A method of and a device for obtaining a temporal offset for synchronizing remote sensing data and motion data

Abstract

A method of obtaining a temporal offset for synchronizing radial velocity data measured by a remote sensing device mounted on a floating platform and motion data of the floating platform is disclosed. The method is performed by a processor and comprises the steps of: deriving an initial benchmark temporal offset for a first time interval, based on motion- compensated wind data for the first time interval; and iteratively calculating a further benchmark temporal offset for a second time interval following the first time interval, with reference to variances calculated based on motion-compensated wind data respectively for the initial benchmark temporal offset, a previous temporal offset one step before the initial benchmark temporal offset, and a subsequent temporal offset one step after the initial benchmark temporal offset. Unlocking insights from Geo-Data, the present invention further relates to improvements in sustainability and environmental developments: together we create a safe and liveable world.

Description

A METHOD OF AND A DEVICE FOR OBTAINING A TEMPORAL OFFSET FOR SYNCHRONIZING REMOTE SENSING DATA AND MOTION DATAFIELD OF THE INVENTION

[0001] The present disclosure generally relates to the field of remote sensing, and more specifically, to a method of and a device for obtaining a temporal offset for synchronizing radial velocity data measured by a remote sensing device mounted on a floating platform and motion data of the floating platform. Unlocking insights from Geo-Data, the present invention further relates to improvements in sustainability and environmental developments: together we create a safe and liveable world.BACKGROUND OF THE INVENTION

[0002] Measuring wind speeds and directions is needed for various purposes, including scientific research, environmental monitoring, and practical applications in various industries. As an example, in the renewable energy sector, accurately measuring wind characteristics helps to determine the potential energy output and feasibility of harnessing wind power.

[0003] For their measurements of wind velocities and directions, fixed ground-based remote sensing devices determine radial velocities of naturally occurring scatterers in the atmosphere along different lines of sight by means of the Doppler effect. At least three of these radial velocities are then used to reconstruct one three-dimensional wind vector. In the reconstruction process, the beam geometry defined by e.g., the azimuth angle and the zenith angle, is considered.

[0004] If no bottom-fixed infrastructure is available at an offshore location, wind site assessment is usually performed by means of remote sensing devices mounted on floating platforms. Under the influence of motion of the floating platform, the remote sensing device, such as a floating lidar system, no longer measures the radial velocities of the scatterers in the atmosphere but the relative radial velocities between the device and the scatterers. Furthermore, the beam geometry is changing under the influence of platform motion and the measurement elevations are no longer identical with the measurement elevations of a fixed lidar.

[0005] Buoy motion influences the wind data from remote sensing devices in a convoluted way. As a result, estimates of turbulence intensity, TI, are increased. Therefore, without motion compensation, estimates of TI from the remoting sensing device, such as floating lidar systems,FLS, are biased by motion and cannot be used for design and yield calculations of offshore wind turbines.

[0006] The existence of the motion-induced error in the calculated TI is studied following different approaches. Relative influence of different motional degrees of freedom on the error has been worked out by a few basic studies. For motion compensation, different approaches have been presented: data filtering, lookup tables, studies based on computer simulations and numerical modelling.

[0007] Prior art that attempts to filter measured wind statistics for the effect of motion has the problem that not each TI estimate will be corrected but only the mean of many values measured in similar conditions might be corrected. If significant environmental parameters are missed in the filtering approach, the results will not be universally valid but only under tested conditions. Filter parameters might need continuous calibration against reference data.

[0008] Attempts that filter measured unaveraged wind vectors suffer from the possibility to overcompensate the data and require unaveraged wind vectors with a high sampling rate that is usually not available from continuous-wave profiling wind lidars and other remote sensing devices.

[0009] If motion compensation is not applied to all six degrees of freedom of motion, the resulting motion compensation will be incomplete. The same accounts for motion compensation without accurate temporal synchronization between line-of-sight and motion data. Simple compensation based on reconstructed wind vectors and motion data alone is not successful because the temporal scanning pattern of the remote sensing device is not considered.

[0010] In view of the above, there is a need for a method for removing the effect of motion from unaveraged wind data that works independent of its sampling rate or prevailing environmental conditions in a cost efficient way.BRIEF SUMMARY OF THE INVENTION

[0011] In one aspect of the invention there is provided a method of obtaining a temporal offset for synchronizing radial velocity data measured by a remote sensing device mounted on a floating platform and motion data of the floating platform. The method is performed by a processor and comprises the steps of:

[0012] - deriving an initial benchmark temporal offset for a first time interval, based on motion-compensated wind data for the first time interval;

[0013] - iteratively calculating a further benchmark temporal offset for a second time interval following the first time interval, with reference to variances calculated based on motion-compensated wind data respectively for the initial benchmark temporal offset, a previous temporal offset one step before the initial benchmark temporal offset, and a subsequent temporal offset one step after the initial benchmark temporal offset.

[0014] Accurate calculation or determination of parameters such as turbulence intensity, TI, from radial velocity data, measured by a non-fixed or floating remote sensing device requires synchronization between the timing of the radial velocity data and that of the motion data of the floating remote sensing device. Determining a correct offset between the timings of the radial velocity data and the motion data can be time consuming, especially when many offsets have to be investigated to find the one that produces an optimal temporal synchronization.

[0015] The method of the present disclosure is based on the insight that computational resources required for calculating many different offsets between timing of the radial velocity data and the timing of the motion data can be significantly reduced when offsets for subsequent time intervals are derived based on an accurately determined initial temporal offset for a first time interval.

