KR100742612B1 - Apparatus and Method for Complex Navigation Using Dead Reckoning and GPS - Google Patents

Abstract

The present invention provides a complex navigation device using a dead reckoning and GPS, and a method thereof, which can be used to provide robust positioning information to an environment by using a filter that is robust to various error environments and is mounted on a vehicle that requires precise positioning information without interruption. A dead reckoning navigation system comprising a moving acceleration measurement means, a moving object rotation measuring means for measuring the rotation of the moving object, a GPS receiving means, and a display means, and a dead reckoning apparatus using GPS. Sigma point generation means for generating sigma points using information of the state variable mean and error covariance of the filter used in Time propagation means for performing dead reckoning (DR) using the sigma points generated by the sigma point generating means; Pseudorange generating means for generating a pseudorange including an error of the dead reckoning using the navigation solution of the dead reckoning DR output from the time propagation means and the positional information of the satellite outputted from the GPS receiving means; And measured value updating means for updating the sigma point of the sigma point generating means by comparing the output information of the GPS receiving means and the pseudo distance generated by the pseudo distance generating means.

FIR filter, dead reckoning (DR) / GPS, error, positioning information, complex navigation system

Description

Apparatus and Method for Complex Navigation Using Dead Reckoning and GPS}

1 is a configuration diagram of an embodiment of a complex navigation device according to the present invention.

2 is a flow diagram of an embodiment of a combined navigation method according to the present invention.

3 is a diagram illustrating an embodiment of a filtering unit of a complex navigation device according to the present invention;

4 is a flow chart of an embodiment of a filtering process of a complex navigation method according to the present invention.

5 to 9 are diagrams illustrating an example of simulation results of the combined navigation apparatus according to the present invention.

* Explanation of symbols for the main parts of the drawings

101: accelerometer 102: gyro

103: sigma point generation unit 104: time transmission unit

105: pseudo range generating unit 106: measured value updating unit

107: GPS receiver 110: filtering unit

120: display unit

The present invention relates to a complex navigation apparatus and method using a robust dead reckoning (DR) system and a satellite positioning system (GPS), and more particularly, is mounted in a vehicle that requires precise positioning information without disconnection, The present invention relates to a dead reckoning, a complex navigation device using GPS, and a method for providing robust positioning information to an environment using a filter having robust characteristics.

With the development of low, small, low power, low cost Global Positioning System (GPS) receivers, GPS receivers have been used as positioning sensors in commercial vehicle navigation systems. Map matching of acquired vehicle location information to digital maps provides users with navigation information such as their location and speed, and location-based services (LBS) such as road guidance and danger zone notifications. have. However, the GPS receiver does not provide accurate location information when the GPS signal is completely or partially blocked, such as in tunnels, underground parking lots, and urban areas.

Therefore, in order to provide the positioning information in which such disconnection occurs without interruption, a DR / GPS complex navigation system is constructed by combining a dead reckoning (DR) system composed of a speedometer, a geomagnetic sensor, and an inertial sensor with a GPS receiver. do.

The DR / GPS complex navigation system may be configured in various forms according to the form of dead reckoning (DR), a coupling technique, and a coupling filter. Here, most of Kalman filters have been used as coupling filters. The Extended Kalman Filter (EKF) is used to apply the Kalman filter used in the linear system to the DR / GPS combined navigation system, which is a nonlinear system.

The Extended Kalman Filter (EKF) can provide accurate positioning in DR / GPS complex navigation systems by accurately estimating errors in good environments, such as small initial estimation errors, small system / measured noise, and no model uncertainty. have. However, suboptimal or divergent characteristics may be exhibited in environments where initial azimuth information cannot be calculated, system / measured noise is high, model uncertainties are present, and the like.

