Passive positioning system and method for indoor complex environment based on ultra-high frequency RFID technology

Abstract

The present invention discloses a passive positioning system and method for complex indoor environments based on ultra-high frequency RFID technology, the system comprising: a plurality of passive RFID tags, an RFID reader / writer, a plurality of antennas, a router and a host computer. The present invention solves the problems of high cost, high power consumption and low precision of existing indoor positioning equipment, and at the same time solves the problem that the existing passive RFID indoor positioning method is seriously interfered by multipath environment and non-line-of-sight environment, resulting in reduced positioning precision or even failure. The system performs passive indoor positioning based on passive RFID tags, realizes high-precision, low-cost, low-power passive indoor positioning in complex environments, and is easy to maintain. It can resist environmental interference in complex indoor environments to realize high-precision passive indoor positioning, has a large sensing range, and can be applied in many fields, and is particularly suitable for inventory and positioning of items in warehousing logistics, library management, retail and medical industries, and has broad prospects for commercial application.

Description

Technical Field

[0001] The present invention belongs to the field of indoor positioning technology, and in particular relates to a passive positioning system and method for indoor complex environments based on ultra-high frequency RFID technology. Background Art

[0002] With the rise of smart city and smart home concepts, indoor positioning methods are becoming increasingly important in modern society, especially in industries such as warehousing and logistics, library management, retail and healthcare. These methods have fundamentally changed the way we live. Although outdoor positioning methods are quite mature, the demand for accurate and reliable indoor positioning methods in complex indoor environments continues to grow. At present, indoor positioning methods face many challenges. For example, infrared positioning methods are limited to short distances and require a line-of-sight environment (LoS) without strong light interference. Ultra-wideband (UWB) methods provide high-precision positioning, but the equipment cost and power consumption are high. Wi-Fi and Bluetooth positioning methods can be deployed using existing networks, but they usually lack sufficient accuracy. Due to the high deployment cost, these methods are difficult to deploy in large quantities in industries that require widespread use and cost control (such as warehousing and logistics and retail).

[0003] Radio Frequency Identification (RFID) is an existing wireless communication identification method that can achieve non-line-of-sight communication and is widely used in smart logistics, transportation, multi-target identification, and location tracking. In contrast, RFID systems are usually inexpensive, and because of their passive wireless communication mode, they are lightweight and less invasive. They have the advantages of integrated communication and positioning, high security, long service life, and low deployment cost, so they are often the subject of research in the field of indoor positioning.

[0004] RFID-based indoor positioning methods usually involve passive RFID tags sending signals to readers through backscatter communication. The reader then uses the energy, transmission time and phase information of the return signal to determine the location of the RFID tag. Most passive RFID positioning methods are hyperbolic positioning methods and triangulation positioning methods based on measurement information such as (TOA, Time of Arrival), Time Difference of Arrival (TDOA), Received Signal Strength Indicator (RSSI) and Space Domain Phase Difference of Arrival (SD-PDOA). Although these methods perform well in outdoor line-of-sight environments, the indoor channel environment is more complex than that of outdoor environments. Due to the structural characteristics of the building itself and the influence of indoor objects blocking, electromagnetic wave signals will refract, reflect, and diffract during propagation. At this time, directly using traditional methods for positioning will produce non-line-of-sight errors, and will also be interfered by multipath effects, resulting in reduced positioning accuracy or even failure. Among passive RFID positioning methods, SD-PDOA hyperbolic positioning method and RSSI triangulation positioning method are more common.

[0005] There are also positioning algorithms based on fingerprint databases for the above RFID return measurement signals. Accuracy can be improved by using the fingerprint of the reference tag and common passive RFID positioning technology to establish a database, but these databases need to be updated regularly, increasing complexity and labor costs. Using machine learning and artificial intelligence technology to analyze and process positioning data can improve accuracy and reliability, but it requires expensive equipment and a lot of energy consumption, and it is necessary to extract and retrain a large number of data sets for different environments. Summary of the invention

[0006] In order to solve the above technical problems, the present invention provides a passive positioning system and method for indoor complex environments based on ultra-high frequency RFID technology, focusing on exploring passive positioning methods in multipath and non-line-of-sight environments. In a non-line-of-sight environment, a non-line-of-sight obstacle avoidance passive positioning method is proposed to realize RFID positioning. In an environment with complex multipath effects, a two-dimensional confidence anti-multipath passive RFID positioning method is proposed to realize anti-multipath interference positioning. An RFID passive indoor positioning system in a complex environment is built in conjunction with a reader and a host computer to realize low-cost, low-power consumption and high-precision RFID positioning in a complex indoor environment.

