Vehicle-mounted inertial navigation system alignment method based on reverse navigation QR-S TCDF

Abstract

The application relates to a vehicle-mounted inertial navigation marching alignment method based on reverse navigation QR-STCDKF. In view of the problems of numerical instability and poor tracking ability of the traditional CDKF filter when the vehicle is maneuvering, the application realizes rough alignment of a moving base through an OBA algorithm; QR decomposition is adopted to replace Cholesky decomposition to improve filter stability, and a time-varying fading factor is introduced to enhance the tracking ability of the carrier sudden motion; and the historical observation data are multiplexed by combining the reverse navigation bidirectional filter to optimize the initial attitude and device zero offset estimation. The method has the advantages of fast convergence speed, high alignment accuracy and strong robustness in a complex dynamic environment, is suitable for self-alignment of a high-maneuvering vehicle-mounted platform during marching, and has good engineering application value.

Description

Technical Field

[0001] This invention relates to the field of initial alignment technology for inertial navigation, and more particularly to an alignment method for a vehicle-mounted inertial navigation system during travel based on reverse navigation QR-STCDKF. Background Technology

[0002] Modern combat equipment places stringent demands on the rapid deployment and strike capabilities of vehicle-mounted weapon platforms. Traditional strapdown inertial navigation systems (SINS) rely on a stationary base for initial alignment, which is time-consuming and cannot meet the real-time combat needs of mobile platforms. Therefore, alignment while in motion has become a core research direction in the industry. In-journey alignment is divided into two stages: coarse alignment and fine alignment. Coarse alignment is responsible for quickly solving the coarse attitude matrix, providing qualified initial values ​​for fine alignment. Fine alignment relies on filtering algorithms to continuously estimate attitude errors, velocity and position errors, and inertial device bias to achieve high-precision alignment. Currently, vector matching algorithms are commonly used for coarse alignment. Among them, the optimized OBA alignment method relies on GPS-assisted velocity and position information to construct observation vectors. Its attitude analysis performance under moving base is better than that of traditional dual-vector alignment, and it has been widely used.

[0003] Currently, in-flight precision alignment commonly employs a nonlinear Kalman filter framework. The Central Difference Kalman Filter (CDKF) eliminates the need to calculate the Jacobian matrix, exhibits strong adaptability to nonlinear systems, and has low computational complexity, making it widely used in vehicle-mounted integrated navigation alignment scenarios. The standard CDKF uses Cholesky decomposition in the covariance matrix square root calculation stage. This decomposition algorithm has an inherent constraint: it requires the covariance matrix to be decomposed to be strictly positive definite.

[0004] During actual vehicle operation, numerous complex dynamic disturbances occur, including rapid acceleration, rapid deceleration, continuous sharp turns, and continuous bumps and impacts from uneven road surfaces. These conditions can cause abrupt changes in IMU measurement noise and system model mismatch, making the prediction covariance matrix in the filtering recursion process highly susceptible to semi-positive definite, singular, or even non-positive definite problems. Once the matrix fails to meet the positive definite condition, the Cholesky decomposition directly fails, the filter values ​​oscillate violently, and in severe cases, the filter diverges completely, resulting in the failure of the alignment results.

[0005] Conventional CDKF lacks an adaptive adjustment mechanism, and the covariance matrix remains constant during the filtering process. When the carrier's motion state changes abruptly or the system model deviates significantly from the actual motion, the filter's innovation residuals accumulate continuously, failing to increase the Kalman gain in time to track the state changes. This results in a significant tracking lag, directly manifesting as slow convergence of attitude errors and poor heading angle alignment accuracy. Summary of the Invention

[0006] To address the aforementioned issues, the present invention aims to provide a method for aligning a vehicle-mounted inertial navigation system while in motion based on reverse navigation QR-STCDKF. This method balances the stability of filter numerical values, the ability to track sudden changes in carrier motion, and the ability to reuse multi-temporal observation information. It enables rapid and high-precision initial alignment while in motion in highly dynamic vehicle environments and is suitable for scenarios such as high-mobility vehicle-mounted weapon platforms and vehicle-mounted mapping and navigation equipment that require rapid initial alignment of the inertial navigation system while in motion.

