CHAPTER 3 Localization Algorithm
3.2 Heterogeneous localization
5. Interpolation and Location Estimation:
We use five target points and their weight to interpolate our location estimation. The weight of every target points is the inverse of Euclidean distance between tracking point and the target point in signal space.
W
Equation 3 displays the weight, TP̅̅̅̅ here is the vector of the means of the RSSI values of each AP, Target̅̅̅̅̅̅̅̅
i is the vector of the means of the RSSI values of each AP at the ith target. And then we calculate weighted sum by targets’ coordinate and weight.
6. Filtering:
Every APs have signal strength fluctuations from minute to minute, and the fluctuations arise from motion people. It course the predicted location is far away from the previous location, Therefore we use filter to correct this location estimation like Collaborative localization [16]
in related work.
3.2 Heterogeneous localization
We use WiFi localization in Nangang exhibition, because there are a lot of APs. But in this paper we focus on the large scale environment, and AP is sparse in more outdoor space. On the other hand, there are a lot of RFID readers in urban environments and on campus. And we can use NFC or RFID tags in the smart phone to get the location from those readers.
Therefore, we proposed a heterogeneous localization algorithm to combine GPS, IOT and WiFi localizations. If we can get two or three locations at the same time, and which location is accuracy and reliability? We use localization possibility to solve this problem. The higher possibility express the localization is more accuracy. Our method gives IOT
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localization possibility is 1 because IOT reader and tag communication distance is very short so the location is reliable. If some GPS devices can get its localization reliability in different area and GPS localization possibility is set to the reliability. But default GPS localization possibility is 0.35, because based on our experience GPS localization possibility is 0.35 on NCCU campus and we will explain in our simulation. GPS localization possibility is not 1, because GPS is not always accurate in outdoor space. WiFi possibility is given by first target point in WiFi localization algorithm. Localization possibility is 0 when the
localization method can’t estimate location. Localization possibility will be reduced when user move in localization time interval because if user can’t estimate his location at a long time and he can use previous localization coordinate but its reliability should be low. And we use the localization possibility to enhance the filter. The filter corrects the location when the location estimation exceeds the human movement capacity. But when the previous location estimation is not accurate then the new location is bounded in the human movement capacity. This causes error localization estimation. So our method compares the possibility between previous location estimation and new location estimation. If the possibility of new location estimation is higher than the previous one and the location estimation will jump.
Figure 11 is an example for heterogeneous localization. Figure 12 is the flow chart of heterogeneous localization. We explain our algorithm by figure 11 and 12. Now user is estimated on Lt by localization system at time t. And then user move. The localization system use GPS WiFi and IOT localization. And they have their location possibility.
GPS’s possibility is given by experience and we will expand that how we set the possibility in simulation. WiFi localization possibility is the similarity of first target in WiFi localization.
IOT’s possibility is 1 when it gets reader’s location. We select the coordinate which have highest possibility at time t+1 and the coordinate is L̃t+1. At the same time system reduces Pt
‧ 國
立 政 治 大 學
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Na tiona
l Ch engchi University
because user moves from time t to t+1. Pt’ is the localization possibility that reduce from Pt
and Pt’ is 0.9. Then we calculate moving speed by localization history. Assume the system refer previous 4 times localizations and calculate the average moving speed. Therefore, we get human movement capacity by multiplying moving speed and time interval from time t to t+1. We paint two human movement capacities around Lt and 𝐿̃t+1. If there are
intersection with two human movement capacities and the localization estimation is on the middle of them. Lt+1 is the localization result. And Pt+1 inherit the higher possibility of Pt’ and 𝑃̃t+1.
Figure 11: An example of heterogeneous localization.
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立 政 治 大 學
‧
Na tiona
l Ch engchi University
Figure 12: Flow chart of heterogeneous localization.
If there is no intersection of two human movement capacities like figure 13. In case A, Pt’ is higher than P̃t+1 and we use filter to correct the location estimation to human movement capacity around Lt. In case B, If Pt’ is lower than P̃t+1 and we think L̃t+1 is more accurate.
So location estimation is on the human movement capacity around L̃t+1.
Figure 13: Two human movement capacities are not intersection.
Case A is using filter.
Case B is jumping to new location.
Case A.
Case B.
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In heterogeneous localization, we used localization possibility to select a localization method from GPS, WiFi and IOT localization and we didn’t use three localization coordinates simultaneously. Because we think some localization is not accurate. Localization accuracy will be down if heterogeneous localization used these inaccurate localization results.
In heterogeneous localization we reduce localization possibility of previous localization estimation. We consider the possibility reduction is respect to moving path like figure 14.
Figure 14: An example of moving path.
If user moves in path 1 and path 2 by the same speed and time. We think the possibility reduction in path 1 is smaller than path 2. Because the moving distance in path 1 is shorter than path 2. And furthermore we consider path 3. The user doesn’t move in path 3 and the localization possibility shouldn’t reduce. Figure 15 is an example for possibility reduction.
When user moves to Lt+1 from Lt at time t+1 and the possibility of Lt should be reduced. We refer the localization estimation from Lt-3 to Lt. First we calculate mean coordinate Lmean of Lt, Lt-1, Lt-2 and Lt-3. Second calculate the distance dt, dt-1, dt-2 and dt-3 from Lt, Lt-1, Lt-2 and
Path 1:
Path 2:
Path 3:
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立 政 治 大 學
‧
Na tiona
l Ch engchi University
Lt-3 to Lmean. Third we calculate the variance of dt, dt-1, dt-2 and dt-3. Forth we use equation 4 to calculate possibility reduction. We consider variance of previous localization and move time interval to propose the equation 4. Pt+1 is localization possibility at time t+1. Pt is previous localization possibility. σ is variance of dt, dt-1, dt-2 and dt-3. ∆t is time interval from t to t+1. D and T are constant. We set D is the distance with user general moving distance in four localizations and set T is general time interval in four localizations.
Localization interval time in our system is constant. The possibility is 0<P<=1, so we use minima with 1 and maxima with 0 in equation 4.
Pt+1 = Pt− min (𝜎𝐷, 1) × min(∆tT, 1) (4)
Figure 15: An example for possibility reduction.
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We proposed a heterogeneous localization algorithm to combine GPS, WiFi and IOT
localizations in large scale environments. And furthermore if I can’t use GPS, WiFi and IOT localization, how do I get my location? So we consider nearby neighbors’ relative WiFi signal strength and location to proposed cooperative localization method. There two problems in cooperative localization. First, how can we scan relative WiFi signal strength between two smart phones? Second, how can we deliver the location to my neighbors. We use hotspot mode to simulate ad-hoc mode in Android 2.2. One smart phone open WiFi hotspot mode and it can transmit signal strength in figure 16. Another smart phone can scan WiFi signal strength and get IP from the smart phone. Therefore, we can open TCP socket between two smart phones and deliver the coordinate of them.
Figure 16: Two HTC Desire smart phones. (right opens hotspot mode)
Figure 17 is an example for cooperative localization. N0 want to estimate or correct location by nearby node N1 and N2. N1 and N2 can scan the relative WiFi signal strength of N0 and their possibility is higher than N0. Then we translate N1’s RSSIN0 and N2’s RSSIN1 to distance D1 and D2 by equation 5. Equation 5 is proposed by RADAR [5].