JP2008039690A - Carrier-wave phase type position measuring instrument - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a carrier-wave phase type position measuring instrument for providing high-accuracy position measurement even in the case where a position measurement side is moving. <P>SOLUTION: The carrier-wave phase type position measuring instrument 34 measures the position of a mobile station, based on satellite data acquired by receiving a satellite signal in the mobile station and at a known point. A movable body model for forecasting the state of the mobile station at the present time from the movement history of the mobile station is adopted as a system model wherein a single or double phase difference is taken as an observation amount of an integration value of the carrier wave phase of the satellite signal received in the mobile station 30 and the known point 20, and the position of the mobile station and a single or double phase difference of integer bias included in the integration value of the wave phase are taken as state variables. Position measurement is performed, by estimating the state variables, based on satellite data at a plurality of epochs. <P>COPYRIGHT: (C)2008,JPO&INPIT

Description

  The present invention relates to a carrier phase type positioning apparatus that measures a mobile station by measuring an integrated value of a carrier phase of a satellite signal.

  In recent years, GPS surveying using a carrier phase has been widely used in the field of surveying. In this GPS survey using the carrier phase, the reference side receiver and the positioning side receiver simultaneously receive satellite signals sent from a plurality of satellites, and the reference side and the positioning side integrate the carrier phase of each satellite signal. Each value is calculated independently. The integrated value of the carrier phase (hereinafter simply referred to as “phase integrated value”) includes an indeterminate element (hereinafter referred to as “integer value bias”) corresponding to an integral multiple of the wavelength of the carrier. Unlike the clock error or the like, this integer value bias cannot be eliminated by taking a single phase difference or a double phase difference of the phase integration value. For this reason, in the field of GPS surveying, a technique has been proposed in which the integral value bias, which is an uncertain factor, is eliminated by taking the triple phase difference of the phase integration value, or the integer value bias itself is obtained.

Here, as a technique for determining an integer value bias, a technique using a Kalman filter is known (see, for example, Patent Document 1). In this technology, a tracking filter that updates the state variable each time observations are made using the position on the positioning side and the integer bias as the state variables, and the single phase difference on the positioning side relative to the reference side as the observation amount is configured. Is done. As another technique for determining the integer value bias, a technique for obtaining an integer value bias of a double phase difference under a predetermined condition by a least square method using a double phase difference of a carrier wave including the integer value bias is known. (For example, refer to Patent Document 2).
JP 2004-77228 A JP 2003-98245 A

  By the way, each of the above-described conventional techniques is a technique in the field of surveying on the assumption that the positioning side is fixed for a long time. Therefore, a technical field in which the positioning side can move (for example, a field in which the position of a mobile station is measured). ) Cannot be applied (that is, the integer value bias cannot be determined with high accuracy).

  Therefore, an object of the present invention is to provide a carrier phase type positioning device that can realize highly accurate positioning even when the positioning side is moving.

In order to solve the above problems, according to one aspect of the present invention, there is provided a carrier phase type positioning device that performs positioning of a mobile station based on satellite data acquired by receiving a satellite signal at a mobile station and a known point,
Single or double phase difference of integrated value of carrier phase of mobile station and satellite signal received at known point is observed amount, and single or two of integer bias included in integrated value of position of mobile station and carrier phase Introducing a mobile body model that estimates the current time state of a mobile station from a mobile station's movement history to a system model having a multiple phase difference as a state variable, the state variable based on satellite data at multiple epochs A carrier wave phase type positioning device is provided, which performs positioning by estimating.

  In this aspect, the movement history of the mobile station may relate to at least one of the position, speed, acceleration, and jerk of the mobile station. The mobile body model may be constructed on the assumption that at least one of speed and acceleration of the mobile station is a first-order Markov process. The estimation of the state variable may be performed by applying a Kalman filter, a least square method or the like least square principle.

  Further, according to one aspect of the present invention, the position of the mobile station is set as a state variable, and at least one of the mobile station's speed, acceleration, and jerk is set as a state variable, and the mobile station is observed at a known point. Using the system model that uses the integrated value of the carrier phase of the satellite signal as observation data, the integer bias included in the integrated value of the carrier phase is estimated, and the position of the mobile station is determined using the estimated integer value bias. A carrier wave phase type positioning device is provided.

  According to the present invention, it is possible to obtain a carrier phase type positioning device that can realize highly accurate positioning even when the positioning side is moving.

  The best mode for carrying out the present invention will be described below with reference to the drawings.