[0007] To achieve the above objectives, the present invention adopts the following technical solution: The vehicle-mounted inertial navigation alignment method based on reverse navigation QR-STCDKF is characterized by the following steps: S1: Collect IMU data during vehicle movement and complete coarse alignment of the vehicle inertial navigation system during travel based on the OBA optimized alignment algorithm; S2: Construct the SINS error state model and the QR-STCDKF framework based on QR decomposition for strong tracking center differential Kalman filtering; S3: Based on the QR-STCDKF framework, it performs forward fine alignment and caches raw IMU measurement data, GPS position and velocity observation data and real-time navigation solution status; S4: Based on the principle of reverse navigation, the data timing is reversed, the IMU gyroscope output, the Earth's rotation angular velocity, and the navigation speed are inverted, and the reverse backtracking filtering is performed based on the QR-STCDKF framework to correct the attitude, velocity, position and inertial device zero bias at the initial alignment moment. S5: Reinitialize the integrated navigation system with the initial state corrected by inverse filtering, and perform forward fine alignment again based on the QR-STCDKF framework. Iterate bidirectional filtering until the attitude error and the zero bias estimation of the inertial device meet the convergence threshold, and output the final result of the vehicle inertial navigation alignment.

[0008] The vehicle-mounted inertial navigation alignment system based on reverse navigation QR-STCDKF includes a processor, a memory, and a computer program stored in the memory. When the processor executes the computer program, it specifically performs the steps in the vehicle-mounted inertial navigation alignment method based on reverse navigation QR-STCDKF as described above.

[0009] A computer storage medium storing a plurality of instructions adapted for loading by a processor and executing the method steps described above.

[0010] The present invention has the following beneficial effects: 1. This invention adopts the OBA coarse alignment combined with the QR-STCDKF fine alignment mechanism, which can quickly obtain a relatively reliable initial attitude during vehicle movement. It can utilize the inertia of the vehicle during actual driving and satellite observation information to achieve autonomous driving alignment without stopping, shortening the alignment time and improving ease of use. 2. This invention uses QR decomposition instead of traditional Cholesky decomposition for covariance square root calculation, reducing the risk of numerical divergence caused by ill-conditioned or non-positive definite covariance matrices during the filtering process. At the same time, it introduces a time-varying fading factor to adaptively adjust the filter gain and predicted covariance based on the observed information, enabling the system to respond more effectively to dynamic changes such as vehicle acceleration and deceleration, turning, and road disturbances, thereby improving stability and robustness under complex working conditions. 3. This invention combines forward filtering with reverse navigation backtracking to transmit the high-precision state information that has converged at the end of the vehicle's journey back to the initial moment. This allows for further optimization of the initial attitude and the zero bias of the gyroscope and accelerometer. After backtracking optimization, forward fine alignment is then implemented, forming a bidirectional iterative correction mechanism that improves the attitude estimation accuracy, the zero bias estimation accuracy, and the overall convergence speed. This invention has strong engineering application value. Attached Figure Description

[0011] Figure 1 This is a flowchart of a filtering algorithm based on QR-STCDKF in one embodiment of the present invention; Figure 2 This is a flowchart of the alignment process during vehicle movement based on the RQR-STCDKF algorithm in one embodiment of the present invention. Detailed Implementation

