JP2007101484A - Device for measuring carrier phase relative position - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a device for measuring a carrier phase relative position capable of quickly and precisely estimating a float solution of integer bias and quickly and precisely calculating estimation processing of a base line vector after decision of the integer bias solution. <P>SOLUTION: Estimation operation for the integer bias float solution by an integer bias float solution estimation part 11 and calculation operation of the base line vector by a base line vector calculation part 14 is carried out under a restriction condition of known information acquired from a relative positional relationship between GPS antennas. As a concrete operation method under the known information restriction condition, methods based on Lagrange multiplier and extended Kalman filter are available. <P>COPYRIGHT: (C)2007,JPO&INPIT

Description

The present invention relates to a carrier phase relative positioning device including a plurality of GPS antennas fixed to a moving body.

As a carrier phase relative positioning device that performs positioning using a carrier phase difference, for example, there is a posture detection device that detects the posture of a moving body using a plurality of GPS antennas fixed to the moving body and inertial sensor units. In this attitude detection device, an integer bias solution is obtained from an observed amount of carrier phase difference generated between an antenna pair and a plurality of satellites, and another antenna (hereinafter referred to as a slave antenna) with respect to a reference antenna is obtained from the obtained integer bias solution. )), That is, a base line vector, and the posture of the moving body is determined from the base line vector.

In such a carrier phase relative positioning device that performs positioning using a carrier phase difference, such as an attitude calculation device, it is a major technical problem to determine an integer bias solution (Integer Ambiguity) quickly and correctly.

FIG. 6 is a diagram for explaining an arithmetic processing procedure for obtaining this integer bias solution. As shown in FIG. 6, the integer bias solution determination is roughly divided into an integer bias float solution estimation process 101, an integer bias solution determination process 102, and an integer bias solution test process 103. it can.

An integer bias float solution estimation process 101 is a process of calculating an integer bias float solution from a phase difference observation amount of a positioning signal, and estimates an integer bias float solution using a Kalman filter or the like (Non-Patent Document 1). . The integer bias solution determination processing 102 calculates an integer bias float solution, then determines an integer bias candidate solution based on a search range determined according to the calculation accuracy of the integer bias float solution, and determines the integer bias candidate solution from the integer bias candidate solution. Determine p integer bias candidate solution sets (N ₁ , N ₂ ,..., N _k ) p. Then, one true integer bias solution set is narrowed down from the determined p integer bias candidate solution sets (N ₁ , N ₂ ,..., N _k ) p to determine integer bias solutions. A known technique such as the LAMBDA technique (Non-Patent Document 2) is used for the integer bias solution determination process 102. The integer bias solution test process 103 is a process for testing whether the integer bias solution determined using known information such as baseline length information is correct.

Once an integer bias solution is determined, the baseline vector can be calculated using a Kalman filter or the like with the integer bias solution known. The posture detection apparatus can obtain the posture of the moving body from the baseline vector.

By the way, various techniques for quickly and correctly determining an integer bias solution have been proposed. For example, Non-Patent Documents 3 and 4 propose a technique using a base length constrain for testing an integer bias float solution or an integer bias solution.

Non-Patent Document 3 discloses that the base line length between two mobile station antennas is known in a configuration in which two GPS mobile stations are fixedly installed on a mobile object with respect to a GPS reference station placed at a known position outside the mobile object. An integer bias solution is estimated as a constraint. Non-Patent Document 3 also presents the use of constraints such as pitch and heading, but these constraints can only be applied after the integer bias is determined.

Non-Patent Document 4 is known in order to efficiently obtain an integer bias candidate solution by reducing the search range of a three-dimensional space in determining an integer bias candidate solution, and for an integer bias test. This technique uses baseline length information.

On the other hand, the present invention, which will be described later, estimates a float solution of an integer bias and a baseline vector using phase difference observation amounts obtained from a plurality of rigidly fixed antennas on a moving body. In this technique, an integer bias float solution or baseline vector is obtained by incorporating known information constraints such as the baseline length directly into the estimation algorithm for estimating the float solution and baseline vector.
Hwang, Patrick Y. C, “Kinematic GPS For Differential Positioning Resolving Integer Ambiguities On The Fly”, Navigation, Vol-31, No. 1 P. J. G. Teunissen, "A New Method for carrier Phase Ambiguity Estimation", proc. IEEE "Position Location and Navigation Symposium PLANS 94", Las Vegas, 11-15 April, 1994, pp 562-573. Shawn D. Weisenburger, "Effect of Constraints and Multiple Receivers for On-The-Fly Ambiguity Resolution", UCGE Reports Number 20109, Department of Geomatics Engineering, University of Calgary, 1997 (http://www.geomatics.ucalgary.ca/Papers/Thesis /MEC/97.20109.SWeisenburger.pdf (as of 2005.10.7)) Lu G. "Development of a GPS Multi-Antenna System for Attitude Determination", UCGE Reports No. 20073, Department of Geomatics Engineering, University of Calgary, 1995 Kornfeld. R. P. Hansman, R.D. J. and Deyst, J.A. J. "Single Antenna GPS Based Aircraft Attitude Determination," Proceedings of the Institute of Navigation National Technical Meeting, Jan. 21-23, 1998, Long Beach, CA, pp. 345-354.