[0016] Based on the insight of the inventor, it is advantageous to not calculate many different offsets for every data interval but only for the first time interval. Offsets for subsequent time intervals can then be found by calculating the variance associated with the previous offset and one value above and below the previous offset. If the motion-compensated wind variance associated with the previous offset is the lowest of the three values, it is the correct value also for this interval. If not, another offset next to the new minimum must be calculated until the new minimum does not lie at the edge of calculated values anymore. In this way, the processing can follow the temporal drift between the wind and motion sensors in a computationally efficient way.

[0017] Based on the above insight of the inventor, the method of the present disclosure starts by calculating an initial benchmark temporal offset for a first time interval. This initial benchmark temporal allows optimal synchronization between the radial velocity data and the motion data in the first time interval.

[0018] For a second time interval following the first time interval, the method tries to find the correct offset by examining the initial benchmark temporal offset and offsets very close tothis initial benchmark temporal offset. The method thereby determines a further offset, which may be the same as the initial benchmark temporal offset, that provides the optimal synchronization between the radial velocity data and the motion data in the second time interval. This involves an iterative procedure that examines a couple of offset values surrounding the initial benchmark temporal offset.

[0019] The iterative procedure can determine the optimal offset for synchronizing the radial velocity data and the motion data in an effective way, without performing extensive computations for many different offsets for each time interval. It is simple to implement and produces accurate results with less computational resources and time.

[0020] In an example of the present disclosure, the step of deriving comprises the steps of:

[0021] - calculating a plurality of wind speed variances based on motion compensated wind data for a plurality of temporal offsets between timings of the motion data and the radial velocity data;

[0022] - determining a temporal offset associated with the smallest wind speed variance; and

[0023] - using the determined temporal offset as the initial benchmark temporal offset for the first time interval.

[0024] The above describes an exemplary way of determining the initial benchmark temporal offset for the first time interval. It is essentially a procedure of checking different temporal offsets between the timings of the motion data and the wind data (or more generally the lidar data) and trying to find the one providing optimal synchronization. The criterion used here is the offset producing the smallest wind speed variance. Those skilled in the art will understand that other criteria may also be used, as long as the correct temporal offset can be determined. The procedure will work with all criteria that are influenced by motion. These can be horizontal or vertical wind speed variance or standard deviation as well as statistics of wind direction fluctuations or a combination of any of these.

[0025] In an example of the present disclosure, the step of iterative calculation comprises the steps of:

[0026] - calculating variances based on motion-compensated wind data of the second time interval, respectively for the initial benchmark temporal offset, a previous temporal offset one step before the initial benchmark temporal offset, and a subsequent temporal offset one step after the initial benchmark temporal offset;

[0027] determining that the lowest variance is associated with the initial benchmark temporal offset;

[0028] - using the initial benchmark temporal offset as the initial benchmark temporal offset for a next time interval.

[0029] In this example, it is decided based on calculation that the initial benchmark temporal offset that provides optimal synchronization for the first time interval is still valid for the second time interval. Therefore, the initial benchmark temporal offset will be kept as it is, which will be the starting point for finding an initial benchmark temporal offset for a time interval following the second time interval.

[0030] In another example of the present disclosure, the step of iterative calculation comprises the steps of:

[0031] - taking the initial benchmark temporal offset as a current reference temporal offset for the second time interval;

[0032] - calculating variances based on motion-compensated wind data of the second time interval, respectively for the current reference temporal offset, a previous temporal offset one step before the current reference temporal offset, and a subsequent temporal offset one step after the current reference temporal offset;

[0033] - determining the lowest variance is associated with the previous temporal offset one step before the current reference temporal offset;

[0034] - taking the previous temporal offset one step before the current reference temporal offset as a current reference temporal offset;

[0035] - computing a variance based on motion-compensated wind data of the second time interval, for a temporal offset one step before the current reference temporal offset;

[0036] - repeating the determining, taking and computing steps until the lowest variance is associated with the current reference temporal offset; and

[0037] using the current reference temporal offset as the initial benchmark temporal offset for the second time interval.

[0038] In still another example of the present disclosure, the step of iterative calculation comprises the steps of:

[0039] - taking the initial benchmark temporal offset as a current reference temporal offset for the second time interval;

[0040] - calculating variances based on motion-compensated wind data of the second time interval, respectively for the current reference temporal offset, a previous temporal offset one step before the current reference temporal offset, and a subsequent temporal offset one step after the current reference temporal offset;

[0041] - determining the lowest variance is associated with the subsequent temporal offset one step after the current reference temporal offset;

[0042] - taking the subsequent temporal offset one step after the current reference temporal offset as a current reference temporal offset;

[0043] - computing a variance based on motion-compensated wind data of the second time interval, for a temporal offset one step after the current reference temporal offset;

[0044] - repeating the determining, taking and computing steps until the lowest variance is associated with the current reference temporal offset; and

[0045] using the current reference temporal offset as the initial benchmark temporal offset for the second time interval.

[0046] In the above two examples, it is found that the initial benchmark temporal offset which allows optimal synchronization for the first time interval is no longer valid for the second time interval. In this case, the temporal offset associated with the lowest variance will be used as a reference offset. The method then calculates one more variance for a further temporal offset which is one step away from the reference offset. This further temporal offset is immediate previous or subsequent to the reference offset, depending on whether the reference offset which now gives the smallest variance instead of the initial benchmark reference lies in time before or after the initial benchmark reference.

[0047] The method then again checks which one of the reference offsets, the one previous to the reference offset and the one subsequent to the reference offset gives the lowest variance. If the reference offset, which is the mid-offset, gives the lowest variance, it shows the correct temporal offset is found. Otherwise, the determining, taking and computing steps are repeated until the mid-offset gives the lowest variance.

