Side-scan sonar mosaic mapping method and system based on polynomial fitting

Abstract

The invention discloses a side-scan sonar mosaic mapping method and system based on polynomial fitting, and relates to the technical field of sonar imaging, and the method comprises the specific steps: obtaining original data, and carrying out the decoding and feature extraction of the original data; constructing a waterfall plot according to the time data and the echo intensity data; judging whether original track points in the original data are shared or not according to the positioning data, and if yes, performing interpolation processing on the original track points by adopting an interpolation method to obtain updated track point data; performing polynomial fitting on the attitude data to obtain an updated course value; calculating coordinates of each sampling point based on the updated track point data and the updated course value; and arranging the waterfall plot according to the coordinates of the sampling points to form the side-scan sonar mosaic plot. According to the method, a course fitting algorithm is added in side-scan sonar data processing, and the phenomena of gaps and cross overlapping caused by course jitter in the mosaic diagram manufacturing process are solved.

Description

Technical Field

[0001] The invention relates to the technical field of sonar imaging, and more particularly to a side-scan sonar mosaic mapping method and system based on polynomial fitting. Background Art

[0002] As a high-precision and high-efficiency underwater detection technology, side-scan sonar is widely used in the fields of seabed geomorphological feature research, seabed bottom identification, underwater archaeology, marine engineering, etc. Its working principle is to transmit a pulse sound signal to the seabed through a transducer. When the beam reaches the seabed, it is backscattered, and the system records the returned beam and its echo intensity data. These data are corrected, geometrically corrected, geocoded, and finally form a sonar image that can reflect the seabed geomorphological features.

[0003] During the side-scan sonar measurement process, each scan data (Ping) is imaged as a unit. However, when the heading jitter is large during the measurement process, these unit images are prone to fan-shaped superposition effects during the high-resolution mosaic (stitching) process. Specifically, adjacent scan strips may have gap discontinuities or cross-overlapping when stitching, which not only affects the visual effect of the image, but more importantly, it makes it impossible to accurately display the seabed image features, thereby affecting the analysis and interpretation of the seabed topography.

[0004] To overcome this problem, existing technologies mainly use methods based on spatial geometric transformation for image stitching, but this method has limited effect when dealing with complex seabed environments and large-scale heading jitter. Therefore, how to solve the fan-shaped superposition effect of side-scan sonar images during the mosaic process and improve the continuity and consistency of the images is an urgent problem that technicians in this field need to solve. Summary of the invention

[0005] In view of this, the present invention provides a side-scan sonar mosaic mapping method and system based on polynomial fitting, which overcomes the above-mentioned defects.

[0006] In order to achieve the above object, the present invention adopts the following technical solution:

[0007] A side scan sonar mosaicking method based on polynomial fitting, the specific steps are:

[0008] Acquire raw data, and decode and extract features from the raw data, where the extracted features include: sampling information, time data, positioning data, attitude data, and echo intensity data;

[0009] constructing a waterfall diagram based on the time data and the echo intensity data;

[0010] Determine whether the original track points in the original data are shared according to the positioning data, and if so, interpolate the original track points using an interpolation method to obtain updated track point data;

[0011] Performing polynomial fitting on the attitude data to obtain an updated heading value;

[0012] Calculate the coordinates of each sampling point based on the updated track point data and the updated heading value;

[0013] The waterfall graph is arranged according to the coordinates of each sampling point to form a side scan sonar mosaic.

[0014] Optionally, before constructing the waterfall diagram, the echo intensity data needs to be preprocessed.

[0015] Optionally, the step of acquiring the updated heading value is:

[0016] Reading the headings of each scan data in the raw data based on the posture data to form a plurality of one-dimensional arrays;

[0017] Adding serial numbers to the plurality of one-dimensional data to form a plurality of the two-dimensional arrays;

[0018] The updated heading value is obtained by utilizing a polynomial regression algorithm based on the plurality of two-dimensional arrays.

[0019] Optionally, the expression of the polynomial regression algorithm is:

[0020] f(x)=m0x n +m1x n-1 +m2x n-2 +m3x n-3 +...+m n ;

[0021] In the formula, x is the serial number; m n is the nth fitted coefficient; n is the largest exponent in the polynomial.

[0022] Optionally, the steps for acquiring the coordinates of the sampling points are:

[0023] Acquire the updated track point data and the updated heading value of each of the scan data;

[0024] Determine the navigation direction and the heading angle according to the updated heading value;

[0025] Calculate the maximum horizontal distance value of each of the scanning data according to the sampling information;

[0026] The coordinates of each sampling point are calculated according to the navigation direction, the heading angle, the sampling information and the maximum horizontal distance value.