However, even if the above-described conventional technique is used, the time required for the convergence of the integer bias float solution greatly depends on the number of GPS satellites. Therefore, when the number of GPS satellites that can be used is the minimum necessary number, the integer bias float is used. It usually takes 20 to 30 minutes or more to finish the solution estimation process 101. In addition, the accuracy of the estimated integer bias float solution also deteriorates. As the accuracy of the integer bias float solution deteriorates, the time required for the subsequent integer bias solution determination process 102 and the integer bias solution test process 103 also increases, and an erroneous integer bias solution occurs.

Furthermore, the time required to determine the integer bias solution and the accuracy of determining the integer bias solution are also affected by multipath observation errors, and sometimes an integer bias solution cannot be obtained or an incorrect integer bias solution may be generated.

In particular, in an apparatus that requires a short baseline length, even a slight error in the integer bias solution gives an unacceptable error in position, velocity, attitude, etc., so it is extremely important to quickly determine the correct integer bias solution.

Also, a GPS / INS integrated device (MA-GPS / INS system) using an inertial sensor has been proposed, but the above-mentioned drawbacks have not been sufficiently overcome. In particular, the determination of the integer bias solution when the power is turned on depends on the performance of the GPS system, and therefore, the merit of integrating the INS is not fully utilized.

The present invention has been made in view of the above problems, and an object of the present invention is to estimate an integer bias float solution at high speed and with high accuracy in the integer bias float solution estimation processing 101. At the same time, an object of the present invention is to obtain a baseline vector at high speed and with high accuracy using the determined integer bias solution after the integer bias solution is determined.

In order to solve the above-mentioned problems, the present invention provides a receiver for obtaining a phase difference observation amount from positioning signals received by a plurality of antennas in a carrier phase relative positioning device including a plurality of antennas fixed on a moving body. And an integer bias float solution estimator for estimating an integer bias float solution from the phase difference observation amount using known information obtained from the relative positional relationship of the plurality of antennas as a constraint.

That is, the integer bias float solution estimation unit directly incorporates a known information constraint condition such as a baseline length into an integer bias float solution estimation algorithm to estimate an integer bias float solution. As a specific technique for estimating an integer bias float solution under a condition with known information constraints, for example, a method using a Lagrange multiplier or an extended Kalman filter is conceivable.

Here, the known information obtained from the relative positional relationship of the plurality of antennas refers to known information obtained from the fact that the plurality of GPS antennas 1 are fixed, for example, baseline length information between the GPS antennas, There is information on the inner product and outer product of the baseline formed between the GPS antennas.

Further, the present invention is characterized in that the baseline vector initial value calculation unit estimates an initial value of the baseline vector, and provides the estimated baseline vector as an initial value of the baseline vector during the integer bias float solution estimation processing. This makes it possible to perform an integer bias float solution estimation process with higher accuracy and speed. Note that the initial value of the baseline vector is estimated by using, for example, a technique for estimating the baseline vector using the pseudo posture angle calculated from the moving body velocity, or estimating the baseline vector using the observation amount of the inertial sensor and the moving body speed. There is a technique to do.

Furthermore, the technique based on the known information constraint in the present invention can be applied not only to the estimation of the integer bias float solution but also to the calculation of the baseline vector after the integer bias solution is determined.

That is, the present invention relates to a carrier phase relative positioning device including a plurality of antennas fixed on a moving body, a receiver for obtaining a phase difference observation amount from positioning signals received by the plurality of antennas, and the phase difference observation amount. The base line vector is calculated from the phase difference observation amount and the integer bias solution, using the integer bias solution calculation unit for calculating the integer bias solution from the above and the known information obtained from the relative positional relationship of the plurality of antennas as constraints. A baseline vector calculation unit is provided.

The baseline vector calculation unit calculates a baseline vector by directly incorporating known information constraint conditions such as a baseline length into the baseline vector estimation algorithm using the integer bias solution calculated by the integer bias solution calculation unit as a known amount. As a specific technique for obtaining the baseline vector under a condition with known information constraints, for example, a method using a Lagrange multiplier or an extended Kalman filter can be considered.

Further, the present invention is characterized in that the baseline vector initial value calculation unit estimates an initial value of the baseline vector, and provides the estimated baseline vector as an initial value of the baseline vector during the baseline vector calculation process. This makes it possible to perform an integer bias float solution estimation process with higher accuracy and speed. Note that the initial value of the baseline vector is estimated by using, for example, a technique for estimating the baseline vector using the pseudo posture angle calculated from the moving body velocity, or estimating the baseline vector using the observation amount of the inertial sensor and the moving body speed. There is a technique to do.

The present invention is further characterized by further comprising means for correcting the inertial sensor error when the observed amount of the inertial sensor is used to estimate the initial value of the baseline vector. That is, it further includes an inertial sensor error correction unit that corrects an observation amount error of the inertial sensor using information obtained after the determination of the integer bias solution, and the baseline vector initial value calculation unit re-determines the integer bias solution, A baseline vector value is obtained as an initial value by estimating a baseline vector using the observed amount of the inertial sensor whose error is corrected by the inertial sensor error correction unit. With this configuration, when the integer bias solution is re-determined, the integer bias float solution and the baseline vector can be obtained with higher accuracy and higher speed.

According to the present invention, by using known information obtained from the relative positional relationship of a plurality of antennas as a constraint condition, a float solution with an integer bias is estimated from the phase difference observation amount, so that conventional estimation without a constraint condition is performed. Compared to the technique, an integer bias float solution can be estimated with higher accuracy and speed. As a result, the integer bias solution can also be determined with high accuracy and high speed.

