KR101458338B1 - Method and apparatus for localization using zigbee communication - Google Patents

Abstract

Disclosed are a method and an apparatus for outdoor localization using Zigbee communications. The outdoor localization method for estimating a current location in a mobile terminal, comprises the steps of mounting a first Zigbee module in the mobile terminal; measuring a distance from a second Zigbee module to the first Zigbee module, wherein the second Zigbee module is fixed to a reference point on the movement path; and estimating the current location of the mobile terminal by using the length of the first Zigbee module and the second Zigbee module.

Description

Technical Field [0001] The present invention relates to a method and apparatus for outdoor positioning using stand-

Embodiments of the present invention relate to positioning techniques for estimating a current location outdoors.

Methods for estimating the current location outdoors include a positioning technique using a global positioning system (GPS), dead beaconing, and a method using a radio frequency identification (RFID) tag.

For example, Korean Patent Laid-Open No. 10-2007-0016795 (published on February 08, 2007) discloses a technique for measuring the position of a mobile communication terminal using GPS in an indoor / outdoor environment.

GPS is the most widely used technology, but GPS data is less accurate to use for walking.

The method of measuring a user's position is classified into a method of using Wi-Fi, Zigbee, and UWB (Ultra-wideband) according to a wireless communication method. Also, according to the implementation method, it is divided into Cell-ID usage method, triangulation method, probabilistic modeling-based method, screen analysis method, and the like.

In case of using Wi-Fi, it does not need to install additional hardware because it uses AP (access point) installed for communication, and it is possible to provide stable location information. However, there is a disadvantage in that the accuracy is lower than that of other methods and radio wave interference occurs.

Recently, it is advantageous in that the tag size is small and the installation is simple. However, it requires a precise propagation model and requires detailed mapping.

Although UWB provides relatively high accuracy and low power consumption, it has a short transmission distance due to its large bandwidth, which is not suitable for use as an outdoor landmark.

The Cell-ID method, also called Proximity method, is the simplest type of positioning method. This is a method for confirming the location of the mobile entity through whether the mobile entity to be tracked exists in a designated space called a cell. However, as the density of the cell increases, the cost increases and the accuracy of the position information varies with the cell radius.

The triangulation method is the most common method of estimating the position, and the position is calculated using the distance from the three reference points to the object. The accuracy depends on the propagation environment such as the existence of obstacles and the refraction refraction caused by the facility.

There is a position estimation method by probabilistic modeling, which is also called a fingerprinting method. It estimates the position of the mobile object by setting a plurality of sample points, storing the propagation characteristic values at all points in the database, and searching for sample points having data similar to the propagation characteristic values of the mobile object. Unlike other methods, it has the advantage of providing the highest accuracy because it reflects the direction of the user and the location of the facility even in the positioning. However, there is a disadvantage in that it is necessary to acquire various propagation characteristic values for a plurality of sample points several times and to update a value every time the arrangement of an obstacle or the like changes, and the database search is complicated.

A positioning method and an apparatus capable of accurately estimating a position of a pedestrian using signal strength between wireless devices instead of GPS are provided.

A positioning method and apparatus are provided that can detect the position of a user by using a distance-based position module that is relatively accurate compared to the existing position without requiring a lot of hardware equipment.

By applying methods based on Gaussian distribution, an optimal positioning method and apparatus for covering the entire area with a minimum number of positioning modules is provided.

An outdoor positioning method for estimating a current position in a mobile terminal, the method comprising the steps of: mounting a first Zigbee Module on the mobile terminal, measuring a distance from a second position module fixed to a reference point on the moving route to the first position module ; And estimating a current location of the mobile terminal using the distance between the first location module and the second location module.

According to an aspect of the present invention, the second locomotion module comprises three locomotion modules fixed to reference points at different positions, and the step of measuring the distance from the second locomotion module to the first locomotion module comprises: The step of estimating the current position of the mobile terminal can estimate the current position of the mobile terminal by the trilateration method using the measured three distance values, .

According to another aspect of the present invention, the step of measuring the distance from the second position module to the first position module may include measuring the distance between the first position module and the second position module in an SDS-TWR (Symmetrical Double-Sided Two-Way Ranging) The distance can be measured by multiplying the packet transmission time between the modules and the packet transmission speed.

According to another aspect, the reference point may be a position determined through a Gaussian distribution based Maximum Bayes Error Estimation technique.

