained ocean environmental parameters, it simulates the propagation characteristics of underwater acoustic signals, simulates and predicts the performance of the underwater acoustic communication system, calculates the communication quality of the underwater acoustic link, iteratively optimizes and solves the local optimal solution of the connectivity performance, and finds the optimal distribution position of each node.

[0013] Preferably, in step 1, the environmental parameter measurement sensor is used to measure the ocean environmental parameters, and the data is transmitted back to the computer module for processing; the ocean environmental parameters include temperature, salinity, depth, Doppler flow velocity, ocean environmental noise level, and seabed environment.

[0014] Preferably, transmitting the data back to the computer module for processing means that the computer module calculates the sound speed profile curve, and the temperature and salinity measured at each depth are calculated by an empirical formula, specifically:

[0015] c = 1449.2 + 4.6T - 0.055T 2 + 0.00029T 3 +(1.34 - 0.01T)(S - 35)+0.016z

[0016] where c is the sound speed, T is the Celsius temperature of the ocean water body, S is the salinity of the ocean water body, and z is the sea water depth.

[0017] Preferably, step 2 is operated as follows: combining the sound speed profile curve and ray acoustics to estimate the propagation loss of the underwater acoustic channel, and obtaining the propagation loss TL(r, z) of the sound wave:

[0018]

[0019] And calculate the propagation loss map of the received signals at each position except the sound source point.

[0020] Preferably, the specific operation of step 3 is as follows. After the signal passes through underwater acoustics, the receiving point is regarded as a passive sonar, and the signal-to-noise ratio SNR of the received signal at each position is expressed by the following formula:

[0021] SNR = SL - TL - (NL - DI)

[0022] where SL is the source level of the sound source, NL is the ocean background noise level, DI is the transducer directivity, and the ocean background noise level is obtained by measuring the environmental parameters with sensors;

[0023] Assume the received signal x(i), which is expressed as

[0024]

[0025] where n(i) is the noise signal, s(i) is the communication signal, H0 represents no signal, and H1 represents the presence of a signal;

[0026] Calculate the test statistic D according to the number of signal points

[0027]

[0028] where t is the time of the received signal in this section;

[0029] The detection result H is

[0030]

[0031] where th is slightly greater than σ 2 , th is the decision threshold, and σ 2 is the power of the noise.

[0032] Preferably, the operation of step 4 is as follows. After the signal is detected, perform a decoding operation on the signal, compare the decoded symbol with the transmitted signal symbol, and obtain the bit error rate BER by dividing the number of incorrect symbols by the total number of symbols

[0033]

[0034] After obtaining the BER of all signal-to-noise ratios, replace the propagation loss diagram of each node with the bit error rate diagram.

[0035] Preferably, the operation of step 5 is as follows

[0036] First, divide the area according to the number and range of nodes to be arranged, select the initial position for arranging nodes according to the center of the area, and arrange the first node at the center of the area around the water surface convergence node;

[0037] According to the BER graph, when there is only one adjacent node near the position where the node needs to be arranged, while ensuring the distance between this node position and the previous node, find the depth and distance with the lowest BER.

[0038]

[0039] Among them, r i represents the horizontal distance from this optimal point to the previous node, and z i represents the layout depth of this node;

[0040] According to this optimal distance, with the previous node (x i , y i ) as the center of the circle and r i as the radius to make a circle, and through the distance from the center position (x j , y j ) of the surrounding blocks, select the minimum distance as the final layout position of this node, and the equation is:

[0041]

[0042] Find the local optimal layout position under the condition of satisfying the above formula.

[0043] Furthermore, when there are multiple adjacent nodes near the position where the node needs to be arranged, find the optimal distance from each adjacent node, and use each adjacent node as the center of the circle and the optimal distance as the radius. Through the multiple intersection points obtained by the intersection of multiple circles, find the intersection point closest to the center of the block as the layout position of this node, and find the appropriate position for the depth by comparing the BER; and compare the BER through a weighted method, and comprehensively use the depth with a smaller BER as the layout depth of this node, so as to find the optimal layout depth.

[0044] Compared with the prior art, the present invention has the following advantages:

[0045] Utilize the ray acoustic model and ocean environmental parameters to simulate and predict the ocean propagation performance and the underwater acoustic communication system. Select the node position through the local optimal solution method to cope with the complex and changeable ocean environment, which can ensure the underwater acoustic communication coverage range while improving the effectiveness and reliability of underwater transmission. Brief description of the drawings:

[0046] Figure 1 It is a flowchart of the underwater unmanned platform distribution method shown in the embodiment of the present invention.

