CN111856527A - Indoor positioning method based on pseudolite space signal spectrum - Google Patents

Indoor positioning method based on pseudolite space signal spectrum Download PDF

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Publication number
CN111856527A
CN111856527A CN202010788744.XA CN202010788744A CN111856527A CN 111856527 A CN111856527 A CN 111856527A CN 202010788744 A CN202010788744 A CN 202010788744A CN 111856527 A CN111856527 A CN 111856527A
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neural network
pseudolite
indoor
signal
indoor positioning
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CN111856527B (en
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蔚保国
黄璐
祝瑞辉
张衡
李爽
梁晓虎
李雅宁
程建强
贾浩男
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CETC 54 Research Institute
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/01—Satellite radio beacon positioning systems transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/13—Receivers
    • G01S19/35—Constructional details or hardware or software details of the signal processing chain
    • G01S19/37—Hardware or software details of the signal processing chain
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
    • G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/42—Determining position
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

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  • Engineering & Computer Science (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Signal Processing (AREA)
  • Position Fixing By Use Of Radio Waves (AREA)

Abstract

The invention discloses an indoor positioning method based on a pseudolite space signal spectrum, and belongs to the technical field of pseudolite indoor positioning. The method comprises two stages of off-line data set construction and on-line positioning. In an off-line stage, constructing an electromagnetic signal map in a space environment by utilizing a carrier noise spectrum density ratio and indoor space position information in received pseudolite original observation data; learning deep layer signal characteristics in the signal map through a deep convolutional neural network, and constructing an indoor positioning model; and the built model is utilized to realize real-time indoor position estimation. The method avoids solving the ambiguity of the whole circle and realizes the indoor absolute positioning of the meter level.

