CN109945870B - Pseudo-satellite indoor positioning method with pseudo-range observed value and carrier-to-noise ratio fused - Google Patents
Pseudo-satellite indoor positioning method with pseudo-range observed value and carrier-to-noise ratio fused Download PDFInfo
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Abstract
The invention discloses a pseudo-range observation value and carrier-to-noise ratio fused pseudolite indoor positioning method, which comprises the following steps: (1) Aiming at an asynchronous pseudolite indoor positioning system, a weighted centroid positioning algorithm is adopted to give a positioning equation; (2) Calculating the carrier-to-noise ratio of the pseudolite received by the receiver to obtain a weight; (3) Pseudo range and carrier-to-noise ratio observation values are fused, redundancy observation values are added, combination of observation value domains is carried out, and positioning accuracy and reliability are improved; and (4) positioning calculation is carried out by adopting unscented Kalman filtering UKF. The method has good inhibition effect on asynchronous sampling time and multipath errors of the receiver, has good positioning effect under the condition of poor geometric distribution of the pseudolite, shows good coarse-error-resistant performance, and is suitable for deep indoor scenes.
Description
Technical Field
The invention relates to the technical field of precise positioning of a pseudolite system, in particular to a pseudolite indoor positioning method fusing a pseudo range observation value and a carrier-to-noise ratio.
Background
With the increasingly wide application of Location Based Services (LBS) in life and military, people have also put forward more requirements on the application scenario of Global Navigation Satellite System (GNSS). GNSS can provide continuous, reliable, high-precision position information in open environments, but cannot solve the "last mile" problem of navigation in occluded environments. At present, wireless signal-based indoor positioning technologies mainly comprise Wi-Fi, bluetooth, ultra-Wide Bandwidth (UWB) and the like, but the WiFi and Bluetooth technologies can only reach meter-level positioning accuracy generally, and huge workload is needed for establishing and maintaining a fingerprint database. Although the UWB technology can achieve higher precision, the signal transmission distance is short, precise clock synchronization is required, the system construction cost is expensive, and large-area deployment and application are difficult.
The pseudolite technology has wide signal coverage range and the same signal system as the GNSS, can realize indoor and outdoor seamless positioning with the GNSS, is receiving more and more attention, and has very wide application prospect in key areas, specific places and even military fields. At present, a pseudolite system with the most mature technology and the most widely applied international is a Locata, can meet the precision positioning requirements in the fields of automatic control, open-pit mining, port measurement, deformation monitoring and the like, can perform industrial-grade and centimeter-grade positioning indoors by inhibiting the multipath effect through a special antenna diversity technology, but the Locata system requires that the pseudolite must keep strict time synchronization, has extremely high requirements on equipment, and can cause huge deployment cost and implementation difficulty. Foreign scholars Yun D and Kee C respectively study synchronous and asynchronous pseudolite indoor positioning systems, and positioning accuracy of a centimeter-millimeter level is achieved by utilizing carrier phase observation values. In order to avoid the problem of time synchronization, borio D adopts a weighted centroid method based on the signal-to-noise ratio of the pseudolite signal to perform an indoor positioning experiment, and obtains meter-level positioning accuracy. In recent years, some indoor positioning research based on pseudolites has been carried out by domestic scholars. GuoX realizes the single-difference single-point positioning between satellites based on a carrier observation value by sharing a crystal oscillator clock through a pseudolite, and the indoor positioning precision reaches the real-time centimeter level. Li X adopts an improved particle swarm algorithm-based Ambiguity Function Method (AFM) to fix the Ambiguity of the indoor pseudolite in a single epoch mode, so that the calculation efficiency and the search capability of the traditional AFM are improved, and centimeter-level positioning accuracy of the indoor pseudolite is realized on a single-frequency software receiver. Xu R develops a new pseudolite indoor positioning system, a transmitter and a receiver use the same clock, pseudolite signals can be processed by a universal receiver, and simulation experiment results show that the system can realize meter-level positioning accuracy.
The above researches usually ignore the influence of multipath effect on the positioning result, or take hardware measures (such as helical antenna, etc.) to suppress multipath error, and the system cost is too high; for an asynchronous pseudolite indoor positioning system, the ranging error caused by the asynchronous receiver sampling time is usually ignored.