[0047] Figure 2 It is the sound speed profile and propagation loss graph shown in the embodiment of the present invention.

[0048] Figure 3The acoustic ray and bit error rate graph shown in the embodiments of the present invention.

[0049] Figure 4 The schematic diagram of the local optimal solution shown in the embodiments of the present invention.

[0050] Figure 5 The comparison graph of the communication coverage ranges between the random points and the topologically optimized points shown in the embodiments of the present invention.

[0051] Figure 6 The comparison graph of the packet transmission rates and throughputs of two methods shown in the embodiments of the present invention. Specific implementation manner:

[0052] The present invention will be further described in detail in combination with the accompanying drawings for the specific implementation manner:

[0053] In view of the fact that the existing point layout method fails to make full use of ocean environmental parameters and cannot adapt to the complex and changeable ocean environment, the present invention designs an environment-adaptive underwater acoustic network topology optimization method, which uses the ray acoustic model and ocean environmental parameters to simulate and predict the ocean propagation performance and underwater acoustic communication system, and selects the node positions in the way of local optimal solution to cope with the changeable ocean environment.

[0054] The following will describe in detail the environment-adaptive underwater acoustic network topology optimization method with specific embodiments:

[0055] Figure 1 The working flow chart of the method is shown, and the steps of the method include S1 to S5.

[0056] Step S1: Obtain ocean environmental parameters.

[0057] In this step, it is necessary to obtain the temperature, salinity, depth, Doppler flow velocity, ocean environmental noise level, seabed environment, etc. in the ocean. The ocean environmental parameters are measured by environmental parameter measurement sensors, and the data is transmitted back to the computer module for processing.

[0058] The first step of the computer module is to calculate the sound speed profile curve, which is calculated by the empirical formula from the temperature and salinity measured at each depth. Specifically:

[0059] c = 1449.2 + 4.6T - 0.055T 2 + 0.00029T 3 +(1.34 - 0.01T)(S - 35)+0.016z

[0060] Where c is the sound speed, T is the Celsius temperature of the ocean water body, S is the salinity of the ocean water body, and z is the seawater depth.

[0061] Step S2: Underwater acoustic communication channel simulation.

[0062] The second step is to estimate the propagation loss of the underwater acoustic channel by combining the sound speed profile curve and ray acoustics.

[0063] In the sound field, the sound pressure at the receiving point is formed by the superposition of multiple beams and can be obtained by separately calculating and finally adding multiple beams.

[0064] Ray acoustics divides the sound field into the eikonal equation and the intensity equation:

[0065]

[0066] where L is the acoustic path, n(x, y, z) is the refractive index at the point (x, y, z), and A is the acoustic wave amplitude. In this example, it is simplified to a two-dimensional plane and can be represented by cylindrical coordinates (r, z). (ξ(s), ζ(s)) is the tangent of the ray, and s is the arc length of the ray, that is:

[0067]

[0068]

[0069] where The sound source position is set to (r0, z0), and the emission angle is α, so

[0070]

[0071] Assuming the initial time is 0, the ray propagation time can be calculated as follows:

[0072]

[0073] The sound pressure amplitude can be calculated from the beam width p(s) and the beam curvature q(s). The relationship between the beam width and the beam curvature is:

[0074]

[0075] where c m is the second derivative of the sound speed with respect to the normal vector of the ray path,

[0076]

[0077] where k(s) is the normal vector of the ray path.

[0078] Assume the initial conditions are as follows

[0079]

[0080] The sound pressure amplitude A(s) and the beam width p(s) can be solved as

[0081]

[0082] δα is the difference between adjacent ray angles and is defined as :

[0083]

[0084] Finally, the sound pressure on the ray acoustic line can be calculated as

[0085]

[0086] The sound pressure at the final receiving point is the sum of the sound pressures of multiple rays:

[0087] P(r,z) = ΣP i (s,k)

[0088] By comparing the sound pressure at the receiving point with the sound pressure of the sound source, the propagation loss TL(r, z) of the sound wave can be obtained:

[0089]

[0090] Through the above method, the propagation loss map of the received signals at each position except the sound source point can be calculated.

[0091] Figure 2 The sound speed profile and the propagation loss map are shown.

[0092] Step S3: Signal detection threshold.

[0093] In this embodiment, direct spread spectrum is used for underwater acoustic communication quality estimation, including direct spread signal detection threshold and bit error rate calculation under different propagation losses. Among them, in this step, after the signal passes through the underwater acoustic channel, the receiving point can be regarded as a passive sonar, and the signal-to-noise ratio SNR at each position can be expressed by the following formula:

[0094] SNR = SL - TL - (NL - DI)

[0095] where SL is the source level of the sound source, NL is the ocean background noise level, DI is the transducer directivity, and the ocean background noise level is obtained by measuring environmental parameters with sensors.