[0027] Optionally, the calculation formula of the heading angle is:

[0028]

[0029] Where α is the heading angle; heading is the updated heading value.

[0030] Optionally, the maximum horizontal distance value is calculated as follows:

[0031]

[0032] In the formula, SR max is the maximum slope distance value; H is the height of the instrument from the bottom.

[0033] Optionally, geocoding is also included, with the following steps:

[0034] Calculate image size based on pixel resolution and geocoded rectangle;

[0035] The pixel coordinates of each sampling point in the geocoding rectangle are calculated according to the image size and the coordinates of each sampling point.

[0036] Optionally, the calculation formula for the pixel point coordinates is:

[0037] w i =(x i -x lt ) / res

[0038] h i =(y i -y rb ) / res;

[0039] In the formula, x i is the x coordinate of the sampling point; lt is the x coordinate of the upper left corner; y i is the y coordinate of the sampling point; rb is the y coordinate of the lower right corner; res is the pixel resolution.

[0040] A side scan sonar mosaic imaging system based on polynomial fitting, comprising:

[0041] A decoding module is used to obtain raw data, and decode and extract features from the raw data, wherein the extracted features include: sampling information, time data, positioning data, attitude data, and echo intensity data;

[0042] A waterfall diagram construction module, used for constructing a waterfall diagram according to the time data and the echo intensity data;

[0043] A track point updating module, used to determine whether the original track points in the original data are shared according to the positioning data, and if so, to interpolate the original track points using an interpolation method to obtain updated track point data;

[0044] A heading update module, used for performing polynomial fitting on the attitude data to obtain an updated heading value;

[0045] A sampling point coordinate calculation module, used to calculate the coordinates of each sampling point according to the updated track point data and the updated heading value;

[0046] The sonar mosaic generation module is used to arrange the waterfall diagram according to the coordinates of each sampling point to form a side scan sonar mosaic.

[0047] It can be seen from the above technical solutions that the present invention discloses a side scan sonar mosaic mapping method and system based on polynomial fitting, which has the following beneficial effects compared with the prior art:

[0048] (1) The present invention adds a heading fitting algorithm to the side scan sonar data processing, which solves the gap and cross-overlap phenomenon caused by heading jitter in the mosaic production process;

[0049] (2) The calculation method of the present invention is simple and easy to operate, and the mapping effect is significantly improved by correcting the posture data;

[0050] (3) The present invention has a good processing effect on side-scan sonar data in various formats. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] 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 embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0052] Figure 1 A schematic diagram of the method provided by the present invention;

[0053] Figure 2 A schematic diagram of track interpolation provided by the present invention;

[0054] Figure 3 A schematic diagram of polynomial heading fitting provided by the present invention;

[0055] Figure 4(a) Schematic diagram of the navigation angle α when 0° < heading ≤ 90° provided by the present invention; Figure 4(b) Schematic diagram of the navigation angle α when 90° < heading ≤ 180° provided by the present invention; Figure 4(c) Schematic diagram of the navigation angle α when 180° < heading ≤ 270° provided by the present invention; Figure 4(d) Schematic diagram of the navigation angle α when 270° < heading ≤ 360° provided by the present invention;

[0056] Figure 5 Is the side-scan sonar waterfall diagram provided by the present invention;

[0057] Figure 6 Is the mosaicked side-scan sonar image obtained without heading fitting provided by the present invention;

[0058] Figure 7 Is the mosaicked side-scan sonar image obtained after heading fitting provided by the present invention. Detailed implementation manners

[0059] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0060] This embodiment discloses a side-scan sonar mosaicking method based on polynomial fitting on the one hand, as Figure 1 shown, the specific steps are as follows:

[0061] Step 1: Obtain the original data, and decode and extract features from the original data. The extracted features include: sampling information, time data, positioning data, attitude data, and echo intensity data;

[0062] Step 2: Construct a waterfall diagram according to the time data and the echo intensity data;

[0063] Step 3: Determine whether the original track points in the original data are shared according to the positioning data. If so, use the interpolation method to interpolate the original track points to obtain updated track point data;

[0064] Step 4: Perform polynomial fitting on the attitude data to obtain an updated heading value;

[0065] Step 5: Calculate the coordinates of each sampling point based on the updated track point data and the updated heading value;

[0066] Step 6: Arrange the waterfall diagram according to the coordinates of each sampling point to form a mosaicked side-scan sonar image.

