Description:TECHNICAL FIELD
[0001] The present disclosure relates to the field of radar. More particularly, the present disclosure pertains to compensating measurements obtained from a sensor mounted on a moving platform.

BACKGROUND
[0002] Background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
[0003] Sensors form an integral part in different measuring systems. In some systems, sensors are mounted on platforms that are not stable, for example, moving platforms like ships, airborne vehicles, etc. In such systems, the sensor along with the platform undergoes significant motion such as a rotational or a translational motion. In such situations, data from the sensor may vary largely between any two sensor sampling instances causing error in a tracking performance of sensor or disturbance such as unwanted motion in camera videos, error in measurement reports, etc., in the sensor output. For example, in a radar system, the variation in sampling instances in the sensor creates a shift in coordinate system causing errors in associating new measurements with an already existing track leading to issues such as track drop, false tracks, bad track estimates, as well as coverage loss, etc.
[0004] Therefore, there is a need for improved techniques for compensating the variations in sensor samples due to the movement of the associated platform.

OBJECTS OF THE PRESENT DISCLOSURE
[0005] Some of the objects of the present disclosure, which at least one embodiment herein satisfies, are as listed below.
[0006] It is an object of the present disclosure to compensate measurements obtained from a sensor mounted on a moving platform.
[0007] It is an object of the present disclosure to correct the measurements received from the sensor on the moving platform based on a roll and pitch information provided by a gyroscope placed on the same platform.
[0008] It is an object of the present disclosure to correct the error associated with the sensor measurements at a measurement level.
[0009] It is an object of the present disclosure to compensate measurements at a digital level.

SUMMARY
[0010] This section is provided to introduce certain objects and aspects of the present disclosure in a simplified form that are further described below in the detailed description. This summary is not intended to identify the key features or the scope of the claimed subject matter.
[0011] In an aspect, the present disclosure relates to a data processing system. The system comprising an input interface to obtain a first set of data from a sensor and a second set of data from an inertial measurement unit (IMU), wherein the sensor and the IMU are placed on a moving platform. Further, the system corrects the first set of data based on the second set of data to compensate for errors due to an effect of motion on the sensor, wherein the effect of motion on the sensor comprises at least one of translational motion or a rotational motion, further wherein the rotational motion includes motion along roll, pitch, and yaw axes. The system is further configured to extrapolate the second set of data based on the first set of data to compensate for errors due to translational motion and subject the first set of data to a set of rotational matrices to compensate for errors due to rotational motion. A cost metric is associated with each rotational matrix in the set of rotational matrices and the system is configured to select at least one rotational matrix, from the set of rotational matrices, providing the least cost. The system further includes an output interface to provide a corrected measurement.
[0012] In another aspect, the present disclosure relates to a method for compensating errors due to an effect of motion on a sensor. The method includes obtaining, by a data processing system, a first set of data from a sensor, a second set of data from an inertial measurement unit (IMU), wherein the sensor and the IMU are mounted on a moving platform, extrapolating the secondt set of data based on the first set of data to compensate for errors due to an effect of translation motion on the sensor, applying a set of rotational matrices on the first set of data to determine a cost metric associated with each rotational matrix in the set of rotational matrices, selecting at least one rotational matrix providing a least cost metric, and compensating the errors due to the rotational motion on the first set of data based on the least cost metric. Further, the method includes timestamping the second set of data and the timestamp is selected to be associated with a time of obtaining the first set of data.

