Piche, R., 'A comparative survey of WLAN location fingerprinting methods,' in Positioning, Navigation and Communication, 2009. IEEE 18th International Symposium on,pp.1-5, 3-7 Sept. Mahonen, P., 'Combining Particle Filtering with Cricket System for Indoor Localization and Tracking Services,' in Personal, Indoor and Mobile Radio Communications, 2007. Pister, K.S.J., 'RF Time of Flight Ranging for Wireless Sensor Network Localization,' in Intelligent Solutions in Embedded Systems, 2006 International Workshop on, pp.1- 12, 30-30 June 2006 Google Scholar To demonstrate the effectiveness of this method, a range-only SLAM algorithm is simulated utilizing this environment. This work describes a simulation method which procedurally generates RSSI values at given distances for wireless nodes utilizing collected data from a given environment type and a Markov chain.

Simulating multipath effects require detailed information about the operating environment's properties (geometry, materials), and can be computationally expensive. However, simulating RSSI can prove difficult due the many environmental factors that impact attenuation, such as multipath interference. To evaluate these methods, it is costly and time consuming to construct hardware to physically test each implementation of an algorithm. Signal strength attenuation models allow distance to be estimated between transceivers and utilized in positioning techniques, such as trilateration and simultaneous localization and mapping (SLAM) algorithms.

Received signal strength indicator (RSSI) is often used in wireless localization applications as it attenuates as the signal propagates through the environment.

Wireless rssi values