A map based dead reckoning protocol for updating location information Sexchat cartoon
Location awareness is the basic requirement for developing new exciting location-based services (LBS).
Compared to common applications supported by outdoor positioning techniques, like the Global Position System (GPS), it is extremely challenging to provide similar ubiquitous and affordable services in indoor environments.
By better modelling of the location error, the improved PF calibrates the location estimation, as well as step direction estimation when the map information is available, while keeping the computational complexity the same as the original PF.
Experimental results show that in a quite dense map constraint environment with corridors, the proposed methods have similar accuracy, but outperform the original PF in terms of accuracy.
Suppose no external information except the initial location is known; the performance of DR totally relies on the measurement accuracy of the sensors.
To overcome the dependency on manually input corridor information in the MM algorithm, as well as the computational complexity in combining two such algorithms, an improved PF is proposed.The infrastructure-based techniques can be categorized into two types, namely training algorithms and non-training algorithms.The training algorithms work based on the assumption that the received signals are different in various locations.The most favored indoor technology has been Wi Fi access points, which is because of their comparable large coverage, and yet, they provide a satisfying solution in certain circumstances .If higher accuracy is demanded, researchers have preferred technologies, like ultrasound and Bluetooth [3,4].
An IMU sensor is able to track its movement by the equipped accelerometers, gyroscopes and magnetometers.