[0048] This procedure is slightly more complicated than in the previous example, but it still requires significantly less computation resources to quickly find the correct temporal offset for the second time interval.

[0049] The procedure carries on as described above to find correct temporal offsets for any time duration and follow the temporal drift between the wind and motion sensors in a computationally efficient way. Except for the first time interval, offset for all subsequent time intervals are determined efficiently in terms of both computational resources and time.

[0050] In an example of the present disclosure, obtaining the motion-compensated wind data comprises the steps of:

[0051] - obtaining radial velocities of each line-of-sight measurement using the remoting sensing device;

[0052] - obtaining, from a motion reference device connected to the remote sensing device, motion of the remote sensing device in six degrees of freedom;

[0053] - projecting the obtained motion in six degrees of freedom onto unit vectors point into lidar beam direction and obtaining magnitude of the projection;

[0054] - respectively subtracting the obtained magnitude of the proj ection from the radial velocities of each line-of-sight measurement.

[0055] The above exemplary method is used to obtain motion-compensated radial velocity data in which the effect from the motion of the remoting sensing device is removed. The motion-compensated radial velocity data is then used to obtain wind speed data, which will be used in the method of the present disclosure to calculate the temporal offsets between the lidar data and motion data.

[0056] It will be understood by those skilled in the art that the motion-compensated wind data can be obtained with a separate computing device, which is then fed to the processor running the method of the present disclosure to find the temporal offset for synchronizing the radial velocity data and the motion data.

[0057] In an example of the present disclosure, obtaining the motion-compensated wind data further comprises reconstructing three-dimensional wind data based on the motion- compensated radial velocities and modified beam geometry.

[0058] In an example of the present disclosure, the remote sensing device comprises a Light Detection And Ranging, lidar sensor.

[0059] As lidar sensors are frequently used in measuring wind data, when they are deployed on a floating platform such as in an offshore environment, the method of the present disclosure can be advantageously used to synchronize the wind data that are obtained by the lidar sensors and the motion of the lidar sensors.

[0060] In an example of the present disclosure, the remote sensing device comprises a continuous-wave lidar sensor.

[0061] Continuous-wave lidar sensors are well suited to measuring wind data in an offshore environment and can be conveniently combined with the method of the present disclosure.

[0062] As can be contemplated, wind data obtained by pulsed lidar may also be processed according to the method of the present disclosure.

[0063] In an example of the present disclosure, the motion data of the floating platform is acquired by a motion sensing device connected to the floating platform.

[0064] As can be understood by those skilled in the art, commercially available profiling wind lidar generally do not provide accurate enough motion data. The motion data thereforecan be recorded by separate instruments like a motion sensing device, such as a motion reference unit. The motion sensing device is connected to the floating platform supporting the lidar, thereby obtaining motion data of the lidar.

[0065] In an example of the present disclosure, each time interval has a duration of ten minutes.

[0066] This is just an exemplary duration of the time intervals. It will be understood by those skilled in the art that other durations may also be used, depending on specific application scenarios of the method.

[0067] In a second aspect of the invention there is provided a device for obtaining a temporal offset for synchronizing radial velocity data measured by a remote sensing device mounted on a floating platform and motion data of the floating platform, the device comprises a processor configured for performing the method according to the first aspect of the present disclosure.

[0068] In a third aspect of the invention there is provided a method for calculating a turbulence intensity based on wind data measured by a remote sensing device mounted on a floating platform, the method performed by a processor and comprising the steps of:

[0069] - obtaining a standard deviation based on a wind speed variance, of a time interval, corresponding to the correct temporal offset obtained according to the method of the first aspect of the present disclosure;

[0070] - obtaining mean wind velocity corresponding to the correct temporal offset;

[0071] - computing the turbulence intensity based on the mean wind velocity and the standard deviation.

[0072] In a fourth aspect of the present disclosure, there is provided a computer program product, comprising a computer readable storage medium storing instructions which, when executed on at least one processor, cause the at least one processor to carry out the method according to the first aspect of the present disclosure.BRIEF DESCRIPTION OF THE DRAWINGS

[0073] In order to describe the manner in which the above-recited and other advantages and features of the disclosure can be obtained, a more particular description of the principles briefly described above will be rendered by reference to specific embodiments thereof which are illustrated in the appended drawings. Understanding that these drawings depict only exemplary embodiments of the disclosure and are therefore not to be considered to be limitingof its scope, the principles herein are described and explained with additional specificity and detail through the use of the accompanying drawings in which:

[0074] FIG. 1 schematically illustrates a floating lidar system.

[0075] FIG. 2 illustrates Standard deviation of motion compensated horizontal wind speed as a function of timing offset between MRU and lidar data.

[0076] FIG. 3 schematically illustrate, in a flow chart, a method of obtaining a temporal offset for synchronizing radial velocity data measured by a remote sensing device mounted on a floating platform and motion data of the floating platform, in accordance with the present disclosure.DESCRIPTION OF ILLUSTRATIVE EMBODIMENTS

[0077] Embodiments contemplated by the present disclosure will now be described in more detail with reference to the accompanying drawings. The disclosed subject matter should not be construed as limited to only the embodiments set forth herein. Rather, the illustrated embodiments are provided by way of example to covey the scope of the subject matter to those skilled in the art.

[0078] The method of the present disclosure will be described with reference to a system employing floating lidars to measure radial velocity data of wind. The method is however not limited to floating lidars. Instead, any remote sensing device suitable for being supported by a floating platform and for measuring the radial velocity data of wind can be used.