[0007] The technical solution adopted by the present invention is: a passive positioning system for indoor complex environments based on ultra-high frequency RFID technology, including: a plurality of passive RFID tags, an RFID reader / writer, a plurality of antennas, a router and a host computer.

[0008] The host computer is connected to the router; the router is connected to one port of the RFID reader; the other ports of the RFID reader are respectively connected to different antennas; the passive RFID tag receives the radio frequency signal sent by the RFID reader through the antenna, and uses the antenna to transmit the signal back to the RFID reader through backscattering.

[0009] The RFID reader / writer uses a universal ultra-high frequency RFID reader / writer, and the ultra-high frequency RFID reader / writer is custom-developed to achieve the function of acquiring signal data, amplitude and phase.

[0010] Furthermore, in the passive positioning system, the working status and communication protocol of the RFID reader and the passive RFID tag are in accordance with the provisions of ISO / IEC 18000-6 Type C standard, and the RFID tag performs RFID tag anti-collision processing according to the anti-collision algorithm in the ISO / IEC18000-6 Type C standard protocol.

[0011] The present invention also provides a passive positioning method for indoor complex environments based on ultra-high frequency RFID technology, and the specific steps are as follows:

[0012] S1, the RFID reader is initialized according to the configuration information of the host computer, and then the platform fixed phase error is eliminated through the reference tag;

[0013] S2, using corresponding algorithms for different complex environments, receiving observation samples to calculate the ranging results and their variance values ​​and determining whether the current environment is a non-line-of-sight environment, if so, proceeding to step S3, if not, proceeding to step S4;

[0014] S3. For non-line-of-sight environments, a non-line-of-sight obstacle avoidance passive positioning method is used to achieve RFID passive indoor positioning;

[0015] S4. In the multipath environment, a two-dimensional confidence anti-multipath passive positioning method is used to achieve anti-multipath interference positioning in the multipath environment;

[0016] S5, evaluate the positioning results. If the positioning results are similar, output the results and go to step S6. If there is a large deviation between the positioning results, it is determined that the environment has changed or the current environment is wrong, and return to step S2;

[0017] S6: The host computer controls the RFID reader to stop, ending the RFID passive positioning.

[0018] Furthermore, the working process of the passive positioning system is as follows:

[0019] The router connects the host computer and the RFID reader / writer. The RFID reader / writer is controlled by the host computer and sends a radio frequency signal to the passive RFID tag group. The radio frequency signal contains radio frequency energy and commands, charges and activates the passive RFID tags in a wireless manner, and controls the activated tags to backscatter and transmit signals to the RFID reader / writer in a manner set by the host computer. The RFID reader / writer receives the signal returned by the passive RFID tag and extracts information data from it. The RFID reader / writer transmits the positioning data to the host computer for RFID passive positioning.

[0020] The passive RFID tag is placed in a complex indoor environment, receives the radio frequency signal sent by the RFID reader through the antenna and converts it into direct current power for its own operation and stores it. When the stored direct current power is sufficient for the passive RFID tag to work normally, the passive RFID tag is powered, waits for the RFID reader to send a command to activate, and transmits the signal back to the reader by backscattering according to the working mode set by the host computer, until the stored direct current power is exhausted, the tag is powered off and stops working, until the next radio frequency signal sent by the reader causes the tag to be powered on and activated again.

[0021] Furthermore, in the RFID reader, the software process is as follows:

[0022] A1. Configure the basic parameters of the RFID reader and wait for the RFID reader to read and write;

[0023] The basic parameters include: IP setting, transmission signal power, signal frequency selection, antenna port number, and reader model.

[0024] A2, start the RFID reader to read and write, collect a certain number of sample signals, and then determine whether the passive positioning system is stable. If it is not stable, re-execute step A2. If it is stable, proceed to step A3;

[0025] Start the RFID reader and writer to read and write. First, measure the reference passive RFID tag N times. The RFID reader and writer receives the signal returned by the passive RFID tag and processes it to obtain the data required for positioning. The data is returned to the host computer. The host computer analyzes whether the data conforms to the statistical characteristics. If the test platform is unstable, the test platform needs to be adjusted and step A2 is re-executed. If the test platform is stable and the data is reliable, go to step A3.

[0026] Among them, the data required for positioning obtained by processing the signal returned by the passive RFID tag includes: EPC code, antenna port number, RSSI value, received signal phase value, Doppler frequency shift and number of readings; the number of passive RFID tag measurements N is set according to actual conditions.

[0027] A3, Eliminate the platform fixed phase error based on the reference passive RFID tag;

[0028] Among them, the phase value of the received signal measured by the RFID reader contains multiple phase errors, including: the initial phase difference of the transmitting and receiving local oscillators, the phase difference of the transmitting and receiving links, the modulation phase offset of the RFID tag, and the receiving phase difference of different receiving antennas; the platform is the passive positioning system.