According to another aspect of the present invention, when the area where the Zigbee signal between the candidate reference points overlap with each other is referred to as an error among the candidate reference points located on the movement path, the second position module calculates a candidate reference point, And may be installed at the reference point determined as a position that minimizes the error while removing the error.

Instead of GPS, the position of a pedestrian can be accurately estimated using the signal strength between wireless devices.

It is possible to identify the user's position by using a relatively accurate distance-based positioning module compared to the existing position without requiring a lot of hardware equipment.

By applying the Gaussian distribution based methods, it is possible to propose an optimal arrangement covering the whole with the minimum number of position modules.

Accurate location estimation is possible at a low cost and a pedestrian such as a visually impaired person can safely move from outdoor to a destination.

1 is an exemplary diagram for explaining a positioning method using a trilateration technique in an embodiment of the present invention.
Fig. 2 shows a signal generation simulation of the locomotive module.
3 is an exemplary diagram for explaining the Bayes decision rule for the maximum error.
4 is an exemplary diagram for explaining a Gaussian distribution-based signal decision boundary.
5 is an exemplary diagram for searching for an optimal module to be used for trilateration.
FIG. 6 shows the comparison result of the distance accuracy between modules.
FIG. 7 shows the results of analyzing the error rate and the loss rate of the received data according to the distance between the modules.
Figure 8 shows a complex residential area with a walkway as an example of a laboratory image.
FIG. 9 is a view showing an area selected as an optimal position with respect to the area of FIG. 8. FIG.
10 is a block diagram for explaining an example of the internal configuration of a computer system for positioning based on a distance communication according to the present invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings.

The present invention proposes a positioning method using Zigbee communication in which the distance is estimated using the strength of a radio signal instead of GPS as a positioning technique suitable for walking. It is possible to estimate the user's position by using the trilateration technique after arranging the position module at a known position and measuring the distance from the module possessed by the moving user. In the present invention, a method of optimizing a fixed position error module using a maximum Bayes error estimation technique based on a Gaussian distribution is proposed. This makes it possible to accurately measure the position of the mobile module while minimizing the space module.

Hereinafter, a trilateration technique for location estimation and an estimation method for landmark optimal placement will be described in detail.

Localization using Zigbee Modules

The location estimation method is divided into a range-based method using a distance and a range-free method using a cell-based coordinate system. In the present invention, a region-based technique known to be relatively accurate is used.

The distance is measured using the SDS-TWR (Symmetrical Double-Sided Two-Way Ranging) method from the fixed module to the mobile module carried by the user. That is, the distance is obtained by multiplying the transmission time and the propagation speed between the two modules. This is advantageous in that precise distance calculation is possible by using the time difference of packet transmission / reception between two modules.

For example, a trilateration technique is used to estimate the location of a moving module by a three-circle equation derived from the location of the three fixed modules and the distance to the moving module. As shown in FIG. 1, the location M of the mobile module can be calculated using the distances R _A , R _B , and R _C to at least three fixed modules (fixed modules A, B, and C). In FIG. 1, the position of a user having a mobile module can be calculated in real time through a position calculation program in a computer equipped with the mobile module.

Optimal Placement of Fixed Nodes

The Bayesian error estimation method based on the Gaussian distribution is applied to accurately measure the position of the user with a minimum number of fixed modules. It is possible to dynamically arrange fixed modules in all possible paths and then find out the locations where errors are minimized by sequentially removing modules with maximum errors at each step.

In the present invention, it is possible to generate a signal so that a Zigbee signal uses a characteristic that the strength decreases according to the distance. A signal is modeled by applying a linear Gaussian distribution as shown in FIG. 2 (b), and the signal is radiated as a circular pattern as shown in FIG. 2 (c) in order to estimate a signal by a Bayes error method. As shown in FIG. 2A, a signal range of a module on a polar coordinate system is represented by a circle having a radius r , and the signal is composed of n concentric circles having offsets of r_off . At this time, in one circle, a constant interval θ signal is emitted within the range of -π to + π. Equation 1 is an equation for calculating r , and Equation 2 is an equation for calculating the position of one point in a circle.

When signals are emitted from each module, signals between adjacent modules are overlapped, and a Bayesian error estimation technique is used to determine the signal decision boundary of the two modules. Define an area where signals overlap, and define a module with the maximum sum of errors as RM (a module to be removed). 3, the signal range of the two modules, each represented by ω ₁ and ω ₂ class, Bayes error region of ω ₁ is ε _1, Bayes error region of ω ₂ can be expressed by ε _2. In FIG. 3, the decision boundaries for distinguishing the errors of the two modules are indicated by dotted lines. As shown in Equation (3), the base error? Can be obtained by integrating a high-dimensional density function in a complex region. In one dimension, p _h ( h | ω _i ) represents the conditional density of h to obtain ω _i .

