Sea ice early warning method and system based on sonar and radar data fusion

Abstract

The present invention discloses a sea ice early warning method and system based on the fusion of sonar and radar data. Through the combination of sonar and radar data, above the water surface, the radar can effectively observe the sea ice exposed above the sea surface, estimate the width, height and distance of the sea ice exposed from the sea surface from the ship, and the sonar equipment with adjustable sound wave emission angle can well detect the width, thickness and distance of the underwater sea ice from the ship. The fusion of sonar and radar data can achieve the detection of the overall situation of the sea ice; the early warning mechanism established according to the safety of ship navigation and the mode of displaying sonar and radar on the same screen help the ship driver to conduct auxiliary observation of the dangerous degree of sea ice ahead, reduce the influence of human factors caused by poor observation and lack of judgment experience of personnel, and enhance the objectivity, accuracy and completeness of the sea ice observation results.

Description

Technical Field

[0001] The present invention relates to the technical fields of data fusion, Arctic ship navigation routes and danger warning, and in particular to a sea ice warning method and system based on sonar and radar data fusion. Background Art

[0002] Maritime transport currently plays a vital role in the global transportation system. Addressing the various challenges and risks inherent in maritime transport is crucial for improving its efficiency and safety. Ships' navigation is affected by the sea area and season. For example, in areas related to the Arctic route and in parts of the Bohai and Yellow Seas during winter, significant sea ice can threaten navigation safety. The presence of sea ice can impact ship construction materials and workmanship, operator handling, and route selection. With Chinese shipping companies developing the Northeast Passage, choosing the Arctic route offers significant savings in time, fuel consumption, and piracy compared to the Malacca and Suez routes. However, the complex distribution of sea ice in the Arctic poses significant safety risks to ships. Being able to predict the presence and location of sea ice in advance would not only protect crew and cargo but also generate significant economic benefits.

[0003] Against this backdrop, the question of how to address sea ice safety issues on Arctic shipping routes has arisen. This primary issue can be broken down into two secondary aspects: obtaining information on sea ice distribution along Arctic shipping routes and identifying and issuing early warnings for sea ice risks.

[0004] Currently, the primary method for obtaining information about sea ice distribution on Arctic shipping routes is through satellite remote sensing data, which captures sea ice conditions over large areas. This method is suitable for route planning, but it is not suitable for complex sea ice conditions during actual navigation. Furthermore, the temporal and spatial resolution of current remote sensing satellites does not meet navigation requirements. Using radar to scan sea ice near ships is incomplete. In the Arctic, radar is also affected by interference from the geomagnetic field, resulting in inaccurate results and only capturing information about sea ice above the water surface.

[0005] As for early warning, in actual shipboard operations, the on-duty pilot often manually reads, identifies, and sounds the alarm based on forward route information acquired by radar and other related equipment. This process is affected by the pilot's personal judgment and driving skills, increasing navigation risks. Specifically, existing technologies lack accurate and effective means of acquiring sea ice distribution information, and early warnings rely too heavily on the pilot's personal experience. Therefore, a new observation and early warning method is needed to address these shortcomings of existing technologies. Summary of the Invention

[0006] The purpose of the present invention is to provide a sea ice warning method and system based on the fusion of sonar and radar data, which is used to solve the problems in the existing technology of lacking accurate and effective means of obtaining sea ice distribution information and over-reliance on the driver's personal experience when making warnings.

[0007] To solve the above technical problems, the present invention provides a first solution: a sea ice early warning method based on sonar and radar data fusion, comprising the following steps:

[0008] S1, collects sonar detection time data, sonar detection space data, radar detection time data and radar detection space data.

[0009] S2, temporally align the sonar detection time data with the radar detection time data and fuse them to obtain a temporally aligned dataset, and spatially align the sonar detection spatial data with the radar detection spatial data and fuse them to obtain a spatially aligned dataset.

[0010] S3, using the Kalman filter method to denoise the temporal registration dataset and the spatial registration dataset to obtain a temporal correction dataset and a spatial correction dataset, respectively.

[0011] S4, obtaining sea ice distance data and sea ice thickness data based on the time correction dataset and the space correction dataset.