Recently, various filters (for example, Sigma Point Kalman Filter (SPKF), Received Horizon Kalman FIR (RHKF) filter, etc.) have been studied. However, the Sigma Point Kalman Filter (SPKF) is robust only to the magnitude of the initial estimation error, and the Receding Horizon Kalman FIR (RHKF) filter does not currently have a complete filter for nonlinear systems. Therefore, there is no filter having robust characteristics in various error environments, and there is a problem that an error may occur in a vehicle navigation system using an extended Kalman filter (EKF).

The present invention has been proposed to solve the above problems, and is provided in a vehicle that requires precise positioning information without disconnection, and is a dead reckoning that can provide robust positioning information to an environment by using a filter having robust characteristics against various error environments. An object of the present invention is to provide a complex navigation device and a method using the GPS.

Other objects and advantages of the present invention can be understood by the following description, and will be more clearly understood by the embodiments of the present invention. Also, it will be readily appreciated that the objects and advantages of the present invention may be realized by the means and combinations thereof indicated in the claims.

The apparatus of the present invention for achieving the above object comprises a dead reckoning comprising moving body acceleration measuring means, a moving body rotating measuring means for measuring the rotation of the moving body, a GPS receiving means and a display means, and a complex navigation using GPS. An apparatus comprising: sigma point generating means for generating sigma points using information of a state variable mean and error covariance of a filter used for dead reckoning (DR); Time propagation means for performing dead reckoning (DR) using the sigma points generated by the sigma point generating means; Pseudorange generating means for generating a pseudorange including an error of the dead reckoning using the navigation solution of the dead reckoning DR output from the time propagation means and the positional information of the satellite outputted from the GPS receiving means; And measured value updating means for updating the sigma point of the sigma point generating means by comparing the output information of the GPS receiving means and the pseudo distance generated by the pseudo distance generating means.

On the other hand, the method of the present invention, a combined navigation method using the dead reckoning and GPS, comprising: a first step of setting the calculation period and the data output period using the internal timer mounted on the complex navigation device; A second step of storing sensor data transmitted from a dead reckoning (DR) sensor in an internal register and calculating an azimuth, speed, and position of the moving object using an accelerometer and a gyro; Generating a pseudorange using the navigation information and GPS receiver output information calculated in the second step, and estimating an error using the generated pseudorange information and the pseudorange information provided by the GPS receiver; And a fourth step of calculating navigation information compensated for by the error estimated in the third step, and displaying the calculated navigation information.

The above objects, features and advantages will become more apparent from the following detailed description taken in conjunction with the accompanying drawings, whereby those skilled in the art may easily implement the technical idea of the present invention. There will be. In addition, in describing the present invention, when it is determined that the detailed description of the known technology related to the present invention may unnecessarily obscure the gist of the present invention, the detailed description thereof will be omitted. Hereinafter, exemplary embodiments of the present invention will be described in detail with reference to the accompanying drawings.

1 is a configuration diagram of an embodiment of a complex navigation device according to the present invention.

As shown in FIG. 1, the complex navigation apparatus according to the present invention includes an accelerometer 101 for calculating a moving distance of a moving object, a gyro 102 for measuring rotation of the moving object, and a GPS for receiving GPS data. The receiver 107 and the accelerometer 101 and the gyro 102 are used to calculate dead reckoning (DR) navigation information, and calculate the calculated dead reckoning (DR) navigation information and the GPS receiver 107 output information. Filtering unit 110 to generate the location information by using, and a display unit 120 for displaying the information output from the filtering unit 110.

The filtering unit 110 generates a sigma point using the state variable mean and error covariance information of the filter used in the dead reckoning DR, the sigma point generating unit 103, and the sigma point generating unit 103. The dead reckoning using the time solution 104 performing the dead reckoning (DR) using the sigma points generated by the dead reckoning (DR), the navigation solution of the dead reckoning (DR), and the position information of the satellite output from the GPS receiver (107). Pseudo distance generating unit 105 for generating a pseudo distance including the error of, and compares the output information of the GPS receiver 107 and the pseudo distance generated by the pseudo distance generating unit 105 by the sigma point generator ( A measurement value updating section 106 for updating the sigma points of 103).