Disclosure of Invention
The invention aims to solve the technical problem of providing a pseudo-satellite indoor positioning method with pseudo-range observation values and carrier-to-noise ratios fused, which has good inhibition effect on asynchronous sampling time and multipath errors of a receiver, has good positioning effect under the condition of poor geometric distribution of pseudo-satellites, shows good coarse error resistance and is suitable for deep indoor scenes.
In order to solve the technical problem, the invention provides a pseudo-satellite indoor positioning method fusing a pseudo-range observation value and a carrier-to-noise ratio, which comprises the following steps:
(1) Aiming at an asynchronous pseudolite indoor positioning system, a positioning equation is given by adopting a weighted centroid positioning algorithm;
(2) Calculating the carrier-to-noise ratio of the pseudolite received by the receiver to obtain a weight;
(3) Pseudo range and carrier-to-noise ratio observation values are fused, redundant observation values are added, observation value domains are combined, and positioning precision and reliability are improved;
(4) Unscented Kalman Filter (UKF) was used for position solution.
Preferably, in step (1), the position of the user receiver is calculated by using a weighted centroid location algorithm according to the following formula:
in the formula, P pl,i Coordinate vectors of the ith pseudolite; w is a i The positioning accuracy of the system is determined for the corresponding weights.
Preferably, in step (2), a weight is calculated by using the carrier-to-noise ratio of the pseudolite received by the receiver, and the expression is as follows:
wherein (C/N) 0 ) i Is the signal carrier to noise ratio of the ith pseudolite.
Preferably, in step (3), the asynchronous pseudolite indoor positioning observation equation is as follows:
in the formula (I), the compound is shown in the specification,
and
for the pseudorange observations from the mobile/reference station for the ith pseudolite,
and
the geometric distance of the mobile/reference station to the ith pseudolite,
and
for non-modeling errors and observation noise,
for the double difference operator, the joint formula (1) and the formula (3) obtain a new observation equation:
in the formula, x u 、y u 、z u I.e. to-be-solved coordinate, upsilon j In order to observe the noise, it is,
and R is an observation noise covariance matrix.
Preferably, in the step (4), an Unscented Kalman Filter (UKF) is used to perform positioning solution, and for the system process noise variance matrix Q and the observation noise variance matrix R, since static positioning is performed, Q is set as a zero matrix, and the measured noise variance matrix is expressed as:
in the formula, γ and η are pseudo-range and carrier-to-noise ratio observation noise error matrixes respectively, and are obtained by analyzing the statistical characteristics of actual observed values.
The invention has the beneficial effects that: the pseudo-satellite indoor positioning method based on pseudo-range observation values and carrier-to-noise ratio fusion provided by the invention adopts a weighted centroid positioning algorithm to improve the positioning accuracy of an asynchronous pseudo-satellite indoor positioning system. The weight is calculated by the carrier-to-noise ratio of the pseudolite received by the receiver, and the performance better than satellite altitude weighting is obtained in a poor signal environment. And the pseudo range and the carrier-to-noise ratio observation value are fused, the redundancy observation value is increased, the combination of the observation value domains is carried out, and the positioning precision and reliability are improved. The unscented Kalman filtering is adopted for positioning calculation, an observation equation is not required to be linearized, the distribution condition of the system state vector estimation value is described by using the determined sample points, and the linearization error of pseudolite positioning can be weakened.
Drawings
FIG. 1 is a schematic flow chart of the method of the present invention.
FIG. 2 is a schematic diagram of the spatial distribution of pseudolites and receivers used in the experiment of the present invention.
Fig. 3 is a diagram illustrating a deviation between a pseudo-range double-difference observation value and a true distance used in an experiment of the present invention.
Fig. 4 is a schematic diagram of the mobile station carrier-to-noise ratio measurements obtained from the experiments of the present invention.
FIG. 5 is a schematic diagram showing alignment error sequence comparison of experiments according to the present invention.
FIG. 6 is a schematic diagram showing the alignment of the two positioning errors obtained in the experiment of the present invention.