[0012] S5, comparing the sea ice distance data and the sea ice volume data with the preset braking distance threshold and the preset icebreaking thickness threshold respectively, and establishing an early warning level. According to the early warning level, an instruction for the ship to break ice or adjust its driving is obtained.

[0013] Specifically, in step S1, the sonar detection time data is the time when the sound wave signal is sent and received between the ship and the sea ice, the sonar detection spatial data is the width of the sea ice below the sea surface, the radar detection time data is the time when the electromagnetic wave signal is sent and received between the ship and the sea ice, and the radar detection spatial data is the direction of the sea ice above the sea surface.

[0014] Specifically, in step S2, the specific steps of time-aligning and fusing the sonar detection time data with the radar detection time data to obtain a time-aligned data set are as follows: using the Lagrange interpolation method to transform the sonar detection time data to obtain sonar transformation time data, and the time in the sonar transformation time data is synchronized with the time in the radar detection time data; after the sonar transformation time data is fused with the radar detection time data, a time-aligned data set is formed.

[0015] Specifically, in step S2, the specific steps of spatially registering and fusing the sonar detection spatial data with the radar detection spatial data to obtain a spatially registered data set are as follows: the sonar detection spatial data is transformed using the UT transformation method to obtain sonar transformed spatial data, and the sonar transformed spatial data and the radar detection spatial data are located in the same coordinate system; after the sonar transformed spatial data and the radar detection spatial data are fused, a spatially registered data set is formed.

[0016] Specifically, in step S3, the specific steps of using the Kalman filter method to denoise the time registration dataset and the spatial registration dataset are as follows: use the Kalman filter method to predict and correct the data in the time registration dataset and the spatial registration dataset one by one, and predict the filter value corresponding to the k moment from the filter value corresponding to the k-1 moment, and then correct the filter value predicted at the k moment based on the observation value obtained at the k moment, remove the noise in the time registration dataset and the spatial registration dataset, and obtain the time correction dataset and the spatial correction dataset.

[0017] Specifically, in step S4, the sea ice distance data is calculated from the propagation time interval in the time-corrected dataset and the propagation speed in the sea area; the underwater thickness and surface thickness of the sea ice are calculated from the spatially corrected dataset and recorded as the sea ice thickness.

[0018] Preferably, in step S5, the preset braking distance threshold satisfies the following expression:

[0019]

[0020] in, 、 are the total mass of the ship and the mass of the additional water respectively; To spread the speed; ; ; is the ship wind load; It is the longitudinal windward area above the water surface of the hull; is the wind speed; is the wind pressure unevenness reduction coefficient; is the wind pressure height change correction coefficient; is the water flow resistance coefficient; is the sea ice density; is the ship width; the preset icebreaking thickness threshold satisfies the following expression:

[0021]

[0022] in, Indicates the maximum ice thickness of the ship. It is expressed as the ship's draft, It is expressed as the height of the sonar installation position from the bottom plate of the ship.

[0023] Preferably, in step S5, the warning level is specifically as follows: when the sea ice distance data is less than 150% of the preset braking distance threshold, it is in a level one alarm state; when the sea ice distance data is greater than or equal to 150% of the preset braking distance threshold and less than 200% of the preset braking distance threshold, it is in a level two alarm state; when the sonar first receives the sound wave return signal of the target sea ice, it is in a level three alarm state.

[0024] Specifically, in step S5, the specific steps for obtaining instructions for ship icebreaking or navigation adjustment according to the warning level are as follows: when the ship is in a level 2 alarm state or a level 3 alarm state, the underwater thickness in the sea ice thickness data is compared with a preset icebreaking thickness threshold. When the underwater thickness in the sea ice thickness data is greater than or equal to the preset icebreaking thickness threshold, the ship is instructed to adjust its navigation; when the underwater thickness in the sea ice thickness data is less than the preset icebreaking thickness threshold, the ship is instructed to navigate and break ice.