As described above, each configuration of the composite navigation apparatus according to the present invention will be described in more detail as follows.

The accelerometer 101 measures the acceleration in the forward direction of the vehicle and calculates the moving distance. Any accelerometer may be mounted and aligned in the forward direction of the vehicle.

The gyro 102 is used to measure the rotation of the vehicle. The gyro 102 calculates the azimuth angle of the vehicle and the roll and the pitch angle due to the change of the ground inclination.

The sigma point generation unit 103 is calculated using information of the average of the state variables (order N) of the filter used and information of the error covariance, and according to the type of sigma point Kalman filter or "2 * N + 1" or "N + 2" and so on.

The time propagation unit 104 performs the dead reckoning (DR) using the generated sigma points, and performs the propagation process during the calculation cycle of position, velocity, azimuth, accelerometer bias, gyro bias, and GPS receiver clock bias. Equation 1 will be performed.

는 상태변수 시그마 포인트 중 j번째를 의미하며 i는 상태변수를 의미한다. 즉, "1"은 북쪽 이동 위치, "2"는 동쪽 이동 위치, "3"은 차량의 전진방향 수평 속도, "4"는 방위각, "5"는 가속도계 바이어스, "6"은 자이로 바이어스, 그리고 "7"은 GPS 수신기 시계 오차를 나타낸다.here,

Is the j th state variable sigma point, and i is the state variable. "1" is north moving position, "2" is east moving position, "3" is horizontal forward speed of vehicle, "4" is azimuth, "5" is accelerometer bias, "6" is gyro bias, and "7" represents GPS receiver clock error.

는 차량의 x축 방향 장착 가속도계 출력을 나타낸 것이며,

는 차량의 z축 방향 장착 자이로의 출력을 나타낸 것이며,

는 시간전파 주기를 의미한다. 아래첨자 k는 측정치 갱신된 시간 k를 나타낸 것이며,

은

시점까지의 측정치를 사용하여 시간 k로 시간전파하는 것을 의미한다.And,

Represents the vehicle's x- axis mounted accelerometer output,

Is the output of the vehicle's z- axis mounting gyro,

Means time propagation period. Subscript k will showing a measured value of the update time k,

silver

It means time propagation by time k using the measurement to the point of time.

The time propagated sigma point performs the time update of state variables, error covariance, and measurement by the method proposed by Sigma Point Kalman Filter (SPKF).

The pseudorange generating unit 105 uses the navigation solution of the dead reckoning DR and the position information of the visible satellite required by the strong coupling technique to calculate a pseudo distance including the error of the dead reckoning DR. ] To create them.

는 i번째 가시위성의 ECEF(Earth-Centered Earth-Fixed) 좌표 계 상의 위치를 나타낸 것이며,

는 추측 항법(DR)로 계산된 위치를 나타낸 것이다. 또한,

는 추정된 수신기 시계 오차에 해당하는 시그마 포인트를 의미한다.Here, i denotes the i th visibility satellite, and j denotes the j th sigma point. And,

Is the position on the Earth-Centered Earth-Fixed (ECEF) coordinate system of the i th visible satellite,

Denotes a position calculated by dead reckoning (DR). Also,

Denotes a sigma point corresponding to the estimated receiver clock error.

When the GPS receiver 107 outputs data, the measured value updating unit 106 updates the sigma point using the information.

In the combined navigation system using the strongly coupled dead reckoning (DR) and GPS, the measured value becomes an error of pseudo range as shown in Equation 3 below.

는 k 시점에서 GPS 수신기(107)가 제공하는 i번째 의사거리를 의미하며 n은 가시위성의 수이다.here,

Denotes the i th pseudorange provided by the GPS receiver 107 at time k and n is the number of visible satellites.