[0079] For their measurements of wind velocities and directions, fixed ground-based remote sensing devices determine radial velocities of naturally occurring scatterers in the atmosphere along different lines of sight by means of the Doppler effect.

[0080] Floating lidar systems are often deployed on buoys or other floating platforms in marine environments. The natural movements of the floating platform, including swaying side to side, heaving up and down, and potential rotation, can introduce variations in the lidar measurements. These variations need to be corrected to obtain accurate wind data

[0081] Under the influence of motion, a floating lidar system no longer measures the radial velocities of the scatterers in the atmosphere but the relative radial velocities between the device and the scatterers. Furthermore, the beam geometry is changing under the influence of platform motion and the measurement elevations are no longer identical with the measurement elevations of a fixed lidar.

[0082] Figure 1 schematically illustrates a floating lidar system 10. In Figure 1, a lidar 11 is shown as being supported by a floating platform 12, which can be for example a buoy. The buoy is anchored to the seabed with a catenary mooring line and floats on the water’s surface. Due to the influence from the sea and other conditions, the floating platform 12 and therefore the lidar 11 can move in all six degrees of freedom. Translational motion in x, y, and z- directions as respectively illustrated by arrows 121, 122 and 123 is called surge, sway, and heave, respectively. Rotational movement as indicated by circle arrows 124, 125 and 126 are respectively referred to as roll, pitch and yaw.

[0083] Information about a direction of each line-of-sight measurement relative to the lidar frame of reference is given in the form of the fixed zenith angle cp and the azimuth angle 9 relative to the heading of the lidar.

[0084] Platform motion correction is a process used to account for motion of a floating platform, such as sway, heave, and rotation, which can introduce errors in lidar measurements. This correction is needed for maintaining the accuracy of the radial velocity data obtained from a floating lidar system, especially when the platform is subject to movements due to waves, wind, or other environmental factors.

[0085] Since commercially available profiling wind lidar do not provide accurate enough motion data, the motion data is normally recorded by separate instruments. In an example, an Advanced Inertial Navigation Systems, INS, or other motion sensors can be employed to precisely measure the motion of the floating platform.

[0086] An INS is a navigation aid that uses a computer, accelerometers, and gyroscopes to continuously calculate the position, orientation, and velocity of a moving object without the need for external references such as GPS. INS can provide accurate information about the platform's acceleration, angular rate, and changes in orientation.

[0087] The information obtained from INS or motion sensors is integrated and fused with the lidar measurements. This integration allows for the transformation of radial velocity data to correct for the platform's motion effects.

[0088] For this purpose, motion compensation algorithms are applied to the lidar data based on the platform motion information. These algorithms consider the direction and magnitude of the platform's movements and adjust the radial velocity data accordingly.

[0089] When correcting the effect caused by the motion of the platform in a floating lidar system, dealing with coordinate systems is important in ensuring accurate measurements. The primary steps involve understanding and transforming data between the coordinate systems ofthe lidar device and the moving platform. Below describes how this process is typically performed.

[0090] First, the coordinate system used by the lidar device to measure wind velocities is defined. This system is typically fixed relative to the lidar instrument itself. Then the coordinate system associated with the floating platform's motion is also defined. This system moves with the platform and experiences the motion-induced effects.

[0091] Next is to establish a clear understanding of the alignment between the lidar coordinate system and the platform coordinate system. This involves defining the angles of rotation and translation between the two systems. Thereafter appropriate coordinate transformations are applied to convert data between the lidar and platform coordinate systems. These transformations may involve rotations, translations, and scaling to account for differences in orientation and position.

[0092] The present disclosure uses information about the orientation and velocity of a buoy-mounted lidar to correct for the effects of motion when processing the wind data measured by the lidar. The method involves a series of processing including subtracting relative motion of the lidar from the measured radial velocities, using modified beam geometry in the wind vector reconstruction process and considering the varying measurement elevation in the sheared and veered wind field. With these three processes in place, motion-compensated wind vectors can be calculated.

[0093] In an example, data from INS or other motion sensors on the platform is used to quantify its motion accurately. Motion correction algorithms that utilize the data from INS to adjust the lidar measurements is developed. These algorithms often involve applying inverse transformations to counteract the effects of platform motion on the lidar data.

[0094] An algorithm for correcting the motion induced error in the wind data may comprise the following steps. First, radial velocities of each line-of-sight measurement are obtained using the remoting sensing device. Then motion of the remote sensing device in six degrees of freedom is obtained from a motion reference device connected to the remote sensing device. After that, the obtained motion in six degrees of freedom is projected onto unit vectors pointing into the lidar beam direction and thereby obtaining magnitude of the projection. By respectively subtracting the obtained magnitude of the projection from the radial velocities of each line-of- sight measurement the motion-compensated wind data is obtained.

[0095] Data from multiple sensors, including the lidar and motion sensors, are thereby integrated into a cohesive dataset. It is ensured that all data are referenced to a common coordinate system, facilitating effective data fusion and analysis.

[0096] As the radial velocity data and the motion data are measured using separate devices, radial velocity data and motion data are not synchronized in time. Since the lidar's radial velocity data and the motion data are not synchronized, they may correspond to different time points. This temporal misalignment makes it challenging to directly correlate the wind measurements with the motion of the platform at specific time instances. To overcome this problem, the datasets must be synchronized temporally.

[0097] Temporal synchronization between lidar data and motion sensors (such as INS) typically requires the application of specialized data synchronization methods. One of the methods used to achieve time synchronization involves using software algorithms to synchronize data. This may involve calibration of data during post-processing to align lidar data and sensor data in time.

