KR100896320B1 - Device and method for purchasing location - Google Patents

Abstract

The present invention relates to a location tracking device and method, comprising an inertial measurement device and a wireless sensor network as one system, and if the observation is not constant in the wireless sensor network, the location is tracked based on the inertial measurement device, and vice versa. If the inertial measurement device's reliability is low, it tracks the location based only on the wireless sensor network, and can selectively use the inertial measurement device and the wireless sensor network as well as perform position prediction by Kalman filter. The position of the moving target can be precisely and reliably tracked.

Wireless Sensor Networks, Inertial Measurement Units, Kalman Filters

Description

Location tracking device and method {DEVICE AND METHOD FOR PURCHASING LOCATION}

The present invention relates to a location tracking technology, and more particularly to a location tracking device and method for tracking the location of a target based on an inertial measurement device and a wireless sensor network.

The present invention is derived from the research conducted as part of the IT new growth engine core technology development project of the Ministry of Information and Communication and the Ministry of Information and Communication Research and Development. [Task Management Number: 2005-S-092-02, Title: USB-based Ubiquitous Robotic Space Technology] Development].

Positioning system based on a wireless sensor network has been applied in various fields such as an inertial measurement system, an inertial navigation system, and a sensor coupling. Specifically, it is applied to the traveling technology of mobile robots, production and process automation, home automation, automobile and aerospace navigation, positioning system, and the like.

However, ZigBee-based positioning systems in wireless sensor networks are difficult to track in real time by tagging moving objects (objects) due to slow responsiveness, while UWB-based positioning systems do not change their position for short distances. It has a disadvantage.

In addition, in the related art, a method of tracking a position by combining a global positioning system (GPS) and an inertial measurement apparatus has been proposed. However, the combination of GPS and inertial measurement devices is effective only when applied outdoors, and it is difficult to recognize the position of a target quickly and accurately when applied indoors.

On the other hand, attempts have been made to combine RFID with an inertial navigation system, but a method of combining a wireless sensor network to track the position of a target has not been proposed.

Accordingly, the present invention has been made to solve the above problems of the prior art, an object of the present invention is to provide a location tracking device and method for tracking the location of things in real time by combining the inertial measurement device and the wireless sensor network. .

On the other hand, another object of the present invention is to provide a position tracking device and method for selectively using the inertial measurement device and the wireless sensor network, or performing position prediction by the Kalman filter.

Location tracking device of the present invention for achieving the above object, the wireless sensor network for predicting the current position of the mobile object through wireless communication; An inertial measurement device for predicting a moving trajectory in response to the movement of the moving body; And generating a state propagation equation from the signal output from the inertial measurement device, and providing a current position information predicted from the wireless sensor network based communication device to the state propagation equation and performing correction to track the position change. It characterized in that it comprises a calculation device.

On the other hand, the position tracking method of the present invention for achieving the above object, (a) determining the movement of the moving object using the inertial measurement device at the current position; (b) In the position calculating device, the signal transmitted from the inertial measurement device in response to the movement of the moving object is determined in the position calculating device while determining whether to correct according to the presence of the predicted current position information based on the wireless sensor network when the moving object is stopped. Tracking the movement trajectory; And (c) storing the last position calculated from the tracked movement trajectory.

As described above, the apparatus and method for tracking the location according to the present invention combines a wireless sensor network-based location recognition system with an inertial measurement device to measure the position of an object in real time, and the observation in the wireless sensor network may not be constant. In this case, the position is tracked based on the inertial measurement device, and if the reliability of the observation signal of the inertial measurement device decreases, the position is tracked based only on the wireless sensor network. In addition to tracking location precisely and reliably, large marts can reliably track the travel of consumers and predict the location of people or objects in airports, gyms, and buildings.

Hereinafter, a position tracking apparatus and a method of the present invention will be described in detail with reference to the accompanying drawings.

1 is a conceptual diagram of a tag equipped with a location tracking device of the present invention.

