Sub-Nanosecond Time of Flight on Commercial Wi-Fi Cards

Time-of-flight, i.e., the time incurred by a signal to travel from transmitter to receiver, is perhaps the most intuitive way to measure distances using wireless signals. It is used in major positioning systems such as GPS, RADAR, and SONAR. However, attempts at using time-of-flight for indoor localization have failed to deliver acceptable accuracy due to fundamental limitations in measuring time on Wi-Fi and other RF consumer technologies. While the research community has developed alternatives for RF-based indoor localization that do not require time-of-flight, those approaches have their own limitations that hamper their use in practice. In particular, many existing approaches need receivers with large antenna arrays while commercial Wi-Fi nodes have two or three antennas. Other systems require fingerprinting the environment to create signal maps. More fundamentally, none of these methods support indoor positioning between a pair of Wi-Fi devices without~third~party~support. In this paper, we present a set of algorithms that measure the time-of-flight to sub-nanosecond accuracy on commercial Wi-Fi cards. We implement these algorithms and demonstrate a system that achieves accurate device-to-device localization, i.e. enables a pair of Wi-Fi devices to locate each other without any support from the infrastructure, not even the location of the access points.Comment: 14 page

Multiple Signal Classification for Determining Direction of Arrival of Frequency Hopping Spread Spectrum Signals

This research extends a MUSIC algorithm to determine DOA of FHSS signals. All incident FHSS signals have unknown DOA and use PSK. Conventional MUSIC algorithm involves multiple MUSIC estimation for all frequency bins. On the other hand, the extended development is meant to execute a single MUSIC algorithm of observations on multiple frequency bins or hops. The new extension shows better performance compared to the conventional MUSIC execution at different SNR levels. Both have the same power accumulation at the true angles of arrival. However, the new development has lower side lobes and hence helps avoid false detections. In addition, the new development has lower side lobes variance resulting in lower error of false detections compared to the normal execution. Simulation results show that the new extension is sensitive to the SNR values and number of samples taken at each frequency bin. However, it is less sensitive to the possible number of frequency hops or hop set and number of array sensors

Sparse methods for blind source separation of frequency hopping rf sources

Blind source separation (BSS) is performed on frequency hopping (FH) sources. These radio frequency (RF) signals are observed by a uniform linear array (ULA) over a Spatial Channel Model (SCM) in four different propagation environments: (i) line-of-sight (LOS), (ii) single-cluster, (iii) multiple-cluster, and (iv) LOS with interference. The sources are spatially sparse, and their activity is intermittent and assumed to follow a hidden Markov model (HMM). BSS is achieved by utilizing direction of arrival (DOA) of the sources and clusters. A sparse detection framework is applied to obtain estimates of the sources\u27 FH and DOA patterns. The solutions are binned according to a frequency grid and a DOA dictionary. A method is proposed to reduce the effect of falsely detected active sources and mitigate the effects of interference, by leveraging the activity model of the intermittent sources. The proposed method is a state filtering technique, referred to as hidden state filtering (HSF), and is used to improve BSS performance. Multiple activity patterns associated with different DOAs are considered similar if they match over a prescribed fraction of the time samples. A method pairing DOA and FH estimates associates the FH patterns to specific sources via their estimated DOAs. Numerical results demonstrate that the proposed algorithm is capable of separating multiple spatially sparse FH sources with intermittent activity, by providing estimates of their FH patterns and DOA

High resolution adaptive arrays based on random processing techniques: frequency hopping modulation

A new architecture for adaptive arrays using frequency hopping modulation is addressed. The resolution of the array and the interference rejection increase substantially applying random processing to the carrier frequency of the signals. The proposed framework is composed of two different stages. The anticipative stage, devoted to minimize the noise and fixed interferences contribution and the GSLC stage which provides cancellation of follower jammers and solves the multiuser collision problem. The developed system requires neither temporal nor spatial reference for its implementation, only the frequency sequence must be known. An adaptive approach has been implemented, allowing a fast convergence to the optimal behavior.Peer ReviewedPostprint (published version