  FIG. 1 is a block diagram of a carrier phase type positioning system according to the present invention. As shown in FIG. 1, the GPS positioning system includes a GPS satellite 10 that orbits the earth, a fixed reference station 20 that is installed at a predetermined position (known point) on the earth, and is positioned on the earth. And a mobile station 30 that can move.

  The GPS satellite 10 always broadcasts navigation messages toward the earth. The navigation message includes orbit information about the corresponding GPS satellite 10, a clock correction value, and an ionospheric correction coefficient. The navigation message is spread by the C / A code, is carried on the L1 carrier (frequency: 1575.42 MHz), and is constantly broadcast toward the earth.

  Currently, 24 GPS satellites 10 orbit the earth at an altitude of about 20,000 km, and each of the four GPS satellites 10 is evenly arranged on six earth-orbiting orbits inclined by 55 degrees. ing. Therefore, as long as the sky is open, at least five GPS satellites 10 can be observed at any time on the earth.

  FIG. 2 is a more detailed configuration diagram of the carrier phase GPS positioning system of FIG. The mobile station 30 includes a GPS receiver 32. In the GPS receiver 32, an oscillator (not shown) whose frequency matches the carrier frequency of the GPS satellite 10 is incorporated. The GPS receiver 32 converts the radio wave (satellite signal) received from the GPS satellite 10 via the GPS antenna 32a into an intermediate frequency, and then uses the C / A code generated in the GPS receiver 32. Synchronize and retrieve navigation messages.

Further, the GPS receiver 32 measures the phase integration value Φ _iu of the carrier phase based on the carrier from each GPS satellite 10 _i . For the phase integrated value Φ _iu , the subscript i (= 1, 2,...) Indicates the number assigned to each GPS satellite 10 _i , and the subscript u is the integrated value on the mobile station 30 side. It shows that there is. As shown in the following equation, the phase integrated value Φ _iu includes the phase Θ _iu (t) of the oscillator at the carrier reception time t and the carrier phase Θ _iu (t−τ) when the satellite signal is generated in the GPS satellite 10 _i. It is obtained as a difference.
Φ _iu (t) = Θ _iu (t) −Θ _iu (t−τ _u ) + N _iu + ε _iu (t) Equation (1)
Here, τ _u represents the travel time from the GPS satellite 10 to the GPS receiver 32, and ε _iu represents noise (error). Note that at the start of observation of the phase difference, the GPS receiver 32 can accurately measure the phase within one wavelength of the carrier wave phase, but cannot determine what wavelength it corresponds to. For this reason, an integer value bias N _iu is introduced to the phase integrated value Φ _iu (t) as an uncertain element as shown in the above equation.

  The mobile station 30 also includes a communication device 33 such as a mobile phone. As will be described later, the communication device 33 is configured to perform bidirectional communication with a communication facility 23 such as a mobile phone base station on the reference station 20 side.

The reference station 20 has a GPS receiver 22 including a GPS antenna 22a. As with the GPS receiver 32 of the mobile station 30, the GPS receiver 22 is based on the carrier wave from each GPS satellite 10 _i and, as shown in the following equation, the integrated value Φ _ik ( t) is measured.
Φ _ik (t) = Θ _ik (t) −Θ _ik (t−τ _k ) + N _ik + ε _ik (t) Equation (2)
N _ik represents an integer value bias, and ε _ik represents noise (error). For the phase integrated value Φ _ik , the subscript k indicates an integrated value on the reference station 20 side. The reference station 20 transmits the measured phase integrated value Φ _ik to the mobile station 30 via the communication facility 23. A plurality of reference stations 20 are installed in a predetermined area. As shown in FIG. 2, each reference station 20 and communication facility 23 (or a plurality of communication facilities) may be connected via a network such as the Internet, or a communication facility 23 may be provided for each reference station 20. . In the former configuration, the mobile station 30 can obtain the information received by each reference station 20 as long as it can communicate with the communication facility 23.

  FIG. 3 is a functional block diagram showing an embodiment of a carrier phase positioning device 34 (hereinafter referred to as “positioning device 34”) according to the present invention mounted on the mobile station 30. As shown in FIG. The positioning device 34 according to the present embodiment is configured with a calculator 40 as the center, and the calculator 40 is connected to the GPS receiver 32 and the communication device 33 described above. Further, various sensors 50 mounted on the mobile station 30 are connected to the computing unit 40. Note that the computing unit 40 may be incorporated in the GPS receiver 32. When the mobile station 30 is a vehicle, the GPS receiver 32, the computing unit 40, and / or the communication device 33 may be mounted in the navigation device.