BRIEF DESCRIPTION OF DRAWINGS
[0013] The accompanying drawings are included to provide a further understanding of the present disclosure and are incorporated in and constitute a part of this specification. The drawings illustrate exemplary embodiments of the present disclosure and, together with the description, serve to explain the principles of the present disclosure. The diagrams are for illustration only, which thus is not a limitation of the present disclosure.
[0014] FIG. 1 illustrates an exemplary network architecture (100), in which or with which a measurement compensation system is implemented, in accordance with an embodiment of the present disclosure.
[0015] FIG. 2 illustrates an exemplary graphical representation (200) of a problem of a shift in coordinate axes of a sensor mounted on a moving platform, in accordance with an embodiment of the present disclosure.
[0016] FIG. 3 illustrates an exemplary graphical representation (300) of a compensated measurement from a sensor mounted on a moving platform, in accordance with an embodiment of the present disclosure.
[0017] FIG. 4 illustrates an exemplary schematic diagram (400) showing six degrees of freedom associated with a moving platfrom on which a sensor is mounted, in accordance with an embodiment of the present disclosure.
[0018] FIG. 5 illustrates an exemplary graphical representation (500) of a unity direction vector in a frame of reference (FoR), in accordance with an embodiment of the present disclosure.
[0019] FIG. 6 illustrates an exemplary graphical representation (600) of components associated with a unity direction vector subjected to a value of pitch, in accordance with an embodiment of the present disclosure.
[0020] FIG. 7 illustrates an exemplary graphical representation (700) of finding a modified direction vector, in accordance with an embodiment of the present disclosure.
[0021] FIG. 8 illustrates an exemplary graphical representation (800) of components associated with a unity direction vector with pitch ‘p’ subjected to a roll ‘r’, in accordance with an embodiment of the present disclosure.
[0022] FIG. 9 illustrates an exemplary graphical representation (900) of components associated with a unity direction vector with pitch ‘p’, roll ‘r’, subjected to a yaw ‘y’, in accordance with an embodiment of the present disclosure.
[0023] FIG. 10 illustrates an exemplary graphical representation (1000) for orienting coordinate frames of a platform, a sensor, and a reference source, in accordance with an embodiment of the present disclosure.
[0024] FIG. 11 illustrates an exemplary tree structure (1100) representing a measurement batch subjected to roll and pitch hypothesis, in accordance with an embodiment of the present disclosure.
[0025] FIG. 12 illustrates an exemplary method (1200) for compensating measurement error from a sensor mounted on a moving platform, in accordance with an embodiment of the present disclosure.
[0026] FIG. 13 illustrates an exemplary computer system (1300) in which or with which embodiments of the present disclosure may be implemented.
[0027] The foregoing shall be more apparent from the following more detailed description of the invention.