As illustrated in FIG. 1, a tag attached to an object, that is, a robot, may be equipped with a wireless sensor network-based location tracking device and an accelerometer-based location tracking device to be implemented in the present invention. The wireless sensor network-based location tracking device is equipped with a ZigBee 10 as an example. Of course, instead of ZigBee, UWB (Ultra Wide Band), Wi-Fi (Wireless Fidelity), and RFID (Radio Frequency IDentification) may be installed. In addition, the accelerometer-based position tracking device is equipped with a MEMS (Micro Electro Mechanical Systems) gyroscope (20) and an MEMS accelerometer (30) that measure angular acceleration and linear acceleration.

In this way, the specific uncertainty boundary is identified through wireless communication based on the wireless sensor network, and the position is calculated by performing a trajectory calculation through the MEMS Gyroscope (20) and the MEMS Accelerometer (30). In the calculation, the correct position is tracked by referring to the position identified through the wireless sensor network.

2 is a block diagram of a position tracking device using an inertial measurement device and a wireless sensor network of the present invention.

As shown in FIG. 2, the position tracking device according to the present invention includes an inertial measurement unit (IMU) 40 including a MEMS gyroscope and a MEMS accelerometer, and a ZigBee 10 which is a wireless sensor network based position tracking device. And an inertial measurement device 40 and a Kalman filter 50 for processing the signal transmitted from the ZigBee 10 to calculate the position. Of course, as described above, a variety of equipment may be used as the wireless sensor network-based location tracking device, and it is well known that various location measurement equipment may be used in place of the Kalman filter 50.

On the other hand, the Kalman filter 50 corrects when there is a signal generated from the state propagation motion equation obtaining unit 51 which processes the signal transmitted from the inertial measurement apparatus 40 to calculate the motion equation, and the wireless sensor network. A motion equation correction and propagation unit 52 for propagating a motion equation by performing the following, and a position observer 53 for processing a signal transmitted from the ZigBee 10 to observe a specific uncertainty boundary. It is.

The position tracking device configured as described above processes the signal transmitted from the inertial measurement device 40 to calculate and propagate the equation of motion, and if there is a position observation based on the wireless sensor network, it performs correction on the equation of motion. Then perform the propagation. This enables more accurate location tracking.

3 is a flowchart illustrating a location tracking method using an inertial measurement device and a wireless sensor network according to the present invention.

As shown in FIG. 3, a tag attached to an object such as a robot identifies an initial position or a current position (S1), and tracks and confirms a position within a specific uncertainty border based on a wireless sensor network in this state. (S2). With this, the inertia measuring device IMU is initialized (S3). Thereafter, the motion of the object is determined (S4), and if there is motion, the position is tracked and confirmed using the inertial measurement device (S5). Of course, the position tracking using the inertial measurement device performs the position tracking while performing the correction based on whether there is a position observation based on the wireless sensor network. On the other hand, when the object is stopped while tracking the position in response to the movement of the object (S6), the current position is confirmed through the return (S7), and then repeated steps S2 to S7 are continuously returned. .

The inertia measuring device is initialized in step S2, because the inertial measuring device cannot be used for a long time due to a phenomenon in which bias noise is accumulated over time. Thus, when the object is stationary, the inertial measurement unit is reset. That is, it is reset to the position based on the wireless sensor network according to the initialization of the inertial measurement device, and when the object moves again, the angular velocity and linear acceleration output from the inertial measurement device based on the current position identified based on the wireless sensor network. Integrate to continuously track the position trajectory of the object.

4 is a flowchart illustrating a position calculation process using an inertial measurement device and a wireless sensor network according to the present invention.

As shown in Figure 4, in performing the position calculation using the inertial measurement device, when the position observation of the object by ZigBee comes out at a certain speed, the state propagation based on the signal transmitted from the inertial measurement device The equation of motion is obtained (S51). At this time, before propagating the obtained equation of motion, it is determined whether there is position information observed from the wireless sensor network (S52), and if there is position information, after performing state variables (states) correction (S53), The equation of motion is propagated (S54). Of course, if there is no position information observed from the wireless sensor network, the equation of motion is propagated without performing correction. Using the motion equation, the position trajectory is tracked to calculate and store the current position (S55).

5 is a view showing a location tracking process using an inertial measurement device and a wireless sensor network according to an embodiment of the present invention.