[0067] Furthermore, the original data in step 1 is an original side scan sonar file.

[0068] In one embodiment, before constructing the waterfall diagram, the echo intensity data needs to be preprocessed, and the preprocessing includes standard interval quantization, slant range correction, image gain, etc.

[0069] Furthermore, after reading the echo intensity data, the echo sound intensity data is first converted into grayscale information, and then the grayscale information is arranged in time series to form a vertically arranged side scan sonar grayscale image, namely a waterfall image.

[0070] Furthermore, the grayscale information data is generally quantized to 0-255 (8-bit). The current sonar hardware output is not necessarily within this quantization range, and 16-bit, 64-bit and other data may appear. Therefore, the data is re-quantized, and the expression is:

[0071]

[0072] For data with poor contrast, enhancement processing is performed, and its expression is:

[0073]

[0074] In the formula, newvalue is the quantized data; rawvalue is the original data, j is the bits recorded value of the original data, k is the re-quantized bits value, and C is the value that can be adjusted according to the waterfall chart display effect.

[0075] In one embodiment, the steps of updating the track point data are:

[0076] The original side scan sonar file records the position information of each scan line. When the sampling frequency of the scan line is greater than the recording frequency of the position information of the GPS receiver, multiple scan data (Ping) will share the same original track point. In order to ensure the accurate geographical location of the sonar image, the track needs to be interpolated. Specifically, interpolation is performed based on two scan data (Ping) with different original track points. Combined with the time interval and ship speed, the real track point of each scan data (Ping) is interpolated. The interpolation diagram is shown as follows: Figure 2 shown.

[0077] In one embodiment, the steps for obtaining the updated heading value are:

[0078] Step 41, reading the heading of each scan data in the original data based on the posture data to form multiple one-dimensional arrays;

[0079] Step 42: adding serial numbers to the multiple one-dimensional data to form multiple two-dimensional arrays;

[0080] Step 43: Obtain an updated heading value using a polynomial regression algorithm based on multiple two-dimensional arrays.

[0081] Further, the heading of each scan data (Ping) is read according to the attitude data exported from the original side scan sonar file to form a one-dimensional array (y);

[0082] Add integer numbers to the one-dimensional array according to the array sequence, with the initial number being 1 and the step length being 1, to form a two-dimensional array (x, y), where the maximum value of x is the number of Pings.

[0083] The two-dimensional array is fitted with the calculation formula through the polynomial regression algorithm, specifically:

[0084] f(x)=m0x n +m1x n-1 +m2x n-2 +m3x n-3 +...+m n (3);

[0085] In the formula, m0~m n is the coefficient after fitting; n is the largest exponent in the polynomial, which is a positive integer.

[0086] First, select the largest exponent n in the polynomial, and then find a set of m0~m n , so that the gap between the fitted value and the actual data is minimized.

[0087] Assume that the sum of the squares of the differences between the fitted value f(x) and the corresponding y is:

[0088] R 2 =(f(x1)-y1) 2 +(f(x2)-y2) 2 +...+(f(x n )-y n ) 2 (4);

[0089] That is to find a set of m0~m n , so that R 2 Minimum, determine the fitting formula.

[0090] Substitute the x value in the two-dimensional array into the fitting formula, calculate the fitted f(x) value, and form a new two-dimensional array;

[0091] Extract the f(x) value in the new two-dimensional array, replace the original heading value with the newly obtained heading value, and complete the heading fitting, such as Figure 3 shown.

[0092] In one embodiment, the steps for obtaining the coordinates of the sampling points are:

[0093] Step 51: Obtain multiple updated track point data and multiple updated heading values;

[0094] Step 52: Determine the sailing direction and heading angle according to the updated heading value;

[0095] Step 53: Calculate the maximum horizontal distance value of each scan data according to the sampling information;

[0096] Step 54: Calculate the coordinates of each sampling point according to the sailing direction, heading angle, sampling information and maximum horizontal distance value.