Using this measure, the measured value of the state variable and the error covariance is updated by the method proposed by the Sigma Point Kalman Filter (SPKF).

In addition, the GPS receiver 107 provides position information of the receiver using a GPS satellite, and serves to provide position and pseudorange information of a visible satellite in a combined navigation apparatus using a strongly coupled dead reckoning and a GPS.

2 is a flowchart illustrating an embodiment of a complex navigation method according to the present invention.

As shown in FIG. 2, the complex navigation method according to the present invention first sets a calculation cycle and a data output cycle using an internal timer mounted in the complex navigation apparatus (201), and A / D (Analog to Digital). The converter converts the analog value of the dead reckoning (DR) sensor into a digital value (202).

Next, the converted sensor data is stored in an internal register of the complex navigation device (203), and the azimuth, speed, and position of the vehicle are calculated (204) using the accelerometer and gyro of the complex navigation device.

Next, a pseudorange is generated using the calculated navigation information and GPS receiver output information (205), and an error is estimated using the generated pseudorange information and pseudorange information provided by the GPS receiver (206). ).

Subsequently, the navigation information with the error compensated is calculated, and when a timer interrupt due to the output period set in the internal timer is generated, the calculated navigation information is transmitted to the external system through serial communication and displayed on the liquid crystal display module (207).

3 is a diagram illustrating an embodiment of a filtering unit of a complex navigation apparatus according to the present invention, and illustrates a concept of a moving section of the filtering unit for positioning.

As shown in FIG. 3, the moving section of the filtering unit for positioning in the complex navigation device includes a hidden section 301 of the current section, an active section 302 of the current section, and a next section. The hidden section (Hidden Section) of 303, and the next section of the active section (Active Section) 304.

The hidden section 301 of the current section exists only at the beginning of the filter and exists before the active section 302 of the current section. The navigation information calculated in this section is not used.

The active section 302 of the current section continuously calculates the navigation information using the information calculated last from the hidden section 301 of the current section, and the navigation information calculated in this section is Useful information and used.

The hidden section 303 of the next section is initialized at the beginning of the section as being driven at the same time as the active section 302 of the current section and the same calculation as the active section 302 of the current section. Have a process. The navigation information calculated in this section is not used.

The active section 304 of the next section continuously calculates the navigation information by using the information calculated last from the hidden section 303 of the next section and the active section of the current section. 302 starts at the end of the section and the section name is changed to the active section of the current section. That is, the active section 302 of the current section is driven independently, such as the hidden section 303 of the next section, and the hidden section 303 of the next section ends, and the active section (302) is finished. If it becomes an Active Section, this section is replaced by an Active Section 302 of the current section.

4 is a flowchart illustrating a filtering process of a complex navigation method according to an exemplary embodiment of the present invention, and shows estimation navigation information calculation and error estimation.

As shown in FIG. 4, the filtering process in the combined navigation method according to the present invention sets a state variable of a filter to be used first (401), and sets a shape and a size of a moving horizon (402). .

Next, sigma points and weights are calculated and set using the state variable initial information (403), and time propagation of the navigation solution of the dead reckoning (DR) system (404).

Next, the filter measurement value is calculated using the GPS output information (405), the error of the dead reckoning (DR) system and the clock error of the GPS receiver are estimated to update the measurement value (406).

Then, the Hidden Section and the Active Section are determined (407). If the Hidden Section is the current section, the process proceeds to the Sigma Point and Weight Setting Process (403). (408) Initialize the Hidden Section of the Interior Horizon (409), proceed with the Sigma Point and Weight Setup process (403), and start the Section while the Active Section is active. Otherwise, after calculating the current horizon and the next section in parallel, the navigation information is output (410), and the sigma point and weight setting process 403 is performed.