[0025] In order to solve the above technical problems, the second solution provided by the present invention is: a sea ice warning system based on sonar and radar data fusion, the sea ice warning system based on sonar and radar data fusion is used to execute the sea ice warning method based on sonar and radar data fusion in the above-mentioned first solution, specifically including: sonar, radar, storage unit, preprocessing unit, target recognition unit, warning judgment unit and display unit; wherein, the sonar is used to collect sonar detection time data and sonar detection space data, the radar is used to collect radar detection time data and radar detection space data, and the data collected by the sonar and radar are stored in The storage unit is used for performing time registration, space registration and denoising based on the data in the storage unit, and transmitting the obtained time correction data set and space correction data set to the target recognition unit; the target recognition unit obtains sea ice distance data and sea ice thickness data based on the time correction data set and the space correction data set, and transmits them to the early warning judgment unit; the early warning judgment unit is used to compare the sea ice distance data and the sea ice volume data with the preset braking distance threshold and the preset icebreaking thickness threshold respectively, obtain the instruction of the ship breaking ice or adjusting the driving according to the early warning level, and send the instruction to the display unit.

[0026] The beneficial effects of the present invention are as follows: different from the existing technology, the present invention provides a sea ice early warning method and system based on the fusion of sonar and radar data. Through the combination of sonar and radar data, above the water surface, the radar can effectively observe the sea ice exposed above the sea surface, estimate the width, height and distance of the sea ice exposed above the sea surface from the ship, and the sonar equipment with adjustable sound wave emission angle can well detect the width, thickness and distance of the underwater sea ice from the ship. The fusion of sonar and radar data can achieve the detection of the overall situation of the sea ice; the early warning mechanism established according to the safety of ship navigation and the method of displaying sonar and radar on the same screen can help ship drivers to assist in observing the degree of sea ice danger ahead, reduce the influence of human factors caused by poor lookout and lack of judgment experience of personnel, and enhance the objectivity, accuracy and completeness of sea ice observation results; at the same time, it improves the ship's perception of the surrounding environment, and provides a more complete safety guarantee for ships sailing in complex sea conditions and icy waters. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 This is a flow chart of an embodiment of a sea ice early warning method based on sonar and radar data fusion in the present invention;

[0028] Figure 2 It is a structural diagram of an embodiment of a sea ice early warning system based on sonar and radar data fusion in the present invention. DETAILED DESCRIPTION

[0029] The following will be combined with the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0030] See also Figure 1 , the first solution proposed in this application, a sea ice early warning method based on sonar and radar data fusion, includes the following steps:

[0031] S1 collects sonar detection time data, sonar detection space data, radar detection time data, and radar detection space data. In this step, sonar and radar are used to obtain detection information. On the one hand, the sonar device emits acoustic pulses to "illuminate" the underwater sea area ahead. Upon detecting a target, an echo is reflected back to the receiver. Sound waves propagate in seawater at a speed of approximately 1500 meters per second. By collecting the time interval between the ship and the target, the distance between the two is calculated, and the width of the target object can also be obtained. On the other hand, radar is used to observe surrounding obstacles. Radar detects targets by emitting electromagnetic waves. Given a known electromagnetic wave propagation speed, the round-trip time of the electromagnetic pulses can be collected to calculate the distance between the two objects. The horizontal width of the radar beam determines the radar's azimuth resolution capability, which can be used to obtain target azimuth information.

[0032] In this embodiment, the sonar detection time data is the time when the sound wave signal is sent and received between the ship and the sea ice, the sonar detection spatial data is the width of the sea ice below the sea surface, the radar detection time data is the time when the electromagnetic wave signal is sent and received between the ship and the sea ice, and the radar detection spatial data is the direction of the sea ice above the sea surface. Through the mutual cooperation of sonar and radar, a more comprehensive perception of sea ice-related information on the route ahead can be achieved.

[0033] In step S2, the sonar detection time data and the radar detection time data are temporally aligned and fused to obtain a temporally registered dataset. The sonar detection spatial data and the radar detection spatial data are spatially aligned and fused to obtain a spatially registered dataset. In this step, because information is acquired using two different detection devices, the acquired information differs in both time and space. Therefore, the sonar and radar data must be fused. The data fusion process primarily involves temporal and spatial registration.

[0034] S21, time alignment.