DETAILED DESCRIPTION
[0028] The following is a detailed description of embodiments of the disclosure depicted in the accompanying drawings. The embodiments are in such detail as to clearly communicate the disclosure. However, the amount of detail offered is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the scope of the present disclosure as defined by the appended claims.
[0029] In the following description, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the present invention. It will be apparent to one skilled in the art that embodiments of the present invention may be practiced without some of these specific details.
[0030] The present disclosure relates to a measuring system, and more particularly, to a system providing measuring compensation for sensors mounted on a moving platform. In an embodiment, the measuring compensation system may include a sensor such as, but not limited to, an image sensor, radio detection and ranging (RADAR), light detection and ranging (LIDAR), etc., mounted on a moving platform such as, but not limited to, ship, aircraft, unmanned aerial vehicle (UAV), etc., for detecting any target object such as, without limitations, vehicle, tree, human, rail track, etc. The proposed system may include an inertial measurement unit (IMU) mounted on the same platform as that of the sensor, wherein the measurement from the IMU is used as a reference signal for compensating the measurement obtained from the sensor. In some embodiments, the compensation is provided at a data processing level.
[0031] In another aspect, the present disclosure relates to a method for compensating errors in a sensor measurement due to a moving nature of the platform or the base on which the sensor is placed. The movement of the platform may be translational or rotational. Hence, the movement compensation needs to be done for both translational motion and rotational motion. Translational motion compensation is achieved based on platform kinematics, whereas rotational motion compensation needs more processing. The proposed method may include obtaining data from the sensor, obtaining data from the IMU having a timestamp near to the time of obtaining data from sensor, and processing the sensor data to compensate for motion error. The method proceeds with extrapolating the IMU data to achieve time synchronization with the sensor data to compensate for errors due to translational motion of the platform. On the other hand, the errors due to rotational motion of the platform may be compensated by applying rotational matrices on the coordinates of the measured data and checking for a cost metric. The rotational matric yielding the least cost metric may be used as compensation data for the obtained measurement data.
[0032] Various embodiments of the present disclosure will be explained with reference to FIGs. 1-13.
[0033] FIG. 1 illustrates an exemplary network architecture (100), in which or with which a measurement compensation system is implemented, in accordance with an embodiment of the present disclosure. In FIG. 1, the exemplary network architecture (100) comprising a sensor (104) mounted on a moving platform, for example, a UAV (102) is shown. The sensor (104) may include, for example, without limitations, a RADAR device, a LIDAR device, or an imaging device such as a camera. The sensor (104) may be used for detecting an object (108) present around its vicinity. The object (106) may include a vehicle, tree, human, rail track, etc. Data captured by the sensor (104) may include a pitch, a roll and a yaw as its coordinate axes. Referring to FIG. 1, an inertial measurement unit (IMU) (106) may be mounted on the moving platform along with the sensor (104), wherein readings from the IMU (106) may be used as reference signal for measurement compensation. The IMU (106) may include a combination of a three-axis accelerometer, a three axis gyroscope, and a magnetometer. The gyroscope measures an angular velocity of the platform, whereas the accelerometer measures the external specific force acting on the platform. The specific force may consist of both acceleration of the platform and the Earth’s gravity. Magnetometers may be used to measure the components of the Earth’s magnetic field vectors to determine the yaw.
[0034] Referring to FIG. 1, the measurement from the sensor (104) and a time stamped measurement from the IMU (106) are sent to a data processing system (110) to obtain a corrected measurement (112) which may be used for further processing. The data processing system (110) may include any system capable of taking sensor input and processing the input to generate an output. For example, without limitations,in radar systems a radar data processing (RDP) moduleidentifies targets from radar data and calculatespositional and kinematics information of respective targets. The time stamped measurement from the IMU (106) may be chosen such that timing is somewhere closer to the time of receiving the measurement from the sensor (104). There may be two types of errors associated with the moving nature of the platform: 1. Error due to a translational motion of the platform, and 2. Error due to a rotational motion of the platform. The data processing system (110) extrapolates the IMU (106) data in accordance with the time stamped data from the sensor (104) in order to achieve compensation with respect to the translational motion of the platform. In some embodiments, data from the IMU (106) may have high data rates and may not be extrapolated. Further, the data processing system (110) applies a set of rotational matrices on the data from the sensor (104) to evaluate a cost metric. The rotational matrix providing the least cost is chosen as the compensation data for compensating rotational error in the sensor data.
[0035] A person of ordinary skill in the art will appreciate that the exemplary architecture (100) may be modular and flexible to accommodate any kind of changes in the architecture (100).
[0036] FIG. 2 illustrates an exemplary graphical representation (200) of a problem of shift in coordinate axes of a sensor mounted on a moving platform, in accordance with an embodiment of the present disclosure.
[0037] In FIG. 2, coordinate axes X, Y, Z associated with a sensor data and the effect of rotational movement of the platform on the sensor data are shown. Due to the motion of the platform, the sensor’s coordinate axes are translated and rotated by Өr degrees from sampling instant t-1 to t. At time t, the measurement should have been (rt, Өt) (110) but due to the platform motion, the actual measurement is (rm, Өm) (120) resulting in a shift in the measurement. The effect of not compensating the shift is illustrated below with reference to FIG. 3.
[0038] FIG. 3 illustrates an exemplary graphical representation (300) of a compensated measurement from a sensor mounted on a moving platform, in accordance with an embodiment of the present disclosure. In FIG. 3, the measurement points obtained from the sensor (104) of FIG. 1 are shown. Referring to FIG. 3, a first measurement taken at time t-1 is given as Zt-1 and a second measurement taken at time t is compensated and represented as Zt,c. FIG. 3 also illustrates an uncompensated measurement point Zt,m. at time t. In a radar tracking scenario, if Zt,m falls within a measuring gate, the target object (108) of FIG. 1 may be seen as maneuvering leading to significant estimation errors. Hence, there is a need to correct the measurement used for data processing. This may be achieved by estimating a three-dimensional platform motion and compensating the measurement.
[0039] FIG. 4 illustrates an exemplary schematic diagram (400) showing six degrees of freedom associated with a platform on which the sensor is mounted, in accordance with an embodiment of the present disclosure. In FIG. 4, the six degrees of freedom comprising roll (rotation about x axis), pitch (rotation about y axis), yaw (rotation about z axis), surge (translation along x axis), sway (translation along y axis), and heave (translation along z axis) are shown. Among the six types of motion, roll and pitch are more prone to error due to the sensor platform’s oscillations or instability.
[0040] FIG. 5 illustrates an exemplary graphical representation (500) of a unity direction vector in a frame of reference (FoR), in accordance with an embodiment of the present disclosure. In FIG. 5, XY, YZ, and XZ planes of the FoR are shown. The XY plane of the FoR is always perpendicular to the direction of the gravitational force and parallel to the perfectly horizontal surface of the earth. The center of gravity of any platform placed in this FoR is always coincident with the origin of this FoR. Pitch is measured in XZ plane positively from +X axis towards +Z axis as shown in FIG. 5. Roll is measured in YZ plane positively from +Y axis towards +Z axis. Yaw is measured in XY plane positively from +X axis towards + Y axis. Bearing is measured similarly as yaw, and elevation is measured similarly as pitch except that it is measured from XY plane towards +Z axis. FIG. 5 shows a random unity vector D in the FoR representing an angular orientation of the sensor (104) mounted on the platform (102) as shown in FIG. 1, which is initially without any pitch, roll, and yaw. The unity vector D is at an elevation e and bearing b. The components of the unity vector along the three axes are given as:

[0041] FIG. 6 illustrates an exemplary graphical representation (600) of components associated with the unity direction vector D subjected to a value of pitch, in accordance with an embodiment of the present disclosure. In FIG. 6, the components of unity vector D subjected to a value ‘p’ of pitch is shown. The unity vector subjected to the pitch is represented by Dp and the components of Dp are given by Dpx, Dpy, and Dpz along the X, Y, and Z axes, respectively.
[0042] FIG. 7 illustrates an exemplary graphical representation (700) of finding a modified direction vector, in accordance with an embodiment of the present disclosure. In FIG. 7, the sum of the projections of the components given by Dpx, Dpy, and Dpz by rotating the vector D on the X, Y, and Z axes are shown. Dpx, Dpy, and Dpz are given as


[0043] FIG. 8 illustrates an exemplary graphical representation (800) of components associated with the unity direction vector with pitch ‘p’ subjected to a roll ‘r’, in accordance with an embodiment of the present disclosure. In FIG. 8, the vector Dp subjected to roll ‘r’ represented by a new vector Dpr is shown. The x, y, and z components of the vector Dp after undergoing rotation through roll r is represented as Dprx, Dpry, and Dprz and given as below:

[0044] Therefore, the new bearing and elevation of the vector Dpr is given as:

[0045] Once the bearing is calculated, it is compensated for yaw as described below with reference to FIG. 9.
[0046] FIG. 9 illustrates an exemplary graphical representation (900) of components associated with the unity direction vector with pitch ‘p’, roll ‘r’, subjected to a yaw ‘y’, in accordance with an embodiment of the present disclosure. In FIG. 9, the bearing obtained in equation (10) above compensated for yaw is shown. The corrected bearing value is shown below as:

[0047] FIG. 10 illustrates an exemplary graphical representation (1000) for orienting the coordinate frames of the platform, the sensor, and a reference source, in accordance with an embodiment of the present disclosure. In FIG. 10, the orientation of coordinate frames with respect to the reference frame is shown. Referring to FIG. 10, a first matrix representing the orientation of the platform (102) of FIG. 1 with respect to the sensor (104) is represented as TPS and it is a fixed matrix as the orientation of the sensor with respect to the platform is fixed. FIG. 10 also shows a second matrix represented as TPR representing the orientation of the reference frame with respect to the platform (102) of FIG. 1, wherein the reference plane is dependent on the rotational matrix of the platform (102) and is calculated using the roll, pitch, and yaw measurements.
[0048] FIG. 11 illustrates an exemplary tree structure (1100) representing a measurement batch subjected to roll and pitch hypothesis. In some embodiments, a single target with constant velocity model is assumed. For example, a vehicle moving with a constant velocity is assumed. The sensor (104) captures measurement from the target (108) as shown in FIG. 1, wherein the captured measurements are subjected to roll, pitch, and yaw rotations. In general, compensating the roll, pitch, and yaw is easy when a prior knowledge related to the order in which roll, pitch, and yaw affect the measurements is available. For example, if the captured measurements are subjected to roll->pitch->yaw in the given order, applying a compensation in a reverse order on the captured measurements might provide the true measurements. However, such order is always unknown in the real time and therefore, there is a need to obtain such order in the real time. Therefore, to obtain the order in which the captured measurement is subjected to roll, pitch, and yaw rotations, a rotation matrix hypothesis is applied on the captured measurement.
[0049] Referring to FIG. 11, an example of applying rotation matrix hypothesis on a batch of three measurements related to the target (108) as captured by the sensor (104) of FIG. 1, that are affected by roll, pitch, and yaw rotations is shown. The three measurements are given as measurement 1, measurement 2, and measurement 3, respectively. ‘A’ refers to a compensation with first roll and then pitch and ‘B’ refers to a compensation with first pitch and then roll. In FIG. 11, the measurement 1 is subjected to first A and then B to produce A1, B1, measurement 2 is subjected to A and B in the same way as measurement 1 to produce A2, B2. Similarly, measurement 3 is also compensated with similar hypotheses. In the present example, for measurements captured from a target moving with a constant velocity, a cost matrix associated with an acceleration value and a heading direction is considered. The cost matrices (CM1 and CM2) are given by :