As shown in FIG. 5, when the object is stationary, the location 305, 315, and 325 of the object is tracked using the ZigBee. Here, although limited to ZigBee in FIG. 5, UWB, Wi-Fi, RFID, etc. may be used instead of ZigBee. However, since ZigBee also includes an error in the position tracking, the estimated position is different from the actual position (300, 310, 320). The locus is estimated by integrating the IMU output value from the estimated position (305 ', 315', 325 '), and when the object is stopped again, the IMU is reset to remove accumulated bias noise. Eventually, this process can be repeated so that the trajectory and position of the object can be tracked within the bounds of uncertainty to some extent, even if it does not provide much improved accuracy. Will be.

In other words, ZigBee is used to track the location of objects and MEMS Gyroscope and MEMS Accelerometer to measure the angular and linear acceleration of objects. At this time, when the position observation of an object is output at a certain speed by ZigBee, noise can be removed by calculating the position by integrating the sensor output value with ZigBee and IMU using a Kalman filter. The Kalman filter can be designed using the navigation equation using the IMU as the dynamic equation of the object and using the position as the observation value from ZigBee. For the Kalman filter design, the state propagation motion equation expressed by Equation 1 is obtained from the IMU.

[Equation 1]

Here, x, y, and z represent positions in three-dimensional space, and θ represents a heading angle in which an object moves. Equation 1 indicates that these state variables are a function of the accelerations f _x , f _y , f _z and the angular accelerations p, q, r rotated about the three axes, which are observed on the x, y, and z axes from the accelerometer. Since the equation of motion is apparent to those skilled in the art to which the present invention pertains, detailed derivation is omitted.

And, if the complexity of [Equation 1], [mathematical formula 1] by simplifying [Equation 1] by the following relationship between the position and the speed given by the following equation (Equation 2], [Equation 3] Equation 4 can be obtained.

[Equation 2]

[Equation 3]

[Equation 4]

(P (t) is the position vector at time t, V (t) is the velocity vector at time t, a (t) is the acceleration vector, and Δt is the sampling interval.)

In Equation 4, the acceleration vector a (t) is an output value of the inertial measurement device and n is process noise. Since wireless sensor networks provide x, y, and z as reference observations, these components are used for the measurement residual. Therefore, the observation equation is simply derived as follows.

[Equation 5]

Where w is the observed noise. And, if the heading angle is observed from a magnetic field sensor or ZigBee, θ can also be used as an observation in the above equation, in which case the system performance is improved.

However, in Equation 1, observations may be much slower than the output period of the IMU, so special consideration is required. This problem is solved by selectively using the IMU output data in accordance with the observation period. For example, observations from ZigBee consist of T1, T2, T3, T4, ... and observations from the IMU are faster than this: t01, t02, t03, ..., t0n, T1, t11, t12 ,. Given .., t1n, T2, t21, t22, ..., t2n, T3 ..., set the update rate of the Kalman filter to the ZigBee observation periods T1, T2, T3, ... The IMU observations are used by averaging t0n-1, t0n, T1, etc., and the IMU observations at t2n are t1n-1, t1n. T2 and the like are used on average. Position tracking between T1, T2, and T3, that is, position tracking using the output of the IMU, is calculated by integrating the IMU output.

를 생략하고), 상태 전파 (state propagation)만을 수행하다가, 관측이 발생하였을 경우 상태 보정 및 상태 전파 과정에 의하여 상태 변수를 예측한다. 그리고, 무선 센서네트워크부터 위치 정보가 비주기적으로 나올 경우, 칼만 필터에서 관측을 통한 상태 변수(states) 보정 단계를 생략하고, 단지 상태 변수 전파(state propagation) 만을 이용하여 위치를 계산한다.Then, the motion equation propagates using the signal from the inertial measurement device. At this time, the state variables are corrected using the location information observed from the wireless sensor network. If there is no observation information from the wireless sensor network, the state correction or state update process is omitted from the Kalman filtering (ie,

O), only state propagation is performed, and when observation occurs, the state variable is predicted by state correction and state propagation. When the location information is aperiodic from the wireless sensor network, the Kalman filter omits the step of correcting the state variables through observation, and calculates the position using only state propagation.

However, if the performance of the inertial measurement device is degraded or the reliability is impaired, it is not possible to secure a (t) in [Equation 4]. In this case, consider a (t) and noise n as external disturbance d as

[Equation 6]

Replace [Equation 4] as follows.