In addition, since the present invention can accurately estimate the initial value of the baseline vector by the baseline vector initial value calculation unit, if the accuracy of the initial value of the baseline vector is good, the accuracy can be regarded as an integer bias directly. To determine the integer bias float solution. Even if the baseline vector initial value cannot be obtained with such high accuracy, the error covariance of the integer bias float solution can be made much smaller than that of the conventional technique. Therefore, the search range for determining the integer bias solution can be narrowed, and as a result, the integer bias solution can be obtained more quickly and accurately.

Furthermore, the technique based on the known information constraint in the present invention can be applied not only to the estimation of the integer bias float solution but also to the calculation of the baseline vector after the integer bias solution is determined.

In addition, the technique based on the known information restriction according to the present invention not only contributes as a measure against the deterioration of estimation accuracy caused by the observation noise of the phase difference observation amount, but also can be expected to be effective as a measure against the deterioration of accuracy due to multipath noise.

(Embodiment 1)
Hereinafter, a carrier phase relative positioning apparatus according to Embodiment 1 of the present invention will be described with reference to the drawings.

FIG. 1 is an explanatory diagram for explaining a navigation coordinate system and a moving body coordinate system used in the present invention.
FIG. 1A is a diagram showing a navigation coordinate system and a moving body coordinate system. As shown in FIG. 1, the reference navigation coordinate system is a right-handed orthogonal coordinate system (Geodetic coordinate system) in which the north and east directions are the X axis and the Y axis, respectively. The moving body coordinate system is a right-handed orthogonal coordinate system in which the moving direction of the moving body and the right hand direction are the X axis and the Y axis, respectively. When the moving body is a ship, the bow direction is the X axis and the starboard direction is the starboard direction. Set as Y-axis. Note that the origins of both coordinate systems do not necessarily match, but here they are matched to help understanding. In the description of the present invention, the right shoulder suffix n indicates a navigation coordinate system, and the right shoulder suffix b indicates a moving body coordinate system (Body coordinate system).

FIG. 1B is a diagram showing the position of the GPS antenna mounted on the moving object coordinate system of FIG. In FIG. 1B, the reference antenna is installed at the origin of the moving object coordinate system, and the slave antenna is installed at a predetermined position. The position of the slave antenna is set to a known arbitrary position where the directions of the plurality of baseline vectors are different. FIG. 1B shows an example of two slave antennas, but one or more slave antennas may be used.

FIG. 2 is a diagram illustrating an example of the configuration of the carrier phase relative positioning device according to the first embodiment of the present invention, in which the carrier phase relative positioning device including three GPS antennas fixed to a rigid body on a moving body. A configuration example is shown.

In FIG. 2, the carrier phase relative positioning device according to the first embodiment of the present invention includes a GPS antenna 1, a GPS receiver 2, and an arithmetic processing unit 3a. The arithmetic processing unit 3 a includes an integer bias float solution estimation unit 11, an integer bias determination unit 12, an integer bias test unit 13, a baseline vector calculation unit 14, and a GPS attitude calculation unit 15.

The GPS antenna 1 is a rigid antenna fixed on a moving body, and each antenna is placed at a known position. Note that the number of GPS antennas 1 fixed on the moving body is not necessarily limited to three, and at least two or more may be sufficient if the inner product and outer product of the baseline are used as known information. In the embodiment of the present invention, the GPS antenna 1 is described as an example. However, the radio wave received by the antenna is not limited to the positioning signal transmitted from the GPS satellite, but the GLONASS satellite, the GALILEO satellite, or the quasi-zenith. Any signal that can receive at least one signal among positioning signals transmitted from a positioning satellite such as a satellite may be used.

The GPS receiver 2 receives positioning signals from a plurality of satellites via the GPS antenna 1 to generate a phase difference observation amount, and together with the reference antenna position and satellite position information (Ephemeris), an integer bias float solution estimation unit 11 Output to. Here, the phase difference observation amount may be a single phase difference or a double phase difference.

The integer bias float solution estimator 11 incorporates the constraint of known information obtained from the relative positional relationship between GPS antennas into the observation equation relating the phase difference observation amount and the integer bias float solution, and thereby the integer bias float solution. Is estimated. The known information obtained from the relative positional relationship between the GPS antennas means known information obtained from the fact that the plurality of GPS antennas 1 are fixed. There is information on the inner product and outer product of the baseline formed. In addition, as a specific technique for the integer bias float solution estimation unit 11 to estimate the integer bias float solution under a condition with known information constraints, for example, a method using a Lagrange multiplier or an extended Kalman filter is conceivable. . Details of this will be described later.

The integer bias determination unit 12 obtains p integer bias candidate solution sets from the integer bias float solution estimated by the integer bias float solution estimation unit 11 using a known LAMBDA technique or the like. Thereafter, one true integer bias solution set is narrowed down from the integer bias candidate solution sets of p integer biases, and integer bias solutions are determined. By the way, the search range of the integer bias candidate solution in the integer bias determination unit 12 depends on the precision of the integer bias float solution obtained by the integer bias float solution estimation unit 11 described above. Therefore, by realizing higher accuracy of the integer bias float solution, the search range of the integer bias candidate solutions can be narrowed, and the speed of the integer bias determination process can be simultaneously realized. Further, depending on the estimation accuracy of the integer bias float solution by the integer bias float solution estimation unit 11, an integer solution of an integer bias can be obtained directly. Therefore, the integer bias determination unit 12 is omitted, and the integer bias float solution estimation unit 11 directly. It is not impossible to test integer bias solutions from

The integer bias test unit 13 tests whether the integer bias solution determined by the integer bias determination unit 12 is correct by a known technique such as a baseline length test or a statistical test.

