Method for evaluating at least one gnss satellite signal using ambiguity resolution

Abstract

The invention relates to a method for evaluating at least one GNSS satellite signal (3), which is received from at least one GNSS satellite (2), in order to determine GNSS sensor data (14) by means of a GNSS sensor (1), comprising at least the following steps: a) resolving ambiguities of at least one carrier frequency of the received GNSS satellite signal (3) by using an evaluation algorithm (7), which determines at least one indication (13) about the estimation accuracy in addition to at least one estimation result (12); b) receiving at least one information (4, 5, 8, 9), which enables an inference about the estimation accuracy in addition to at least one indication (13) about the estimation accuracy, which originates from the evaluation algorithm (7); c) adjusting the indication (13) about the estimation accuracy, which originates from the evaluation algorithm (7), by using at least one information (4, 5, 8, 9), which is determined in step b).

Description

Technical Field

[0001] This invention relates to a method for evaluating at least one GNSS satellite signal, a computer program for performing the method, a machine-readable storage medium having the computer program, and a positioning device for performing the method. The method can, for example, be used in conjunction with autonomous driving. Background Technology

[0002] With the help of Global Navigation Satellite Systems (GNSS), geospatial positioning can be achieved virtually anywhere on Earth. GNSS satellites orbit the Earth and transmit coded signals, which GNSS receivers use to calculate the distance or interval between the receiver and the satellites by estimating the time difference between the signal reception time and the transmission time. If enough satellites are tracked (typically more than five), the estimated distance to the satellites can be converted into an estimate of the receiver's position. Currently, there are over 130 GNSS satellites orbiting the Earth, meaning that typically up to 65 are visible on the local horizon. In particular, with the advent of GNSS quad-constellations, third harmonics, and / or external atmospheric constraints, this can be advantageously facilitated by, for example, so-called PPP (Precise Point Positioning) users, and / or by using ambiguity resolution, achieving centimeter-level accuracy using GNSS-based or GNSS / INS (Inertial Navigation System) positioning sensors. In this context, the focus is particularly on further improving local solutions for performing ambiguity resolution. Summary of the Invention

[0003] Here, a method is proposed for evaluating at least one GNSS satellite signal received from at least one GNSS satellite in order to determine GNSS sensor data using a GNSS sensor. The method includes at least the following steps:

[0004] a) By using an estimation algorithm to resolve the ambiguity of at least one carrier frequency of the received GNSS satellite signal, the estimation algorithm determines at least one indication of the estimation accuracy in addition to at least one estimation result;

[0005] b) Receive at least one piece of information, which, in addition to at least one indication of the estimation accuracy derived from the estimation algorithm, also enables the inference of the estimation accuracy;

[0006] c) Adjust the indication of estimation accuracy derived from the estimation algorithm by using at least one piece of information determined in step b).

[0007] To perform this method, steps a), b), and c) can be performed at least once and / or repeated in a given order, for example. Furthermore, steps a), b), and c), particularly steps a) and b), can be performed at least partially in parallel or simultaneously.

[0008] In particular, this method can help provide the most reliable possible indication of the uncertainty of measurements or estimates of GNSS-based positioning sensors. Here, the method proposes for the first time to adjust the indication of accuracy according to step c), wherein the indication of the accuracy of the estimate can be artificially degraded in step c) in a particularly advantageous manner. For example, at least one ambiguity variance can be artificially degraded or increased, especially if the ambiguity variance is determined to be a floating-point number. The term "accuracy" here is understood specifically in the sense of the "confidence" of the estimate.

[0009] GNSS sensors can be, for example, positioning sensors configured to perform GNSS sensor positioning and / or vehicle positioning based at least on GNSS measurements. Preferably, GNSS sensors or positioning sensors can also be configured to perform GNSS sensor positioning and / or vehicle positioning based on a combination or fusion of GNSS measurements and inertial measurements (inertia measurements) and / or vehicle sensor data (e.g., environmental sensor data). For example, steering angle sensors and / or wheel rotation sensors can be used as vehicle sensors. For example, cameras, radar sensors, lidar sensors, and / or ultrasonic sensors can be used as environmental sensors. Furthermore, map data from digital maps and / or messages from other vehicles can also be used for positioning.