[0035] In practical applications, differences in sensor sampling start times, sampling frequencies, and transmission delays can lead to time asynchrony between multi-sensor data, which in turn degrades subsequent data fusion performance. Therefore, prior to data fusion, the asynchronous data from multiple sensors must be time-aligned. Time alignment involves aligning the asynchronous observation data of the same target from each sensor to the same time reference. This paper employs Lagrangian interpolation to time-align radar and sonar. After fusing the sonar transformation time data with the radar detection time data, a time-aligned dataset is formed. Specifically, the Lagrangian interpolation algorithm is as follows:

[0036] In this embodiment, the sonar sampling time is aligned with the radar sampling time, and the sonar sampling period is recorded as T. Assume 、 、 The sonar measurement data at this moment is:

[0037] (1)

[0038] (2)

[0039] (3)

[0040] Since the target does not move much distance within the sampling interval, 、 、 Assuming that they are equally spaced, that is, To calculate the interpolation points The measured value at the moment, assuming that the interpolation point time is ,and , then the Lagrange three-point interpolation method is used to calculate Measurement value at time for

[0041] (4)

[0042] in,

[0043] (5)

[0044] (6)

[0045] (7)

[0046] Similarly, a similar method can be used to align the radar sampling time with the sonar sampling time. The specific steps are similar to those mentioned above and will not be repeated here.

[0047] S22, spatial registration.

[0048] When performing multi-sensor data fusion, the data measured by each sensor is based on its own coordinates. Sonar uses its own coordinate system as a reference to determine the target's azimuth, while radar uses its own coordinate system as a reference to determine the corresponding rectangular coordinates. To effectively fuse these data, the data measured by these different sensors must be converted to a common coordinate system, known as spatial registration. The accuracy of this spatial registration significantly impacts the target fusion accuracy and operational efficiency of the fusion system.

[0049] In this embodiment, UT transformation is adopted. UT transformation has the following characteristics: it does not require the specific details of the nonlinear function and can be treated as a data processing module, which is highly versatile; the processed data can achieve an accuracy of more than 2nd order moments; the computational complexity is low and does not require a lot of time and hardware overhead. The general method is: the measured data is used to generate a Sigma point set, and then these points are used to generate another set of Sigma point sets through a nonlinear function. Finally, the transformed point set is weighted and integrated to obtain the transformed mean and variance. The system adopts a symmetrical sampling method, assuming that the measured quantity The mean and autocovariance of and , we can get the Sigma point set as follows:

[0050] (8)

[0051] in Represents the Sigma point set after UT transformation, , the corresponding weighted value is

[0052] (9)

[0053] in After obtaining the corresponding weights, we can calculate the mean and variance of the data we want to obtain.

[0054] (10)

[0055] (11)

[0056] After calculation, the data is unified into the radar rectangular coordinate system. Targets are then fused, and identical targets are merged within the tolerance range. The sonar-transformed spatial data is fused with the radar-detected spatial data to form a spatially registered dataset. Similarly, a similar method can be used to register the radar spatial data to the sonar coordinate system. The specific steps are similar to those described above and are not detailed here.

[0057] S3: Denoise the temporal registration dataset and the spatial registration dataset using the Kalman filter method to obtain a temporally corrected dataset and a spatially corrected dataset, respectively. In this step, the Kalman filter method is used to predict and correct the data in the temporal registration dataset and the spatially corrected dataset one by one. The filter value corresponding to time k is predicted from the filter value corresponding to time k-1. The filter value predicted at time k is then corrected based on the observed value obtained at time k, thereby removing noise from the temporal registration dataset and the spatially corrected dataset to obtain the temporally corrected dataset and the spatially corrected dataset.

[0058] Due to the sonar and radar equipment and registration algorithms, noise and registration errors may occur. This paper uses Kalman filtering to eliminate these errors and improve data fusion accuracy. The Kalman filtering process is an iterative process. Each filter value is obtained by first predicting the target state based on the previous filter result to obtain a priori information. The predicted value is then corrected by the radar observation value to obtain the target's posterior information. This maximizes the use of known information to predict the target state and obtain the optimal estimate.