[0098] A general outline of how synchronization is conventionally achieved is described in the following.

[0099] Firstly, radial velocity data and motion data are collected. For radial velocity data, it is gathered from the lidar system, providing measurements of radial wind velocities along the lines of sight. For the motion data, it is collected from motion sensors, such as an INS or other devices, recording the motion of the floating platform.

[0100] Following that, a temporal offset analysis is performed. At this stage, the temporal offsets between the radial velocity data and the motion data are studied, which evaluates different temporal offsets between the motion data and the radial velocity data. This involves analyzing the datasets at various time lags to understand the correlation between platform motion and wind measurements.

[0101] Following that, an algorithm that compensates for the temporal misalignment is devised. This algorithm typically involves aligning the radial velocities and the platform motion so that motion compensated radial velocities are obtained.

[0102] The algorithm may be designed to test multiple temporal offsets systematically to find the optimal alignment.

[0103] Thereafter, an iterative process is implemented to refine the temporal alignment. This might involve adjusting the temporal offset in smaller increments to achieve finer synchronization.

[0104] An ooptimization criterion may be used to determine the optimal temporal alignment. A known criterion is minimizing the difference between motion-compensated velocities and reference data,

[0105] In the following, the method used by the present disclosure to synchronize the wind data and the motion data will be described in detail.

[0106] For testing and validating the method of the present disclosure, the main criteria to compare is turbulence intensity, defined as standard deviation of horizontal wind speeds divided by their mean.

[0107] That is, the method seeks to minimize the wind speed fluctuations, i.e., the variance or standard deviation of wind speed data after motion compensation. The idea behind it is motion add variance to the measured wind speed data. Motion compensation can entirely remove this added variance again. But only if the timing is right. If instead, the timing is slightly wrong, the motion-compensated wind speeds still contain some of the effect of motion and are not minimal. Thus, if the variance after motion compensation is minimal, the optimal temporal alignment is reached.

[0108] As can be understood by those skilled in the art, offsets are present between the timings of the radial data and that of the motion data due to the fact that different clocks are used by the lidar sensor and the motion sensor. Each sensor operates independently and has its own internal clock mechanism to record the timing of data points, as a result there is slight variations or offsets in the timing of data collected by the lidar sensor and the motion sensor.

[0109] The inventor has found, by way of experiments and experiences, that the timing offset between the lidar data and the motion data exhibit a drift over time. This drift is attributed to factors such as clock drift in one or both sensors, temperature variations affecting clock accuracy and so on.

[0110] In practice, the internal clock of the lidar is reset periodically as part of automatized procedures. This reset can occur hourly or at other regular intervals to ensure accurate timekeeping.[OHl] Based on the above factors, the inventor has the insight that the temporal offset between the lidar data and the motion data falls within a range, and the range can be determined experimentally.

[0112] This drift in temporal offset poses a challenge when calculating turbulence intensity, which is a parameter that characterizes the temporal variations in wind speed. Turbulence intensity is defined as a measure of variability or fluctuations in wind speed over a specific time interval. It is typically calculated using statistical metrics such as the standard deviation of wind speed within a certain time interval.

[0113] A statistical metric used for calculating the turbulence intensity is advantageously used in the present disclosure to solve for a correct temporal offset between the lidar data andthe motion data. This is in consideration that the temporal offset can be decided on the basis of time intervals as well.

[0114] The approach of the present disclosure involves deriving the correct temporal offset between the lidar data and the motion data with reference to a certain time interval. The method seeks to find a correct offset between the lidar data and motion data by considering wind speed variances calculated based on different possible temporal offsets between the lidar data and the motion data. The variances are calculated on time interval basis. For each time interval, a temporal offset associated with the lowest variance represents the correct offset between the lidar data and the motion data.

[0115] To be more specific, for a defined time interval, a number of offset values are considered. Motion compensation is performed for radial velocity data obtained by the lidar sensor, while using each of the offset values to align the radial velocity data and the motion data. The motion compensated radial velocity data is then used to construct 3D wind vectors in the defined time interval, which will in turn be used to calculate variances of the wind data. The temporal offset associated with the lowest variance is considered the correct offset between the lidar data and the motion data.

[0116] In the present disclosure, when performing motion compensation considering a temporal offset, the timestamps or timing of the radial velocities are shifted based on the temporal offset, while the radial velocity values remain unchanged.

[0117] According to the method of the present disclosure, temporal synchronization between the radial velocity data and the motion data is done by calculating motion- compensated wind data for many different temporal offsets between motion data and line-of- sight data. The correct temporal offset between motion data and line-of-sight data is the value corresponding to the result with the lowest variance in motion-compensated wind data.

[0118] With the synchronization performed, a final dataset that represents accurate wind measurements, accounting for the platform's motion is obtained.

[0119] In the following, an example of temporally synchronizing the radial velocity data and the motion data, in accordance with the present disclosure, will be described. The motion data is collected by a motion reference unit, MRU, and the radial velocity data is acquired using a floating lidar.

[0120] As a preparation step, both the radial velocity data and the motion data are obtained. It is assumed that both data have a same sample rate such as for example 50 Hz. Both the lidar and the motion sensor may be independently synchronized with a common GPS time server,but they are not synchronized with each other. Therefore, offsets are expected between the timestamps of the MRU and the lidar.

[0121] To tackle this issue, a method is implemented to synchronize the two measurement devices. Its underlying basic assumption is that the motion of the buoy caused by waves, current, and local wind is independent of the simultaneous wind vectors at measurement height. From this assumption it follows that the motion-induced error and the current wind velocity are also independent variables. The turbulence measured by a floating lidar in motion must therefore be larger than measurements with a fixed lidar of the same type.