[0097] Furthermore, the position of the track point and the heading angle of each scan line are obtained, and the coordinates of each sampling point are calculated to realize the geographical homing of the sampling point. The specific process is as follows:

[0098] Step 51 is specifically: Read the updated track point data (i.e., track point coordinate value) P0(x0, y0) and the updated heading value heading of each scan line;

[0099] Step 52 is specifically: Judge the sailing direction and calculate the heading angle α by the magnitude of the updated heading value heading; among them, the calculation formula of the heading angle α is:

[0100]

[0101] Step 53 is specifically: Obtain the maximum slant range value SR from the decoded data of the original side-scan sonar file max , the total number of unilateral samplings N; According to the maximum slant range value SR max and the instrument height above the bottom H, calculate and obtain the maximum horizontal distance value GR of each scan data (Ping) max , the calculation formula is:

[0102]

[0103] Step 54 is specifically: The 4 sailing directions judged by the updated heading value heading, and the sailing angle α of each sailing direction is as Figure 4(a)-Figure 4(d) shown. When collecting side-scan sonar data, it advances along the track line, which is divided into the starboard side and the port side, and the sampling point counting is carried out separately to the starboard side and the port side with the track line as the starting point. The coordinates P i (x i , y i ) of the i-th sampling point are calculated as:

[0104] (1) When the sailing direction is northeastward and 0° < heading ≤ 90°, the formula is as follows:

[0105] Port side:

[0106] Starboard:

[0107] (2) When the course is southeast and 90° < heading ≤ 180°, the formula is as follows:

[0108] Port:

[0109] Starboard:

[0110] (3) When the course is southwest and 180° < heading ≤ 270°, the formula is as follows:

[0111] Port:

[0112] Starboard:

[0113] (4) When the course is northwest and 270° < heading ≤ 360°, the formula is as follows:

[0114] Port:

[0115] Starboard:

[0116] In one embodiment, it further includes geocoding, and the specific steps are as follows:

[0117] Calculate the image size based on the pixel resolution and the geocoding rectangle;

[0118] Calculate the pixel coordinates of each sampling point in the geocoding rectangle according to the image size and the coordinates of each sampling point.

[0119] Furthermore, geocoding is actually a pixel resampling process based on the geospatial, mainly including determining the pixel resolution, the geocoding rectangle, calculating the image size, and performing image point coordinate transformation, etc., to produce a sidescan sonar image.

[0120] The image size is the width and height of the image, represented by (w, h). Let the geocoding rectangle be (x rb , y rb , x lt , y lt ), x rb is the x coordinate of the lower right corner, y rb is the y coordinate of the lower right corner, x lt is the x coordinate of the upper left corner, y lt is the y coordinate of the upper left corner. If the pixel resolution is res, then the width and height of the geocoding rectangle are:

[0121]

[0122] After calculating the image size, according to the sampling point coordinates P i (x i ,y i ) can calculate the pixel coordinates of the sampling point in the geocoding rectangle (w i ,h i ), the calculation formula is:

[0123]

[0124] Step 6 is specifically as follows: arranging the grayscale information of each sampling point according to the sampling point coordinates to form a side scan sonar mosaic.

[0125] This embodiment implements the method disclosed in the above embodiment with specific data, specifically:

[0126] This embodiment uses the collected side scan sonar data, the original data format is JSF, the measurement time is December 2013, the data collection instrument is EdgeTech, and the spatial range is located in the Bohai Sea area.

[0127] Step 1. Decode the original data: Decode the original side scan sonar JSF format file. The side scan sonar data stored in JSF format is composed of multiple ping information, stored in binary, and stored in little endian. Each Ping is a separate data packet, which contains a message header, a data header, and data information. By decoding the side scan sonar data in JSF format, the sampling information, time data, positioning data, attitude data, sampling value, weight factor, etc. of each Ping are obtained. The JSF data echo intensity data needs to be calculated based on the sampling value (samplevalue) and the weight factor (F). The calculation formula is as follows:

[0128] BS = sample value × 2 -F (17);

[0129] After analysis, the data of this embodiment totals 862 pings, and the ping numbers are 10415-11276.

[0130] Step 2, waterfall chart display: The echo intensity obtained by the previous step is converted into grayscale information. By decoding the original data, it can be known that the accuracy of the side scan sonar data in this embodiment is 16-bit. The grayscale information is quantized according to formula (1). The processed value is between 0 and 255. The waterfall chart is as follows: Figure 5 In this embodiment, j is 16 and k is 8.

[0131] Step 3, track interpolation: According to the original data decoding results, the track point coordinates are output, a total of 862 Ping data. From Table 1, it can be seen that different Pings share coordinate data. According to two different coordinate values, the cubic spline interpolation method is used to interpolate the coordinate value of the middle Ping to ensure that each Ping has a unique coordinate value after calculation. Table 1 is the original track point coordinates. It can be seen from the table that it is common for multiple Pings to share a coordinate value. Table 2 is the track point after cubic spline interpolation, and each Ping has a unique coordinate value.