[0010] At least one or every GNSS satellite signal is typically received on at least one carrier frequency. Particularly advantageously, GNSS satellite signals provided on at least two carrier frequencies (L1, L2) can also be received.

[0011] GNSS sensor data may be, for example, the (own) position, (own) velocity, (own) orientation, and / or (own) acceleration of a GNSS sensor and / or a vehicle equipped with a GNSS sensor. The GNSS sensor data preferably includes at least one (own) position of the GNSS sensor and / or the vehicle equipped with a GNSS sensor. The vehicle may be, for example, a motor vehicle, such as an automobile. The vehicle is preferably configured for at least partially autonomous or self-driving operation.

[0012] In step a), at least one ambiguity of the received GNSS satellite signal's carrier frequency is resolved using an estimation algorithm that, in addition to at least one estimation result, determines at least one indication of estimation accuracy. The relevant estimation algorithm can be performed, for example, using an ambiguity filter. The indication of estimation accuracy can be, for example, at least one ambiguity variance and / or ambiguity (co)variance matrix. Estimation algorithms for ambiguity resolution are known. For example, least squares fitting can be used as an estimation algorithm.

[0013] Ambiguity resolution can optionally be performed in different modes, such as either integer mode or floating-point mode. In integer mode, the resolution can include resolving integer ambiguities. In floating-point mode, the resolution can include resolving the ambiguity to a floating-point number. This method is particularly suitable for resolution in floating-point mode.

[0014] When operating in integer mode, the following factors should be considered in particular: to achieve the highest possible probability of correctly resolving integer ambiguities, the residual measurement error should be less than a quarter wavelength. This is often not the case, making the methods for determining integer ambiguities very complex. This is a challenging task, especially in ambiguity correction in online applications such as those in the automotive industry. The reliability of integer ambiguity estimates depends on several factors. First, it depends on the strength of the underlying GNSS model, which is determined by measurement noise, uncertainties in applied tropospheric and ionospheric corrections, satellite geometry, and the number of frequencies. Second, it depends on the integer estimation method applied.

[0015] When running in floating-point mode, the following factors can be specifically considered: To resolve ambiguity, a standard least-squares fit can be performed, discarding the integer nature of the ambiguity. As a result, a so-called floating-point solution is obtained for the ambiguity or, if necessary, other parameters (e.g., position / baseline components and / or possible additional parameters, such as atmospheric delay), as well as an indication of the estimation accuracy, such as variance.

[0016] In this scenario, for example, the (real-valued) floating-point solution of the ambiguity can be adjusted to account for integer constraints, thus obtaining an integer ambiguity solution. Several tests exist to determine whether to accept the integer solution. Several tests have been proposed in the literature and are currently in use in practice. Examples include the ratio test, distance test, and projector test. If the test fails, the floating-point ambiguity solution can be determined as the final solution.

[0017] Furthermore, it can be argued that having floating-point solutions is often problematic, not only because it ignores the integer nature of ambiguity, but also because of the typically (unrealistically) small (and therefore unrealistic) initial ambiguity variance, which is obtained, for example, by the conventional least squares method used for ambiguity resolution. This unrealistically small ambiguity variance often leads to an overly optimistic carrier range variance, which in turn leads to over-reliance on the carrier range, such as calculating code measurements downwards. Therefore, an overly optimistic output variance can be identified for the estimated output, particularly the ambiguity estimation results and / or (and therefore) the GNSS sensor data, which underestimates the actual error of the signal.

[0018] To counteract the aforementioned problems, particularly in the case of floating-point solutions, this paper proposes for the first time to manually or retrospectively adjust the indication (output variance) of the estimation accuracy derived from the ambiguity estimation algorithm to obtain a more realistic indication (output variance).

[0019] In step b), at least one piece of information is received, which, in addition to at least one indication of the estimation accuracy derived from the estimation algorithm, enables an inference of the estimation accuracy. This information can be received, for example, from the vehicle's sensors, particularly those present in addition to GNSS sensors. Preferably, this information can be received from a (GNSS) correction data service. In particular, this information can be received or determined from OSR correction data and / or SSR correction data, or it can include OSR correction data and / or SSR correction data. Preferably, this information includes (along with the correction data) the received correction data variance, which is provided, for example, by an external SSR or OSR server. The aforementioned correction data or correction data service is known. Here, OSR stands for observation space representation, and SSR stands for state space representation.