  The computing unit 40 may be composed of a microcomputer, and includes a satellite position calculation unit 42 and an integer value bias estimation unit 48 as shown in FIG.

Based on the orbit information of the navigation message received by the GPS receiver 32, the satellite position calculator 42 determines the position (X _i (t), Y) of each observable GPS satellite 10 _i in the world coordinate system at time t. _i (t), Z _i (t)) is calculated. Since the GPS satellite 10 is one of artificial satellites, its movement is limited to a certain plane (orbital plane) including the center of gravity of the earth. The orbit of the GPS satellite 10 is an elliptical motion with the earth's center of gravity as one focal point, and the position of the GPS satellite 10 on the orbital plane can be calculated by sequentially calculating the Kepler equation. Further, the position (X _i (t), Y _i (t), Z _i (t)) of each GPS satellite 10 _i at the carrier wave reception time t is determined by the orbital plane of the GPS satellite 10 and the equatorial plane of the world coordinate system. In consideration of the rotational relationship, the position of the GPS satellite 10 on the orbital plane is obtained by three-dimensional rotational coordinate conversion. As shown in FIG. 4, the world coordinate system is defined by an X axis and a Y axis that are orthogonal to each other within the equator plane, and a Z axis that is orthogonal to both axes, with the center of gravity as the origin.

In the integer value bias estimation unit 48, observation data relating to each GPS satellite 10 _i (particularly, the phase integrated value Φ _ik on the reference station 20 side received by the mobile station 30 via the communication device 33 and the phase on the mobile station 30 side). The integer value bias is estimated based on the integrated value Φ _iu ). The procedure will be described below.

The double phase difference between the phase integration values for the two GPS satellites 10 _j , 10 _h (i = j, h, where j ≠ h) at time t is expressed by the following equation.
Φ ^jh _ku = (Φ _jk (t) −Φ _ju (t)) − (Φ _hk (t) −Φ _hu (t)) Equation (7)
On the other hand, the double phase difference Φ ^jh _ku of the phase integration value is based on the physical meaning of (distance between GPS satellite 10 _i and GPS receiver 22 or 32) = (wavelength L of carrier wave) × (phase integration value). It becomes as follows.

ここで、式（８）における［Ｘ_k（ｔ）、Ｙ_k（ｔ）、Ｚ_k（ｔ）］は、時刻ｔにおける基準局２０のワールド座標系における座標値（既知）であり、［Ｘ_u（ｔ）、Ｙ_u（ｔ）、Ｚ_u（ｔ）］は、時刻ｔにおける移動局３０の座標値（未知）であり、［Ｘ_j（ｔ）、Ｙ_j（ｔ）、Ｚ_j（ｔ）］及び［Ｘ_h（ｔ）、Ｙ_h（ｔ）、Ｚ_h（ｔ）］は、時刻ｔにおける各ＧＰＳ衛星１０_j、１０_hの座標値（衛星位置算出部４２により算出）である。Ｎ^jh _kuは、整数値バイアスの２重位相差である（即ち、Ｎ^jh _ku＝（Ｎ_jk−Ｎ_ju）−（Ｎ_hk−Ｎ_hu））。尚、時刻ｔは、例えばGPS時刻で同期が取られているものとする。

Here, [X _k (t), Y _k (t), Z _k (t)] in equation (8) is a coordinate value (known) in the world coordinate system of the reference station 20 at time t, and [X _u (T), Y _u (t), Z _u (t)] are coordinate values (unknown) of the mobile station 30 at time t, and [X _j (t), Y _j (t), Z _j (t) ]] And [X _h (t), Y _h (t), Z _h (t)] are the coordinate values (calculated by the satellite position calculation unit 42) of the GPS satellites 10 _j and 10 _h at time t. N ^jh _ku is a double phase difference of an integer value bias (that is, N ^jh _ku = (N _jk −N _ju ) − (N _hk −N _hu )). The time t is assumed to be synchronized with, for example, GPS time.

In the integer value bias estimation unit 48, the following state equation is set.
η (i + 1) = F · η (i) + u (i) Equation (9)
Here, eta (i) represents the state variables of the observation period i (= 1,2 ...), the position of the mobile station _{30 [X u (i),} Y u (i), Z u (i )], the speed of the mobile station 30 [Vx (i), Vy (i), Vz (i)] and, the double difference of the integer bias when n _z number of GPS satellites _{10 1～Nz} is observable in _{and, η = [X u, Y} u, Z u, Vx, Vy, Vz, N 11 ku, N 12 ku,. . . , N ^1nz _ku ] ^T ( ^T represents transposition). U is a disturbance (system noise: normal white noise).