[0059] In this implementation, the main work of the Kalman filter can be divided into two processes: prediction and correction. The prediction process mainly predicts the current target state based on the target state at the previous moment by constructing a state transition matrix. The correction process mainly compares the predicted value with the actual observation value and makes adjustments to reduce data noise. The basic process of the Kalman filter is as follows:

[0060] (12)

[0061] (13)

[0062] (14)

[0063] (15)

[0064] (16)

[0065] in, is the prior information at time k; is the posterior information; is the observed value of the target at time k under the current system; A is an (N, N) matrix; B is an (N, 1) matrix; Q and R are defined as the covariance matrices of the state transition noise and observation noise in the prediction process; ; .

[0066] The above is the basic process of Kalman filtering. It can be seen that there are no complex calculation problems in the entire process of Kalman filtering, and it has excellent noise processing capabilities. Although there are certain restrictions on the conditions of use, high efficiency and excellent noise processing capabilities are important advantages that enable Kalman filtering to be widely used once it appears.

[0067] S4. Sea ice distance and thickness data are obtained based on the time-corrected and space-corrected datasets. In this step, the sea ice distance data is inferred from the propagation time interval and propagation velocity in the sea area in the time-corrected dataset. The underwater and surface thicknesses of the sea ice are inferred from the space-corrected dataset and recorded as sea ice thickness.

[0068] Specifically, based on the coordination of data information fused by sonar and radar, a comprehensive evaluation of the sea ice conditions above and below the water can be made. The sea ice above the sea surface can be scanned by radar to obtain relevant data information, and the sea ice below the sea surface can be scanned by sonar. Since the sonar emits sound wave pulses as a plane beam, its installation location should be determined by considering the draft of the ship, the structural strength of the ship and the maximum thickness of sea ice that can be broken autonomously. Indicates the maximum ice thickness of the ship. It is expressed as the height of the sonar installation from the water surface. It is expressed as the ship's draft, It is expressed as the height from the installation position of the sonar to the bottom plate of the ship. It is expressed as the distance between the ship and the sea ice detected by the sonar. Taking into account that there will be sea ice on the surface, and the volume of the iceberg above and below the water is approximately 1 / 8 and 7 / 8 of its total volume respectively, the underwater part is much larger than the surface part. As time goes by, the sea ice gradually melts, especially the sea ice on the water surface. Its shape also changes into large areas of ice blocks as the sea ice melts, and its height gradually decreases, resulting in a smaller radar detection distance. The sea ice further melts and decomposes to form large pieces of drifting ice. Therefore, in this embodiment, the sonar installation position is selected to be 60% of the thickness of the sea ice that can be broken autonomously by the ship. It can be seen that the maximum icebreaking thickness of the ship, that is, the preset icebreaking thickness threshold satisfies the following relationship:

[0069] (17)

[0070] The preset braking distance threshold satisfies the following expression:

[0071] (18)

[0072] in, 、 are the total mass of the ship and the mass of the additional water respectively; To spread the speed; ; ; is the ship wind load; It is the longitudinal windward area above the water surface of the hull; is the wind speed; is the wind pressure unevenness reduction coefficient; is the wind pressure height change correction coefficient; is the water flow resistance coefficient; is the sea ice density; is the ship's width.

[0073] Specifically, the sea ice distance data indicates the distance between the ship and the sea ice detected by sonar. , whose expression is: =The time interval from sonar emission to reception of sound wave pulse x the propagation speed of sound wave pulse in the sea water in the area.

[0074] Echoes from sonar and radar scans reveal the underwater and surface thickness of sea ice, allowing for an assessment of its volume. By extracting in-situ sea ice data, detailed information about the site, including ice size distribution, can be analyzed. This information, combined with meteorological data, can be used to make predictions, effectively mitigating potential risks.

[0075] S5: Compare the sea ice distance data and sea ice volume data with the preset braking distance threshold and preset icebreaking thickness threshold, respectively, and establish a warning level. Based on the warning level, instructions are issued for icebreaking or vessel navigation adjustments. The warning levels established in this step are: Level 1 when the sea ice distance data is less than 150% of the preset braking distance threshold; Level 2 when the sea ice distance data is greater than or equal to 150% of the preset braking distance threshold and less than 200% of the preset braking distance threshold; and Level 3 when the sonar receives the initial acoustic return signal from the target sea ice.