[0020] In step c), the indication regarding the estimation accuracy derived from the estimation algorithm is adjusted using at least one piece of information determined in step b). Specifically, this adjustment is made such that a (penalty) appendage is made to the (output) indication (e.g., output variance) from step a) to obtain the (output) indication, such as the output variance. For example, based on at least one piece of information determined in step b), a (penalty) appendage, such as a penalty variance, can be determined and applied in step c) for adjustment.

[0021] Therefore, an embodiment can be advantageously provided in which, particularly when using OSR or SSR correction data and / or PPP-based positioning, a penalty variance is added to the estimated ambiguity variance. This is particularly to achieve, as far as possible, a more realistic or reliable estimate of the uncertainty of the output signal (ambiguity estimate and / or GNSS sensor data).

[0022] In other words, a particularly preferred embodiment can also be described as follows: to avoid overly optimistic variance of the output signal (ambiguity estimation and / or GNSS sensor data) (in the PPP scheme), especially for the output position of GNSS / INS-based positioning sensors, the variance of SSR or OSR correction data received from an external server is used to adjust the (initial) indication regarding accuracy.

[0023] For example, adjustments may include multiplying by a scaling factor and / or adding a (penalty) surcharge. This (penalty) surcharge can be determined specifically based on information about the accuracy of the (GNSS) correction data. Adjustments are particularly made in the case of floating-point ambiguity resolution. Specifically, the result of the adjustment is the output ambiguity variance.

[0024] The scaling factor can be set, for example, so that at least one weight in the positioning by the GNSS sensor can be adjusted by adding a (penalized) appendage (penalized variance). In particular, the scaling factor is set so that the weights can be adjusted between different measurement types (particularly including code and phase measurements).

[0025] According to an advantageous implementation, in step a), the ambiguity of the carrier frequency is resolved using an ambiguity filter that determines the covariance matrix as an indication of the estimation accuracy. The ambiguity filter may, for example, include a least-squares filter. The ambiguity filter may be a component of, and / or connected to, a GNSS sensor or positioning device. In addition to positioning filters such as Kalman filters, ambiguity filters may be provided or integrated into positioning filters.

[0026] According to another advantageous embodiment, at least one piece of information received in step b) includes one or more of the following: information from a GNSS antenna, information from an inertial sensor, information from a velocity sensor, and information from a GNSS calibration data source. The calibration data service can, for example, be used as a GNSS calibration data source. On the vehicle side, calibration data can be received, for example, from an antenna (e.g., a GNSS antenna) and / or a radio connection and / or an internet connection.

[0027] According to another advantageous embodiment, at least one piece of information received in step b) includes information from a GNSS correction data source. Specifically, this information may be received or determined from (OSR and / or SSR) correction data, or it may include (OSR and / or SSR) correction data. In addition to the actual correction information, this correction data may also contain information about the accuracy and / or reliability of the correction information.

[0028] According to another advantageous embodiment, at least one piece of information received in step b) includes information from a GNSS correction data source describing the accuracy and / or reliability of the GNSS correction data. Preferably, this information includes (alongside the correction data) an indication of the accuracy and / or reliability of the correction data or its correction information, particularly at least one correction data variance, which is provided, for example, by an external SSR or OSR server.

[0029] According to another advantageous embodiment, at least one piece of information received in step b) is provided by at least one sensor of a vehicle equipped with a GNSS sensor. This sensor may, for example, be an inertial sensor and / or an environmental sensor and / or a (wheel) speed sensor of the vehicle.

[0030] According to another advantageous implementation, in step c), the indication of estimation accuracy derived from the estimation algorithm is artificially degraded by using at least one piece of information determined in step b). For example, a (penalty) supplement can be determined by using at least one piece of information determined in step b) and applied to the artificial degradation.

[0031] According to another aspect, a computer program for performing the methods presented herein is proposed. In other words, this specifically relates to a computer program (product) comprising instructions that, when executed by a computer, cause the computer to perform the methods described herein.