[0029] A4. Start the RFID reader to read and write, process the signal returned by the passive RFID tag to obtain the data required for positioning and upload it to the host computer for passive RFID positioning;

[0030] A5. Determine whether the host computer sends a stop command. If not, receive the host computer's start RFID reader / writer read and write instruction and return to step A4. If yes, receive the host computer's stop command, suspend reading and writing, clear data, and end RFID tag positioning.

[0031] Further, in step S3, the non-line-of-sight obstacle avoidance passive positioning method is specifically as follows:

[0032] S31, initializing a compatible positioning filter algorithm, receiving observation samples and calculating the ranging result and its variance value;

[0033] The compatible positioning filter algorithm is an algorithm that is compatible with standard Kalman filtering and biased Kalman filtering.

[0034] The core equation of the Kalman filter algorithm is as follows:

[0035] x k =F k x k-1 +w k (1)

[0036] z k =H k x k +v k (2)

[0037] Where k represents the number of discrete time variable tests, x k represents the kth state vector, H k represents the observation matrix, w k represents the process noise in the kth state update, v k represents the observation noise, F k represents the state transfer matrix, z k Represents the kth observation value. The Kalman filter expression is as follows:

[0038]

[0039]

[0040] in, represents the prior estimate of the state at time k, represents the optimal state estimate at time k, P k and P k - They represent the estimation error co-equation matrix and the prediction error co-equation matrix respectively, Qk represents the covariance matrix of process noise, () T represents the transpose operation of the matrix; then the Kalman gain is obtained according to the prediction error co-equation matrix, and the expression is as follows:

[0041]

[0042] Among them, K k represents the Kalman gain, R k represents the observation noise covariance matrix.

[0043] The estimated error co-equation matrix is ​​updated according to the Kalman gain, and the expression is as follows:

[0044]

[0045] Among them, I represents the unit matrix. Finally, the optimal estimate is obtained according to formula (7): The expression is as follows:

[0046]

[0047] The compatible positioning filtering algorithm adjusts the filter parameters to reduce the noise deviation σ caused by non-line-of-sight when performing optimal estimation. NLOS To reduce the error caused by non-line-of-sight error to the optimal estimate, the expression is as follows:

[0048]

[0049]

[0050]

[0051] Where λ represents the covariance matrix R k The weight factor is , and λ>1; at the same time, the observation value is obtained according to the process noise covariance matrix after weighted adjustment.

[0052] S32, judging whether the observed sample is a non-line-of-sight signal according to the variance value of the observed sample ranging result obtained in step S31, if so, proceeding to step S33, if not, proceeding to step S34;

[0053] The judgment is to judge whether the local variance of the ranging result of the observed sample meets the 3k principle to distinguish whether it is a non-line-of-sight signal.

[0054] S33, adjusting the Kalman filter parameters, iterating the positioning data obtained by the SD-PDOA hyperbolic positioning, using a biased Kalman filter for estimation, and judging whether the iteration is completed. If so, proceed to step S35, otherwise, continue to iterate the positioning data of the hyperbolic positioning;

[0055] S34, iterate the positioning data obtained by SD-PDOA hyperbolic positioning, use the standard Kalman filter to estimate, and judge whether the iteration is completed. If so, go to step S35, if not, continue to iterate the positioning data of hyperbolic positioning;

[0056] S35: Output the result to obtain the RFID passive indoor positioning result in the non-line-of-sight environment.

[0057] Further, in step S4, the two-dimensional confidence anti-multipath passive positioning method is specifically as follows:

[0058] S41, the RFID reader receives the backscattered signal of the passive RFID tag, extracts the RSSI value and the SD-PDOA value therefrom, inputs the RSSI value into step S42, and inputs the SD-PDOA value into step S43;

[0059] S42, substituting the RSSI value of the passive RFID tag read in step S41 into the logarithmic path attenuation model to calculate the ranging value, constructing a positioning set through RSSI multilateral positioning, and then obtaining a more stable positioning set through Kalman filtering, and establishing a two-dimensional confidence region according to the positioning set;

[0060] S43, constructing a set of hyperbolic equations according to the SD-PDOA value read in step S41 to estimate the coordinate position, solving the estimated positioning value, and judging whether it satisfies the confidence region generated according to the positioning set in step S42, if so, proceeding to step S44, if not, judging that the value is an interference signal generated by multipath interference and discarding it;

[0061] S44. Obtain positioning results and complete fast and accurate positioning in a multipath environment.