[0012] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments: In this embodiment, a vehicle-mounted inertial navigation alignment method based on reverse navigation QR-STCDKF is provided, characterized by the following steps: S1: Collect IMU data during vehicle movement and complete coarse alignment of the vehicle inertial navigation system during travel based on the OBA optimized alignment algorithm; S2: Construct the SINS error state model and the QR-STCDKF framework based on QR decomposition for strong tracking center differential Kalman filtering; S3: Based on the QR-STCDKF framework, it performs forward fine alignment and caches raw IMU measurement data, GPS position and velocity observation data and real-time navigation solution status; S4: Based on the principle of reverse navigation, the data timing is reversed, the IMU gyroscope output, the Earth's rotation angular velocity, and the navigation speed are inverted, and the reverse backtracking filtering is performed based on the QR-STCDKF framework to correct the attitude, velocity, position and inertial device zero bias at the initial alignment moment. S5: Reinitialize the integrated navigation system using the initial state corrected by inverse filtering, and perform forward fine alignment again based on the QR-STCDKF framework. Iterate bidirectional filtering until the attitude error and inertial device zero-bias estimation meet the convergence threshold, and output the final alignment result of the vehicle inertial navigation system. In this embodiment, the Northeastern Sky (ENU) is selected as the navigation coordinate system of the vehicle-mounted strapdown navigation system. ), assuming it is in The attitude differential equation at time t is: (1) In the formula, For time t, the carrying system In relation to navigation system angular velocity at The projection on the system, among which for antisymmetric matrix, Let be the attitude matrix at time t.

[0013] The velocity equation of the carrier in the navigation frame can be expressed as: (2) During the alignment process in motion; based on the matrix chain multiplication relationship, the time-varying strapdown attitude matrix during the alignment process in motion is... It can be decomposed into the following three matrix chain multiplication forms: (3) in, express time Tie The attitude transformation matrix of the system, further... Decompose: (4) In the above formula, , and These represent the longitude and latitude at the start of the rough alignment during the march, respectively. and These represent the real-time longitude and real-time latitude of the carrier, respectively. A dual-vector observation can be constructed based on the SINS specific force equation: (5) in, (6) in, and Called a two-vector, This can be obtained via GPS. Over time. The changes, the two vectors can be obtained through equation (6). In the vector... and Given the information, the attitude can be determined using the least squares method. Therefore, the OBA algorithm decomposes the initial pose matrix into... , and The three parts all use inertial space as a reference and are fully analytical in their calculation methods, enabling them to achieve a relatively good alignment effect in the coarse alignment of the vehicle-mounted rapid moving base.

[0014] In this embodiment, the vehicle-mounted inertial navigation precision alignment error model is constructed as follows: The vehicle-mounted inertial navigation system adopts the East-North-Sky (NED) coordinate system, and the system error propagation equation is as follows: Attitude error equation: (7) in, The attitude error angle is... yes A frame of reference relative to an inertial frame ( The projection of the angular velocity error of the first system onto the navigation system, i.e. .

[0015] Velocity error equation (8) in, For accelerometer in The output of the system, For the velocity information of the inertial navigation system in the navigation system, , , These are the calculation errors for Earth's rotation angular velocity, navigation system rotation, and gravity.

[0016] Position error equation (9) in, The radius of curvature of the Earth's meridian. The radius of curvature of the Earth's geoid. Latitude For height, , and These represent latitude error, longitude error, and altitude error, respectively. For northbound speed, For eastward speed, This represents the northbound velocity error. This refers to the eastward velocity error. This refers to the upward velocity error; IMU Inertial Device Error Model (10) in, For zero bias of the gyroscope, This refers to the gyroscope scaling factor error. To account for gyroscope installation error, To achieve zero bias in the accelerometer, For accelerometer scale factor error, Due to accelerometer installation error, For the quadratic term error, For the inner arm error; Traditional CDKF algorithms typically use Choliski decomposition to solve for the square root of the error covariance matrix during the recursive process. This requires the matrix to be positive definite, which can cause filter divergence, especially when the vehicle is making sharp turns or the road surface is bumpy. This application proposes to use QR decomposition instead of Choliski decomposition to solve for the square root of the error covariance matrix. QR decomposition does not require the matrix to be positive definite, thus ensuring the stability of the recursive process.