The baseline vector calculation unit 14 uses the integer bias solution passed through the integer bias test unit 13 as a known amount, and the known information obtained from the relative positional relationship between the GPS antennas in the phase difference observation equation linearized with respect to the baseline vector The baseline vector is calculated by incorporating the constraints. At this time, the baseline vector calculation unit 14 uses a new phase difference observable with the integer bias solution known. As a specific method for obtaining a baseline vector under a condition with known information constraints, a Lagrange multiplier or an extended Kalman filter technique can be applied as described above. Details of this will be described later.

The GPS attitude calculation unit 15 obtains an attitude angle from the baseline vector calculated by the baseline vector calculation unit 14 using a known technique such as a TRIAD technique or a QUEST technique.

The integer bias solution calculation unit 16 illustrates a block for calculating an integer bias solution from the phase difference observation amount. For example, the integer bias float solution estimation unit 11, the integer bias determination unit 12, and the integer bias test unit described above. 13 is a component.

Next, the process performed by the integer bias float solution estimation unit 11 will be described in more detail.
For example, the carrier phase difference observation amount ΔΦ _ij in the navigation coordinate system in the single difference observation model can be expressed by the following equation (Equation 1). Here, i and j indicated by subscripts at the lower right indicate the i-th slave antenna and the j-th satellite, respectively.

In the following description, a single baseline vector is considered for the sake of simplicity. Therefore, the lower right subscript i attached to the baseline vector is omitted. If a plurality of baseline vectors are considered, an expression corresponding to the baseline vector may be formed, and the technique described below can be similarly applied.

Now, when the observation equation [Equation 1] is linearized and expressed as a matrix, it can be expressed as the following equation [Equation 2]. Note that t (1, 2,..., N) indicated by the lower right subscript indicates an observation epoch.

The integer bias float solution estimation unit 11 includes a baseline vector (x _t , y _t , z _t ) and integer bias solutions (ΔN ₁ , ΔN ₂ ,..., ΔN _k ) based on the observation equation [Equation 2]. The state Xt is estimated under a constraint condition of known information obtained from the relative positional relationship between GPS antennas.

Here, in order to estimate the state Xt based on the observation equation of [Equation 2], it is required that the number of observations be equal to or greater than the estimated unknown number. In the case of a single phase difference observation model, the number of observations obtained per baseline vector is n × k, and the number of unknowns is 3 × n + k. Therefore, the required observation epoch must be n ≧ k / k−3. I must. Therefore, an observation amount of 4 observation epochs or more is required with 4 or more satellites (k = 4).

Therefore, the observation equation formed in the necessary observation epoch is again expressed as the following equation [Equation 3].

The integer bias float solution estimator 11 uses the Lagrange multiplier or the extended Kalman filter based on the observation equation [3] to obtain known information obtained from the relative positional relationship between the GPS antennas. State X is estimated under the constraints.

Hereinafter, as an estimation method of the state X using the Lagrange multiplier and the extended Kalman filter, a case where the known information obtained from the relative positional relationship between the GPS antennas is the baseline length information will be described as an example. .

First, a technique using a Lagrange multiplier will be described.
(1) Technique Using Lagrange Multiplier In the Lagrange multiplier technique, an observation equation [Equation 3] and a linear constraint equation [Equation 4] are considered.

Since one base line vector is considered here, C is the base line length and is a scalar. The optimal problem under the constraint condition by the Lagrange multiplier can be converted into the following equation [Equation 5] from [Equation 3] and [Equation 4]. Μ is a Lagrange multiplier.

The optimum solution of the state X of [Equation 5] can be obtained by the following equation [Equation 6]. In order to estimate the optimum solution of the state X more accurately and at high speed, it is desirable to give the initial value of the baseline vector v (x, y, z) as accurately as possible.

At this time, the Lagrange multiplier μ can be obtained by the following equation [Equation 7] by substituting [Equation 6] into [Equation 4].

By the way, the optimum solution of the state X obtained by [Equation 6] is a solution for the linear constraint function. However, when the baseline length information is used as the constraint condition, the constraint function is nonlinear as shown in the following equation [Equation 8]. It becomes.

Therefore, it is necessary to linearize [Equation 8] around the baseline vector estimated value as shown in [Equation 9] using Taylor's next approximation, and to obtain an optimum solution of the state X by iterative calculation.

In this case, B in [Equation 4] can be calculated from the following equation [Equation 10].

Here, the case where the baseline length is used as a constraint condition has been described. However, when known information other than the baseline length is used as a constraint condition, the relational expression between the applied known amount C and the state X is considered. It can be processed similarly.

Next, a technique using an extended Kalman filter will be described.
(2) Technique Using Extended Filter It is also possible to estimate integer bias float solutions and baseline vectors with known information constraints using a known extended Kalman filter. The extended Kalman filter is a technique in which an initial value of a state quantity to be estimated is given and an estimated value of the state quantity is sequentially calculated based on the initial value and the time history of the observed quantity. This extended Kalman filter is convenient for practical use because it has an advantage that the state can be estimated while sequentially updating the error covariance based on the observation amount for each observation epoch.