Detailed Description
As shown in fig. 1, a pseudo-range observation value and carrier-to-noise ratio fused pseudolite indoor positioning method specifically includes the following steps:
step (1), aiming at an asynchronous pseudolite indoor positioning system, a weighted centroid positioning algorithm is adopted, and the position of a user receiver can be calculated by the following formula:
in the formula, P pl,i Coordinate vectors of the ith pseudolite; w is a i The positioning accuracy of the system is determined for the corresponding weights.
Step (2), calculating the carrier-to-noise ratio of the pseudolite received by the receiver to obtain a weight, wherein the expression is as follows:
wherein (C/N) 0 ) i Is the signal carrier to noise ratio of the ith pseudolite.
And (3) fusing pseudo range and carrier-to-noise ratio observed values, and increasing a redundancy observed value, wherein an asynchronous pseudolite indoor positioning observation equation is as follows:
in the formula (I), the compound is shown in the specification,
and
for the pseudorange observations from the mobile/reference station for the ith pseudolite,
and
the geometric distance of the mobile/reference station to the ith pseudolite,
and
for non-modeling errors and observation noise,
is a double difference operator. The joint formula (1) and the formula (3) obtain a new observation equation:
in the formula, x u 、y u 、z u I.e. the coordinate to be solved, upsilon j In order to observe the noise, it is,
and R is an observation noise covariance matrix.
And (4) positioning and resolving by adopting Unscented Kalman Filtering (UKF), wherein for a system process noise variance matrix Q and an observation noise variance matrix R, because of static positioning, Q is set as a zero matrix, and the noise measurement variance matrix is expressed as follows:
in the formula, γ and η are pseudo-range and carrier-to-noise ratio observation noise error matrixes respectively, and are obtained by analyzing the statistical characteristics of actual observed values.
In order to verify the reliability of the algorithm, pseudolite static observation data of about 20 minutes are collected, the data sampling rate is 1Hz, two groups of different data are selected according to the geometric distribution of the pseudolites for indoor positioning calculation, and the indoor positioning calculation is compared with the traditional positioning method. In order to comprehensively evaluate the performance of the positioning algorithm, in the first experiment, positioning calculation is performed by using all 8 pseudolites to ensure good geometric distribution of the pseudolites and sufficient redundant observation, the spatial distribution of the pseudolites and the receiver is shown in fig. 2, and the indoor environment is complex.
Fig. 3 shows the deviation between the pseudo-range double-difference observed value acquired by the u-blob receiver and the true geometric distance between the true pseudolite and the receiver (the reference satellite is the pseudolite No. 2), and it can be seen that the pseudo-range double-difference observed value has poor quality and oscillation and deviation which change along with time due to the influence of asynchronous sampling time and multipath and indoor signal propagation effect. The short baseline is subjected to double-difference processing, and observation errors and noise are amplified, so that the positioning result is seriously deviated from a true value and even diverged.
Fig. 4 shows pseudolite carrier-to-noise ratio observed quantities acquired by a mobile station receiver, pseudo-range double-difference observed values are compared, and the carrier-to-noise ratio of static observation is relatively stable and is suitable for being used as observation information to participate in indoor positioning calculation. However, due to the change of the surrounding environment of the receiver (such as the movement of people) and a certain degree of signal interference, the signal-to-noise ratio of part of the pseudolite signals has a large fluctuation range.
The error sequence of the positioning result is shown in fig. 5, and the three positioning methods all adopt a static mode to perform coordinate calculation. As can be seen from comparison, the error of the fusion positioning method is minimum, and the stability of the positioning result is greatly improved compared with pseudo-range double-difference positioning. Due to the influence of clock drift, indoor multipath and signal propagation effects, the pseudorange double-difference positioning error is large and has a slight divergence trend. The positioning result using the weighted centroid method is better than the pseudorange double differences due to its more stable carrier-to-noise ratio observation output, sufficient pseudolite numbers, and good pseudolite distribution.
Table 1 shows the statistics of the positioning results of the three methods, and the accuracy of the external coincidence and the internal coincidence of the fusion positioning method respectively reaches the decimeter level and the centimeter level. Compared with pseudo-range double-difference positioning, the positioning errors in the east direction and the north direction are respectively reduced by 95.0 percent and 98.5 percent; compared with the weighted centroid method, the positioning errors in the east direction and the north direction are reduced by 75.4% and 96.5%, respectively.