[0062] Beneficial effects of the present invention: The system of the present invention includes: a plurality of passive RFID tags, an RFID reader, a plurality of antennas, a router and a host computer. The present invention solves the problems of high cost, high power consumption and low precision of existing indoor positioning equipment, and at the same time solves the problem that the existing passive RFID indoor positioning method is seriously interfered by multipath environment and non-line-of-sight environment, resulting in reduced positioning precision or even failure. The system performs passive indoor positioning based on passive RFID tags, realizes high-precision, low-cost, low-power passive indoor positioning in complex environments, and is easy to maintain. It can resist environmental interference in complex indoor environments to realize high-precision passive indoor positioning, has a large sensing range, and can be applied in many fields. It is particularly suitable for inventory and positioning of items in warehousing logistics, library management, retail and medical industries, and has broad prospects for commercial applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 This is a structural diagram of a passive positioning system for complex indoor environments based on ultra-high frequency RFID technology according to the present invention.

[0064] Figure 2 The present invention is a flow chart of a passive positioning method for complex indoor environments based on ultra-high frequency RFID technology.

[0065] Figure 3 Flow chart of RFID reader software in an embodiment of the present invention.

[0066] Figure 4 The figure is a flow chart of a non-line-of-sight obstacle avoidance passive positioning method in an embodiment of the present invention.

[0067] Figure 5 It is a distribution histogram of SD-PDOA positioning data measured in line-of-sight and non-line-of-sight environments in an embodiment of the present invention.

[0068] Figure 6 The figure is a flow chart of a two-dimensional confidence anti-multipath passive positioning method in an embodiment of the present invention. DETAILED DESCRIPTION

[0069] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0070] like Figure 1 As shown, a passive positioning system for indoor complex environments based on ultra-high frequency RFID technology of the present invention includes: a plurality of passive RFID tags, an RFID reader / writer, a plurality of antennas, a router and a host computer.

[0071] The host computer is connected to the router; the router is connected to one port of the RFID reader; the other ports of the RFID reader are respectively connected to different antennas; the passive RFID tag receives the radio frequency signal sent by the RFID reader through the antenna, and uses the antenna to transmit the signal back to the RFID reader through backscattering.

[0072] The RFID reader uses a general-purpose ultra-high frequency RFID reader, which has a transmission power range of 10dBm to 31.5dBm, is equipped with four antenna ports, and can be expanded to 32 antenna ports through a specific extender, and has an RS-232 interface and a network port control input port. In order to facilitate users to control the RFID reader, users can develop the RFID reader according to personal needs through the development interface of the reader. This embodiment customizes the development of the ultra-high frequency RFID reader to achieve the acquisition function of signal data, amplitude and phase.

[0073] In this embodiment, in a system where multiple RFID tags are working, that is, the passive positioning system, in order to solve the collision problem, the working status and communication protocol of the RFID reader and the passive RFID tag are in accordance with the provisions of ISO / IEC 18000-6Type C standard, and the RFID tag performs RFID tag anti-collision processing according to the anti-collision algorithm in the ISO / IEC 18000-6Type C standard protocol to improve the overall efficiency of the system, realize the multi-tag anti-collision function, and have universality.

[0074] like Figure 2 As shown, in this embodiment, the present invention also provides a passive positioning method for indoor complex environments based on ultra-high frequency RFID technology, and the specific steps are as follows:

[0075] S1, the RFID reader is initialized according to the configuration information of the host computer, and then the platform fixed phase error is eliminated through the reference tag;

[0076] S2, using corresponding algorithms for different complex environments, receiving observation samples to calculate the ranging results and their variance values ​​and determining whether the current environment is a non-line-of-sight environment, if so, proceeding to step S3, if not, proceeding to step S4;

[0077] S3. For non-line-of-sight environments, a non-line-of-sight obstacle avoidance passive positioning method is used to achieve RFID passive indoor positioning;

[0078] S4. In the multipath environment, a two-dimensional confidence anti-multipath passive positioning method is used to achieve anti-multipath interference positioning in the multipath environment;

[0079] S5, evaluate the positioning results. If the positioning results are similar, output the results and go to step S6. If there is a large deviation between the positioning results, it is determined that the environment has changed or the current environment is wrong, and return to step S2;

[0080] S6: The host computer controls the RFID reader to stop, ending the RFID passive positioning.

[0081] like Figure 1 As shown, in this embodiment, the working process of the passive positioning system is specifically as follows:

[0082] The router is connected to the host computer and the RFID reader / writer. The RFID reader / writer is controlled by the host computer and sends a radio frequency signal to the passive RFID tag group. The radio frequency signal contains radio frequency energy and commands, charges the passive RFID tag in a wireless manner and activates it, and controls the activated tag to backscatter and transmit the signal to the RFID reader / writer in a manner set by the host computer. The RFID reader / writer receives the signal returned by the passive RFID tag and extracts information data (including: EPC code, antenna port number, RSSI value, received signal phase value, Doppler frequency shift and number of reads) from it. The RFID reader / writer transmits these positioning data to the host computer for RFID passive positioning. Due to the complex environment of the RFID tag and the complex and diverse data received by the reader / writer, the positioning information transmitted back to the host computer is unreliable and there are many interference separations. It is necessary to execute the passive positioning method proposed in this embodiment at the host computer to accurately estimate the RFID position.