[0132] Table 1 Example of original track point coordinates (first 80 points)

[0133]

[0134]

[0135] Table 2 Example of track point coordinates after interpolation (first 80 points)

[0136]

[0137]

[0138] Step 4, heading fitting: According to the decoding result of the original data, the heading data is output, and the heading is numbered starting from 1 to form a two-dimensional array, with a total of 862 pairs of numbers. Table 3 shows the first 60 points of the original heading.

[0139] Table 3 Example of raw heading data (first 60 points)

[0140] x heading x heading x heading 1 323.6 21 321.69 41 319.39 2 323.1 22 321.69 42 317.69 3 323.39 23 321.69 43 317.69 4 324.19 24 321.39 44 317.19 5 324.19 25 322.3 45 318.69 6 323.8 26 321.5 46 320.39 7 323.19 27 320 47 320.1 8 322.19 28 320.5 48 315.89 9 320.89 29 320.89 49 314.1 10 319.6 30 321.69 50 314.1 11 320 31 343 51 312.6 12 320.69 32 343.5 52 313.1 13 323.5 33 318.5 53 315.3 14 324.5 34 316.39 54 316.89 15 324.6 35 315.6 55 316.8 16 324.6 36 316.5 56 315.1 17 323.3 37 317.5 57 313.39 18 320.69 38 318.39 58 312.1 19 320.8 39 318.39 59 311.8 20 321 40 319.69 60 312.1

[0141] Perform polynomial fitting based on 862 number pairs, with n selected as 3, and obtain the following fitting formula.

[0142] f(x)=-1.78441×10 -7 x 3 +0.000341061x 2 -0.166167x+323.084 (18);

[0143] The R of this formula 2 =0.637973.

[0144] Substitute the serial number x in the original two-dimensional array, calculate and obtain the fitted heading value f(x), and replace the original heading data.

[0145] Step 5, Sampling Point Coordinate Calculation: According to Steps 3 and 4, the coordinate values and heading values for each ping can be obtained. Among them, the coordinate values need to be projected to convert longitude and latitude into plane rectangular coordinates. The coordinate unit is in metric system. In this embodiment, the projection parameters are UTM projection, WGS84 ellipsoid, and Zone 50N. After statistics, the minimum value of the heading of this data is 298.98°, the maximum value is 322.92°, 270° < heading ≤ 360°, and the heading is northwest. The calculation formula for the α angle is as follows:

[0146] α = 360 - heading (19);

[0147] And based on Formula (13) and Formula (14), the coordinates of each sampling point are calculated.

[0148] Step 6, Geocoding: Pixel resampling and geocoding are performed on each sampling point. Let the geocoding rectangle be (x rb , y rb , x lt , y lt ), where x rb is the x coordinate of the lower right corner, y rb is the y coordinate of the lower right corner, x lt is the x coordinate of the upper left corner, y lt is the y coordinate of the upper left corner, and the pixel resolution is res;

[0149] After calculating the image size, according to the sampling point coordinates P i (x i , y i ) obtained from the previous step, the pixel point coordinates (w i , h i ) of the sampling point in the geocoding rectangle can be calculated according to Formula (16).

[0150] Step 7, Mosaic Map Generation: Arrange the gray-scale information of each sampling point obtained by the calculation in Step 2 according to the sampling point coordinates obtained by the calculation in Step 6 to form a sidescan sonar mosaic map, as shown in Figure 6 and Figure 7 shown. Figure 6 is the sidescan sonar image mosaic map obtained without heading fitting; Figure 7 is the sidescan sonar image mosaic map obtained after heading fitting.

[0151] On the other hand, this embodiment discloses a sidescan sonar mosaicking mapping system based on polynomial fitting, which loads the method disclosed in the above embodiment. The specific structure includes:

[0152] The decoding module is used to obtain the original data, decode the original data and extract features. The extracted features include: sampling information, time data, positioning data, attitude data and echo intensity data;

[0153] A waterfall chart building module, used for building a waterfall chart according to time data and echo intensity data;

[0154] The track point update module is used to determine whether the original track points in the original data are shared according to the positioning data. If so, the original track points are interpolated by using the interpolation method to obtain updated track point data;

[0155] A heading update module is used to perform polynomial fitting on the attitude data to obtain an updated heading value;

[0156] A sampling point coordinate calculation module is used to calculate the coordinates of each sampling point according to the updated track point data and the updated heading value;

[0157] The sonar mosaic generation module is used to arrange the waterfall chart according to the coordinates of each sampling point to form a side scan sonar mosaic.