In the technique using the extended Kalman filter, known information equivalent to a Lagrange multiplier is obtained by adding, as a pseudo-observed quantity, known information obtained from the relative positional relationship between GPS antennas to the k observed quantities of [Equation 2]. Can perform constrained estimation. At this time, in order to estimate more accurately and faster to the optimal solution of the state X _t [Equation 2] is preferably'll give baseline vector v (x, y, z) the initial value of as accurately as possible.

An observation model of the pseudo observation amount yc added as a pseudo observation amount to the k observation amounts of [Equation 2] can be defined by the following equation [Equation 11].

Although νc is essentially zero, in order to adjust the weight between the pseudo observation amount and the actual observation amount, pseudo observation noise having an average value of zero and variance Rc can be used. B is an observation matrix, and when the known information as the constraint condition is the base line length, the observation matrix is determined by [Equation 10] as described above.

As described above, the integer bias float solution estimation unit 11 uses the (1) Lagrange multiplier or (2) the extended Kalman filter, and the integer bias under the restriction of the known information obtained from the relative positional relationship between the GPS antennas. A float float solution can be estimated. In the above example, the case where only the baseline length information is used as the constraint condition has been described, but the same can be said when other known information is used as the constraint condition. Of course, the more information you impose as constraints, the better the results you get.

Next, the process performed by the baseline vector calculation unit 14 will be described in detail.
The baseline vector calculation unit 14 obtains a baseline vector from the observation equation [Equation 2] using the integer bias solution determined by the integer bias test unit 12 as a known amount. Specifically, the following processing is performed.

First, the integer bias solution that has passed the integer bias test unit 12 is substituted into the integer bias solution (ΔN ₁ , ΔN ₂ ,..., ΔN _k ) of the observation equation [Equation 2]. Then, the baseline vector calculation unit 14 obtains a baseline vector from the phase difference observation equation using a new phase difference observation amount whose integer bias solution is known. At this time, similar to the processing in the integer bias float solution estimation unit 11 described above, known information obtained from the relative positional relationship between the GPS antennas is directly incorporated into the estimation algorithm as a constraint condition. As a specific technique for estimating the state with the known information constraint, for example, there is a technique using a Lagrange multiplier or an extended Kalman filter, and the baseline vector v (x, y, It is desirable to provide an initial value for z). Note that the details of these processing contents are the same as those described in the processing in the integer bias float solution estimation unit 11 described above, and thus description thereof is omitted.

As described above, according to the carrier phase relative positioning apparatus according to Embodiment 1 of the present invention, the float solution with integer bias is estimated under the constraint condition of known information obtained from the relative positional relationship between GPS antennas. This makes it possible to determine the integer bias with high accuracy and high speed. In addition, according to the carrier phase relative positioning apparatus according to Embodiment 1 of the present invention, the base line vector is obtained under the constraint condition of the known information obtained from the relative positional relationship between the GPS antennas. It becomes possible to calculate the baseline vector.

In the first embodiment of the present invention, the integer bias float solution estimation process by the integer bias float solution estimation unit 11 and the baseline vector calculation process by the baseline vector calculation unit 14 are both imposed with constraint conditions. However, only one of the processes may be imposed with a constraint condition.

(Embodiment 2)
As described above, in order to obtain an integer bias float solution and a baseline vector accurately and at high speed using a Lagrange multiplier or an extended Kalman filter, it is desirable to provide an accurate initial value of the baseline vector.

Therefore, Embodiment 2 of the present invention proposes a technique for giving a more accurate baseline vector initial value when calculating an integer bias float solution or a baseline vector.

(1) Technique for Estimating Baseline Vector Using Pseudo Attitude Angle Calculated from Moving Body Speed FIG. 3 is a diagram for explaining a technique for estimating a baseline vector using a pseudo attitude angle calculated from moving body speed. is there.

In FIG. 3, the carrier phase relative positioning device includes a GPS antenna 1, a GPS receiver 2, and an arithmetic processing unit 3b. The arithmetic processing unit 3b includes an integer bias float solution estimation unit 11, an integer bias determination unit 12, an integer bias test unit 13, a baseline vector calculation unit 14, a GPS attitude calculation unit 15, a speed detection unit 21, and a baseline. The vector initial value calculation unit 22 is configured. In addition, the same code | symbol is attached | subjected about the component similar to the carrier phase relative positioning apparatus by Embodiment 1 of this invention, and description is abbreviate | omitted here.

The speed detection unit 21 detects a moving body speed in the navigation coordinate system, and the moving body speed can be calculated by using, for example, a side position signal received by the GPS antenna 1.

The baseline vector initial value calculation unit 22 estimates a baseline vector using the pseudo posture angle calculated from the moving body speed, and gives an optimum initial value of the baseline vector to the integer bias float solution estimation unit 11 and the baseline vector calculation unit 14. Is.

In the baseline vector initial value calculation unit 22, a certain baseline vector initial value can be calculated based on the relationship of the following equation.

Here, the rotation matrix from the moving body coordinate system to the navigation coordinate system defined by the rotation sequence of the 3-2-1 axis (the 3-2-1 sequence) can be expressed by the following equation. Note that θr, θp, and θy are roll, pitch, and yaw, respectively.

Baseline vectors in the moving body coordinate system are known information because a plurality of GPS antennas 1 are installed at known positions. Therefore, if θr, θp, and θy can be calculated by some method, a baseline vector in the navigation coordinate system can be calculated.