[0076] The specific steps for obtaining instructions for ship icebreaking or navigation adjustment according to the warning level are as follows: when the ship is in the second-level alarm state or the third-level alarm state, the underwater thickness in the sea ice thickness data is compared with the preset icebreaking thickness threshold. When the underwater thickness in the sea ice thickness data is greater than or equal to the preset icebreaking thickness threshold, the ship is instructed to adjust its navigation, that is, the ship's navigation direction and status need to be adjusted at this time; when the underwater thickness in the sea ice thickness data is less than the preset icebreaking thickness threshold, the ship is instructed to break ice, that is, the ship can rely on itself to break ice at this time without adjusting its navigation direction and status; after the ship adjusts its navigation direction and status, the aforementioned steps S1 to S5 can still be executed, thereby realizing real-time monitoring of the ship's navigation status and the distribution status of the surrounding sea ice.

[0077] More specifically, the above instructions can be transmitted to the display unit via the warning and judgment unit, allowing the crew to assess the vessel's navigational status. During navigation, when both the sonar and radar echoes are absent, indicating safe navigation with no ice obstacles ahead, the screen displays a green light. When the radar echoes but the sonar echoes are absent, sea ice is present, but the thickness of the underwater ice has not reached the limit of the vessel's inherent icebreaking capabilities. The pilot should maintain a vigilant lookout and observe the ice's thickness and clearness ahead. A yellow light will be displayed to warn the pilot. When both the sonar and radar echoes are present, indicating significant ice clearing and ice thickness exceeding the vessel's maximum autonomous icebreaking capability, the screen displays a red light and an alarm sounds to warn the pilot of the ice threat ahead. This warning takes into account the comprehensive safe braking distance for the vessel under varying speeds, drafts, cargo loads, and wind speeds. In addition to the vessel's own influence, the distribution of sea ice in different ice zones also affects the safe distance between the following vessel and the icebreaker. The more severe the ice conditions, the smaller the safe distance between the vessels. In mild ice conditions, the braking performance of a ship in ice and ice-free waters is roughly the same. However, in severe ice conditions, the sea ice has a greater impact on navigation, and the braking performance in ice and ice-free waters differs significantly.

[0078] When a ship issues a sea ice danger alert, it automatically adjusts the sonar's acoustic wave transmission angle and scans downward for relevant sea ice data, obtaining comprehensive sea ice information. During the downward scanning process, the thickness of the sea ice underwater can be calculated based on the transmission angle and the time interval between the sonar transmission and the reception of the acoustic pulse. The calculation of underwater sea ice thickness can be expressed as:

[0079] (19)

[0080] (20)

[0081] in, It is expressed as the thickness of sea ice scanned downward by the sonar; It is expressed as the distance from the sonar to the bottom of the sea ice; Expressed as the sonar downward deflection angle; Expressed as the thickness of the underwater sea ice. By scanning the sea ice downward, sea ice-related information is obtained, providing more safety information to ship officers on duty, improving navigation safety and security.

[0082] See also Figure 2The second solution proposed in this invention provides a sea ice warning system based on sonar and radar data fusion. This system is used to implement the sea ice warning method based on sonar and radar data fusion described in the first solution. The system specifically includes a sonar unit, a radar unit, a storage unit, a preprocessing unit, a target recognition unit, a warning judgment unit, and a display unit. Each component is described in detail below.

[0083] Specifically, the sonar is used to collect sonar detection time data and sonar detection space data, and the radar is used to collect radar detection time data and radar detection space data. The data collected by the sonar and radar are stored in a storage unit. The preprocessing unit performs time alignment, space alignment, and denoising based on the data in the storage unit, and transmits the obtained time-corrected data set and space-corrected data set to the target recognition unit. The target recognition unit obtains sea ice distance data and sea ice thickness data based on the time-corrected data set and space-corrected data set, and transmits them to the early warning judgment unit. The early warning judgment unit is used to compare the sea ice distance data and sea ice volume data with the preset braking distance threshold and the preset icebreaking thickness threshold, respectively, and obtain instructions for the ship to break ice or adjust its driving according to the warning level, and send the instructions to the display unit.