[0083] The passive RFID tag is placed in a complex indoor environment, receives the radio frequency signal sent by the RFID reader through the antenna and converts it into direct current power for its own operation and stores it. When the stored direct current power is sufficient for the passive RFID tag to work normally, the passive RFID tag is powered on, and waits for the RFID reader to send a command to activate it. According to the working mode set by the host computer, the signal is transmitted back to the reader by backscattering until the stored direct current power is exhausted, the tag is powered off and stops working, until the next radio frequency signal sent by the reader causes the tag to be powered on and activated again, thereby realizing intermittent positioning information monitoring of passive RFID tags in complex environments.

[0084] like Figure 3 As shown, in this embodiment, in the RFID reader, the software process is as follows:

[0085] A1. Configure the basic parameters of the RFID reader and wait for the RFID reader to read and write;

[0086] The basic parameters include: IP setting, transmission signal power, signal frequency selection, antenna port number, and reader model.

[0087] A2, start the RFID reader to read and write, collect a certain number of sample signals, and then determine whether the passive positioning system is stable. If it is not stable, re-execute step A2. If it is stable, proceed to step A3;

[0088] Start the RFID reader to read and write. In this embodiment, the reference passive RFID tag is first measured 1000 times. The RFID reader receives the signal returned by the passive RFID tag and processes it to obtain the data required for positioning, and returns the data to the host computer. The host computer analyzes whether the data conforms to the statistical characteristics. If the test platform (the passive positioning system) is unstable, the test platform needs to be adjusted and step A2 is re-executed. If the test platform is stable and the data is reliable, enter step A3.

[0089] The data required for positioning obtained by processing the signal returned by the passive RFID tag include: EPC code, antenna port number, RSSI value, received signal phase value, Doppler frequency shift and number of reads.

[0090] A3, Eliminate the platform fixed phase error based on the reference passive RFID tag;

[0091] Among them, the phase value of the received signal measured by the RFID reader contains multiple phase errors, including: the initial phase difference of the transmitting and receiving local oscillators, the phase difference of the transmitting and receiving links, the modulation phase offset of the RFID tag, and the receiving phase difference of different receiving antennas; the platform is the passive positioning system.

[0092] A4. Start the RFID reader to read and write, process the signal returned by the passive RFID tag to obtain the data required for positioning and upload it to the host computer for passive RFID positioning;

[0093] A5. Determine whether the host computer sends a stop command. If not, receive the host computer's start RFID reader / writer read and write instruction and return to step A4. If yes, receive the host computer's stop command, suspend reading and writing, clear data, and end RFID tag positioning.

[0094] The complex environment in step S2 of this embodiment includes: non-line-of-sight environment and multipath environment.

[0095] like Figure 4 As shown, in this embodiment, in step S3, the non-line-of-sight obstacle avoidance passive positioning method is specifically as follows:

[0096] S31, initializing a compatible positioning filter algorithm and receiving an observation sample variance value;

[0097] This embodiment proposes a compatible positioning filtering algorithm, which is an algorithm that is compatible with standard Kalman filtering and biased Kalman filtering.

[0098] Kalman filtering is an algorithm for estimating system status and is widely used in the field of indoor positioning. The core equation of the Kalman filtering algorithm is as follows:

[0099] x k =F k x k-1 +w k (1)

[0100] z k =H k x k +v k (2)

[0101] Where k represents the number of discrete time variable tests, x k represents the kth state vector, H k represents the observation matrix, w k represents the process noise in the k-th state update, v k represents the observation noise, F k represents the state transfer matrix, z k Represents the kth observation value. The Kalman filter expression is as follows:

[0102]

[0103]

[0104] in, represents the prior estimate of the state at time k, represents the optimal state estimate at time k, P k and P k - They represent the estimation error co-equation matrix and the prediction error co-equation matrix respectively, Qk represents the covariance matrix of process noise, () T represents the transpose operation of the matrix; then the Kalman gain is obtained according to the prediction error co-equation matrix, and the expression is as follows:

[0105]

[0106] Among them, K k represents the Kalman gain, R k represents the observation noise covariance matrix.

