A Design of Indoor RTLS by Use of the UWB-WSN based Two Reference Points Ardiansyah Musa, Gde Dharma Nugraha, Deokjai Choi, Seungho Seo, Juseok Kim Contact:
[email protected] /
[email protected]
Presented at AEMT 2018, Lombok, Indonesia
Introduction • Recently, Real-Time Locating Systems (RTLS) play as the key and importance role for many applications in the Internet of Things (IoT) that require real-time and precise position information. • It is very useful for assisted navigation, tracking, and others location-based services.
Introduction • The most pervasive technology of RTLS exist is the Global Navigation Satellite System (GNSS). • However, in indoor application, signal from GNSS faces difficulties that makes GNSSRTLS functionality suddenly disappears. • Hence, any solution for indoor RTLS application is required.
Introduction • At present, there are several indoor RTLS solutions that are commonly using Radio Frequency Identification (RFID), ZigBee Technology, and Ultra-Wide-Band (UWB). • Among the solutions for indoor RTLS, the use of UWB ranging is indispensable in order to obtain very high accuracy positioning system [1]. • Their large bandwidth guarantee accurate estimation, that can theoretically reduce the impact of source of errors caused by wall and obstacles.
Introduction • Moreover, after the definition of the IEEE 802.15.4a Wireless Sensor Networks (WSN) standard [2], the interest on UWB-WSN system for indoor RTLS is growing fast. • There are some commercially positioning devices already available. For example, DecaWave DW1000 UWB ranging chip. • For research and development purpose, the company provide EVB 1000 Evaluation Kit [3].
Introduction • However, as depicted below, the common approach of the existing UWB-WSN system in the literature [4-7], require three or more reference points to determine the position of a target. Our Goal: Reduce the number of reference points in order to • Decrease cost • Reduce the number of packet collision • Reduce the overall power consumption
Literature Review • For UWB ranging, due to their nature of the inverse relationship of time and frequency, the lifetime of UWB signal is very short. • Consequently, the time-based scheme can achieve a far more accurate result than received signal strength based scheme
Literature Review • In the time-based scheme, the distance between the target object and a number of references measured using time-of-flight (TOF) estimation. • It can be derived from ranging mechanism between the tag and the references. • For UWB-WSN system, the basic mandatory mechanism is two-way-ranging (TWR) based on IEEE 802.15.4a standard [2].
Literature Review • In general, the TWR mechanism consists of the following steps 1. 2. 3. 4.
The reference point transmits a message to the tag and records the departure time (t1) The tag receives the message and sends back a reply The reference point records the arrival time of the reply (t2) The reference point then calculates the time difference Tr between the message departure time and the reply arrival time. 𝑇" = 𝑡& – 𝑡(
(1)
𝑇)* = 𝑇" ⁄2
(2)
5. The estimation ToF between the reference point and the tag, is given by 6. The reference point then calculates the distance between the tag and the reference using following formula 𝑑 = 𝑐𝑇)* where c is the speed of light.
(3)
Literature Review • In more details, there are 2 main methods of determining a tagged target’s location in an area using time-based schemes 1. TOA-based algorithm • TOA stands for Time of Arrival, is the algorithm which estimates the location of the tag by the intersection of circles from references. • The distance between the tag and the reference become the radius r of the circles, as depicted in figure. Combing with equation (3), we got 𝑟0 =
𝑥0 − 𝑥
&
+ (𝑦0 − 𝑦)& = 𝑑0
(4)
where 𝑑0 is the estimated distance between tag and anchor unit i, (xi, yi) represents the coordinate of the anchor unit i, and (x, y) represent the coordinate of the tag/target object.
y
r2
p1
P2 (h,0,0)
r1 T (x,y,0)
p3
r3
x
Literature Review 2. TDOA-based algorithm • TDOA stands for Time Difference of Arrival. • The merit of TDOA in comparison with TOA is that there is no need for synchronization between tag and references. • As tag and references are not synchronized, there is a time offset of ToF which need to calculate. • The measuring unit in TDOA resulting a hyperbolic multilateration. • The tag should be at the intersection of a hyperbola formed. • The equation of the hyperbola 𝑟0,8 =
&
&
(𝑥 0 − 𝑥9 + (𝑦0 − 𝑦)& − (𝑥 0 − 𝑥9 + (𝑦0 − 𝑦)& = 𝑐(𝑇)*: − 𝑇)*; )
(5)
Where (xi,yi) and (xj,yj) represent the coordinate of anchors i and j, (x,y) represent the coordinate of the tag, 𝑇)*: and 𝑇)*; represent the signal arrival time in anchors i and j.
Proposed Method • As mentioned before, our goal is to reduce the number of reference points in order to decrease cost, the number of packet collision, and the overall power consumption. • The idea to reduce the number reference point in UWB/WSN system for indoor RTLS firstly proposed in [10]. • However, their work is focused on a static object and its only done in Matlab simulation.