In Equation 4, X represents an observation vector, which is a classification value that determines which class category belongs. And h (X) defines a decision rule, also called a discriminant function, which is used here as a test function to find the maximum error. M _i denotes the expected vectors, and S _i denotes the covariance matrices. Since the same type of module is used here, Σ is indicated because Σ ₁ and Σ ₂ are the same. P ₁ / P ₂ is a threshold value of a likelihood ratio for making a decision, in which the extent to which signal points overlap can be heuristically determined based on probabilistic results .

FIG. 4 shows a linear function used in the present invention to calculate a Gaussian distribution-based decision boundary. FIG. 5 shows an example in which an optimum module to be used in the trilateration technique is extracted by removing one RM from each step by applying the method proposed in the present invention.

Accuracy Estimation Using Distance of Zigbee Modules (Distance of Zigbee Modules)

In order to verify the efficiency of the scheme proposed in the present invention, a module for estimating the distance using only the RSSI (received field strength) of the signal and a module for measuring the distance using the SDS-TWR scheme using the packet transmission / Respectively. In Xbee ™ using only RSSI, additional work is required to convert the signal strength into a distance value. NanoLOC ™ was used as a module to calculate the distance using the time difference of signal transmission and reception. Figure 6 shows the accuracy of the distance measured using the two types of modules. The horizontal axis represents the actual distance, and the vertical axis represents the distance measured by the module. As the experimental results show, the RSSI signal of Xbee ™ is greatly influenced by the surrounding environment and causes a large error. The nanoLOC showed very high accuracy without filtering.

Analysis and Optimization of Factors Influencing Module Placement

In order to perform accurate positioning at the minimum cost, the present invention determines the factors that affect the positioning accuracy and changes the value of each element to some extent. As a target factor, the sampling rate of the landmark module and the interval between modules are selected.

The first factor is the sampling rate of one module. In order to confirm the accuracy according to the change of the sampling rate, the factors excluding the operation variable sampling rate are defined as control variables. That is, the module interval is fixed at 20 m, and the number of modules is not considered because it is independent of deriving the sampling rate result.

Table 1 shows experimental results on the signal loss and the accuracy of the distance measurement according to the variation of the sampling rate. The sampling rates were changed to 10, 20, 40, 50, and 100 Hz, and 300, 600, 1200, 1500, and 3000 data were analyzed for 30 seconds respectively. The signal loss represents the percentage of data lost in the data by sampling rate, and the accuracy represents the ratio of the error between the measured value and the true value of the distance data including the lost data. The range in which the measured value is accepted as a true value shall be within ± 1m in consideration of the speed limit. As shown in Table 1, the accuracy in the sampling rate range of 10 to 40 Hz was not affected by the signal loss and was proportional to the amount of data received.

Also, when the sampling rate is 40 to 100 Hz, the bottleneck phenomenon occurs due to an increase in the amount of data, resulting in a drastic increase in signal loss, resulting in a remarkably reduced accuracy. As the amount of received data increases, the accuracy increases overall. However, it is confirmed that when the sampling rate exceeds a certain value, the signal loss increases sharply and the accuracy is lowered. In this experiment, 40 Hz is the optimal sampling rate.

For landmark optimal placement, it is important to determine the optimal distance between modules. In the present invention, the distance between modules is selected as a second factor statistically for module placement. In this experiment, we set the distance as an operational variable to find the range of the signal of the module and set the sampling rate determined in the previous experiment as the control variable.

In order to change the distance between the modules, the modules were arranged at regular intervals in an environment where signal interference was minimized and data were measured. In the present invention, an error rate means a ratio of data determined as an error in the received distance data. The error rate is defined as data larger than a reference error of ± 1 m in consideration of the constriction. Loss rate refers to the data rate lost over a period of time. FIG. 7 shows the results of analyzing the error rate and hand extension rate of the received data according to the distance between the two modules. Each module installed at each unit distance was analyzed with 1200 data measured for 30 seconds at the optimal sampling rate determined in the previous experiment.

As shown in FIG. 7, there is no error within 40 m, and an error occurs after 40 m. Also, it can be seen that the loss rate increases linearly with the distance between the two modules. According to the experimental results, in the present invention, the optimal distance between the two modules is determined as 40m, which is the maximum distance without error, considering the cost. The loss rate is about 25% at 40m, but considering the walking speed and sampling rate, the loss rate does not affect the application of the trilateration technique.