  This state equation is based on a mobile object model that predicts the current time state of the mobile station 30 from the movement history of the mobile station 30. Here, assuming that the speed v of the mobile station 30 is a first-order Markov process, the state equation regarding the speed can be expressed as follows.

但し、αは時定数の逆数である。この仮定より、移動局３０の状態方程式は、

Where α is the reciprocal of the time constant. From this assumption, the state equation of the mobile station 30 is

と表現でき、これを離散化すると上記の式（９）を得る。

When this is discretized, the above equation (9) is obtained.

  In equation (9),

である。

It is.

  The covariance matrix of system noise u (i) is

となる。但し、

It becomes. However,

であり、σ^２ _ｖは、移動局３０の速度の分散であり、例えばSingerモデルにおける確率分布により導出してもよい。

Σ ² _v is a variance of the speed of the mobile station 30 and may be derived, for example, by a probability distribution in the Singer model.

The integer value bias estimation unit 48 employs the following observation equation.
Z (i) = H (i) · η (i) + V (i) Equation (10)
Here, Z and V indicate an observation amount and an observation noise (normal white noise), respectively. The observation amount Z in the equation (10) is a double phase difference (see the above equation (7)) of the phase integration value. State equation of the equation (9) is a linear, observation quantity Z is the state variable X _u, Y since it is non-linear with respect to _u and Z _u, each term state variable X _u of the formula (8), Y _u And Z _u are partially differentiated to obtain the observation matrix H of Equation (10).

但し、状態変数η＝［Ｘ_u、Ｙ_u、Ｚ_u、Ｎ¹¹ _ku、Ｎ¹² _ku、．．．、Ｎ^1nz _ku、Ｖｘ、Ｖｙ、Ｖｚ］^Ｔとし、数８のＨ_１は、状態変数η＝［Ｘ_u、Ｙ_u、Ｚ_u、Ｎ¹¹ _ku、Ｎ¹² _ku、．．．、Ｎ^1nz _ku］^Ｔとした場合の観測行列である。従って、数８のＨは、観測行列Ｈ_１に、状態変数η＝［Ｖｘ、Ｖｙ、Ｖｚ］^Ｔに対応するゼロ行列を横長に加えたものとなっている。

However, the state variable _{η = [X u, Y u} , Z u, N 11 ku, N 12 ku,. . . _{^{, N 1nz ku, Vx, Vy}} , and ^{Vz] T,} _{H 1} number 8, the state variable _{η = [X u, Y u} , Z u, N 11 ku, N 12 ku,. . . , N ^1nz _ku ] ^T. Accordingly, H in Equation 8 is obtained by adding the zero matrix corresponding to the state variable η = [Vx, Vy, Vz] ^T to the observation matrix H ₁ in the horizontal direction.

When the Kalman filter is applied to the state equation of the above equation (9) and the observation equation of the above equation (10), the following equation is obtained.
As time update,
η (i) ⁽⁻⁾ = η (i−1) ⁽⁺⁾ Equation (11)
P (i) ⁽⁻⁾ = P (i−1) ⁽⁺⁾ + Q (i−1) Formula (12)
As an observation update,
K (i) = P (i) ⁽⁻⁾ · H ^T (i) · (H (i) · P (i) ⁽⁻⁾ · H ^T (i) + R (i)) ⁻¹ Formula (13)
η (i) ⁽⁺⁾ = η (i) ⁽⁻⁾ + K (i) · (Z (i) −H (i) · η (i) ⁽⁻⁾ ) Equation (14)
P (i) ⁽⁺⁾ = P (i) ⁽⁻⁾ −K (i) · H (i) · P (i) ⁽⁻⁾ Equation (15)
Here, Q and R represent the covariance matrix of the disturbance u and the covariance matrix of the observation noise V, respectively. The above equations (11) and (14) are filter equations, the above equation (13) is a filter gain, and the above equations (12) and (15) are covariance equations. In addition, ^(-) and ⁽⁺⁾ indicated by superscript indicate before and after updating.

  FIG. 5 is a flowchart showing main processing executed by the positioning device 34 of the present embodiment. The processing routine shown in FIG. 5 is executed every calculation period (each observation period) of the phase integration value by the GPS receivers 22 and 32.