[Equation 7]

In this case, it is not necessary to observe the acceleration and angular acceleration signals in the inertial measurement device, and the Kalman filter is designed by considering the motion of the object (or person) as external perturbation. The observation is the same as given in [Equation 5].

As a result, as shown in FIG. 5, the position is predicted from the wireless sensor network, the trajectory of the target is predicted from the IMU, and the actual position is identified and corrected. Throughout this process, the predicted and actual position of the target gradually becomes similar.

In the above-described embodiments, the concept of tracking the trajectory of a moving target by integrating the inertial measurement device and the wireless sensor network has been proposed. Since the present invention enables tracking of a moving target in real time, the present invention enables accurate and reliable tracking of the location of a vehicle or other moving target in the navigation of a mobile robot or indoors. It also allows customers to reliably track the movement of consumers in large marts and to predict the location of people or objects in airports, indoor gyms, and buildings.

Although the present invention has been described in more detail with reference to some embodiments, the present invention is not necessarily limited to these embodiments, and various modifications can be made without departing from the spirit of the present invention.

1 is a conceptual diagram of a configuration of a tag equipped with a location tracking device of the present invention;

2 is a configuration diagram of a position tracking device using an inertial measurement device and a wireless sensor network of the present invention;

3 is a flowchart illustrating a location tracking method using an inertial measurement device and a wireless sensor network according to the present invention;

4 is a flowchart illustrating a position calculation process using an inertial measurement device and a wireless sensor network according to the present invention;

5 is a view showing a location tracking process using an inertial measurement device and a wireless sensor network according to an embodiment of the present invention.

<Explanation of symbols for the main parts of the drawings>

10: ZigBee

20: MEMS Gyroscope

30: MEMS Accelerometer

40: inertial measurement unit

50: Kalman Filter

51: state propagation motion equation acquisition unit

52: motion equation correction and propagation unit

53: position observation unit

Claims (10)

A wireless sensor network for predicting a current position of a mobile object through wireless communication; An inertial measurement device for predicting a moving trajectory in response to the movement of the moving body; And Generates a state propagation equation from the signal output from the inertial measurement device, calculates the position by tracking the change of position by providing the current position information predicted from the wireless sensor network based communication device to the state propagation equation and performing correction Device Position tracking device comprising a. According to claim 1, wherein the position calculating device, A position observer configured to process the signal generated from the wireless sensor network and observe the current position information; A state propagation motion equation obtaining unit configured to process a signal transmitted from the inertial measurement device to calculate a state propagation motion equation; And Motion equation correction and propagation unit for propagating after providing current position information transmitted from the position observation unit to the state propagation motion equation Position tracking device, characterized in that consisting of. The position tracking device according to claim 2, wherein the position calculating device is a Kalman filter. 3. The apparatus of claim 2, wherein the current location information is determined within a certain uncertainty boundary. The method of claim 1, wherein the communication method used for the wireless sensor network uses any one selected from ZigBee, Ultra Wide Band (UWB), Wireless Fidelity (Wi-Fi), and Radio Frequency IDentification (RFID). Tracking device. The apparatus of claim 1, wherein the inertial measurement device is a micro electro mechanical systems (MEMS) gyroscope and a micro electro mechanical systems (MEMS) acceleration sensor for measuring angular acceleration and linear acceleration. (a) determining whether a moving object is moved by using an inertial measurement unit at a current position; (b) In the position calculating device, the signal transmitted from the inertial measurement device in response to the movement of the moving object is determined in the position calculating device while determining whether to correct the movement according to the presence of the predicted current position information based on the wireless sensor network when the moving object is stopped. Tracking the movement trajectory; And (c) storing the last position calculated from the tracked movement trajectory Position tracking method comprising a. 8. The position tracking method according to claim 7, wherein the values measured from the inertial measurement apparatus are angular acceleration and linear acceleration. The method of claim 7, wherein step (b), Calculating a state propagation motion equation based on the signal transmitted from the inertial measurement unit; Correcting a state variable when there is a predicted current position information based on the wireless sensor network; And Calculating a current position by propagating a state propagation motion equation with the state variable corrected Position tracking method, characterized in that consisting of. The position tracking method according to claim 7, wherein the inertial measurement unit is reset when the moving object is stopped.

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