Proposed Method • In this paper, we aim to develop indoor RTLS which focuses on the moving object as the tagged object. • In order to locate the moving object, we estimate the continuous location of the moving object by utilizing • the information of initial tag position • the knowledge of estimated tag positions from TWR mechanism process
Proposed Method The detail steps of our system as follows 1. A sensor node as a reference point, which builds a set of positioning scheme of UWB-WSN system for indoor RTLS
2. We then divide the system into several interest regions. Each region will consist of two reference points. For example region 1: consist reference points A0(x0, y0) and A1(x1, y1)
Proposed Method The detail steps of our system as follows 3. We then estimate the tag position T(x,y,z) by utilizing the TOAbased algorithm. • The estimation distance between A0 and T is the radius/radii r1 with center P1. • The estimation distance between A1 and T is the radius/radii r2 with center P2. • The estimated tag positions are the intersection points between A0 and A1 which could be either intersection between 2 circles in 2D space or between 2 spheres in 3D space
Proposed Method The detail steps of our system as follows 3. (cont), In order to simplify the problem in 3D space, we made these assumptions: • All references and tag can be located at the fixed height, regarded as zero (z=0). • The geometric calculation can be simplified using Fang method [11]. • So, the center P1 is located at the origin of the elevated plane A0(0, 0, 0) and P2 along the x-axis A1(a, 0, 0). • Which resulted 𝑝(& (vector line from A0 to A1), 𝑒𝑥 (unit vector of 𝑝(& ) and 𝑒𝑦 (unit vector of y axis), as depicted in the next figure.
Proposed Method
Geometrical Approach of the Estimated Position.
Proposed Method The detail steps of our system as follows 4. With the geometrical approach, we then calculate the estimated tag position by following equations 𝑥= 𝑦=
"> ? @"? ? @A? &A
|𝑟( & − 𝑥 & |
(6), (7),
5. Since x value is only dependent on the 𝑝(& , so we can apply x to the 𝑒𝑥 to find the x estimated position of the tag. However, for the y value, we will have 2 possible tag positions T1(x,y) and T2 (x,-y).
Proposed Method The detail steps of our system as follows 6. In the final step, in order to solve the problem mentioned in step 5, • We find the best tag estimate position by utilizing the information of the initial tag position InitialTag. • We then calculate the distance of the initial tag position InitialTag with possible tag positions. • In order to determine the correct position TagNewPosition of the tag, • We select the minimum distance tp between the InitialTag and possible tag positions 𝑡 = min ( (𝑥 − 𝑥 )& + (𝑦 − 𝑦 )& + (𝑧 − 𝑧 )& , C
(
(
(
(𝑥 − 𝑥& )& + (𝑦 − 𝑦& )& + (𝑧 − 𝑧&)& ) The selected position will be picked as the new initial position of the tag in iterative steps of our system
Where (x, y, z) represents the initial tag position, (x1, y1, z1) represent the estimated tag T1 and (x2, y2, z2) represent the estimated tag T2.
Proposed Method The summary of our algorithm is depicted in Algorithm 1. Algorithm 1. Indoor RTLS using two-reference points 1: InitialTag(InitX, InitY) 2: ReferencePos(A0X, A0Y, A1X, A1Y) 3: function toa() 4: compute p12 ß vector line from A0 to A1 5: compute a ß scalar value of p12 6: get r1 ß distance from reference A0 to tag 7: get r2 ß distance from reference A1 to tag 𝑟12 −𝑟22 +𝑎 2
8:
compute x ß with formula 𝑥 =
9: 10:
compute y ß with formula 𝑦 𝑥2| compute new tag position for x value ß with unit vector p12 multiply to x/a define two possible y value for new tag position ß T1(x, y) and T2(x, -y) compute tp1 as distance between InitialTag(InitX, InitY) and T1(x, y) compute tp2 as distance between InitialTag(InitX, InitY) and T2(x, -y) if tp1 < tp2 TagNewPosition = pos1(x, y) else TagNewPosition = pos2(x, -y) return TagNewPosition
11: 12: 13: 14: 15: 16: 17: 18:
2𝑎 = +|𝑟12 −
Result • In order to validate our proposed method, we implement our algorithm in the Decawave EVB1000 Evaluation Kits as depicted in the figure
Experimental Setup with DW1000 Evaluation Kits
Real vs Estimated Tag Position
Result
Result • In order to measure the accuracy of indoor RTLS, we then calculate the root-mean-square (RMS) and R95 error value with following formula 𝑅𝑀𝑆𝐸 =
? ? ? ∑P :Q>(M@M: ) N(O@O: ) N(R@R: )
S
(9),
Where (x, y, z) represent the value of the real tag position and (xi, yi, zi) represent the estimated value of the tag position.
• Our evaluation shows RMSE of estimation is about 12.79 cm and 22.26 cm within R95 accuracy.
Summary • In this paper, we presented our initial work, a design of indoor RTLS using UWBWSN based 2 reference points. • We estimate the continuous location of the moving object by utilizing the information of initial tag position and the knowledge of estimated tag positions from TWR mechanism process. • In order to determine the correct position of the tag, we select the minimum distance between the initial tag position and estimated tag positions. • The selected position will be picked as the new initial position of the tag in iterative steps of our system. • Our evaluation show RMSE of estimation is about 12.79 cm and 22.26 cm within R95 accuracy. • It is shown that our approach is feasible to be implemented for indoor RTLS.
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