Privacy in Inter-Vehicular Networks: Why simple pseudonym change is not enough

Inter-vehicle communication (IVC) systems disclose rich location information about vehicles. State-of-the-art security architectures are aware of the problem and provide privacy enhancing mechanisms, notably pseudonymous authentication. However, the granularity and the amount of location information IVC protocols divulge, enable an adversary that eavesdrops all traffic throughout an area, to reconstruct long traces of the whereabouts of the majority of vehicles within the same area. Our analysis in this paper confirms the existence of this kind of threat. As a result, it is questionable if strong location privacy is achievable in IVC systems against a powerful adversary.\u

GPS Carrier Tracking Loop Performance in the presence of Ionospheric Scintillations

The performance of several GPS carrier tracking loops is evaluated using wideband GPS data recorded during strong ionospheric scintillations. The aim of this study is to determine the loop structures and parameters that enable good phase tracking during the power fades and phase dynamics induced by scintillations. Constant-bandwidth and variable-bandwidth loops are studied using theoretical models, simulation, and tests with actual GPS signals. Constant-bandwidth loops with loop bandwidths near 15 Hz are shown to lose phase lock during scintillations. Use of the decision-directed discriminator reduces the carrier lock threshold by ∼1 dB relative to the arctangent and conventional Costas discriminators. A proposed variablebandwidth loop based on a Kalman filter reduces the carrier lock threshold by more than 7 dB compared to a 15-Hz constant-bandwidth loop. The Kalman filter-based strategy employs a soft-decision discriminator, explicitly models the effects of receiver clock noise, and optimally adapts the loop bandwidth to the carrier-to-noise ratio. In extensive simulation and in tests using actual wideband GPS data, the Kalman filter PLL demonstrates improved cycle slip immunity relative to constant bandwidth PLLs.Aerospace Engineering and Engineering Mechanic

Review of Unbiased FIR Filters, Smoothers, and Predictors for Polynomial Signals

Extracting an estimate of a slowly varying signal corrupted by noise is a common task. Examples can be found in industrial, scientific and biomedical instrumentation. Depending on the nature of the application the signal estimate is allowed to be a delayed estimate of the original signal or, in the other extreme, no delay is tolerated. These cases are commonly referred to as filtering, prediction, and smoothing depending on the amount of advance or lag between the input data set and the output data set. In this review paper we provide a comprehensive set of design and analysis tools for designing unbiased FIR filters, predictors, and smoothers for slowly varying signals, i.e. signals that can be modeled by low order polynomials. Explicit expressions of parameters needed in practical implementations are given. Real life examples are provided including cases where the method is extended to signals that are piecewise slowly varying. A critical view on recursive implementations of the algorithms is provided

Robust Gaussian Filtering using a Pseudo Measurement

Many sensors, such as range, sonar, radar, GPS and visual devices, produce measurements which are contaminated by outliers. This problem can be addressed by using fat-tailed sensor models, which account for the possibility of outliers. Unfortunately, all estimation algorithms belonging to the family of Gaussian filters (such as the widely-used extended Kalman filter and unscented Kalman filter) are inherently incompatible with such fat-tailed sensor models. The contribution of this paper is to show that any Gaussian filter can be made compatible with fat-tailed sensor models by applying one simple change: Instead of filtering with the physical measurement, we propose to filter with a pseudo measurement obtained by applying a feature function to the physical measurement. We derive such a feature function which is optimal under some conditions. Simulation results show that the proposed method can effectively handle measurement outliers and allows for robust filtering in both linear and nonlinear systems