[0107] The estimated error co-equation matrix is ​​updated according to the Kalman gain, and the expression is as follows:

[0108]

[0109] Among them, I represents the unit matrix. Finally, the optimal estimate is obtained according to formula (7): The expression is as follows:

[0110]

[0111] The compatible positioning filtering algorithm is mainly used for passive positioning in non-line-of-sight environments. The compatible positioning filtering algorithm adjusts the filter parameters and, when performing optimal estimation, subtracts the noise deviation σ caused by non-line-of-sight. NLOS To reduce the error caused by non-line-of-sight error to the optimal estimate, the expression is as follows:

[0112]

[0113]

[0114]

[0115] Where λ represents the covariance matrix R k The weight factor is λ>1, because the process covariance of the signal in the non-line-of-sight channel is usually larger than the process covariance matrix under the line-of-sight channel. At the same time, the observation value is obtained according to the process noise covariance matrix after weighted adjustment.

[0116] S32, judging whether the observed sample is a non-line-of-sight signal according to the variance value of the observed sample ranging result obtained in step S31, if so, proceeding to step S33, if not, proceeding to step S34;

[0117] The judgment is to judge whether the local variance of the ranging result of the observed sample meets the 3σ principle to distinguish whether it is a non-line-of-sight signal.

[0118] S33, adjusting the Kalman filter parameters, iterating the positioning data obtained by the SD-PDOA hyperbolic positioning, using a biased Kalman filter for estimation, and judging whether the iteration is completed. If so, proceed to step S35, otherwise, continue to iterate the positioning data of the hyperbolic positioning;

[0119] S34, iterate the positioning data obtained by SD-PDOA hyperbolic positioning, use the standard Kalman filter to estimate, and judge whether the iteration is completed. If so, go to step S35, if not, continue to iterate the positioning data of hyperbolic positioning;

[0120] S35: Output the result to obtain the RFID passive indoor positioning result in the non-line-of-sight environment.

[0121] Since the passive positioning data usually measured conforms to the Gaussian distribution, the measurement error n caused by non-line-of-sight environment nlosIt is usually expressed as an exponential distribution model related to the signal delay, as shown in equation (11). The distribution of SD-PDOA positioning data measured in line-of-sight and non-line-of-sight environments is as follows: Figure 5 As shown, Figure 5 (a) is the line-of-sight environment, Figure 5 (b) Non-line-of-sight environment.

[0122]

[0123] Where x represents the length of the signal propagation path and τ represents the signal attenuation constant.

[0124] Based on the derivation of the above theory, a non-line-of-sight obstacle avoidance passive positioning method is proposed in a non-line-of-sight environment. The SD-PDOA of the signal is extracted by using multiple antennas to receive the backscattered signal of the RFID tag. The ranging information is calculated by the hyperbolic positioning algorithm. The compatible positioning filter algorithm is used to process the SD-PDOA ranging information to obtain the RFID passive indoor positioning result in a non-line-of-sight environment. The process is as follows: Figure 4 As shown in FIG, this method first distinguishes whether it is a non-line-of-sight signal by judging whether the local variance of the observed sample conforms to the 3σ principle. For the non-line-of-sight signal, a biased Kalman filter is used for estimation, while for the line-of-sight signal, a standard Kalman filter is used for estimation.

[0125] like Figure 6 As shown, in this embodiment, in step S4, the two-dimensional confidence anti-multipath passive positioning method (this method is generally applicable to line-of-sight environments) is specifically as follows:

[0126] In the positioning system in a multipath environment, the RFID reader not only receives the line-of-sight transmission data of the passive RFID tag, but also receives the multipath signals generated by the environment. Because the multipath signal transmission path is long and the signal attenuation is serious, it can be screened by the received signal RSSI value.

[0127] S41, the RFID reader receives the backscattered signal of the passive RFID tag, extracts the RSSI value and the SD-PDOA value therefrom, inputs the RSSI value into step S42, and inputs the SD-PDOA value into step S43;

[0128] S42, substituting the RSSI value of the passive RFID tag read in step S41 into the logarithmic path attenuation model to calculate the ranging value, constructing a positioning set through RSSI multilateral positioning, and then obtaining a more stable positioning set through Kalman filtering, and establishing a two-dimensional confidence region according to the positioning set;

[0129] S43, constructing a set of hyperbolic equations according to the SD-PDOA value read in step S41 to estimate the coordinate position, solving the estimated positioning value, and judging whether it satisfies the confidence region generated according to the positioning set in step S42, if so, proceeding to step S44, if not, judging that the value is an interference signal generated by multipath interference and discarding it;

[0130] S44. Obtain positioning results and complete fast and accurate positioning in a multipath environment.

[0131] The two-dimensional confidence anti-multipath passive positioning method has the advantages of the wide positioning area of ​​the RSSI multilateral positioning algorithm and the high accuracy of the SD-PDOA hyperbolic positioning method. At the same time, it overcomes the shortcomings of low RSSI positioning accuracy and limited SD-PDOA positioning range, and fully improves the passive positioning accuracy in multipath environments.

