Localization Using Extended Kalman Filters in Wireless Sensor Networks

Localization arises repeatedly in many location-aware applications such as navigation, autonomous robotic movement, and asset tracking. Analytical localization methods include triangulation and trilateration. Triangulation uses angles, distances, and trigonometric relationships to locate an object. Trilateration, on the other hand, uses only distance measurements to identify the position of the target. Figure 1A, describes a simple example of trilateration. Using three reference points S1, S2, and S3 with known locations and distances d1, d2, and d3 to the target object, the object can be located at the intersecting point of the three circles. However, in a dynamic system where distance measurements are noisy and fluctuate, the task of localizing becomes difficult. This can be seen in Figure 1B, where with fluctuating distances, regions within the circles become possible locations for the tracked object. In this case, rather than the object being located at a single point at the intersection of the circles as in Figure 1A, the object can be located anywhere in the dark overlapped region in Figure 1B.