Where ‘N’ corresponds to the number of hypotheses considering the batch of measurements and applied rotations matrices. a1, a2…aN corresponds to the accelerations of each permutation, hd1, hd2…hdN corresponds to the heading differences of each hypotheses. The cost matrix with the sum of squares of acceleration nearly equal to zero or minimum and sum of squares of the heading difference near to zero or minimum is considered as the least cost matrix.
[0050] From FIG. 11, the possible hypotheses are given by A1->A2->A3, A1->A2->B3, A1->B2->A3, A1->B2->B3, B1->A2->A3, B1->A2->B3, B1->B2->A3, B1->B2->B3 i.e., for a batch of three measurements, rotation matrices hypothesis for roll and pitch yields eight different hypothesis values. The rotation matrix having a lower hypothesis value may be considered as the best order for achieving compensation.
[0051] FIG. 12 illustrates an exemplary method (1200) for compensating measurement error from a sensor mounted on a moving platform, in accordance with an embodiment of the present disclosure. The method (1200) includes obtaining a first set of measurements, at step 1202, from a sensor (104) mounted on a moving platform (102) as shown in FIG. 1, obtaining a second set of measurements, at step 1204, from an IMU (106) as shown in FIG. 1, and extrapolating, at step 1206, the first set of measurements to compensate for transitional motion associated with the moving platform (102). The extrapolation includes selecting a data from the IMU (106) having a timestamp nearer to the time of obtaining the measurements from the sensor (104) and modifying the value of the measurements from theIMU (106)to approximately reach the selected timestamped value of the sensor (104).
[0052] Referring to FIG. 12, the method (1200) includes applying, at step 1208, a set of rotational matrices on the measurement captured by the sensor (104), wherein the rotational matrices provides a hypothetical cost metric associated with the order in which the sensor (104) might have undergone rotational motion such as roll, pitch, and yaw. The method (1200) proceeds with selecting, at step 1210, a rotational matrix with least cost, and compensating, at step 1212, the first set of measurements based on the rotational matrix having the least cost.
[0053] FIG. 13 illustrates an exemplary computer system (1300) in which or with which embodiments of the present disclosure may be implemented.
[0054] As shown in FIG. 13, the computer system (1300) may include an external storage device (1310), a bus (1320), a main memory (1330), a read only memory (1340), a mass storage device (1350), communication port(s) (1360), and a processor (1370). A person skilled in the art will appreciate that the computer system (1300) may include more than one processor (1370) and communication port(s) (1360). The processor (1370) may include various modules associated with embodiments of the present disclosure. The communication port(s) (1360) may be any of an RS-242 port for use with a modem based dialup connection, a 10/100 Ethernet port, a Gigabit or 10 Gigabit port using copper or fiber, a serial port, a parallel port, or other existing or future ports. The communication port(s) (1360) may be chosen depending on a network, such a Local Area Network (LAN), Wide Area Network (WAN), or any network to which computer system connects. The memory (1330) may be Random Access Memory (RAM), or any other dynamic storage device commonly known in the art. The read-only memory (1330) may be any static storage device(s) e.g., but not limited to, a Programmable Read Only Memory (PROM) chips for storing static information e.g., start-up or basic input/output system (BIOS) instructions for the processor (1370). The mass storage device (1350) may be any current or future mass storage solution, which can be used to store information and/or instructions. Exemplary mass storage solutions include, but are not limited to, Parallel Advanced Technology Attachment (PATA) or Serial Advanced Technology Attachment (SATA) hard disk drives or solid-state drives (internal or external, e.g., having Universal Serial Bus (USB) and/or Firewire interfaces), one or more optical discs, Redundant Array of Independent Disks (RAID) storage.
[0055] The bus (1320) communicatively couples the processor (1370) with the other memory, storage, and communication blocks. The bus (1320) may be, e.g. a Peripheral Component Interconnect (PCI) / PCI Extended (PCI-X) bus, Small Computer System Interface (SCSI), Universal Serial Bus (USB) or the like, for connecting expansion cards, drives and other subsystems as well as other buses, such a front side bus (FSB), which connects the processor (1370) to the computer system (1300).
[0056] Optionally, operator and administrative interfaces, e.g. a display, keyboard, and a cursor control device, may also be coupled to the bus (1320) to support direct operator interaction with the computer system (1300). Other operator and administrative interfaces may be provided through network connections connected through the communication port(s) (1360). Components described above are meant only to exemplify various possibilities. In no way should the aforementioned exemplary computer system (1300) limit the scope of the present disclosure.
[0057] While the foregoing describes various embodiments of the invention, other and further embodiments of the invention may be devised without departing from the basic scope thereof. The scope of the invention is determined by the claims that follow. The invention is not limited to the described embodiments, versions or examples, which are included to enable a person having ordinary skill in the art to make and use the invention when combined with information and knowledge available to the person having ordinary skill in the art.