Meanwhile, in the step 402 of setting the shape and size of the moving section, the size of the filter is set in consideration of the convergence speed of the filter, and the moving interval of the moving section is set based on the size of the set filter. The method includes dividing a section into a hidden section and an active section. Here, the size setting of the moving section determines the size of the moving section which is twice the size of the active section of FIG. 2. This size should generally be at least twice the size of the filter order. In other words, if the size of the moving section is large, the advantage of the FIR filter is reduced. If the size of the moving section is large, the convergence of the filter is lowered.

Meanwhile, the sigma point and weight calculation process 403 may be performed by a method suggested by a Sigma Point Kalman Filter (SPKF).

In addition, in the process of time propagating the navigation solution of the dead reckoning (DR) system, the relationship between the state variables is established, and then the relationship between the sigma points is established between the state variables. Formulate using And time propagating the sigma points according to the relational expression, and repeating the time propagation by the number of sigma points.

Meanwhile, the navigation information estimated by the measured value update process 406 is output and used as navigation information when the section is the time at which the active section starts.

In addition, the step 409 of initializing the next section includes initializing the state variable, initializing the error covariance matrix, and initializing the variable. That is, the next section initialization process 409 initializes the hidden section of the next section at the start of the active section of the current section. The state variable is the final covariance matrix of the current section. Is initialized to the error covariance matrix set at the beginning of the filter driving.

In the process of calculating the current section and the next section in parallel, the time propagation and measurement values are updated using the variables used in the current section, and the time propagation and measurement values are updated using the variables used in the next section. do.

The navigation information output process may include extracting a state variable from a measurement value update of the current section, classifying the extracted state variable into output or non-output, and converting the output state variable into SI units to use the navigation information. To the device.

5 to 9 is an exemplary explanatory diagram showing a simulation test result of a complex navigation device according to the present invention, an Extended Kalman Filter (EKF), Sigma Point Kalman Filter (SPKF), performed in various test environments, The estimation error of the result of the filter is shown.

1, 2, and 3 shown on the x-axis of each figure mean an Extended Kalman Filter (EKF), a Sigma Point Kalman Filter (SPKF), and a filter according to the present invention.

The test environment of FIG. 5 is as follows.

Accelerometer 101 and gyro 102 have a random walk component error and the filter models the sensor error as a random walk.

In the test environment of FIG. 5, the magnitudes of the estimation errors of the three filters are almost similar to those of the complex navigation apparatus of FIG. 1. This shows that the performance of the three filters is similar in good circumstances.

The test environment of FIG. 6 is as follows.

The accelerometer 101 and the gyro 102 have a random walk component error, and the filter models the sensor error as a random constant.

When the combined navigation apparatus of FIG. 1 is performed in the test environment of FIG. 6, it can be seen that the magnitude of the estimation error of the proposed type of filter is smaller than that of the Extended Kalman Filter (EKF) and the Sigma Point Kalman Filter (SPKF). As a result, when there is a modeling error, it can be seen that the proposed filter has better performance than other filters.

The test environment of FIG. 7 is as follows.

Accelerometer 101 and gyro 102 have a random constant error, and the filter models the sensor error as a random constant. In addition, there is a temporary accelerometer uncertainty bias error of magnitude between 30 and 60 seconds of the total 120-second running interval.

When the complex navigation apparatus of FIG. 1 is performed in the test environment of FIG. 7, it may be seen that the estimated error of the proposed type of filter is smaller than that of the extended Kalman filter (EKF) and the Sigma Point Kalman Filter (SPKF). As a result, we can see that the proposed type of filter has better performance than other filters in case of temporary sensor uncertainty error.

The test environment of FIG. 8 is as follows.

The accelerometer 101 and the gyro 102 have errors of random walk components, and the filter models the sensor errors as random walks. The initial azimuth estimation error is set to 160 degrees.

