6.9 Performance evaluation of the geo-location algorithm
6.9.2 Experiment location results – Discussion
Discussion and reasoning on error results is given below:
• Experiment Location 1: The location of position 1 (figure 6-32) lies in the room with sensor 3. The room has a steel pole as shown in figure 5-4. The location is such that the client is beyond the steel pole with respect to sensor 1 and sensor 3. This could be expected to cause additional losses. It is also very near to the second wall with respect to sensor 1. In addition to these factors the reported signal strengths are expected to vary by 3 to 4 dBm. For this particular example the reported signal strength should have been less than 69.73 dBm (threshold S1) for the algorithm to have correctly assumed that the client is beyond a single wall. The RSS received is 72.4 dBm (A.1.2), an error of 2.5 dBm. The algorithm would be prone to these errors on clients placed at a boundary line near the wall inside or outside. Similarly sensor 3 also reported extra absorption for this position of client due to possibly the same reasons. Experiment locations 3, 4, 5 and 20 also show that the client placed near to a wall has caused the algorithm to determine an inaccurate number of walls. But, as the other three sensors have correctly detected the walls, the data fusion effectively counters any negative effect and manages to keep the error as low as possible.
Despite the errors in detecting the number of walls in this example, the positional error reported is only 1.69m. After correction of walls the circles (figure A-1) pass close to the client’s position. It is obvious that in such scenarios the circle formed after correction will not ideally cut the other 3 circles and will not overlap with one or more other circles as can be observed in the
and brought near to the client as shown in the next two results of figure A-1. Forced overlapping has been explained in sections 4.8 and 6.8.
• Experiment Location 3 (A.3): Again the client is placed close to the wall in the room with sensor 3. Sensor 1 has a reported loss equal to 71.69 dBm. The threshold for determining two walls is 69.73. The reason explained for experiment position 1 applies here as well.
• Experiment Location 13: Sensor 2 has not determined the number of walls correctly. The RSS reported is 67.61 dBm, which is compared to the threshold of 68.06 dBm. As already explained 3 to 4 dBm variation is to be expected. Here the threshold is missed by 0.46 dB only. The client’s RSS is very close to threshold.
• Experiment Location 18: Sensor 3 should have detected 2 walls instead of 1 resulting in the circle for sensor 3 after correction (A.18 first figure) falling short of the actual location. It is worth noting that the error is intelligently picked up by algorithm and the forced overlapping feature has brought it close to the actual position.
• Experiment Location 19: Walls are correctly detected but the error is on the higher side. Observing figure A.19, it is shown that two sets of triangulation results in the combination of three circles produces good results – an error of 1.6 and 2.7m. However, two set of triangulation results produce larger errors of 5.1 and 8.1m.
An interesting example which highlights that the placement of client also has an effect with respect to sensors on the reported accuracy (Jonge, 2005). Observing the triangulation result in figure A.19 which produces an 8.1m error is quite perplexing. All three circles are passing very close to the client’s actual position and yet the error reported is very high. Sensors 2 and 3 are in line with the client’s actual position (the square). This causes the curvature effect of the circle
to induce an error as the intersection point shifts with a slight increase of distance between the two circles (Stansfield, 1947). The same issue causes error in location 17 and location 12. Location 12 is one of the results where the location error is 4.26m when walls are detected and 3.8m when walls are not detected. If we observe the results A.12, and A.12.1, the overlapping is good when walls are detected. But due to the circle curvature error as explained in the above paragraph, the reported error high. It can also be seen that the location is correct in the x axis and is in the same room. Where as in figure A.12.1 (appendix A), the position is reported outside the wall and the averaging resulted in a reported error of 3.8 m, outside the test area.
The following can be deduced from the above discussion:
• The correct detection of walls increase the accuracy and reliability of location estimation.
• Clients placed close to the walls, at times, causes the algorithm to report an incorrect detection of wall. This is, however, random due to the residual variation in RSS values.
• Location of the sensors, if possible, should not be in line to avoid a common line of bearing for the client from two sensors since this causes circle curvature error and causes dilution of precision as described in Location experiment 19 above.
• The forced overlap is working to good effect.
• The algorithm design provides good redundancy. If the RSS is not accurate; it is compensated by some extent by the forced overlap. If one set of three sensors produces an inaccurate result; the effect is minimized by averaging the triangulation results (data fusion) from other sensors.
• The RSS values are demonstrated to be varying in a range of 3 to 5 dB and above 5dB for clients beyond 2 walls. Frequency diversity has given some control over RSS variation but without a sophisticated algorithm getting better positional accuracies is not possible; especially at longer distances when slight change in RSS value causes a lot or error in reported distances.