Here, as disclosed in Non-Patent Document 5, θr, θp, and θy are obtained from the pseudo attitude angle (pseudo attitude) calculated by the following equation [Equation 14] from the velocity detected by the velocity detector 21. . Here, L is an acceleration obtained by differentiating the velocity and lift acceleration calculated from gravity, and P is a horizontal reference vector calculated from the velocity and Gravity. L · P represents the inner product of L and P.

Note that θy can be obtained from an orientation sensor such as a gyrocompass.

(2) A technique for estimating a base line vector using an observation amount of an inertial sensor and a mobile body speed When a slave antenna is installed on the mobile body coordinate system ^Xb axis, a roll for obtaining the base line vector, The pitch can be obtained from the observation amount of the inertial sensor. Moreover, yaw can also be calculated | required from an azimuth | direction detection part output.

FIG. 4 is a diagram for explaining a technique for estimating the baseline vector using the observed amount of the inertial sensor and the output of the azimuth detection unit. The INS system is integrated with the carrier phase relative positioning device according to the first embodiment. Indicates.

In FIG. 4, the carrier phase relative positioning device includes a GPS antenna 1, a GPS receiver 2, an arithmetic processing unit 3 b, and an IMU 4. The arithmetic processing unit 3b includes an integer bias float solution estimation unit 11, an integer bias determination unit 12, an integer bias test unit 13, a baseline vector calculation unit 14, a GPS attitude calculation unit 15, a speed detection unit 31, and a baseline. The vector initial value calculation unit 32, the INS navigation calculation unit 33, and the INS navigation error estimation unit 34 are configured. In addition, the same code | symbol is attached | subjected about the component similar to the carrier phase relative positioning apparatus by Embodiment 1 of this invention, and description is abbreviate | omitted here.

The IMU 4 is an inertial sensor composed of an angular velocity sensor and an acceleration sensor, and is fixed to a rigid body on the moving body like the GPS antenna 1. FIG. 5 is a diagram showing an inertial sensor and a GPS antenna position mounted on the moving object coordinate system of FIG. As shown in FIG. 5, the IMU 4 is composed of an acceleration sensor and an angular velocity sensor mounted on three orthogonal axes of the moving body coordinate system, and a sensitivity axis (sensitive axis) of each sensor for each of the three axes. ) Are mounted one by one with each of the acceleration sensor and the angular velocity sensor. Thereby, the observation amount obtained from the inertial sensor can be used as it is for the acceleration and angular velocity in the front-rear direction, the left-right direction, and the vertical direction of the moving body. Of course, the three orthogonal axes defined by IMU4 do not necessarily have to be matched if the rotation amount from the moving object coordinate system is known, but by performing arithmetic processing for axis rotation on the observation amount of the inertial sensor. The acceleration and angular velocity in the front-rear direction, the left-right direction, and the up-down direction of the moving body can be obtained. The inertial sensor only needs to be provided with the minimum necessary sensor according to the information to be measured or the measurement accuracy, and it is not always necessary to provide the acceleration sensor and the angular velocity sensor in each of the three axial directions.

The speed detection unit 31 detects a moving body speed in the navigation coordinate system, and the moving body speed can be calculated by using, for example, a side position signal received by the GPS antenna 1.

The baseline vector initial value calculation unit 32 gives an optimum initial value of the baseline vector to the integer bias float solution estimation unit 11 and the baseline vector calculation unit 14. Since the base line vector in the moving body coordinate system is known as described above, the base line vector initial value calculation unit 32 calculates θr, θp, and θy, thereby calculating the navigation from the relational expressions of [Expression 12] and [Expression 13]. A baseline vector in the coordinate system can be obtained. In the case where the velocity vector of the moving object matches the mobile coordinate system ^{X b-axis,} the base line vector in the mobile coordinate system ^{(x b, y b, z} b) T is ^(x b, ^{0,0) T} and Therefore, the base line vector can be estimated using only the pitch θ _P and the direction θ _y from the relational expressions of [Equation 12] and [Equation 13].

θr and θp receive the observed amount of acceleration and angular velocity from the IMU 4, and directly or after performing LPF processing for removing high frequency noise components included in these observed amounts, the IMU 4 observed amount and the output of the velocity detection unit 31 are output. And can be calculated from the following [Equation 19]. Further, θy can be calculated from [Expression 20] described later using the output of the speed detector 31. Details will be described later.

The INS navigation calculation unit 33 receives the observation amount from the IMU 4, the position from the GPS receiver, the velocity output, the attitude angle from the GPS attitude calculation unit 14, and the like, and the position, velocity, roll, pitch, direction, etc. are known by a known method. Perform navigation calculations.

The INS error estimator 34 corresponds to an inertial sensor error corrector. The position, speed, and attitude angle obtained from the INS navigation calculator 33 and those values obtained from the GPS receiver and the GPS attitude calculator 15 are used. Based on the difference, for example, a sensor error or navigation error of the IMU 4 is estimated using a Kalman filter, and these errors are removed by negative feedback. Thereby, at the time of integer bias re-determination (Ambiguity resolution) after the instantaneous interruption of GPS, the baseline vector initial value calculation unit 21 can calculate the calculation of the roll and the pitch using the calibrated observation amount of the inertial sensor. A highly accurate calculation result can be obtained.

Next, the calculation process of the roll θ _r and the pitch θ _p by the baseline vector initial value calculation unit 21 will be described in detail.