[0122] An ideal motion compensation algorithm can reduce the measured turbulence down to exactly the level of a fixed lidar if the timing between motion and lidar data is correct. The above considerations form the basis for finding the correct timing between MRU and lidar data. According to the method of the present disclosure, motion compensation is calculated for different time lag values between MRU and lidar, then an offset at which the motion- compensated turbulence reaches its minimum is considered the correct temporal offset between the MRU and the lidar data.

[0123] The ideal motion compensation algorithm can reduce the measured turbulence down to exactly the level of a fixed lidar if the timing between motion and lidar data is correct.

[0124] Figure 2 shows the result of this procedure for an arbitrary ten-minute interval. The y-axis shows motion-corrected standard deviation values of the horizontal wind speed <5^hor averaged over all measurement heights. The corresponding time lag between the MRU and the lidar timestamp is shown on the x-axis. The absolute minimum is found at -0.16 s. This is the average offset between the MRU and the lidar data for this ten-minute interval. It will be understood by those skilled in the art that the standard deviation is the square root of the variance and both parameters follow the same variation pattern.

[0125] A periodicity of the waves is visible from Figure 2, and leads to local minima each separated by approximately 2.5 s. Figure 2 also shows that the offsets between the lidar data and motion data shift, following generally a linear trend, within a defined range. In an example of the present disclosure, the offset between the lidar data and the motion data varies between about -0.6 second to about 2.0 second. In implementing the method of the present disclosure, a number of offset values within the range of for example -1.0 second to 2.0 second can be tested.

[0126] The processing of motion-compensated wind data is computationally expensive when many different offsets need to be tested to find the absolute minimum of variance. It is therefore advantageous to not calculate many different offsets for every data interval but onlyfor the first time interval. Offsets for subsequent time intervals can then be found by calculating the variance associated with the previous offset and one value above and below the previous offset.

[0127] If the motion-compensated wind variance associated with previous offset is the lowest of the three values, it is the correct value also for this interval. If not, another offset next to the new minimum must be calculated until the new minimum does not lie at the edge of calculated values anymore. In this way, the processing can follow the temporal drift between the wind and motion sensors in a computationally efficient way.

[0128] The calculation procedure can be briefly summarized as two steps. That is, first, an initial benchmark temporal offset for a first time interval is derived, based on motion- compensated wind data for the first time interval. After that, for a second time interval following the first time interval, a further benchmark temporal offset is iteratively calculated with reference to variances calculated based on motion-compensated wind data respectively for the initial benchmark temporal offset, a previous temporal offset one step before the initial benchmark temporal offset, and a subsequent temporal offset one step after the initial benchmark temporal offset.

[0129] The procedure then repeats to calculate the temporal offset for a third time interval following the second time interval by using the calculated benchmark temporal offset for the second time interval as a reference temporal offset for the third time interval.

[0130] The above process is described in detail in the following with reference to Figure 3.

[0131] Figure 3 schematically illustrate, in a flow chart, a method 30 of obtaining a temporal offset for synchronizing radial velocity data measured by a remote sensing device mounted on a floating platform and motion data of the floating platform, in accordance with the present disclosure.

[0132] At step 31, an initial benchmark temporal offset for a first time interval is derived based on motion-compensated wind data for the first time interval.

[0133] This step involves calculating motion-compensated wind speeds for example according to a known method, for a range of temporal offsets for a first interval, followed by deriving a plurality of wind speed variances based on a number of temporal offsets being tested and then identifying a temporal offset associated with the lowest wind speed variance. The identified temporal offset is the correct offset between the lidar data and the motion data and is considered as the initial benchmark temporal offset for the first time interval.

[0134] Specifically, for each temporal offset that is being considered, motion-compensated radial velocities are derived following a method such as the one described above. Following that motion-compensated radial velocity measurements from multiple azimuth angles and elevation angles are combined to estimate the wind speed and direction in three dimensions.

[0135] The reconstructed wind vector is unaveraged and represents the wind speed and direction at discrete points in space. Each reconstructed wind vector corresponds to a specific location and time instance.

[0136] Averaged wind statistics for the first time interval are then calculated from the unaveraged reconstructed wind vectors, which provide insights into the overall wind characteristics over this time period and the considered spatial region.

[0137] A wind speed variance is then derived for this first time period, based on wind data considering the temporal offset being tested. It will be understood that the variance can be derived based on horizontal or vertical wind speed. The above procedure is performed for each temporal offset under consideration to derive a plurality of variances.

[0138] As can be understood by those skilled in the art, if the offset is not the correct offset, the results or output of the motion compensation calculations will be a time series of wind speeds that are not correctly motion compensated. They have a wind speed variance that is higher than the wind speed variance of the correctly motion-compensated data. Calculations are therefore performed several times to find out which of offset is correct and shall be used.

[0139] In an example, the interval has a duration of ten minutes. An exemplary sampling rate for the motion data is 5Hz, while the lidar frequency is 50Hz. The motion data is then upsampled to the lidar frequency (50Hz). The upsampling is done for example using interpolation, with some filtering to avoid aliasing effects.

[0140] Temporal offsets can be calculated with the same step size (50Hz) or coarser. It will be understood by those skilled in the art that a larger time step used to test the plurality of possible temporal offsets allows required computational resources to be reduced.

[0141] Wind speed variances for each temporal offset within a range of temporal offsets are calculated. Then the temporal offset associated with the lowest wind speed variance is determined. This is a correct temporal offset between the radial velocity data and the motion data for this time interval.