[0084] The display unit is a crucial component of sonar and radar data fusion. It displays real-time intensity and range images generated by sonar and radar detection of sea ice targets. The display plays a crucial role in image analysis, improving the performance of image display and image processing. It is also a crucial tool for analyzing and understanding the performance of sonar and radar data fusion. By analyzing the resulting data, we can intuitively understand the performance of the ship's sonar and radar, guiding safe navigation in re-icing areas.

[0085] In this embodiment, the radar display is centrally positioned, with sonar information superimposed as a background display. The display also displays the current warning signal and danger level information. A manual adjustment knob for adjusting the sonar transmission angle and an area displaying underwater sea ice thickness are also included. In other embodiments, the specific data display method on the display unit can be adaptively adjusted based on actual needs, which is not listed here.

[0086] Different from the existing technology, the present invention provides a sea ice early warning method and system based on the fusion of sonar and radar data. Through the combination of sonar and radar data, above the water surface, the radar can effectively observe the sea ice exposed above the sea surface and estimate the width, height and distance of the sea ice exposed from the sea surface from the ship. The sonar equipment with adjustable sound wave emission angle can well detect the width, thickness and distance of the underwater sea ice from the ship. The fusion of sonar and radar data can achieve the detection of the overall situation of the sea ice; the early warning mechanism established according to the safety of ship navigation and the sonar and radar display on the same screen can help the ship driver to assist in observing the degree of sea ice danger ahead, reduce the influence of human factors caused by poor lookout and lack of judgment experience of personnel, and enhance the objectivity, accuracy and completeness of the sea ice observation results; at the same time, it improves the ship's perception of the surrounding environment, and provides a more complete safety guarantee for ships sailing in complex sea conditions and icy waters.

[0087] The above-described embodiments merely illustrate the implementation methods of the present invention. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent. It should be noted that a person skilled in the art would be able to make various modifications and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the patent for this invention shall be determined by the appended claims.

Claims

1. A sea ice early warning method based on sonar and radar data fusion, characterized in that: The steps include: S1, collects sonar detection time data, sonar detection space data, radar detection time data and radar detection space data; S2, using a Lagrange interpolation method to temporally register the sonar detection time data with the radar detection time data, and fuse them to obtain a temporally registered data set; using a UT transformation method to spatially register the sonar detection spatial data with the radar detection spatial data, and fuse them to obtain a spatially registered data set; S3, performing denoising processing on the temporal registration dataset and the spatial registration dataset using a Kalman filter method to obtain a temporal correction dataset and a spatial correction dataset, respectively; S4, obtaining sea ice distance data and sea ice thickness data based on the time-corrected dataset and the space-corrected dataset; S5, comparing the sea ice distance data and the sea ice volume data with a preset braking distance threshold and a preset icebreaking thickness threshold, respectively, and establishing an early warning level, and obtaining instructions for icebreaking or navigation adjustment of the ship according to the early warning level.

2. The sea ice early warning method based on sonar and radar data fusion according to claim 1 is characterized in that: In step S1, the sonar detection time data is the time when the sound wave signal is sent and received between the ship and the sea ice, the sonar detection spatial data is the width of the sea ice below the sea surface, the radar detection time data is the time when the electromagnetic wave signal is sent and received between the ship and the sea ice, and the radar detection spatial data is the direction of the sea ice above the sea surface.

3. The sea ice early warning method based on sonar and radar data fusion according to claim 2 is characterized in that: In step S2, the specific steps of using the Lagrange interpolation method to time-align the sonar detection time data with the radar detection time data and fuse them to obtain a time-aligned data set are as follows: The sonar detection time data is transformed using a Lagrange interpolation method to obtain sonar transformation time data, wherein the sonar transformation time data is synchronized with the time in the radar detection time data; The sonar conversion time data and the radar detection time data are fused to form the time registration data set.

4. The sea ice early warning method based on sonar and radar data fusion according to claim 2 is characterized in that: In the step S2, the specific steps of using the UT transformation method to spatially register the sonar detection spatial data and the radar detection spatial data and fuse them to obtain a spatial registration data set are as follows: The sonar detection spatial data is transformed using a UT transformation method to obtain sonar transformed spatial data, wherein the sonar transformed spatial data and the radar detection spatial data are located in the same coordinate system; The sonar transformation spatial data and the radar detection spatial data are fused to form the spatial registration data set.