TABLE 1 comparison of results of a positioning experiment
In order to better embody the advantages of the algorithm, the observation data of the pseudolites 1, 2, 3, 4 and 8 are selected in the second experiment to perform indoor positioning calculation, and the geometric distribution of the pseudolites in the scene is poor. The positioning error sequence is shown in fig. 6, and it can be seen that the positioning result obtained by the fusion positioning method has the best precision, and the plane errors are all within 1 m. As shown in table 2, the outer coincidence accuracy and the inner coincidence accuracy of the fusion positioning method reach sub-meter level and centimeter level respectively, and the positioning errors in the east direction and the north direction are reduced by 55.4% and 81.1% respectively compared with pseudo-range double-difference positioning; compared with the weighted centroid method, the positioning errors in the east and north directions are reduced by 33.3% and 25.7%, respectively. The experiment can find that the positioning precision is remarkably improved by adopting the fusion positioning method under the condition that the geometric distribution of the pseudolite is poor, so that the method can be suitable for indoor scenes with poor observation environment, the positioning equation is more stable by adding new observation information (carrier-to-noise ratio) on the basis of the original pseudo-range observation value, and the reduction of the positioning precision caused by poor geometric distribution of the pseudolite is effectively inhibited.
TABLE 2 comparison of the results of the two experimental positions
The pseudo-range observation value and carrier-to-noise ratio fused pseudolite indoor positioning method provided by the invention has a good inhibiting effect on asynchronous sampling time and multipath errors of a receiver, has a good positioning effect under the condition of poor geometric distribution of pseudolites, shows good coarse error resistance and is suitable for deep indoor scenes.
Claims (3)
1. A pseudo-range observation value and carrier-to-noise ratio fused pseudolite indoor positioning method is characterized by comprising the following steps:
(1) Aiming at an asynchronous pseudolite indoor positioning system, a positioning equation is given by adopting a weighted centroid positioning algorithm; by adopting a weighted centroid location algorithm, the position of the user receiver is calculated by the following formula:
in the formula, P pl,i Coordinate vectors of the ith pseudolite; w is a i Determining the positioning accuracy of the system for the corresponding weight;
(2) Calculating the carrier-to-noise ratio of the pseudolite received by the receiver to obtain a weight;
(3) Pseudo range and carrier-to-noise ratio observation values are fused, redundancy observation values are added, combination of observation value domains is carried out, and positioning accuracy and reliability are improved; the indoor positioning observation equation of the asynchronous pseudolite is as follows:
in the formula (I), the compound is shown in the specification,
and
pseudorange observations to the i-th pseudolite for the mobile station and the reference station,
and
the geometric distance of the mobile station and the reference station to the ith pseudolite,
and
for non-modeling errors and observation noise,
for the double difference operator, the joint formula (1) and the formula (3) obtain a new observation equation:
in the formula, x u 、y u 、z u I.e. the coordinate to be solved, upsilon j In order to observe the noise, it is,
r is an observation noise covariance matrix;
(4) And positioning resolving is carried out by adopting unscented Kalman filtering UKF.
2. The pseudo-range observation value and carrier-to-noise ratio fused pseudolite indoor positioning method as claimed in claim 1, wherein in step (2), a weight is obtained by calculation using a pseudolite carrier-to-noise ratio value received by a receiver, and the expression is as follows:
wherein (C/N) 0 ) i The signal carrier to noise ratio of the ith pseudolite.
3. The pseudo-range observation and carrier-to-noise ratio fused pseudolite indoor positioning method as claimed in claim 1, wherein in step (4), unscented kalman filter UKF is used for positioning solution, and for a system process noise variance matrix Q and an observation noise variance matrix R, since static positioning is adopted, Q is set as a zero matrix, the measurement noise variance matrix is expressed as:
in the formula, γ and η are pseudo-range and carrier-to-noise ratio observation noise error matrixes respectively, and are obtained by analyzing the statistical characteristics of actual observed values.
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