[0132] In summary, the present invention solves the problems of high cost, high power consumption and low precision of existing indoor positioning equipment, and at the same time solves the problem that the existing passive RFID indoor positioning method is seriously interfered by multipath environment and non-line-of-sight environment, resulting in reduced positioning precision or even failure. The system performs passive indoor positioning based on passive RFID tags, realizes high-precision, low-cost and low-power passive indoor positioning in complex environments, and is easy to maintain. It can resist environmental interference and realize high-precision passive indoor positioning in complex indoor environments, has a large sensing range, and can be applied in many fields. It is particularly suitable for inventory and positioning of items in warehousing and logistics, library management, retail and medical industries, and has broad prospects for commercial application.

[0133] Those skilled in the art will appreciate that the embodiments described herein are intended to help readers understand the principles of the present invention, and should be understood that the scope of protection of the present invention is not limited to such specific statements and embodiments. For those skilled in the art, the present invention may have various changes and variations. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of the claims of the present invention.

Claims

1. A passive positioning system and method for indoor complex environments based on ultra-high frequency RFID technology, as follows: The passive positioning system comprises: Several passive RFID tags, an RFID reader / writer, several antennas, a router and a host computer; The host computer is connected to the router; The router is connected to one port of the RFID reader; the other ports of the RFID reader are respectively connected to different antennas; the passive RFID tag receives the radio frequency signal sent by the RFID reader through the antenna, and transmits the signal back to the RFID reader through the antenna in a backscattering manner; The RFID reader / writer uses a general-purpose ultra-high frequency RFID reader / writer, and the ultra-high frequency RFID reader / writer is custom-developed to realize the acquisition function of signal data, amplitude and phase; The passive positioning method for indoor complex environment based on ultra-high frequency RFID technology has the following specific steps: S1, the RFID reader is initialized according to the configuration information of the host computer, and then the platform fixed phase error is eliminated through the reference tag; S2, using corresponding algorithms for different complex environments, receiving observation samples to calculate the ranging results and their variance values ​​and determining whether the current environment is a non-line-of-sight environment, if so, proceeding to step S3, if not, proceeding to step S4; S3. For non-line-of-sight environments, a non-line-of-sight obstacle avoidance passive positioning method is used to achieve RFID passive indoor positioning; S4. In the multipath environment, a two-dimensional confidence anti-multipath passive positioning method is used to achieve anti-multipath interference positioning in the multipath environment; S5, evaluate the positioning results, if the positioning results are consistent, then output the results and go to step S6, if the positioning results are inconsistent, then determine that the environment has changed or the current environment is wrong, and return to step S2; S6, the host computer controls the RFID reader to stop, ending the RFID passive positioning; In step S3, the non-line-of-sight obstacle avoidance passive positioning method is specifically as follows: S31, initializing a compatible positioning filter algorithm, receiving observation samples and calculating the ranging result and its variance value; The compatible positioning filtering algorithm is an algorithm that is compatible with standard Kalman filtering and biased Kalman filtering; The core equation of the Kalman filter algorithm is as follows: x k =F k x k-1 +w k (1) z k =H k x k +v k (2) Where k represents the number of discrete time variable tests, x k represents the kth state vector, H k represents the observation matrix, w k represents the process noise in the k-th state update, v k represents the observation noise, F k represents the state transfer matrix, z k Represents the kth observation value; the Kalman filter expression is as follows: in, represents the prior estimate of the state at time k, represents the optimal state estimate at time k, P k and P k - They represent the estimation error co-equation matrix and the prediction error co-equation matrix, Q k represents the covariance matrix of process noise, () T represents the transpose operation of the matrix; then the Kalman gain is obtained according to the prediction error co-equation matrix, and the expression is as follows: Among them, K k represents the Kalman gain, R k represents the observation noise covariance matrix; The estimated error co-equation matrix is ​​updated according to the Kalman gain, and the expression is as follows: Among them, I represents the unit matrix; Finally, the optimal estimate is obtained according to formula (7) The expression is as follows: The compatible positioning filtering algorithm adjusts the filter parameters to reduce the noise deviation σ caused by non-line-of-sight when performing optimal estimation. NLOS To reduce the error caused by non-line-of-sight error to the optimal estimate, the expression is as follows: Where λ represents the covariance matrix R k The weight factor is , and λ>1; at the same time, the observation value is obtained according to the process noise covariance matrix after weighted adjustment; S32, judging whether the observed sample is a non-line-of-sight signal according to the variance value of the observed sample ranging result obtained in step S31, if so, proceeding to step S33, if not, proceeding to step S34; The judgment is to judge whether the local variance of the ranging result of the observed sample meets the 3σ principle to distinguish whether it is a non-line-of-sight signal; S33, adjusting the Kalman filter parameters, iterating the positioning data obtained by the SD-PDOA hyperbolic positioning, using a biased Kalman filter for estimation, and judging whether the iteration is completed. If so, proceed to step S35, otherwise, continue to iterate the positioning data of the hyperbolic positioning; S34, iterate the positioning data obtained by SD-PDOA hyperbolic positioning, use the standard Kalman filter to estimate, and judge whether the iteration is completed. If so, go to step S35, if not, continue to iterate the positioning data of hyperbolic positioning; S35, output the result, and obtain the RFID passive indoor positioning result in the non-line-of-sight environment; In step S4, the two-dimensional confidence anti-multipath passive positioning method is specifically as follows: S41, the RFID reader receives the backscattered signal of the passive RFID tag, extracts the RSSI value and the SD-PDOA value therefrom, inputs the RSSI value into step S42, and inputs the SD-PDOA value into step S43; S42, substituting the RSSI value of the passive RFID tag read in step S41 into the logarithmic path attenuation model to calculate the ranging value, constructing a positioning set through RSSI multilateral positioning, and then obtaining a more stable positioning set through Kalman filtering, and establishing a two-dimensional confidence region according to the positioning set; S43, constructing a set of hyperbolic equations according to the SD-PDOA value read in step S41 to estimate the coordinate position, solving the estimated positioning value, and judging whether it satisfies the confidence region generated according to the positioning set in step S42, if so, proceeding to step S44, if not, judging that the value is an interference signal generated by multipath interference and discarding it; S44. Obtain positioning results and complete fast and accurate positioning in a multipath environment.