First, at step 100, the satellite information (position information of each GPS satellite 10 _i, the phase integrated value of the reference station 20 side for each GPS satellite 10 _i, and the phase accumulation value of the mobile station 30 side with respect to each GPS satellite 10 _i) is observed Acquired every cycle.

For the first observation period i (= 1), the processing of steps 110 and 120 is executed. That is, in step 110, an initial position calculation process using a code double phase difference is executed. Specifically, pseudo distances ρ _k and ρ measured on both the reference station 20 side and the mobile station 30 side based on a PRN code (pseudo noise code) such as a C / A code or a P code included in the satellite signal. as observed amount of double difference of _u, by using the least squares method or Kalman filter, the position of the mobile station 30 estimates a _{[X u (i), Y} u (i), Z u (i)]. In step 120, the estimated value initial bias value based on _{[X u (i), Y} u (i), Z u (i)] (= initial value of the double difference of the integer bias) is estimated Is done. Since these estimated values may include a relatively large error, they may be used exclusively as the initial value η ₀ of the state variable to speed up the convergence of the solution.

  For the observation period i (> 1) after the first time, the process of step 130 is repeatedly executed for each observation period, and the above-described moving body model is introduced based on satellite information obtained in a plurality of observation periods. State variables are derived (estimated) by the Kalman filter. That is, in step 130, time and observation are updated by the above formulas (11) to (15) for each observation period, and positioning calculation is performed while taking over the covariance and state variable estimates of the previous period. .

In step 140, processing for calculating and outputting a positioning solution is executed. The estimated value of the integer bias obtained from step 130 is obtained as a real number solution. However, since the integer value bias is actually an integer value, the closest integer solution (that is, wave number) is obtained with respect to the obtained real number solution. As this method, the integer value bias is decorrelated, and the search space for the integer solution is narrowed to specify the solution.
An MBDA method or the like may be used. In addition, when the GPS receivers 22 and 32 are two-frequency receivers that can receive both the L1 wave and the L2 wave emitted from the GPS satellite 10, the above-described operation is performed for each of the L1 wave and the L2 wave. Similar estimation may be performed simultaneously and in parallel to create a sum of both periods (wide lane) to narrow down integer solution candidates with integer bias. In addition, a single or double phase difference of a pseudorange derived using a C / A code or P code or a PRN code (pseudo noise code) carried on a satellite signal may be incorporated into the above state variable. .

  When the integer solution of the integer value bias is thus determined, the position of the mobile station 30 can be calculated with high accuracy by positioning using the integer solution of the integer value bias unless a cycle slip occurs thereafter.

  As described above, according to the present embodiment, it is possible to calculate a state variable (positioning, integer value bias) while the mobile station 30 is moving, and it is possible to move the current time from the past movement state of the mobile station 30. By introducing a mobile model that predicts the state into the Kalman filter, the model estimation error due to the movement of the mobile station 30 is reduced (the bias of the estimation error can be removed), and the calculation accuracy of the state variable is improved. Further, since the model error can be eliminated in a feedforward manner, the calculation accuracy of the state variables (positioning, integer value bias) is improved, and the time until the positioning is started can be shortened.

  In the above-described embodiment, the mobile body model is constructed assuming that the velocity v of the mobile station 30 is a primary Markov process. For example, the acceleration of the mobile station 30 is assumed to be a primary Markov process. You may comprise a mobile body model. That is, the mobile object model may be configured using arbitrary parameters that can represent the movement state of the mobile station 30 such as position, velocity, acceleration, and jerk (differential value of acceleration).

  FIG. 6 is test data demonstrating high positioning accuracy realized by the positioning device 34 according to the present embodiment. FIG. 6A shows the positioning result by latitude / longitude according to the configuration to which the algorithm of the present embodiment is applied (the configuration in which the moving body model is introduced into the Kalman filter). FIG. 6B shows a positioning result by a configuration in which the moving body model is not introduced into the Kalman filter as a control.

  When the moving body model is not introduced into the Kalman filter, as shown in FIG. 6B, when the moving body 30 (vehicle) travels on a curve or the like, it cannot follow a large change in the movement, resulting in an error in the solution. On the other hand, when the algorithm of this embodiment is applied, a solution with good accuracy can be obtained as shown in FIG.