[0017] Let the deviation matrix be: (11) in, Then to Perform QR decomposition: (12) in, It is an orthogonal matrix. It is an upper triangular matrix. Therefore, the square root of the predicted covariance is... ,Right now Similarly, in the measurement update step, the observation bias matrix and the square root matrix of the measurement noise are combined and then subjected to QR decomposition to obtain the square root of the measurement covariance matrix.

[0018] To address the system model mismatch problem caused by sharp turns / acceleration / deceleration of vehicles, a strong tracking filter is used to introduce a fading factor to force the residual sequences to be mutually orthogonal, thereby enabling the tracking ability of the system's abrupt state. At the same time, the strong tracking CDKF uses central difference transformation to approximate the posterior mean and covariance of the nonlinear Gaussian system state, eliminating the need to calculate complex Jacobian matrices, thus improving computational efficiency and filtering accuracy.

[0019] To make the filter robust to uncertainties in the system model, a fading factor is introduced. Adjusting the prediction covariance matrix: (13) (14) in, The operator for finding the trace of a matrix, Let be the covariance of the residuals.

[0020] (15) in, For the current information, Forgetting factor, usually taken as By monitoring the actual measurement residual statistical characteristics online and comparing them with... When comparisons are made and the actual residual is found to significantly exceed the theoretical expectation, the value is automatically increased. This amplifies the prediction covariance, increases the Kalman gain, and enhances the filter's ability and robustness in tracking model errors and state changes.

[0021] The computation process of the improved algorithm: 1. Time Update Calculate Sigma point generation: (16) in, It is the state dimension. , and It's about adjusting parameters. , For matrix The List. Obtained from the QR decomposition of the covariance matrix (17) Sigma point propagation, (18) State prediction (19) QR decomposition predicts covariance (20) Calculate the new information covariance using the strong tracking fading factor. (twenty one) in For the current information, Forgetting factor. Gradual fading factor update: (twenty two) covariance is (twenty three) Measurement Update Measure Sigma points, (twenty four) Predicted values, (25) QR decomposition measures covariance (26) Calculation of measurement variance and cross-covariance (27) Calculate filter gain and state update (28) The filtering process based on the QR-STCDKF algorithm is as follows: Figure 1 As shown: Based on the preceding formula derivation, a 15-dimensional filter was designed to estimate the initial attitude angle and IMU zero bias of the system. The state variables of the 15-dimensional filter can then be expressed as follows: (29) in, For the system's attitude error, For the system's speed error, For the system's position error, and These represent the constant errors of the gyroscope and accelerometer, respectively.

[0022] The state transition equation of the system is (30) In the formula, Let be the state transition matrix of the system. The noise is Gaussian white noise, and the state matrix is... Represented as (31) State transition matrix The elements in are as follows: , , Using the difference between velocity and position from inertial navigation and GPS as the observed quantity, then (32) In the formula, To measure Gaussian white noise, The measurement matrix can be represented as (33) The reverse navigation algorithm is an inertial navigation processing method based on data backtracking. This method stores the output data of the gyroscope and accelerometer in the strapdown inertial navigation system as a time series, and then performs navigation calculations in reverse time.

[0023] Assuming the inertial navigation system is in the time period to If a person moves from point A to point B, then reverse navigation uses stored inertial data to perform a reverse calculation from point B to point A, thereby improving the accuracy of navigation state estimation.

[0024] The attitude update equation is (34) The velocity update equation is (35) The position update equation is (36) in, , , , in, The overall sampling time of the IMU. The time period number during navigation ( From equations (27) to (29), it can be seen that during the reverse navigation process, the calculated speed needs to be inverted. The reverse navigation algorithm can be rewritten as follows: (37) Assumption You can get (38) Simplifying the above equation yields the expression for the reverse navigation algorithm. (39) The algorithm for the reverse navigation process is basically the same as that for the forward navigation process. It simply involves inverting the data output from the gyroscope and reversing it along with the data output from the accelerometer, while also inverting the Earth's rotation speed. The velocity at any given moment is inverted.