We selected two factors and analyzed the characteristics of the module by heuristic experiments based on statistical analysis. Even if the module is changed, the optimum value can be found by experimenting based on the two factors proposed by the present invention.

Modules Placement Using Proposed Methods

Let's try to arrange the modules virtually using the previously determined factors. In order to verify the efficiency of the proposed scheme, we implemented a similar overlapping area estimation technique similar to the proposed method. This method is to select the minimum module to apply trilateration technique by sequentially removing large overlapping modules. The range in which one position signal affects the other positions is stored as the sum of the overlap areas, and the module that overlaps most with other modules is defined as RM. Area _{( P , Q )} in Equation (5 ₎ represents an overlap region of two modules and d _{( P , Q )} represents an Euclidean distance between two modules.

There are two steps to finding the optimal placement of a module. In the first step, one of the two estimation schemes is selected and the removable candidate module (RMC) is determined after performing the technique. In the second step, it is determined whether the RMC selected in the previous step is a module required in the calculation of the trilateration technique and determines whether or not RM is selected.

The experiment was carried out in a 500m × 400m area corresponding to a complicated residential area with a walkable road as shown in FIG. It is assumed that there are 52 POIs in the area and 9500 candidate locations where modules can be installed on the route.

Table 2 shows the experimental results for the two techniques. The number of minimum modules for trilateration was determined to be 76 for the proposed method and 112 for the overlapped region estimation. The cost savings are compared to the case of all 9500 modules installed for accurate location estimation. The proposed method is 99.20% max and the overlap region estimation is 98.82% max. FIG. 9 is a graph showing a location where an optimum module position is selected by applying the two techniques to the region of FIG. 8 (red point represents POI). The optimal location using the proposed method is represented by black points, and the module layout using the overlap area technique is represented by black points and white points. It can be seen that the overlapped region scheme requires more modules than the proposed scheme by the white point location. Therefore, using the proposed method, it is possible to effectively locate a wider area with a small number of modules, as well as considerable time and cost savings.

In this way, according to the embodiment of the present invention, by proposing a positioning method using a distance-based positioning module, it is possible to arrange the positioning modules at a known position and measure the distance from the module of the moving user, Can be estimated. Also, by using the maximum Bayesian error estimation based on the Gaussian distribution, it is possible to estimate the position of the mobile module possessed by the user with a minimum number of fixed position modules by providing an optimum arrangement of the fixed position module.

10 is a block diagram for explaining an example of an internal configuration of a computer system in an embodiment of the present invention. The computer system 1000 may be a computer equipped with the above-described moving module.

10, the computer system 1000 includes at least one processor 1010, a memory 1020, a peripheral interface 1030, an input / output subsystem (not shown) I / O subsystem) 1040, a power circuit 1050, and a communication circuit 1060.

10 is merely an example of the computer system 1000, and the computer system 1000 may further include additional components not shown in Fig. 10, or may have a configuration or arrangement to couple two or more components . In particular, the computer system for a mobile terminal may further include various sensors (photo sensor, proximity sensor, illuminance sensor, etc.), a touch screen, etc. in addition to the components shown in FIG. 10, May be included. Components that may be included in computer system 1000 may be implemented in hardware, software, or a combination of both hardware and software, including one or more signal processing or application specific integrated circuits.

The memory 1020 can include, for example, a high-speed random access memory, a magnetic disk, SRAM, DRAM, ROM, flash memory or non-volatile memory. have. The memory 1020 may include software modules, a set of instructions, or various other data required for operation of the computer system 1000. At this point, accessing memory 1020 from other components, such as processor 1010 or peripheral device interface 1030, may be controlled by processor 1010.

The peripheral device interface 1030 may couple the input and / or output peripheral devices of the computer system 1000 to the processor 1010 and the memory 1020. The processor 1010 may perform various functions and process data for the computer system 1000 by executing a software module or a set of instructions stored in the memory 1020. [

The input / output subsystem 1040 can couple various input / output peripherals to the peripheral interface 1030. For example, input / output subsystem 1040 may include a controller for coupling a peripheral, such as a monitor, keyboard, mouse, printer, or a touch screen or sensor, as needed, to peripheral interface 1030. According to another aspect, the input / output peripheral devices may be coupled to the peripheral device interface 1030 without going through the input / output subsystem 1040.