In the test environment of FIG. 8, the extended Kalman filter (EKF) can be seen that the filter diverges when the complex navigation apparatus of FIG. 1 is performed, and the magnitude of the estimation error of the Sigma Point Kalman Filter (SPKF) and the filter of the proposed type. You can see the little thing. As a result, when the magnitude of the initial estimation error is large, the Extended Kalman Filter (EKF), which requires the calculation of the Jacobian matrix, is emitted, but the Sigma Point Kalman Filter (SPKF), which does not require the Jacobian matrix, and the proposed type of filter It can be seen that it has performance.

The test environment of FIG. 9 is as follows.

The accelerometer 101 and the gyro 102 have errors of random walk components, and the filter models the sensor errors as random walks. The number of visible satellites is set to two in the 45 second and 53 second intervals, and the number of visible satellites is set to zero in the 54 second and 60 second intervals.

In the test environment of FIG. 9, the performance of the three filters is not significantly different when the combined navigation apparatus of FIG. 1 is performed. However, the estimation error of the Sigma Point Kalman Filter (SPKF) and the proposed type of filter is greater than that of the Extended Kalman Filter (EKF). You can see that the size of is small. As a result, SPKF (Sigma Point Kalman Filter) and the proposed type of filter have good performance when the number of visible satellites changes like urban area.

In addition, the present invention is not limited to the above-described embodiments, and it is apparent that the present invention can be used by those skilled in the art in navigation systems such as vehicles, walking, aviation, and navigation.

As described above, the method of the present invention may be implemented as a program and stored in a recording medium (CD-ROM, RAM, ROM, floppy disk, hard disk, magneto-optical disk, etc.) in a computer-readable form. Since this process can be easily carried out by those skilled in the art will not be described in more detail.

The present invention described above is capable of various substitutions, modifications, and changes without departing from the technical spirit of the present invention for those skilled in the art to which the present invention pertains. It is not limited by the drawings.

The present invention as described above, the technical and structural limitations of the Extended Kalman Filter (EKF) error is increased / divergent in the environment, such as modeling error, temporary sensor uncertainty error, large initial estimation error, the number of visible satellites changes to four or less It is effective in overcoming and providing robust positioning information to the user.

In addition, the present invention can be used as a filter that provides navigation information such as position, speed, attitude, etc. in pedestrian, aviation, and navigational navigation systems as well as vehicles, and various sensors fusion as well as dead reckoning (DR) / GPS. There is an effect that can be used by modification of the filter state variable in the system.

Claims (17)

delete In the combined navigation device using a dead reckoning and GPS comprising a moving body acceleration measuring means, a moving body rotation measuring means for measuring the rotation of the moving body, a GPS receiving means, and a display means, Sigma point generation means for generating sigma points using information of the state variable mean and error covariance of the filter used for the dead reckoning DR; Time propagation means for performing dead reckoning (DR) using the sigma points generated by the sigma point generating means; Pseudorange generating means for generating a pseudorange including an error of the dead reckoning using the navigation solution of the dead reckoning DR output from the time propagation means and the positional information of the satellite outputted from the GPS receiving means; And Measured value updating means for updating the sigma point of the sigma point generating means by comparing the output information of the GPS receiving means and the pseudo distance generated by the pseudo distance generating means Complex navigation device comprising a. delete delete The method of claim 2, The sigma point generating means, And a predetermined number according to the type of sigma point Kalman filter, which is calculated using information of the average of the state variables (order N) of the filter used and information of the error covariance. The method of claim 2, The time propagation means, By performing the dead reckoning (DR) using the sigma point generated by the sigma point generating means, the propagation process during the calculation cycle of position, velocity, azimuth, accelerometer bias, gyro bias, GPS receiver clock bias The navigation system as described in [Equation], wherein the time propagated sigma point is a time navigation of the state variable, the error covariance and the measured value by the method proposed by the Sigma Point Kalman Filter (SPKF). [Equation]

here,

Is the jth point of the state variable sigma point, i is the state variable ("1" is the north moving position, "2" is the east moving position, "3" is the forward horizontal speed of the vehicle, and "4" is the azimuth angle). "5" is accelerometer bias, "6" is gyro bias, and "7" is GPS receiver clock error),