[0025] The flowchart of the initial alignment process of QR-STCDKF based on reverse navigation during vehicle movement is as follows: Figure 2 As shown.

[0026] The entire algorithm consists of OBA coarse alignment, forward and reverse QR-STCDKF fine alignment, and reverse QR-STCDKF fine alignment, which together complete the high-precision initial alignment during vehicle movement.

[0027] During the coarse alignment phase of OBA travel, angular velocity and specific force information measured by IMU are used, combined with GPS velocity observations to construct vector observations corresponding to the navigation coordinate system and the body coordinate system. The initial attitude matrix during vehicle motion is obtained by solving the Wahba optimal attitude estimation problem, providing a reliable initial attitude for subsequent fine alignment.

[0028] Using the initial attitude obtained from coarse alignment as the initial condition, the system enters the forward QR-STCDKF fine alignment process. SINS navigation calculations are performed using IMU data, a SINS error state model is established, and GPS position and velocity are used as measurement information for filtering and updating. QR-STCDKF recursively estimates the attitude error, gyroscope and accelerometer bias, and feeds the estimation results back to the inertial navigation system for correction. Based on this, the system enters the reverse QR-STCDKF fine alignment stage. Historical data saved during the forward navigation process is read in reverse chronological order, and IMU measurement data, GPS observation data, and navigation status are reverse-processed according to the backtracking navigation principle. The attitude error and inertial device bias are estimated in reverse, allowing the high-precision navigation information that has converged at the termination of the forward filtering to be propagated to the initial time, thereby further improving the estimation accuracy of the initial attitude and inertial device error parameters.

[0029] After completing the reverse alignment, the system re-enters the forward QR-STCDKF fine alignment process, restoring the normal time sequence of IMU and GPS observation data. The integrated navigation system is then re-initialized using the backtracked corrected initial attitude, velocity, position, and inertial device zero bias, followed by another forward QR-STCDKF filter. Because the initial state has been effectively optimized, the filter converges faster, further improving the estimation accuracy of attitude and inertial device zero bias. This process is repeated until a stable and reliable initial alignment result is obtained. Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0030] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0031] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0032] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0033] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A vehicle-mounted inertial navigation alignment method based on reverse navigation QR-STCDKF, characterized in that, Includes the following steps: S1: Collect IMU data during vehicle movement and complete coarse alignment of the vehicle inertial navigation system during travel based on the OBA optimized alignment algorithm; S2: Construct the SINS error state model and the QR-STCDKF framework based on QR decomposition for strong tracking center differential Kalman filtering; S3: Based on the QR-STCDKF framework, it performs forward fine alignment and caches raw IMU measurement data, GPS position and velocity observation data and real-time navigation solution status; S4: Based on the principle of reverse navigation, the data timing is reversed, the IMU gyroscope output, the Earth's rotation angular velocity, and the navigation speed are inverted, and the reverse backtracking filtering is performed based on the QR-STCDKF framework to correct the attitude, velocity, position and inertial device zero bias at the initial alignment moment. S5: Reinitialize the integrated navigation system with the initial state corrected by inverse filtering, and perform forward fine alignment again based on the QR-STCDKF framework. Iterate bidirectional filtering until the attitude error and the zero bias estimation of the inertial device meet the convergence threshold, and output the final result of the vehicle inertial navigation alignment.

2. The vehicle-mounted inertial navigation alignment method based on reverse navigation QR-STCDKF according to claim 1, characterized in that, The OBA-based optimized alignment algorithm is used to achieve coarse alignment of the vehicle-mounted inertial navigation system during travel, as detailed below: The Northeast ENU is used as the navigation coordinate system for the vehicle strapdown navigation system. Suppose it is in The attitude differential equation at time t is: ； In the formula, For time t, the carrying system In relation to navigation system angular velocity at The projection on the system, among which for antisymmetric matrix, Let be the attitude matrix at time t; The velocity equation of the vehicle in the navigation frame is expressed as: ； During the alignment process in motion; based on the matrix chain multiplication relationship, the time-varying strapdown attitude matrix during the alignment process in motion is... It can be decomposed into: ； in, express time Tie The attitude transformation matrix of the system, further... Decompose: ； In the above formula, ， ； ； and These represent the longitude and latitude at the start of the rough alignment during the march, respectively. and These represent the real-time longitude and real-time latitude of the carrier, respectively. A dual-vector observation can be constructed based on the SINS specific force equation: ； in, ; and It is a two-vector. Obtained via GPS; in vector and Given the information, the attitude is determined using the least squares method. .