Acceleration associated with the rotational angular velocity of the earth with respect to the rotational angular velocity and the inertial coordinate system of the navigation coordinate system with respect to Earth, neglecting smaller than the force of gravity (gravity), the acceleration sensor output in a mobile coordinate system (specific force) A ^b is next It can be approximated by the equation [Equation 15]. Here, g is known because it is gravity. The velocity component in the moving object coordinate system can be set to v ^b (v ^b _x , 0, 0) because the X axis of the moving object coordinate system is set in the traveling direction of the moving object.

The rotation matrix (rotation matrix) from the navigation coordinate system to the moving body coordinate system defined by the rotation sequence of the 3-2-1 axis (the 3-2-1 sequence) can be expressed by the following equation [Equation 16].

If the moving body does not perform a high dynamic motion such as sudden start or stop, the motion acceleration can be ignored. Even if the motion acceleration cannot be ignored, the LPF processing for removing the high frequency noise component included in the acceleration observation amount is performed as described above, or the GPS speed observation amount support information with a small error in the low frequency band is used. By using, it is possible to reduce the motion acceleration included in the acceleration observation amount by signal processing. Therefore, [Equation 15] can be approximated by the following equation [Equation 17].

When the slave antenna is installed on the moving body coordinate system ^Xb axis, the velocity vector in the moving body coordinate system is only the x component, and the magnitude of the velocity vector in the navigation coordinate system and the moving body coordinate system does not change. Therefore, v ^b _x can be obtained by the following equation [Equation 18].

Accordingly, the roll θ _r and the pitch θ _p of the moving body are calculated from the observed amount obtained from the IMU 4 and the speed information of the moving body in the X-axis direction obtained from the speed detecting unit 31 as in the following equation [Equation 19]. Can do.

As described above, the roll θ _r and the pitch θ _p obtained from the inertial sensor can be used in place of the roll θ _r and the pitch θ _p obtained by [Equation 14], but are used together in order to expect better accuracy. You can also

なお、θｙについてはジャイロコンパス等の方位センサから情報を得ることも可能である。 Further, the azimuth θ _y can be obtained by the following equation using the output of the speed detector 31.

Note that information about θy can be obtained from an orientation sensor such as a gyrocompass.

Thus, the errors of the roll θ _r , the pitch θ _p , and the direction θ _y determined by the methods (1) and (2) are within about several degrees. In this case, when the baseline length is 1 m, the error in the initial value of the baseline vector can be determined with sufficient accuracy within several centimeters.

Although the description has been given here of determining the roll θ _{r in} consideration of the velocity V _x ^b of the moving object in the moving object coordinate system, it is possible to move by ignoring the speed and motion acceleration of the moving object in the moving object coordinate system. It is also possible to calculate the roll θ _r of the moving body without speed information regarding the moving direction of the body.

As described above, according to the carrier phase relative positioning device according to the second embodiment of the present invention, the integer bias float solution estimation unit 11 and the baseline vector calculation unit 14 are accurately compared from the baseline vector initial value calculation units 21 and 31. By giving the initial value of the baseline vector, the arithmetic processing in the integer bias float solution estimation unit 11 and the baseline vector calculation unit 14 can be performed more accurately and at high speed.

The first and second embodiments described above are examples of the best embodiment, and various modifications can be made without departing from the spirit of the present invention. The present invention is not limited to the above-described embodiments. Absent.

(Experimental result)
Tables 1 and 2 show examples of simulation experiment results of base line length-constrained integer bias float solutions and baseline vector estimation characteristics according to the Lagrange multipliers of the present invention.

(1) Performance Evaluation Experiment of Integer Bias Float Solution Estimator 11 Assuming that external attitude angle support data is obtained, the initial value of the baseline vector when the slave antenna is installed on the mobile coordinate system ^Xb axis is It can be obtained from the above [Equation 12] from the pitch θ _P and the direction θ _y . Therefore, an experiment was performed based on the initial values of the baseline vectors calculated from the attitude angles having pitch / azimuth errors of 3 °, 5 °, and 10 °, respectively. The number of satellites used was 5, and the distance between the antennas (baseline length) was 0.5 m.

Table 1 shows the result of obtaining the integer bias float solution using the Lagrange multiplier based technique with the baseline length constraint under the above conditions.

As shown in Table 1, when the ship is stationary, the pitch / heading error is 3 ° (σ), and when the ship is shaking, the pitch / heading error is within 10 ° (σ), and the number of satellites is five and 30 seconds. The integer bias solution (integer Ambiguity) could be directly estimated within 100% probability.

In addition, when the pitch / azimuth error is 5 ° and 10 ° at rest, the decision rate decreases to 89% and 74%, respectively, but the difference from the true value of the integer bias solution is ± 1 to ± 2 at most. It was an error.

In this experiment, it was confirmed that the decision rate of the integer bias solution (integer Ambiguity) at the time of shaking was higher than that when the ship was stationary. The reason is that even if the initial value of the baseline vector is different, the initial value of the baseline vector approaches the initial value of the baseline vector due to the movement of the ship, that is, there is a timing when the initial value of the baseline vector matches the actual baseline vector position. It is done.

(2) Performance Evaluation Experiment of Baseline Vector Calculation Unit 14 Table 2 shows posture angle conversion errors when a baseline vector is obtained using a baseline length constrained technique based on a Lagrange multiplier. The number of satellites used was 5, and the distance between the antennas (baseline length) was 0.5 m.