[0096] In this embodiment, the energy detection method is used for signal detection. When actually calculating, discrete signals are used. Assuming the received signal x(i), it is expressed as

[0097]

[0098] where n(i) is the noise signal, s(i) is the communication signal, H0 represents no signal, and H1 represents the existence of a signal.

[0099] Furthermore, the test statistic D is calculated according to the number of signal points

[0100]

[0101] Among them, t is the time of the received signal in this section.

[0102] When detecting the signal, a decision threshold th needs to be set, and based on this, it is judged whether there is a signal. Since D is calculated by using the average power method for the signal, the setting of the detection threshold is related to the power σ of the noise 2 and the detection result H is

[0103]

[0104] Among them, th is slightly greater than σ 2 .

[0105] Step S4: Calculate the bit error rate.

[0106] Furthermore, after the signal is detected, a decoding operation is performed on the signal, and the decoded symbol is compared with the transmitted signal symbol. The bit error rate BER is obtained by dividing the number of incorrect symbols by the total number of symbols

[0107]

[0108] After obtaining the BER of all signal-to-noise ratios through this method, the propagation loss diagram of each node is replaced with a bit error rate diagram.

[0109] Figure 3 The ray and the bit error rate diagram are shown. Among them, the blue dots indicate that the signal sent can be received well at this point, the green dots indicate that the effect of receiving the signal is poor, and the red dots indicate that the signal cannot be received.

[0110] Step S5: Estimate the local optimal solution of the topological structure.

[0111] In this step, since the bit error rate diagram and the set communication range are known, it is carried out by finding the local optimal solution.

[0112] When distributing the point positions, first divide the area according to the number and range of the nodes to be arranged, select the initial position for arranging the nodes according to the center of the area, and the first node is arranged at the center of the area around the water surface convergence node.

[0113] Furthermore, according to the made bit error rate diagram, when there is only one adjacent node near the position of the node to be arranged, while ensuring the distance between this node position and the previous node, find the depth and distance with the lowest bit error rate

[0114]

[0115] Among them, r iIndicates the horizontal distance from the optimal point to the previous node, z i Indicates the placement depth of this node.

[0116] Furthermore, based on this optimal distance, with the previous node (x i , y i ) as the center, a circle is drawn with radius r i . By comparing the distances to the central positions (x j , y j ) of the surrounding blocks, the minimum distance is selected as the final placement position of this node. The equation is:

[0117]

[0118] The local optimal placement position can be obtained when the above equation is satisfied.

[0119] When there are multiple adjacent nodes near the position of the node to be placed, similarly, find the optimal distance to each adjacent node, and with each adjacent node as the center and the optimal distance as the radius, among the multiple intersection points obtained by the intersection of multiple circles, find the intersection point closest to the center point of the block as the placement position of this node, and find a suitable position for the depth by comparing the bit error rates.

[0120] For example, if there are two adjacent nodes (x1, y1) and (x2, y2) near the position of the node to be placed, and the center position of the block is (x j , y j ), and the optimal distances and depths are (r1, z1) and (r2, z2) respectively. The intersection equations are:

[0121]

[0122] In this way, the locally optimal horizontal position of this node can be found. Furthermore, the optimal placement depth of this node needs to be found. By comparing the bit error rates in a weighted manner, the depth with a smaller comprehensive bit error rate is used as the placement depth of this node. The method is as follows:

[0123]

[0124] z = min(α, β)

[0125] Thus, the optimal placement depth can be found.

[0126] Figure 4 Shows the local optimal solution method diagram, where the green points represent adjacent nodes, the dashed lines represent the ranges with the optimal distance as the radius, the red point represents the proposed central node of the block, and the black points represent the node coordinates after topological optimization.

[0127] Figure 5The figure shows a comparison diagram of the communication coverage of random points and topologically optimized points. The left figure is the communication coverage diagram of randomly generated points, and the right figure is the communication coverage diagram after topological optimization. It can be seen that after topological optimization, a larger range can be stably covered.

[0128] Figure 6 The figure shows a comparison diagram of the packet transmission rate and throughput of two methods. The physical layer protocol, network layer protocol, etc. of both are the same in the figure. The left figure is the comparison diagram of the packet transmission rate with the sending time interval, and the right figure is the comparison diagram of the throughput with the sending time interval.

[0129] Furthermore, by using this local optimal method multiple times, the positions where each node needs to be arranged can be found to optimize the network topology structure, and finally a layout diagram of the entire underwater acoustic communication network is formed.