[0142] In the present disclosure, the correct temporal offset obtained as described above is referred to as an initial benchmark temporal offset, and will be used as a reference temporal offset for calculating a correct offset for a second time interval following the first time interval, as indicated at step 32 of Figure 3.

[0143] In an example as illustrated in Figure 2, the offset between the clocks of the MRU and the lidar with a value of -0.16s allows the lowest variance to be achieved and therefore is the initial benchmark temporal offset which will be used for calculating a correct temporal offset for a subsequent time interval.

[0144] At step 33, variances are calculated based on motion-compensated wind data of the second time interval, respectively for the initial benchmark temporal offset, a previous temporal offset one step before the initial benchmark temporal offset, and a subsequent temporal offset one step after the initial benchmark temporal offset.

[0145] As can be seen, here the number of temporal offsets being considered is significantly reduced as only three offset values are considered in performing the motion compensation and variance calculation.

[0146] At step 34, it is determined whether the smallest variance is associated with the initial benchmark temporal offset. When a positive determination result is obtained for step 34, it shows that the initial benchmark temporal offset is still valid for the second time interval. The flow therefore goes back to step 32, considering it as the initial benchmark temporal offset for the second time interval, which will be used as a reference offset for a next, that is, a third time interval following the second time interval.

[0147] When a negative determination result is obtained for step 34, the flow proceeds to step 35. At step 35, the offset associated with the smallest variance is referred to as mid-offset for convenience purpose. The mid-offset becomes the new reference offset and may be referred to as the "ref-left" or "ref-right" offset, depending on whether it is associated with the timestep before (left) or after (right) the previous reference offset. Depending on whether the mid-offset is "ref-left" or "ref-right" offset, one more variance is now calculated for a temporal offset which is one time step to the left or right of it. This is due to consideration that the offset drifts over time, the choice of the extra variance to be calculated is in line with this drifting trend.

[0148] Following that, at step 36, it is determined whether the smallest variance is associated with the most recently processed reference offset. When a positive determination result is obtained for step 36, it shows that the most recently processed reference offset can be considered as a benchmark offset, this is done at step 37. The flow then proceeds to a next time interval, and the newly obtained benchmark offset will be used as a reference offset for the next time interval.

[0149] On the other hand, if a negative decision result is obtained at step 36, the flow returns to step 35, taking the offset associated with the smallest variance as a new mid-offset and again calculating one more offset which follows the drifting trend of the offset. Steps 35and 36 are repeated until the offset associated with the smallest variance is the middle one of the three offsets that are considered. A benchmark offset is therefore found for the current second time interval.

[0150] As an example, when the initial benchmark offset derived from the first time interval is tested versus two offsets which are respectively one step away from the initial benchmark offset for the second time interval, it shows that the one to the left of the initial benchmark offset gives the smallest variance, then an extra offset still to the left is considered. Therefore, a variance is calculated for a temporal offset which is still one step left to the current temporal offset associated with the smallest variance (which is referred to the mid-offset for convenience purpose). The procedure is repeated until the smallest variance is associated with the mid-offset, that shows that the mid-offset is the correct offset for the second time interval.

[0151] An offset “left” to the initial benchmark offset / mid-offset as used herein refers to an offset with a value of “the initial benchmark offset minus a time step”. Similarly, an offset “right” to the initial benchmark offset / mid-offset as used herein refers to an offset with a value of “the initial benchmark offset plus a time step”.

[0152] The thus found mid-offset is now used as the initial benchmark offset for the second time interval, which will be used as a reference offset for the third time interval following the second time interval.

[0153] The above method of the present disclosure significantly reduces the computational resources required for find optimal temporal offset between the motion data and the radial velocity data. This is especially advantageous when wind data obtained during a long period of time has to be corrected.

[0154] An example of using the present disclosure to find the correct offset between the lidar data and the motion data is briefly described to facilitate the understanding of the present disclosure. It is assumed that a step size of 0.1 second (or 10Hz) is used, and the initial benchmark temporal offset for the first time interval of 10 minutes is found to be 0.6 second. Then, for the second time interval, that is, the next set of 10 minute, lidar and motion data will be processed three times in a first iteration. It will be processed for the offsets 0.5 second, 0.6 second, and 0.7 second. That means motion-compensation calculations are done only three times for this ten minute interval.

[0155] That means we get three sets of reconstructed wind speed data. One complete set for each offset 0.5 second, 0.6 second and 0.7 second. Then the variances of each of the three sets of wind speeds are calculated. Likely the data set from processing with 0.6 second temporaloffset shows the lowest variance. Then 0.6 second is the correct offset also for this 10-minute interval (second time interval).

[0156] In this case, the correct temporal offset is found by calculating only three variances.

[0157] If the data set from processing with 0.7 second shows the lowest variance of the three, considering that the offset drifts, it is known that the correct offset is either 0.7s or anything above. So, to make sure, the motion-compensation processing is done a fourth time with 0.8 second temporal offset (to the right of 0.7 second). Accordingly, the variance of the resulting fourth set of motion-compensated reconstructed wind speed data is calculated. If the variance value associated with 0.7 second is the lowest of the four, it is known this was the correct offset for the second time interval. This involves computation of only one extra variance.

[0158] Following that, the method proceeds with the next third interval which considers the temporal offset 0.6 second, 0.7 second, and 0.8 second in the first iteration.

[0159] The method of the present disclosure therefore reduces the required computational resources significantly without compromising accuracy of the solved correct temporal offset. It is especially advantageous when wind data over a long period of time has to be processed.