Represents the vehicle's x- axis mounted accelerometer output,

Is the output of the vehicle's z- axis mounting gyro,

Is the time propagation period, subscript k is the updated time k of the measurement,

silver

Means time propagation in time k using measurements up to point in time. The method of claim 2, The pseudo-range generating means, And a pseudo distance including an error of the dead reckoning DR using the navigation solution of the dead reckoning DR and position information of the visible satellite as shown in Equation below. [Equation]

I is the i th visibility satellite, j is the j th sigma point,

Represents the position on the Earth-Centered Earth-Fixed (ECEF) coordinate system of the i th visible satellite,

Represents the position calculated by dead reckoning (DR),

Denotes a sigma point corresponding to the estimated receiver clock error. The method of claim 2, The measurement value updating means, The sigma point is updated using the information output from the GPS receiving means, but in the combined navigation using the dead reckoning DR and the GPS, the measured value becomes an error of the pseudo distance as shown in the following Equation. And updating the measured values of the state variable and the error covariance by the method proposed by the Sigma Point Kalman Filter (SPKF). [Equation]

here,

Is the i- th pseudorange provided by the GPS receiver at point k and n is the number of visible satellites. delete In the combined navigation method using the dead reckoning and GPS, A first step of setting a calculation period and a data output period by using an internal timer mounted in the complex navigation device; A second step of storing sensor data transmitted from a dead reckoning (DR) sensor in an internal register and calculating an azimuth, speed, and position of the moving object using an accelerometer and a gyro; Generating a pseudorange using the navigation information and GPS receiver output information calculated in the second step, and estimating an error using the generated pseudorange information and the pseudorange information provided by the GPS receiver; And A fourth step of calculating navigation information compensated for by the error estimated in the third step and displaying the calculated navigation information; Complex navigation method comprising a. The method of claim 10, The third step, A state variable setting step of setting a state variable of a filter used; A moving section setting step of setting a shape and size of a moving section of the moving object; A sigma point and weight calculation step of calculating and setting sigma points and weights using the set state variable initial information; A time propagation step of time propagating a navigation solution of a dead reckoning (DR) system; A filter measurement step of calculating filter measurements using GPS output information; A measurement value updating step of estimating an error of the dead reckoning system and a clock error of a GPS receiver to update a measurement value; And The Hidden Section and the Active Section are determined. If the current section is a hidden section, the sigma point and weight calculation step is performed. If the start of the Active Section, the next section (Position Horizon) After initializing the hidden section, proceed to the sigma point and weight calculation step, and if the active section is not the start point of the section, the current section and the next section are calculated in parallel After the navigation information is output, the navigation information using step proceeds to the sigma point and weight calculation step. Complex navigation method comprising a. The method of claim 11, The moving section setting step, Setting the size of the filter in consideration of the convergence speed of the filter, setting the moving interval of the moving section according to the set size of the filter, and dividing the moving section into a hidden section and an active section; Complex navigation method comprising a. The method of claim 12, The sigma point and weight calculation step, Substantially, the composite navigation method, characterized in that performed by the method proposed by Sigma Point Kalman Filter (SPKF). The method of claim 12, The time propagation step, After establishing the relation between the state variables, the relation between the sigma points is established using the relation between the state variables, time propagating the sigma point according to the relational expression, and the time propagation the sigma point. Complex navigation method comprising the step of repeating the number of times. delete The method of claim 12, The navigation information using step, In the process of calculating the current section and the next section in parallel, time propagation and measurement values are updated using the variables used in the current section, and time propagation and measurement values are updated using the variables used in the next section. Compound Navigation Method. The method of claim 12, The navigation information using step, In the process of outputting the navigation information, the state variable is extracted from the update of the measured value of the current section. A composite navigation method characterized by the above-mentioned.

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