2. According to claim 1, a passive positioning system and method for indoor complex environments based on ultra-high frequency RFID technology is characterized in that: The working process of the passive positioning system is as follows: The router connects the host computer and the RFID reader / writer. The RFID reader / writer is controlled by the host computer and sends a radio frequency signal to the passive RFID tag group. The radio frequency signal contains radio frequency energy and commands, charges the passive RFID tag wirelessly and activates it, and controls the activated tag to backscatter and transmit the signal to the RFID reader / writer in the mode set by the host computer. The RFID reader / writer receives the signal sent back by the passive RFID tag and extracts information data from it. The RFID reader / writer transmits the positioning data to the host computer for RFID passive positioning. The passive RFID tag is placed in a complex indoor environment, receives the radio frequency signal sent by the RFID reader through the antenna and converts it into direct current power for its own operation and stores it. When the stored direct current power is sufficient for the passive RFID tag to work normally, the passive RFID tag is powered, waits for the RFID reader to send a command to activate, and transmits the signal back to the reader by backscattering according to the working mode set by the host computer, until the stored direct current power is exhausted, the tag is powered off and stops working, until the next radio frequency signal sent by the reader causes the tag to be powered on and activated again.

3. According to claim 1, a passive positioning system and method for indoor complex environments based on ultra-high frequency RFID technology is characterized in that: In the RFID reader, the software process is as follows: A1. Configure the basic parameters of the RFID reader and wait for the RFID reader to read and write; The basic parameters include: IP settings, transmission signal power, signal frequency selection, antenna port number, and reader model; A2, start the RFID reader to read and write, collect a certain number of sample signals, and then determine whether the passive positioning system is stable. If it is not stable, re-execute step A2. If it is stable, proceed to step A3; Start the RFID reader to read and write, first measure the reference passive RFID tag N times, the RFID reader receives the signal returned by the passive RFID tag and processes it to obtain the data required for positioning, and returns the data to the host computer, which analyzes whether the data conforms to the statistical characteristics. If the test platform is unstable, it is necessary to adjust the test platform and re-execute step A2. If the test platform is stable and the data is reliable, proceed to step A3; The data required for positioning obtained by processing the signal returned by the passive RFID tag include: EPC code, antenna port number, RSSI value, received signal phase value, Doppler frequency shift and reading times; the passive RFID tag measurement times N are set according to the actual situation; A3, Eliminate the platform fixed phase error based on the reference passive RFID tag; The phase value of the received signal measured by the RFID reader contains a variety of phase errors, including: the initial phase difference of the transmitting and receiving local oscillators, the phase difference of the transmitting and receiving links, the modulation phase offset of the RFID tag, and the receiving phase difference of different receiving antennas; the platform is the passive positioning system; A4. Start the RFID reader to read and write, process the signal returned by the passive RFID tag to obtain the data required for positioning and upload it to the host computer for passive RFID positioning; A5. Determine whether the host computer sends a stop command. If not, receive the host computer's start RFID reader / writer read and write instruction and return to step A4. If yes, receive the host computer's stop command, suspend reading and writing, clear data, and end RFID tag positioning.

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