[0160] Following the determination of the correct temporal offset between the lidar data and motion data, parameters such as turbulence intensity may be obtained based on the corrected wind data.

[0161] The invention has been described by reference to certain embodiments discussed above. It will be recognized that these embodiments are susceptible to various modifications and alternative forms well known to those of skill in the art.

[0162] Further modifications in addition to those described above may be made to the structures and techniques described herein without departing from the spirit and scope of the invention. Accordingly, although specific embodiments have been described, these are examples only and are not limiting upon the scope of the invention.

Claims

CLAIMS1. A method of obtaining a temporal offset for synchronizing radial velocity data measured by a remote sensing device mounted on a floating platform and motion data of the floating platform, the method performed by a processor and comprising the steps of: deriving an initial benchmark temporal offset for a first time interval, based on motion-compensated wind data for the first time interval; iteratively calculating a further benchmark temporal offset for a second time interval following the first time interval, with reference to variances calculated based on motion-compensated wind data respectively for the initial benchmark temporal offset, a previous temporal offset one step before the initial benchmark temporal offset, and a subsequent temporal offset one step after the initial benchmark temporal offset.

2. The method according to claim 1, wherein the step of deriving comprises the steps of: calculating a plurality of wind speed variances based on motion compensated wind data for a plurality of temporal offsets between timings of the motion data and the radial velocity data; determining a temporal offset associated with the smallest wind speed variance; and using the determined temporal offset as the initial benchmark temporal offset for the first time interval.

3. The method according to claim 1 or 2, wherein the step of iterative calculation comprises the steps of: taking the initial benchmark temporal offset as a current reference temporal offset for the second time interval; calculating variances based on motion-compensated wind data of the second time interval, respectively for the current reference temporal offset, a previous temporal offset one step before the current reference temporal offset, and a subsequent temporal offset one step after the current reference temporal offset; determining that the lowest variance is associated with the current reference temporal offset; using the current reference temporal offset as the initial benchmark temporal offset for the second time interval.

4. The method according to claim 1 or 2, wherein the step of iterative calculation comprises the steps of: taking the initial benchmark temporal offset as a current reference temporal offset for the second time interval; calculating variances based on motion-compensated wind data of the second time interval, respectively for the current reference temporal offset, a previous temporal offset one step before the current reference temporal offset, and a subsequent temporal offset one step after the current reference temporal offset; determining the lowest variance is associated with the previous temporal offset one step before the current reference temporal offset; taking the previous temporal offset one step before the current reference temporal offset as a current reference temporal offset; computing a variance based on motion-compensated wind data of the second time interval, for a temporal offset one step before the current reference temporal offset; repeating the determining, taking and computing steps until the lowest variance is associated with the current reference temporal offset; and using the current reference temporal offset as the initial benchmark temporal offset for the second time interval.

5. The method according to claim 1 or 2, wherein the step of iterative calculation comprises the steps of: taking the initial benchmark temporal offset as a current reference temporal offset for the second time interval; calculating variances based on motion-compensated wind data of the second time interval, respectively for the current reference temporal offset, a previous temporal offset one step before the current reference temporal offset, and a subsequent temporal offset one step after the current reference temporal offset; determining the lowest variance is associated with the subsequent temporal offset one step after the current reference temporal offset; taking the subsequent temporal offset one step after the current reference temporal offset as a current reference temporal offset; computing a variance based on motion-compensated wind data of the second time interval, for a temporal offset one step after the current reference temporal offset;repeating the determining, taking and computing steps until the lowest variance is associated with the current reference temporal offset; and using the current reference temporal offset as the initial benchmark temporal offset for the second time interval.

6. The method according to any of the previous claims, wherein obtaining the motion- compensated wind data comprises the steps of: obtaining radial velocities of each line-of-sight measurement using the remoting sensing device; obtaining, from a motion reference device connected to the remote sensing device, motion of the remote sensing device in six degrees of freedom; projecting the obtained motion in six degrees of freedom onto unit vectors point into lidar beam direction and obtaining magnitude of the projection; respectively subtracting the obtained magnitude of the proj ection from the radial velocities of each line-of-sight measurement.

7. The method according to claim 6, further comprising a step of: reconstructing three-dimensional wind data based on the motion-compensated radial velocities and modified beam geometry.

8. The method according to any of the previous claims, wherein the remote sensing device comprises a Light Detection and Ranging, lidar sensor.

9. The method according to any of the previous claims, wherein the remote sensing device comprises a continuous-wave lidar sensor.

10. The method according to any of the previous claims 1 to 8, wherein the remote sensing device comprises a pulsed lidar sensor.

11. The method according to any of the previous claims, wherein the motion data of the floating platform is acquired by a motion sensing device connected to the floating platform.

12. The method according to any of the previous claims, wherein each time interval has a duration of ten minutes.

13. A device for obtaining a temporal offset for synchronizing radial velocity data measured by a remote sensing device mounted on a floating platform and motion data of the floating platform, the device comprises a processor configured for performing the method according to any of the previous claims 1 to 12.

14. A method for calculating a turbulence intensity based on wind data measured by a remote sensing device mounted on a floating platform, the method performed by a processor and comprising the steps of: obtaining a standard deviation based on a wind speed variance, of a time interval, corresponding to the correct temporal offset obtained according to the method of any of the previous claims 1 to 12; obtaining mean wind velocity corresponding to the correct temporal offset; computing the turbulence intensity based on the mean wind velocity and the standard deviation.

15. A computer program product, comprising a computer readable storage medium storing instructions which, when executed on at least one processor, cause the at least one processor to carry out the method according to any of the claims 1 to 12.

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