  The preferred embodiments of the present invention have been described in detail above. However, the present invention is not limited to the above-described embodiments, and various modifications and substitutions can be made to the above-described embodiments without departing from the scope of the present invention. Can be added.

  For example, in the above-described embodiment, the Kalman filter is applied to the state equation of the above equation (9) and the observation equation of the above equation (10). However, the present invention is not limited to other methods such as the least square method or the Bayes method. The present invention can also be applied to a configuration to which the estimation method is applied.

  In the embodiment described above, an observation amount related to the speed v of the mobile station 30 may be introduced into the observation equation of the above equation (10). In this case, for example, the speed v of the mobile station 30 may be observed based on the calculation result of the Doppler shift of the satellite signal (radio wave), and the observation result of the pseudo distance change (pseudo distance change rate) Based on this, the speed v of the mobile station 30 may be observed. In addition, when the mobile station 30 is a vehicle, detection values of in-vehicle sensors such as a vehicle speed sensor and a yaw rate sensor may be used cooperatively.

Further, in the embodiments described above, without a velocity v of the mobile station 30 as the state variables of the state equation of the equation (9), the state variables of the above formula _{(11) [X u (i} ), Y u (i) , Z _u (i)], the dynamic state quantity based on the moving body model as described above may be considered.

  In the above-described embodiment, the influence of the initial phase of the oscillator and the clock error in the GPS receivers 22 and 32 is eliminated by taking the double phase difference as described above. The configuration may be a single phase difference that can eliminate only the initial phase and the GPS clock error, or a configuration that does not take any phase difference. Further, in this embodiment, the ionospheric refraction effect, the tropospheric refraction effect, and the influence of multipath are ignored, but these may be taken into consideration.

In the above description, for convenience, there is a case that a reference satellite to a GPS satellite 10 _1, depending on the position of the mobile station 30 and the reference station 20, the other GPS satellites ₁₀ i (= 2,3 · ·・) Can be a reference satellite.

  In the above description, a vehicle is given as an example of the mobile station 30, but the mobile station 30 may be a hawk lift, robot, receiver 32, and / or arithmetic unit in which the receiver 32 and / or the arithmetic unit 40 is mounted. An information terminal such as a mobile phone incorporating the device 40 is included.

1 is a configuration diagram of a carrier phase GPS positioning system according to the present invention. It is a more detailed block diagram of the carrier wave phase type GPS positioning system of FIG. 2 is a functional block diagram showing an embodiment of a positioning device 34 according to the present invention mounted on a mobile station 30. FIG. It is a figure which shows the relationship between a world coordinate system and a local coordinate system, and the relationship between a local coordinate system and a body coordinate. It is a flowchart of the process implement | achieved by the positioning apparatus 34 of a present Example. It is test data which demonstrates the high positioning accuracy implement | achieved by the positioning apparatus 34 by a present Example.

Explanation of symbols

DESCRIPTION OF SYMBOLS 10 GPS satellite 20 Reference station 22 Reference station side GPS receiver 30 Mobile station 32 Mobile station side GPS receiver 34 Carrier phase type positioning device 40 Calculator 42 Satellite position calculation part 48 Integer value bias estimation part

Claims (5)

A carrier phase type positioning device that performs positioning of a mobile station based on satellite data acquired by receiving satellite signals at a mobile station and known points,
Single or double phase difference of integrated value of carrier phase of mobile station and satellite signal received at known point is observed amount, and single or two of integer bias included in integrated value of position of mobile station and carrier phase Introducing a mobile model that predicts the current time state of the mobile station from the movement history of the mobile station to a system model having a multiple phase difference as a state variable, the state variable based on satellite data in a plurality of epochs A carrier phase type positioning device characterized in that positioning is performed by estimating.   The carrier phase type positioning device according to claim 1, wherein the movement history of the mobile station relates to at least one of a position, velocity, acceleration, and jerk of the mobile station.   The carrier phase type positioning device according to claim 1, wherein the mobile body model is constructed on the assumption that at least one of a velocity and acceleration of a mobile station is a first-order Markov process.   The carrier phase type positioning device according to claim 1, wherein the estimation of the state variable is performed by applying a Kalman filter, a least square method or the like least square principle.   Using the position of the mobile station as a state variable and at least one of the mobile station's speed, acceleration and jerk as the state variable, observe the integrated value of the carrier phase of the satellite signal observed at the mobile station and a known point Using a system model as data, an integer bias included in an integrated value of the carrier phase is estimated, and the position of the mobile station is measured using the estimated integer bias. apparatus.

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