James W. Scrofani

A stochastic multirate signal processing approach to high-resolution signal reconstruction

This paper addresses the problem of reconstructing a signal at some high sampling rate from a set of signals sampled at a lower rate and subject to additive noise and distortion. A set of periodically time-varying filters are employed in reconstructing the underlying signal. Results are presented for a one-dimensional case involving simulated data, as well as for a two-dimensional case involving real image data where the image is processed by rows. In both cases, considerable improvement is evident after the processing.

Cellular Synchronization Assisted Refinement (CeSAR): A Method for Accurate Geolocation in LTE-A Networks

The vulnerability of cellular networks to location-based attacks via uplink timing management commands has been studied since the advent of GSM. However, the introduction of heterogenous networks as the answer to increasing demand for data throughput has resulted in a new vulnerability to such attacks. In this work, we propose Cellular Synchronization Assisted Refinement (CeSAR), an entirely passive method of leveraging available LTE-A downlink synchronization messaging to refine uplink timing advance commands issued by the network. Our results suggest that CeSAR is capable of providing positioning improvement, not only in LTE-A networks but also in legacy deployments, of up to 254 meters.

Scheme for enhanced tracking of mobile subscribers in a WiMAX network

In this paper, the base station identification and timing adjust measurements are used to geolocate mobile subscribers in a WiMAX network. The uplink and downlink subframes of the physical layer and management messages of the medium access control layer are examined to extract the necessary data for geolocation. Using a hidden Markov model [1] based algorithm to estimate the track of the mobile subscriber, we demonstrate that the position error can be further reduced by incorporating timing adjust measurements. Simulation results of the proposed scheme are included to demonstrate the effectiveness of the combined use of base station ID and timing adjust measurements.

Location Privacy in LTE: A Case Study on Exploiting the Cellular Signaling Plane's Timing Advance

Location privacy is an oft-overlooked, but exceedingly important niche of the overall privacy macrocosm. An ambition of this work is to raise awareness of concerns relating to location privacy in cellular networks. To this end, we will demonstrate how user location information is leaked through a vulnerability, viz. the timing advance (TA) parameter, in the Long Term Evolution (LTE) signaling plane and how the position estimate that results from that parameter can be refined through a previously introduced method called Cellular Synchronization Assisted Refinement (CeSAR) [1]. With CeSAR, positioning accuracies that meet or exceed the FCC’s E-911 mandate are possible making CeSAR simultaneously a candidate technology for meeting the FCC’s wireless localization requirements and a demonstration of the alarming level of location information sent over the air. We also introduce a geographically diverse data set of TAs collected from actual LTE network implementations utilizing different cell phone chipsets. With this data set we show the appropriateness of modeling the error associated with a TA as normally distributed.

Maximum likelihood geolocation in LTE cellular networks using the timing advance parameter

Wireless geo location is an increasingly relevant area of research as cellular technology becomes ever more ubiquitous. In this work we consider the timing advance parameter in Long Term Evolution cellular networks to this end. We also evaluate a previously introduced method of position estimation augmentation called Cellular Synchronization Assisted Refinement (CeSAR). We first develop the concept of geometric dilution of precision and the Cramer-Rao Lower Bound (CRLB) to motivate an intuition for positioning accuracy. We then derive an exact maximum likelihood estimator (MLE) for TA-specific position estimation. While the exact MLE proves to be computationally difficult, it is shown to be equivalent to the MLE in the general case of Gaussian noise. This equivalency is then used to propose an approximate MLE (AMLE). Through simulation, the AMLE is shown to meet the CRLB. Real-world data that includes urban, suburban, line-of-sight, and non-line-of-sight channels are also presented and used to build a realistic model of the TA-specific channel. This model is then used to evaluate the performance of the AMLE and CeSAR in non-line-of-sight and line-of-sight conditions. Our results suggest that the proposed method may be appropriate for meeting the Federal Communication Commissions E-911 requirements.

A configurable fingerprint-based hidden-Markov model for tracking in variable channel conditions

A novel scheme for mobile subscriber positioning is proposed based on the hidden-Markov model (HMM) and the cell-ID maximum-likelihood database correlation method also known as fingerprinting. Using a simulated channel environment, based on the Clearwire deployment of WiMAX base stations in San Jose, CA, we show that matching the right configuration of the model to the deployment environment can realize significant gains in performance. The proposed scheme balances the scalability inherent in hidden-Markov-based motion models deployed in large areas of interest against the existing channel conditions and computational capability. By utilizing a simulated channel this paper demonstrates the effect of base station deployment and shadowing on the fingerprint-based HMM motion model. Further, the benefits gained through scaling the HMM are explored.

Target migration path morphology of moving targets in spotlight SAR

This paper examines the signature characteristics of moving targets in spotlight synthetic aperture radar (SAR) image data. This analysis considers the special case in which the radar sensor is assumed to move with constant speed and heading on a level flight path with broadside imaging geometry. It is shown that the resulting defocused smear signature in the spotlight SAR image exhibits range migration effects, as has been shown previously for strip map SAR analysis. In particular, cases of uniform target motion exhibit simply curved range migration paths, whereas non-uniform target motion can cause complicated smear shapes.

On mobile positioning via Cellular Synchronization Assisted Refinement (CeSAR) in LTE and GSM networks

Inferring user equipment (UE) location in cellular networks via uplink timing management presents attractive possibilities in an increasingly mobile-connected world. Power constrained mobile devices with ever more common applications that rely on location-based services demand an alternate solution to positioning than a Global Navigation Satellite System (GNSS) and its derivatives. In this paper we provide analysis of mobile positioning via timing advance (TA), an LTE parameter also accounted for in legacy network deployments. Specifically, we provide analysis for a TA-based method Cellular Synchronization Assisted Refinement (CeSAR). We demonstrate through analysis and simulation how CeSAR can increase accuracy in timing advance-based positioning by several hundreds of meters with no impact to radio link level traffic. CeSAR is shown to be robust to situations where unassisted TA-based positioning can provide poor positioning accuracy.

A Scalable Hidden-Markov Model Algorithm for Location-Based Services in WiMAX Networks

Hidden-Markov Models (HMM) have shown promise as viable solutions to providing location based services (LBS) within cellular networks. Previously established work includes a scheme to merge the stochastic contribution of the HMM and maximum likelihood decisions based on signal strength measurements and timing adjust parameters. A novel scalable positioning algorithm that utilizes the aforementioned techniques along with reorientation of the state vector in order to favor the local measurements within the area of interest is proposed in this paper. The resulting scheme is presented and its performance validated through simulations built from a scenario based on a real world WiMAX network. The results demonstrate improved performance over previous work, and the effect of scaling the algorithm is discussed.

A spatiotemporal clustering approach to maritime domain awareness

Spatiotemporal clustering is the process of grouping objects based on both their spatial and temporal similarity. This approach is useful when considering the distance between objects and how that distance changes through time. Spatiotemporal clustering analysis is applied to the maritime domain in this paper, specifically to a defined area of water, during a period of time, in order to gain behavioral knowledge of vessel interactions and provide the opportunity to screen such interactions for further investigation. The proposed spatiotemporal clustering algorithm spatially clusters vessels in the water space using k-means clustering analysis, kinematically refines the clusters based on the similarity of vessel headings, speeds and the distance between them, and temporally analyzes the continuity of membership of the kinematic clusters through time to determine which clusters are moving. The algorithm is implemented in the MATLAB programming environment, verified with a synthetic data scenario, and validated with two real-world datasets.