[0158] Through example comparison, it can be seen that the quality of the side-scan sonar mosaic image after polynomial track fitting processing is significantly improved, and it can reflect the seabed topography characteristics more accurately and clearly.

[0159] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0160] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A side scan sonar mosaicking method based on polynomial fitting, characterized in that: The specific steps are: Acquire raw data, and decode and extract features from the raw data, where the extracted features include: sampling information, time data, positioning data, attitude data, and echo intensity data; constructing a waterfall diagram based on the time data and the echo intensity data; Determine whether the original track points in the original data are shared according to the positioning data, and if so, interpolate the original track points using an interpolation method to obtain updated track point data; Performing polynomial fitting on the attitude data to obtain an updated heading value; Calculate the coordinates of each sampling point based on the updated track point data and the updated heading value; The waterfall graph is arranged according to the coordinates of each sampling point to form a side scan sonar mosaic.

2. The side scan sonar mosaicking method based on polynomial fitting according to claim 1, characterized in that: Before constructing the waterfall diagram, the echo intensity data needs to be preprocessed.

3. The side scan sonar mosaicking method based on polynomial fitting according to claim 1, characterized in that: The steps for obtaining the updated heading value are: Reading the headings of each scan data in the raw data based on the posture data to form a plurality of one-dimensional arrays; Adding serial numbers to the plurality of one-dimensional data to form a plurality of two-dimensional arrays; The updated heading value is obtained by utilizing a polynomial regression algorithm based on the plurality of two-dimensional arrays.

4. The side scan sonar mosaicking method based on polynomial fitting according to claim 3 is characterized in that: The expression of the polynomial regression algorithm is: f(x)=m0x n +m1x n-1 +m2x n-2 +m3x n-3 +...+m n ; In the formula, m n is the nth fitted coefficient; n is the largest exponent in the polynomial; x is the sequence number.

5. The side scan sonar mosaicking method based on polynomial fitting according to claim 3 is characterized in that: The steps for obtaining the coordinates of the sampling points are: Acquire a plurality of the updated track point data and a plurality of the updated heading values; Determine the navigation direction and the heading angle according to the updated heading value; Calculate the maximum horizontal distance value of each of the scanning data according to the sampling information; The coordinates of each sampling point are calculated according to the navigation direction, the heading angle, the sampling information and the maximum horizontal distance value.

6. The side scan sonar mosaicking method based on polynomial fitting according to claim 5, characterized in that: The calculation formula of the heading angle is: Where α is the heading angle; heading is the updated heading value.

7. The side scan sonar mosaicking method based on polynomial fitting according to claim 5, characterized in that: The calculation formula of the maximum horizontal distance value is: In the formula, SR max is the maximum slope distance value; H is the height of the instrument from the bottom.

8. The side scan sonar mosaicking method based on polynomial fitting according to claim 1, characterized in that: It also includes geocoding, the specific steps are: Calculate image size based on pixel resolution and geocoded rectangle; The pixel coordinates of each sampling point in the geocoding rectangle are calculated according to the image size and the coordinates of each sampling point.

9. The side scan sonar mosaicking method based on polynomial fitting according to claim 8, characterized in that: The calculation formula of the pixel point coordinates is: In the formula, x i is the x coordinate of the sampling point; lt is the x coordinate of the upper left corner; y i is the y coordinate of the sampling point; rb is the y coordinate of the lower right corner; res is the pixel resolution.

10. A side-scan sonar mosaicking system based on polynomial fitting, characterized in that: include: A decoding module is used to obtain raw data, and decode and extract features from the raw data, wherein the extracted features include: sampling information, time data, positioning data, attitude data, and echo intensity data; A waterfall diagram construction module, used for constructing a waterfall diagram according to the time data and the echo intensity data; A track point updating module, used to determine whether the original track points in the original data are shared according to the positioning data, and if so, to interpolate the original track points using an interpolation method to obtain updated track point data; A heading update module, used for performing polynomial fitting on the attitude data to obtain an updated heading value; A sampling point coordinate calculation module, used to calculate the coordinates of each sampling point according to the updated track point data and the updated heading value; The sonar mosaic generation module is used to arrange the waterfall diagram according to the coordinates of each sampling point to form a side scan sonar mosaic.

Images