Description

Indoor positioning method based on pseudolite space signal spectrum
Technical Field
The invention relates to the technical field of pseudolite indoor positioning navigation, in particular to an indoor positioning method based on a pseudolite space signal map.
Background
Currently, a positioning method based on a navigation satellite basically meets the navigation of people in an outdoor environment. However, in indoor environments, GNSS signals cannot provide accurate positioning information due to occlusion. Therefore, the research and development of the indoor positioning navigation technology are attracting more and more attention. The pseudolite positioning system has the capability of transmitting signals the same as those of a satellite on the sky, can provide stable and reliable satellite signals for an indoor environment, enables indoor and outdoor seamless positioning and navigation to be possible based on the existing hardware conditions of the smart phone, and therefore becomes a research hotspot in the field of indoor positioning.
However, in the existing pseudolite indoor positioning technology, for example, a position resolving technology based on a pseudorange or a carrier phase smooth pseudorange faces technical problems of solving an integer ambiguity, a near-far effect and the like, and because indoor multipath signals have a large influence on the method, the existing pseudolite-based indoor positioning is difficult to be widely applied.
Disclosure of Invention
In view of this, the invention provides an indoor positioning method based on a pseudolite space signal map, which is simple and easy to implement and has higher robustness.
In order to achieve the purpose, the invention adopts the technical scheme that:
an indoor positioning method based on a pseudolite space signal map comprises the following steps:
(1) constructing a signal map of the pseudolite signal in an indoor space environment in an off-line manner;
(2) constructing a deep convolutional neural network, inputting the signal map obtained in the step (1) into the neural network so as to train the signal map, and obtaining a trained neural network; the output of the neural network is a spatial position coordinate;
(3) and inputting the pseudosatellite data received by the receiver into the trained neural network to position the receiver in real time.
Further, the specific mode of the step (1) is as follows:
(101) setting up a multi-channel pseudo satellite positioning system in an indoor positioning area, wherein each channel is connected with a transmitting antenna, and arranging and installing pseudo satellite antennas;
(102) dividing an indoor positioning area into grids with equal size, taking angular points of the grids as sampling points, placing a receiver at each sampling point, and calibrating the position of each receiver and the position of each pseudo-satellite antenna by using a total station;
(103) for each sampling point, acquiring the carrier noise spectrum density ratio of each pseudo satellite antenna at the point, arranging the carrier noise spectrum density ratios into a line, and attaching the horizontal and vertical coordinates of the corresponding sampling point behind the line so as to form complete line data; arranging the data of each sampling point up and down to form a data set;
(104) preprocessing the data set, and reconstructing each row of data into a single-channel image with M multiplied by M pixels, wherein the M value is larger than the maximum value of the horizontal and vertical coordinates of all the pseudolite antenna positions; and after preprocessing, forming a signal map in a (a, M, M,1) format by using each sub-image, wherein a is the number of sampling points.
Further, the neural network includes a convolutional layer, a pooling layer, and a fully-connected layer.
Further, the method for training the neural network in the step (2) is as follows:
dividing all signal maps into a plurality of different training sets and test sets, training the neural network by using the training sets, testing the trained neural network by using the test sets, carrying out parameter optimization on the neural network according to a test result, and repeatedly training and testing to obtain the trained neural network.
Compared with the prior art, the invention has the following beneficial effects:
(1) the invention provides a method for constructing a pseudolite electromagnetic signal spectrum under an indoor environment by utilizing a pseudolite carrier noise spectrum density ratio and space position information, which can realize effective utilization of the pseudolite signal space distribution characteristics.
(2) The invention provides an indoor positioning method based on a deep convolutional neural network model by combining a constructed pseudo-satellite signal map, a positioning model is trained by learning deep features through multilayer convolutional processing, and the positioning model can be used for indoor real-time positioning.
(3) Compared with the traditional indoor pseudo-satellite positioning method, the method does not need to solve the whole-cycle ambiguity, and reduces the calculation complexity. Moreover, pseudo range, carrier wave and other pseudo satellite information are not utilized in the method, so that the influence of indoor multipath on positioning accuracy is reduced, and the method has higher robustness.
Drawings
Fig. 1 is a flowchart of a positioning method according to an embodiment of the present invention.
Fig. 2 is a schematic diagram of a positioning system in an embodiment of the invention.
FIG. 3 is a diagram illustrating a format of a data set according to an embodiment of the present invention.
Detailed Description
The invention is further described with reference to the following figures and detailed description.
An indoor positioning method based on a pseudolite space signal map comprises two stages of off-line data set construction and on-line positioning. In the method, the pseudo satellite base station can send a navigation signal with a unique C/A code, the method is compatible with a GPS L1 code and a BDS B1 code, all channel signals are generated by 1PPS at the same time, and a commercial GNSS receiver can receive the pseudo satellite signals. In the off-line stage, the carrier noise spectral density ratio C/N in the received pseudolite original observation data is utilized0Establishing an electromagnetic signal map in the space environment by the indoor space position information; learning deep layer signal features in pseudolite signal maps through deep convolutional neural networksAcquiring, and constructing an indoor positioning model; in the on-line positioning stage, real-time indoor position estimation is realized by using the constructed model, and the flow of the method is shown in fig. 1.
The method specifically comprises the following steps:
step 1: a multi-channel pseudo satellite positioning system is set up in an indoor positioning area, each channel is connected with a transmitting antenna, and the pseudo satellite antennas are arranged and installed at different indoor positions.
As shown in fig. 2, the multichannel pseudolite indoor positioning system comprises a multichannel signal transmitter, antennas, a receiver and an intelligent terminal, wherein the multichannel signal transmitter transmits navigation signals through each transmitting antenna, each pseudolite transmitting antenna in the antennas corresponds to one pseudolite channel of the multichannel signal transmitter, the signal of each pseudolite channel has a unique C/a code, each pseudolite channel is modulated by an L1 code of a GPS and a B1 code of a BDS, and each channel signal is generated by 1PPS at the same time; the receiver comprises a GNSS receiving chip and a receiving antenna.
Step 2: and (2) establishing a coordinate system in the positioning area in the step (1), dividing indoor area grids at equal intervals according to positioning requirements, and taking corner points of each grid as sampling points, as shown in fig. 2. Placing a receiver at each sampling point, and calibrating the position u of the receiver by using a total stationi(i is 1,2, …, a), a is the number of sample points, and the position p of each antennaj(j ═ 1,2, …, b), and b represents the number of satellite antennas.
And step 3: collecting carrier noise spectrum density ratio C/N of each antenna of pseudolite at all sampling point positions0Corresponding sample point position uiThe data set dataset comprising a row a and (b +2) columns is shown in fig. 3, and x and y represent the abscissa and ordinate of the corresponding sampling point.
And 4, step 4: preprocessing the data set dataset described in step 3, reconstructing each line of data into a single channel image of M × M pixels, wherein the determination of the M value depends on the coordinate position p of the pseudolite antenna, i.e. the M value is greater than the maximum of the abscissa/ordinate of all antenna positions. And after preprocessing, forming a signal map Radiomap in a (a, M, M,1) format by using each image.
And 5: and constructing a multilayer convolutional neural network model consisting of a convolutional layer, a pooling layer and a full-connection layer, inputting the preprocessed pseudolite signal map, and outputting the preprocessed pseudolite signal map as a space position coordinate.
Step 6: and (4) dividing the Radiomap constructed in the step (4) into different training sets and test sets by using a cross validation mode, training the constructed multilayer convolutional neural network model, performing parameter tuning, and storing the trained positioning model.
And 7: in real-time positioning, pseudolite data received by the receiver is input into the positioning model in step 6, and the current position coordinates of the receiver are estimated.
The method realizes real-time indoor positioning at an online stage by constructing an electromagnetic signal map of a pseudolite signal in an indoor space environment in an off-line manner and learning deep signal characteristics by utilizing a deep convolutional neural network. The pseudo satellite can transmit navigation signals with unique C/A codes, is compatible with GPS L1 codes and BDS B1 codes, and all channel signals are generated by 1PPS at the same time. The signal map is formed by carrier noise spectral density ratio C/N in pseudolite original observation data0And indoor spatial location information. The deep convolutional neural network is used as a feature extraction mode, learns the space distribution features of the pseudo satellite signals and is used for constructing a position estimation model.
In a word, the invention avoids the problems of complex time synchronization and ambiguity resolution in the traditional pseudolite indoor positioning method, and provides the indoor positioning method based on the pseudolite indoor signal atlas. In this method, a multichannel signal transmitter is used to transmit different PRN codes, the signals of which are compatible with GPS and BDS signals. Commercial GNSS receivers can receive these signals and build signal transmission systems. By creating a novel off-line data set construction mode, the distribution of the carrier noise spectral density ratio of the visible pseudo-satellite in an indoor space environment is combined with a deep convolutional neural network model, deep representative characteristics are fully learned, and an indoor positioning model is obtained through training. Accurate position estimation can be achieved in the real-time positioning stage. The method is verified through tests, and results show that the method can provide meter-level dynamic and static absolute positioning accuracy without an initial position, reduces the operation complexity of a user and improves the coverage and continuity of a positioning result compared with the traditional indoor pseudo-satellite ambiguity resolution method.