[0032] According to another aspect, a machine-readable storage medium is proposed on which the computer program proposed herein is stored. A machine-readable storage medium is typically a computer-readable data carrier.

[0033] According to another aspect, a positioning device is also proposed, which is configured to perform the method described herein. The positioning device is particularly for vehicles. The positioning device may be formed by, for example, a GNSS sensor, or may include a GNSS sensor. Furthermore, the positioning device may include an ambiguity filter.

[0034] The positioning device may include, for example, an arithmetic logic unit (ALU) and / or a controller, which can execute commands to perform the method. For this purpose, the ALU or controller may, for example, execute a given computer program. For instance, the ALU or controller may access a given storage medium to enable the execution of the computer program. The positioning device may, for example, be a motion and position sensor, particularly arranged in or on a vehicle.

[0035] The details, features, and advantageous implementations discussed in conjunction with the methods can also appear in the computer program and / or storage medium and / or apparatus presented herein, and vice versa. For this purpose, full reference is made to the corresponding description for a more detailed description of the features. Attached Figure Description

[0036] The solutions presented herein and their technical context will now be explained in more detail with reference to the accompanying drawings. It should be noted that the invention should not be limited to the embodiments shown. In particular, unless explicitly stated otherwise, certain aspects of the facts explained in the drawings may be extracted and combined with other parts of the drawings and / or other components of this specification. Wherein, schematically:

[0037] Figure 1 An exemplary flow of the method described herein is shown, and

[0038] Figure 2 An exemplary positioning device described herein is shown. Detailed Implementation

[0039] Figure 1 An exemplary flow of the method described herein is illustrated schematically. This method is used to evaluate at least one GNSS satellite signal 3, received from at least one GNSS satellite 2, in order to determine GNSS sensor data 14 (see GNSS sensor 1) using GNSS sensor 1. Figure 2 The order of steps a), b), and c) indicated by boxes 110, 120, and 130 is exemplary and can be performed at least once in the indicated order to execute the method.

[0040] In block 110, according to step a), the ambiguity of at least one carrier frequency of the received GNSS satellite signal 3 is resolved by using estimation algorithm 7, which, in addition to at least one estimation result 12, determines at least one indication 13 regarding estimation accuracy. In block 120, according to step b), at least one piece of information is received, which, in addition to the at least one indication 13 regarding estimation accuracy derived from estimation algorithm 7, enables an inference of the estimation accuracy. In block 130, according to step c), the indication 13 regarding estimation accuracy derived from estimation algorithm 7 is adjusted using the at least one piece of information determined in step b).

[0041] Figure 2 An exemplary positioning device 16 described herein is shown. The positioning device 16 is arranged, for example, in a vehicle 10 to determine the vehicle 10's own position, for example, by means of GNSS satellite signals 3 from GNSS satellite 2. The positioning device 16 is configured to perform the methods described herein. For this purpose, the positioning device 16 exemplary includes a GNSS sensor 1 and an ambiguity filter 6. Here, the self-position is an example of GNSS sensor data 14.

[0042] Ambiguity filter 6 can acquire data 15 from GNSS sensor 16, which is (still) ambiguous due to the ambiguity of the carrier frequency. The ambiguity of the carrier frequency can be resolved using ambiguity filter 6. For this purpose, ambiguity filter 6 can include estimation algorithm 7. Estimation algorithm 7 can optionally output an integer solution (lower left arrow) or a floating-point solution (vertical down arrow). This solution includes an estimation result 12 and an indication 13 regarding the estimation accuracy, which can (jointly) be transmitted to GNSS sensor 16. For example, at least one variance and / or a covariance matrix can be determined as the indication 13 regarding the estimation accuracy.

[0043] Instruction 13 may, by way of example, include carrier range variance (symbol: σ_C), which is the sum of measurement variance (symbol: σ_Cmeas) and estimation variance (symbol: σ_Cest):

[0044]

[0045] Here, the measurement variance of the carrier range typically depends on the carrier phase measurement variance (symbol: σ_phase) plus the ambiguity variance (symbol: σ_amb):

[0046]

[0047] Specifically, if the output is a floating-point solution (a downward-pointing arrow), then indicator 13 is adjusted in the exemplary adder 11. For this, a penalty variance (symbol: σ_pen) can be added:

[0048]

[0049] This means that, as in the following example, and where necessary as in step c), the indication 13 regarding the estimation accuracy derived from estimation algorithm 7 can be artificially degraded. This is advantageously achieved by using at least one piece of information determined in step b).

