G lobal navigation satellite systems (GNSSs) have been the prevalent positioning, navigation, and timing technology over the past few decades. However, GNSS signals suffer from four main limitations: 1) They are extremely weak and unusable in certain environments (e.g., indoors and deep urban canyons) [1]. 2) They are susceptible to unintentional interference and intentional jamming [2], [3]. 3) Civilian signals are unencrypted, unauthenticated, and specified in publicly available documents, making them spoofable (i.e., hackable) [3]. 4) Their position estimate suffers from a large vertical estimation uncertainty due to the lack of GNSS space vehicle (SV) angle diversity, which is particularly problematic for aerial vehicles [4]. As such, standalone GNSSs will not deliver the stringent demands of future systems such as autonomous vehicles, intelligent transportation systems, and location-based services. Research over the past few years has revealed the potential of signals of opportunity as an alternative or a complement to GNSSs. Signals of opportunity are ambient signals not intended for positioning, navigation, and timing, such as cellular, AM/FM radio, satellite communication, digital television, and Wi-Fi. Among these signals, cellular signals are particularly attractive due to their abundance, geometric diversity, high carrier frequency, large bandwidth, and high received power. This article presents a multisignal software-defined receiver (SDR) architecture for navigating with cellular code division multiple access (CDMA) and long-term evolution (LTE) signals. When GNSS signals are unavailable or compromised, the SDR extracts navigation observables from cellular signals, producing a navigation solution in a standalone fashion. When GNSS signals are available, the cellular navigation observables are fused with GNSS observables, yielding a superior navigation solution to a standalone GNSS solution. Exploiting the abundant cellular signals in the environment provides a more robust and accurate navigation solution.

No abstract

Mast cell responses can be altered by cytokines, including those secreted by Th2 and regulatory T cells (Treg). Given the important role of mast cells in Th2-mediated inflammation and recent demonstrations of Treg-mast cell interactions, we examined the ability of IL-4 and TGF-β1 to regulate mast cell homeostasis. Using in vitro and in vivo studies of mouse and human mast cells, we demonstrate that IL-4 suppresses TGF-β1 receptor expression and signaling, and vice versa. In vitro studies demonstrated that IL-4 and TGF-β1 had balancing effects on mast cell survival, migration, and FcεRI expression, with each cytokine cancelling the effects of the other. However, in vivo analysis of peritoneal inflammation during Nippostrongylus brasiliensis infection in mice revealed a dominant suppressive function for TGF-β1. These data support the existence of a cytokine network involving the Th2 cytokine IL-4 and the Treg cytokine TGF-β1 that can regulate mast cell homeostasis. Dysregulation of this balance may impact allergic disease and be amenable to targeted therapy.

Mapping multiple unknown terrestrial signals of opportunity (SOP) transmitters via multiple collaborating receivers is considered. The receivers are assumed to have knowledge about their own states, make pseudorange observations on multiple unknown SOPs, and fuse these pseudoranges through a central estimator. Two problems are considered. The first problem assumes multiple receivers with random initial states to pre-exist in the environment. The question of where to optimally place an additional receiver so to maximize the quality of the estimate of the SOPs' states is addressed. A novel, computationally efficient optimization criterion that is based on area-maximization is proposed. It is shown that the proposed optimization criterion yields a convex program, the solution of which is comparable to two classical criteria: minimization of the geometric dilution of precision (GDOP) and maximization of the determinant of the inverse of the GDOP matrix. The second problem addresses the optimal mapping performance as a function of time and number of receivers in the environment. It is demonstrated that such optimal performance assessment could be generated off-line without knowledge of the receivers' trajectories or the receivers' estimates of the SOP. Experimental results are presented demonstrating collaborative mapping of an unknown terrestrial SOP emanating from a cellular tower for various receiver trajectories versus the optimal mapping performance.

No abstract