Development of Geolocation Data Compression for Transportation Target Identification

Abstract

Applications of an intelligent transportation system (ITS) often require wireless communication networks to support navigation and location identification, which is emerging as an important research issue in ITS. Of the various infrastructures for supporting ITS development, the wireless sensor network is the most commonly used medium to transmit data from a target source to the receiving sensors, between sensors, or both. Such data, often referred to as geolocation data, can then be used to calculate the time difference of arrival (TDOA) to the various sensors for the purpose of precise location identification. However, the limitation on the system bandwidth and energy resources motivates the use of data compression within the network when data are transmitted between sensors. This paper presents three data compression approaches specifically designed for geolocation data transmitted between sensors within a wireless communication network for the purposes of transportation target identification and location-finding. The approaches include the wavelet transform with arithmetic encoding method, the wavelet packet analysis with the Fisher-information method, and the Texas Southern University model (the monotone increase method). The effectiveness of each presented method is evaluated by calculation of the precision of TDOA from the decompressed data, which are used for determination of source location. Sensitivity analysis is conducted on all the presented methods in relation to compression ratio, signal-to-noise ratio, and computational time. A comparison of the three methods permits recommendation of the optimal compression tool for various applications.