The power circuit 1050 can power all or a portion of the components of the terminal. For example, the power circuitry 1050 may include one or more power sources such as a power management system, a battery or alternating current (AC), a charging system, a power failure detection circuit, a power converter or inverter, And may include any other components for creation, management, distribution.

The communication circuitry 1060 may enable communication with other computer systems using at least one external port. Alternatively, as needed, communication circuitry 1060 may communicate with other computer systems by sending and receiving RF signals, also known as electromagnetic signals, including RF circuits.

The methods according to embodiments of the present invention may be implemented in the form of a program instruction that can be executed through various computer systems and recorded in a computer-readable medium.

The program according to the present embodiment can be configured as a PC-based program or an application dedicated to a mobile terminal. An app for location calculation in the present embodiment may be implemented as an independent program or as an in-app form of a specific application so that it can be operated on the specific application.

The apparatus described above may be implemented as a hardware component, a software component, and / or a combination of hardware components and software components. For example, the apparatus and components described in the embodiments may be implemented within a computer system, such as, for example, a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable gate array (FPGA) , A programmable logic unit (PLU), a microprocessor, or any other device capable of executing and responding to instructions. The processing device may execute an operating system (OS) and one or more software applications running on the operating system. The processing device may also access, store, manipulate, process, and generate data in response to execution of the software. For ease of understanding, the processing apparatus may be described as being used singly, but those skilled in the art will recognize that the processing apparatus may have a plurality of processing elements and / As shown in FIG. For example, the processing unit may comprise a plurality of processors or one processor and one controller. Other processing configurations are also possible, such as a parallel processor.

The software may include a computer program, code, instructions, or a combination of one or more of the foregoing, and may be configured to configure the processing device to operate as desired or to process it collectively or collectively Device can be commanded. The software and / or data may be in the form of any type of machine, component, physical device, virtual equipment, computer storage media, or device , Or may be permanently or temporarily embodied in a transmitted signal wave. The software may be distributed over a networked computer system and stored or executed in a distributed manner. The software and data may be stored on one or more computer readable recording media.

The method according to an embodiment may be implemented in the form of a program command that can be executed through various computer means and recorded in a computer-readable medium. The computer-readable medium may include program instructions, data files, data structures, and the like, alone or in combination. The program instructions to be recorded on the medium may be those specially designed and configured for the embodiments or may be available to those skilled in the art of computer software. Examples of computer-readable media include magnetic media such as hard disks, floppy disks and magnetic tape; optical media such as CD-ROMs and DVDs; magnetic media such as floppy disks; Magneto-optical media, and hardware devices specifically configured to store and execute program instructions such as ROM, RAM, flash memory, and the like. Examples of program instructions include machine language code such as those produced by a compiler, as well as high-level language code that can be executed by a computer using an interpreter or the like. The hardware devices described above may be configured to operate as one or more software modules to perform the operations of the embodiments, and vice versa.

While the present invention has been particularly shown and described with reference to exemplary embodiments thereof, it is to be understood that the invention is not limited to the disclosed exemplary embodiments. For example, it is to be understood that the techniques described may be performed in a different order than the described methods, and / or that components of the described systems, structures, devices, circuits, Lt; / RTI > or equivalents, even if it is replaced or replaced.

Therefore, other implementations, other embodiments, and equivalents to the claims are also within the scope of the following claims.

Claims (5)

An outdoor positioning method for estimating a current position in a mobile terminal,
The mobile terminal is equipped with a first Zigbee Module,
Measuring a distance from a second position module fixed to a reference point on a moving path to the first position module; And
Estimating a current location of the mobile terminal using the distance between the first location module and the second location module
Lt; / RTI >
The reference point is a position determined through a Gaussian distribution based maximum Bayes Error Estimation technique,
Wherein the second space module is composed of three spatial modules fixed to different reference positions, and when an area in which the spatial signals (Zigbee signal) between the reference reference points are overlapped among the reference reference points located on the movement path is an error, The reference point being determined to be a position that minimizes the error while eliminating a candidate reference point having a maximum sum of errors,
Wherein measuring the distance from the second position module to the first position module comprises:
Measuring a distance from each reference point to the first position space module,
Estimating a current location of the mobile terminal,
Estimating the current position of the mobile terminal by trilateration using three measured distance values
. ≪ / RTI > delete The method according to claim 1,
Wherein measuring the distance from the second position module to the first position module comprises:
The distance is measured by multiplying the packet transmission time between the first location module and the second location module by the SDS-TWR (Symmetrical Double-Sided Two-Way Ranging) method and the packet transmission speed
. ≪ / RTI > delete delete

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