[0130] The above is only an illustration of the preferred embodiments of the present invention, but it should not be construed as a limitation of the claims. Any equivalent process transformation using the specification of the present invention is included in the patent protection scope of the present invention.

Claims

1. An environment-adaptive underwater acoustic network topology optimization method, characterized by: The following steps are included: Step 1: Obtain ocean environment parameters; Step 2: Simulate the underwater acoustic communication channel according to the ocean environment parameters; Step 3: signal detection threshold; Step 4: Calculate the bit error rate; Step 5: Estimate the local optimal solution of the topological structure.

2. The method for optimizing the topology of an environmentally adaptive underwater acoustic network according to claim 1, characterized in that: In step 1, the ocean environmental parameters are measured using environmental parameter measurement sensors, and the data are transmitted back to the computer module for processing; the ocean environmental parameters include temperature, salinity, depth, Doppler flow velocity, ocean environmental noise level, and seabed environment.

3. The method for optimizing the topology of an environmentally adaptive underwater acoustic network according to claim 2, characterized in that: The data is sent back to the computer module for processing. The computer module calculates the sound velocity profile curve using the temperature and salinity measured at each depth using an empirical formula, specifically: c=1449.2+4.6T-0.055T 2 +0.00029T 3 +(1.34-0.01T)(S-35)+0.016z Among them, c is the speed of sound, T is the ocean water temperature in Celsius, S is the salinity of the ocean water, and z is the depth of sea water.

4. The method for optimizing the topology of an environmentally adaptive underwater acoustic network according to claim 1, characterized in that: Step 2 is as follows: the sound velocity profile curve is combined with ray acoustics to estimate the propagation loss of the underwater acoustic channel and obtain the propagation loss TL(r, z) of the sound wave: And calculate the propagation loss diagram of the signal received at each location except the sound source point.

5. The method for optimizing the topology of an environmentally adaptive underwater acoustic network according to claim 1, characterized in that: The specific operation of step 3 is as follows: after the signal passes through the underwater sound, the receiving point is regarded as a passive sonar, and the signal-to-noise ratio (SNR) of the received signal at each position is expressed by the following formula: SNR=SL-TL-(NL-DI) Among them, SL is the sound source level of the sound source, NL is the ocean background noise level, DI is the directivity of the transducer, and the ocean background noise level is obtained by environmental parameter measurement sensor measurement; Assume that the received signal x(i) is expressed as Where n(i) is the noise signal, s(i) is the communication signal, H0 means no signal, and H1 means the presence of a signal; Calculate the test statistic D based on the number of signal points Among them, t is the time of receiving the signal in this section; The test result H is Among them, th is slightly larger than σ 2 , th is the threshold of decision, σ 2 is the power of the noise.

6. The method for optimizing the topology of an environmentally adaptive underwater acoustic network according to claim 1, characterized in that: Step 4 is as follows: after the signal is detected, the signal is decoded, the decoded codewords are compared with the transmitted signal codewords, and the bit error rate BER is obtained by dividing the number of erroneous codewords by the total number of codewords. After obtaining the BER of all signal-to-noise ratios, the propagation loss graph of each node is replaced by the bit error rate graph.

7. The method for optimizing the topology of an environmentally adaptive underwater acoustic network according to claim 1, characterized in that: Step 5 is as follows: First, divide the blocks according to the number and range of nodes to be deployed, and select the initial position of the node deployment according to the center of the block. The first node is deployed at the center of the block around the water surface convergence node. According to the bit error rate graph, when there is only one adjacent node near the node position to be arranged, find the depth and distance with the lowest bit error rate while ensuring the distance between the node position and the previous node. Among them, r i Indicates the horizontal distance between the optimal point and the previous node, z i Indicates the node's layout depth; According to this optimal distance, the previous node (x i ,y i ) is the center of the circle, r i Draw a circle with the radius of the surrounding blocks (x j ,y j ) and select the minimum distance as the final labeling position of the node. The equation is: Under the condition that the above formula is satisfied, find the local optimal point location.

8. The method for optimizing the environment-adaptive underwater acoustic network topology according to claim 7, characterized in that: When there are multiple adjacent nodes near the node to be arranged, find the optimal distance from each adjacent node, and take each adjacent node as the center of the circle and the optimal distance as the radius. Through the intersection of multiple circles, find the intersection closest to the center of the block as the arrangement position of the node. The depth is found by comparing the bit error rate; and the bit error rate is compared in a weighted manner. The depth with the smallest comprehensive bit error rate is used as the placement depth of the node, so as to find the optimal arrangement depth.

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