As can be seen from Table 2, the baseline vector estimation characteristic with the baseline length constraint by the Lagrange multiplier can be improved by about 30% compared to the case of no constraint. In addition, an improvement of about 10% was observed for multipath.

In addition, although the simulation experiment result using a Lagrange multiplier was shown here, the result by the Kalman filter was also almost the same.

Explanatory drawing for demonstrating the navigation coordinate system and moving body coordinate system which are used by this invention 1 is a block diagram showing an example of the configuration of a carrier phase relative positioning device according to Embodiment 1 of the present invention. The figure for demonstrating the technique which estimates a baseline vector using the pseudo posture angle calculated from the moving body speed of this invention The figure for demonstrating the technique which estimates a baseline vector using the observation amount of an inertial sensor of this invention, and a direction detection part output The figure which shows the inertial sensor and GPS antenna position which were mounted on the moving body coordinate system Explanatory diagram for explaining an integer bias solution decision processing procedure

Explanation of symbols

DESCRIPTION OF SYMBOLS 1 GPS antenna 2 GPS receiver 3a, 3b, 3c Arithmetic processing part 4 IMU
DESCRIPTION OF SYMBOLS 11 Integer bias float solution estimation part 12 Integer bias determination part 13 Integer bias test part 14 Baseline vector calculation part 15 GPS attitude | position calculation part 16 Integer bias solution calculation part 21, 31 Speed detection part 22, 32 Baseline vector initial value calculation part 33 INS Navigation calculation unit 34 INS navigation error estimation unit 101 Integer bias float solution estimation process 102 Integer bias solution determination process 103 Integer bias solution test process

Claims (12)

In a carrier phase relative positioning device comprising a plurality of antennas fixed on a moving body,
A receiver for obtaining a phase difference observation amount from positioning signals received by the plurality of antennas;
An integer bias float solution estimator for estimating an integer bias float solution from the phase difference observation amount, with known information obtained from the relative positional relationship of the plurality of antennas as a constraint condition. Positioning device. In the carrier phase relative positioning device according to claim 1,
The carrier phase relative positioning apparatus, wherein the integer bias float solution estimation unit performs integer bias float solution estimation processing using a Lagrange multiplier. In the carrier phase relative positioning device according to claim 1,
The carrier phase relative positioning device, wherein the integer bias float solution estimation unit performs integer bias float solution estimation processing using an extended Kalman filter. In the carrier phase relative positioning device according to any one of claims 1 to 3,
A speed detector for detecting the moving body speed;
A baseline vector initial value computing unit that estimates a baseline vector using a pseudo attitude angle calculated from the moving body velocity and gives the baseline vector as an initial value of the baseline vector during the integer bias float solution estimation processing; A carrier phase relative positioning device. In the carrier phase relative positioning device according to any one of claims 1 to 3,
An inertial sensor for measuring acceleration and angular velocity of the moving object;
A speed detector for detecting the moving body speed;
A baseline vector initial value calculation unit that estimates a baseline vector from the observed amount of the inertial sensor and the moving body speed, and that provides the baseline vector as an initial value of the baseline vector during the float solution estimation process of the integer bias; Characteristic carrier phase relative positioning device. In a carrier phase relative positioning device comprising a plurality of antennas fixed on a moving body,
A receiver for obtaining a phase difference observation amount from positioning signals received by the plurality of antennas;
An integer bias solution calculation unit for calculating an integer bias solution from the phase difference observation amount;
Carrier phase relative, comprising: a baseline vector calculation unit that calculates a baseline vector from the phase difference observation amount and the integer bias solution using known information obtained from a relative positional relationship of the plurality of antennas as a constraint condition Positioning device. In the carrier phase relative positioning device according to claim 6,
The carrier phase relative positioning device, wherein the integer bias float solution estimation unit performs a baseline vector calculation process using a Lagrange multiplier. In the carrier phase relative positioning device according to claim 6,
The carrier phase relative positioning device, wherein the integer bias float solution estimation unit performs a baseline vector calculation process using an extended Kalman filter. In the carrier phase relative positioning device according to any one of claims 6 to 8,
A speed detector for detecting the moving body speed;
A baseline vector initial value calculation unit that estimates a baseline vector using a pseudo posture angle calculated from the moving body velocity and gives the baseline vector as an initial value of the baseline vector during the baseline vector calculation process; Carrier phase relative positioning device. In the carrier phase relative positioning device according to any one of claims 6 to 8,
An inertial sensor for measuring acceleration and angular velocity of the moving object;
A speed detector for detecting the moving body speed;
It further comprises a baseline vector initial value calculation unit that estimates a baseline vector from the observed amount of the inertial sensor and the moving body speed and gives the baseline vector as an initial value of the baseline vector during the baseline vector calculation process. Carrier phase relative positioning device. In the carrier phase relative positioning device according to any one of claims 5 and 10,
The baseline vector initial value calculation unit calculates the pitch of the moving body from the observation amount of the inertial sensor, calculates the direction from the speed information from the speed detection unit, and estimates the baseline vector from the pitch and direction information. A carrier phase relative positioning device. In the carrier phase relative positioning device according to any one of claims 5 and 10,
Using an information obtained after determination of the integer bias solution, further comprising an inertial sensor error correction unit for correcting an observation amount error of the inertial sensor;
The baseline vector initial value calculation unit estimates a baseline vector to be provided as an initial value by using the observation amount of the inertial sensor corrected for error by the inertial sensor error correction unit when an integer bias solution is redetermined. Carrier phase relative positioning device.

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