ADVANTAGES OF THE DISCLOSURE
[0058] The present disclosure performs measurement compensation at a digital level, thereby reducing the cost involved in measurement compensation using hardware.
[0059] In the present disclosure, measurement compensation is performed in the data processing module, thereby reducing the processing complexity.
[0060] In the proposed disclosure, error correction is done at signal measurements level, thereby reducing the processing need associated with a signal processor. 
, Claims:1. A data processing system (110), comprising:
an input interface to:
obtain a first set of data from a sensor (104); and
obtain a second set of data from an inertial measurement unit (IMU) (106), wherein the first set of data is corrected based on the second set of data to compensate for errors due to an effect of motion on the sensor (104); and
an output interface to:
provide a corrected measurement (112).

2. The data processing system (110) as claimed in claim 1, wherein the sensor (104) and the IMU (106) are mounted on a moving platform (102).
3. The data processing system (110) as claimed in claim 1, wherein the effect of motion on the sensor (104) comprises at least one of: translational motion or a rotational motion, and wherein the rotational motion comprises motion along roll, pitch, and yaw axes.
4. The data processing system (110) as claimed in claim 3, wherein the second set of data is extrapolated based on the first set of data to compensate for errors due to the translational motion.
5. The data processing system (110) as claimed in claim 3, wherein the first set of data is subjected to a set of rotational matrices to compensate for errors due to the rotational motion.
6. The data processing system (110) as claimed in claim 5, wherein a cost metric is associated with each rotational matrix in the set of rotational matrices.
7. The data processing system (110) as claimed in claim 6, wherein said data processing system (110) is configured to select at least one rotational matrix, from the set of rotational matrices, providing a least cost metric.
8. A method (1200) for compensating errors due to an effect of motion on a sensor (104), said method (1200) comprising:
obtaining (1202), by a data processing system (110), a first set of data from a sensor (104);
obtaining (1204), by the data processing system (110), a second set of data from an inertial measurement unit (IMU) (106);
extrapolating (1206), by the data processing system (110), the second set of data based on the first set of data to compensate for errors due to an effect of translation motion on the sensor (104);
applying (1208), by the data processing system (110), a set of rotational matrices on the first set of data to determine a cost metric associated with each rotational matrix in the set of rotational matrices;
selecting (1210), by the data processing system (110), at least one rotational matrix, from the set of rotational matrices, providing a least cost metric; and
compensating (1212), by the data processing system (110), errors due to a rotational motion on the first set of data based on the least cost metric.
9. The method (1200) as claimed in claim 8, wherein the second set of data is a timestamped data, and wherein the timestamp is selected to be associated with a time of obtaining the first set of data..
10. The method (1200) as claimed in claim 8, comprising mounting the sensor (104) and the IMU (106) on a moving platform (102).