Claims (4)

1. An indoor positioning method based on a pseudolite space signal map is characterized by comprising the following steps:
(1) constructing a signal map of the pseudolite signal in an indoor space environment in an off-line manner;
(2) constructing a deep convolutional neural network, inputting the signal map obtained in the step (1) into the neural network so as to train the signal map, and obtaining a trained neural network; the output of the neural network is a spatial position coordinate;
(3) and inputting the pseudosatellite data received by the receiver into the trained neural network, and positioning the receiver in real time.
2. The indoor positioning method based on the pseudolite spatial signal spectrum as claimed in claim 1, wherein the specific manner of step (1) is as follows:
(101) setting up a multi-channel pseudo satellite positioning system in an indoor positioning area, wherein each channel is connected with a transmitting antenna, and arranging and installing pseudo satellite antennas;
(102) dividing an indoor positioning area into grids with equal size, taking angular points of the grids as sampling points, placing a receiver at each sampling point, and calibrating the position of each receiver and the position of each pseudo-satellite antenna by using a total station;
(103) for each sampling point, acquiring the carrier noise spectrum density ratio of each pseudo satellite antenna at the point, arranging the carrier noise spectrum density ratios into a line, and attaching the abscissa and the ordinate of the corresponding sampling point behind the line so as to form complete line data; arranging the data of each sampling point up and down to form a data set;
(104) preprocessing the data set, and reconstructing each row of data into a single-channel image with M multiplied by M pixels, wherein the M value is larger than the maximum value of the horizontal and vertical coordinates of all the pseudolite antenna positions; and after preprocessing, forming a signal map with a (a, M, M,1) format by each image, wherein a is the number of sampling points.
3. The pseudo-satellite spatial signal map-based indoor positioning method of claim 1, wherein the neural network comprises a convolutional layer, a pooling layer and a fully connected layer.
4. The indoor positioning method based on the pseudo-satellite space signal spectrum as claimed in claim 3, wherein the training of the neural network in step (2) is performed by:
dividing all signal maps into a plurality of different training sets and test sets, training the neural network by using the training sets, testing the trained neural network by using the test sets, carrying out parameter optimization on the neural network according to a test result, and repeatedly training and testing to obtain the trained neural network.
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113267191A (en) * 2021-05-26 2021-08-17 中国电子科技集团公司第五十四研究所 Unmanned navigation system and method based on pseudolite indoor signal map correction

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105182382A (en) * 2015-08-05 2015-12-23 中国电子科技集团公司第五十四研究所 Centimeter-level positioning method of pseudo satellite
CN106941718A (en) * 2017-04-07 2017-07-11 南京邮电大学 A kind of mixing indoor orientation method based on signal subspace fingerprint base
CN110456307A (en) * 2019-07-31 2019-11-15 东南大学 A kind of method of locating terminal based on indoor Pseudolite signal carrier-to-noise ratio
CN110716217A (en) * 2019-10-29 2020-01-21 中国电子科技集团公司第五十四研究所 Array pseudo satellite indoor positioning method and system

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105182382A (en) * 2015-08-05 2015-12-23 中国电子科技集团公司第五十四研究所 Centimeter-level positioning method of pseudo satellite
CN106941718A (en) * 2017-04-07 2017-07-11 南京邮电大学 A kind of mixing indoor orientation method based on signal subspace fingerprint base
CN110456307A (en) * 2019-07-31 2019-11-15 东南大学 A kind of method of locating terminal based on indoor Pseudolite signal carrier-to-noise ratio
CN110716217A (en) * 2019-10-29 2020-01-21 中国电子科技集团公司第五十四研究所 Array pseudo satellite indoor positioning method and system

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113267191A (en) * 2021-05-26 2021-08-17 中国电子科技集团公司第五十四研究所 Unmanned navigation system and method based on pseudolite indoor signal map correction

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