3. The vehicle-mounted inertial navigation alignment method based on reverse navigation QR-STCDKF according to claim 2, characterized in that, The construction of the SINS error state model is as follows: Attitude error equation: ； in, The attitude error angle is... yes The projection of the angular velocity error of the system relative to the inertial frame onto the navigation frame, i.e. ; Velocity error equation ； in, For accelerometer in The output of the system, For the velocity information of the inertial navigation system in the navigation system, , , These are respectively the calculation errors of Earth's rotation angular velocity, navigation system rotation calculation error, and gravity error; Position error equation: ； in, The radius of curvature of the Earth's meridian. The radius of curvature of the Earth's geoid. Latitude For height, , and These represent latitude error, longitude error, and altitude error, respectively. For northbound speed, For eastward speed, This represents the northbound velocity error. This refers to the eastward velocity error. This refers to the upward velocity error; IMU Inertial Device Error Model ； in, For zero bias of the gyroscope, This refers to the gyroscope scaling factor error. To account for gyroscope installation error, To achieve zero bias in the accelerometer, For accelerometer scale factor error, Due to accelerometer installation error, For the quadratic term error, This refers to the error of the inner arm.

4. The vehicle-mounted inertial navigation alignment method based on reverse navigation QR-STCDKF according to claim 1, characterized in that, The QR-STCDKF framework based on QR decomposition uses QR orthogonal triangular decomposition instead of Cholesky decomposition to solve for the square root of the covariance matrix, eliminating the positive definite constraint of the matrix and improving the numerical stability of the filter. Furthermore, a strong tracking time-varying fading factor is introduced to calculate the filter information residual in real time, adaptively amplify the prediction covariance matrix, and enhance the filter's ability to track sudden acceleration, deceleration, and sharp turns of vehicles. The concept of reverse navigation is introduced to reuse the full-process time-series inertial observation data and reverse the initial alignment state using the high-precision information of the end convergence.

5. The vehicle-mounted inertial navigation alignment method based on reverse navigation QR-STCDKF according to claim 1, characterized in that, The forward alignment is divided into two stages: time update and measurement update. In the time update stage, Sigma points are generated based on the square root of the covariance of QR decomposition, and a fading factor is introduced to adjust the prediction covariance. In the measurement update stage, QR decomposition is performed on the observation matrix to solve for the filter gain and complete the error state feedback correction.

6. The method for aligning a vehicle-mounted inertial navigation system during travel based on reverse navigation QR-STCDKF according to claim 1, characterized in that, The reverse navigation processing method is as follows: the IMU gyroscope output, Earth rotation angular velocity, and navigation calculation velocity are inverted, all measurement data and GPS observation data are arranged in reverse time order, the reverse navigation attitude, velocity, and position update equations are solved in reverse, and the backtracking estimation is completed using the forward filtering endpoint state as the reverse filtering initial value.

7. A vehicle-mounted inertial navigation alignment system based on reverse navigation QR-STCDKF, characterized in that, It includes a processor, a memory, and a computer program stored in the memory. When the processor executes the computer program, it specifically performs the steps in the vehicle inertial navigation alignment method based on reverse navigation QR-STCDKF as described in any one of claims 1-6.

8. A computer storage medium, characterized in that, The computer storage medium stores a plurality of instructions adapted for loading by a processor and executing the method steps as claimed in any one of claims 1-6.

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