5. The sea ice early warning method based on sonar and radar data fusion according to claim 2 is characterized in that: In step S3, the specific steps of using the Kalman filter method to perform denoising on the temporal registration dataset and the spatial registration dataset are as follows: The Kalman filter method is used to predict and correct the data in the temporal registration dataset and the spatial registration dataset one by one, and the filter value corresponding to the k moment is predicted by the filter value corresponding to the k-1 moment. The filter value predicted at the k moment is then corrected based on the observation value obtained at the k moment to remove the noise in the temporal registration dataset and the spatial registration dataset, and obtain the temporal correction dataset and the spatial correction dataset.

6. The sea ice early warning method based on sonar and radar data fusion according to claim 2 is characterized in that: In step S4, the sea ice distance data is calculated based on the propagation time interval in the time-corrected dataset and the propagation speed in the sea area; The underwater thickness and the surface thickness of the sea ice are calculated from the spatially corrected data set and are recorded as the sea ice thickness.

7. The sea ice early warning method based on sonar and radar data fusion according to claim 2 is characterized in that: In the step S5, the preset braking distance threshold satisfies the following expression: in 、 are the total mass of the ship and the mass of the additional water respectively; To spread the speed; ; ; is the ship wind load; It is the longitudinal windward area above the water surface of the hull; is the wind speed; is the wind pressure unevenness reduction coefficient; is the wind pressure height change correction coefficient; is the water flow resistance coefficient; is the sea ice density; is the ship's width; The preset ice breaking thickness threshold satisfies the following expression: in, Indicates the maximum ice thickness of the ship. It is expressed as the ship's draft, It is expressed as the height of the sonar installation position from the bottom plate of the ship.

8. The sea ice early warning method based on sonar and radar data fusion according to claim 2 is characterized in that: In the step S5, the warning level is specifically: When the sea ice distance data is less than 150% of the preset braking distance threshold, it is in a level one alarm state; When the sea ice distance data is greater than or equal to 150% of the preset braking distance threshold and less than 200% of the preset braking distance threshold, the vehicle is in a level 2 alarm state; When the sonar first receives the acoustic return signal from the target sea ice, it is in level 3 alert status.

9. The sea ice early warning method based on sonar and radar data fusion according to claim 8 is characterized in that: In step S5, the specific steps for obtaining instructions for icebreaking or navigation adjustment of the ship according to the warning level are as follows: When the ship is in the second-level alarm state or the third-level alarm state, the underwater thickness in the sea ice thickness data is compared with the preset icebreaking thickness threshold, and when the underwater thickness in the sea ice thickness data is greater than or equal to the preset icebreaking thickness threshold, an instruction to adjust the ship's travel is executed; When the underwater thickness in the sea ice thickness data is less than the preset icebreaking thickness threshold, an instruction to travel and break ice is executed on the ship.

10. A sea ice early warning system based on sonar and radar data fusion, characterized in that: The sea ice warning system based on sonar and radar data fusion is used to execute the sea ice warning method based on sonar and radar data fusion according to any one of claims 1 to 9, and specifically comprises: a sonar, a radar, a storage unit, a preprocessing unit, a target recognition unit, a warning judgment unit, and a display unit; The sonar is used to collect sonar detection time data and sonar detection space data, and the radar is used to collect radar detection time data and radar detection space data, and the data collected by the sonar and radar are stored in the storage unit; The preprocessing unit performs time registration, space registration and noise removal based on the data in the storage unit, and transmits the obtained time correction data set and space correction data set to the target recognition unit; The target recognition unit obtains sea ice distance data and sea ice thickness data based on the time correction data set and the space correction data set, and transmits the data to the early warning judgment unit; The early warning judgment unit is used to compare the sea ice distance data and the sea ice volume data with a preset braking distance threshold and a preset icebreaking thickness threshold respectively, obtain instructions for ship icebreaking or driving adjustment according to the early warning level, and send the instructions to the display unit.

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