[0050] The information received in step b) may include one or more of the following: information from GNSS antenna 9, information from inertial sensor 4, information from velocity sensor 5, and information from GNSS correction data source 8.

[0051] Preferably, at least one piece of information includes information from GNSS correction data source 8. Particularly preferably, at least one piece of information includes information from GNSS correction data source 8 that describes the accuracy and / or reliability of the GNSS correction data.

[0052] In addition, the information from the GNSS correction data source 8 preferably includes (along with the correction data) an indication of the accuracy and / or reliability of the correction data or its correction information (in particular at least one correction data variance, which is provided, for example, by an external SSR or OSR server).

[0053] The penalized variance can be determined based on information from the GNSS correction data source 8 and applied to the indicator 13 in the adder 11. Here, the penalized variance can be generated, for example, based on the SSR or OSR line-of-sight correction variance multiplied by a scaling factor.

[0054] Alternatively or additionally, at least one piece of information may be provided by a sensor of the vehicle 10 equipped with a GNSS sensor 1. In particular, the GNSS antenna 9, the inertial sensor 4, and / or the speed sensor 5 are considered herein as sensors of the vehicle 10.

[0055] Therefore, a particularly advantageous method can be given for obtaining a more realistic uncertainty for the estimated position based on a (GNSS / INS-based) positioning sensor, given the solution of the ambiguity of the carrier phase.

[0056] In particular, adding a penalty variance here helps in estimating the ambiguity variance. The penalty variance can be determined or calculated using the received correction data (SSR or OSR).

[0057] To calculate the penalty method, one can multiply the sum of the variances of ionospheric, tropospheric, orbital, clock, and phase distortion corrections by a scaling factor.

[0058] The scaling factor can be adjusted such that at least one weight in the positioning via GNSS sensor 1 can be adjusted by adding a penalty variance. In particular, the scaling factor can be adjusted such that the weights can be adjusted between different measurement types (especially including code and phase measurements).

Claims

1. A method for evaluating at least one GNSS satellite signal (3) received from at least one GNSS satellite (2) in order to determine GNSS sensor data (14) by means of a GNSS sensor (1), the method comprising at least the following steps: a) resolving ambiguities of at least one carrier frequency of the received GNSS satellite signal (3) by using an estimation algorithm (7) which determines at least one indication (13) on the estimation accuracy in addition to at least one estimation result (12); b) receiving at least one information which enables an inference on the estimation accuracy in addition to the at least one indication (13) on the estimation accuracy resulting from the estimation algorithm (7); c) adjusting the indication (13) on the estimation accuracy resulting from the estimation algorithm (7) by using the at least one information determined in step b); wherein in step c) the indication (13) on the estimation accuracy resulting from the estimation algorithm (7) is artificially degraded by using the at least one information determined in step b). In step a) the ambiguities of the carrier frequencies are resolved by means of an ambiguity filter (6) which determines a covariance matrix as an indication (13) on the estimation accuracy. The at least one information received in step b) comprises one or more of the following information: information from a GNSS antenna (9), information from an inertial sensor (4), information from a speed sensor (5), information from a GNSS correction data source (8). The at least one information received in step b) comprises information from a GNSS correction data source (8) which describes the accuracy and / or the reliability of GNSS correction data. The at least one information received in step b) is provided by a sensor of a vehicle (10) equipped with a GNSS sensor (1).

2. The method of claim 1, wherein, 6. A computer program product comprising instructions which, when executed by a computer, cause the computer to carry out the method according to any one of claims 1 to 5.

3. The method of claim 1 or 2, wherein, 7. A machine-readable storage medium having stored thereon a computer program which, when executed by a computer, causes the computer to carry out the method according to any one of claims 1 to 5.

4. The method of claim 1 or 2, wherein, 8. A positioning device (16) arranged to carry out the method according to any one of claims 1 to 5.

5. The method of claim 1 or 2, wherein, ​ ​ ​ ​

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