Passive Localization Through Channel Estimation of On-the-Air LTE Signals

Abstract

Wireless communication signal is a kind of coherent illuminator, and it can shed light on surrounding environments. The mobile terminal receives the signal and estimate the propagation channel, and can sense the moving objects passively. The localization module and communication module share the RF front-end and baseband processing, and thus greatly reduce the implementation cost in mobile terminals. Passive localization is based on the delay estimation of the dynamic reflection path. However, several practical factors prohibit the accurate estimation of the propagation delays. The multipath reflection and scattering are abundant in urban and indoor environments, and the signal bandwidth is usually not large enough to reach a fine delay resolution. The synchronization error, sampling clock drift, frequency offset and phase noise will severely impact the estimation performance. In this article, we propose a set of methods to make the practical application of this method possible. We also derive the Cramer-Rao lower bound of the delay estimation, and analyze the estimation error related with various impact factors. A prototype system is built to test the performance in real environments, and various experiments have been done to verify its feasibility. We believe that the fusion of wireless communication and sensing is a potential enhancement of next generation cellular system, and the capability of passive location will bring interesting applications for smart phones.

Utilization of non-coherent accumulation for LTE TOA estimation in weak LOS signal environments

“…According to the discussion in [14,34,35], since a multipath channel frequency response can be represented as a harmonic model, the SRA can be applied to TOA estimation. It has a better multipath estimation capability than the first-peak detection algorithm.…”

“…Let the new covariance matrix can be then obtained as Benefit from P ′ > L and the small dispersion of the noise eigenvalues extracted from Rnch , the signal subspace of RMDL will be same as that of Rnch and { L , L+1 , • • • , P ′ −1 } can be approximated as the estimation of noise variance. Therefore, MDL estimator can achieve multipath number estimation according to RMDL , and it can be realized by substituting the following equation into (35) as Once P ′ satisfies P ′ ≪ P and P ′ > L , the size of P ′ has little effect on the performance of MDL. So, the selection of P ′ is relatively loose.…”

“…There are also some channel order estimation methods which are suitable for any scenarios but require a lot of calculation [40,41]. (35) MDL[ξ ] = log…”

Is Centimeter Accuracy Achievable for LTE-CSI Fingerprint-Based Indoor Positioning?

“…where The channel estimation in a practical receiver may suffer from synchronization error, sampling clock drift, frequency offset, phase noise, and received noise [9], [10]. Taking these impairments into account, the estimate of h p,b,r,k at time t can be expressed aŝ…”

Pengaruh Interferensi Hidden Node Terhadap Model Prediksi Ketersediaan Bandwidth Di Jaringan Nirkabel

“…Saat ini beberapa model matematika untuk memprediksi ketersediaan bandwidth telah dikembangkan berdasarkan indikator-indikator tertentu [10]. Hal ini disebabkan karena informasi yang didapatkan dari evaluasi ini sangat bermanfaat untuk aplikasi yang bergantung pada ketersediaan bandwidth (misalnya aplikasi multimedia streaming) sebelum ditransmisikan untuk mencapai jaminan QoS [11]. Walaupun demikian, tidak ada mekanisme terstandarisasi untuk mengevaluasi ketersediaan bandwidth pada jaringan yang menghubungkan antarperangkat (node) [12].…”