We propose, analyze, and demonstrate an architecture for scalable cooperative reception. In a cluster of N+1 receive nodes, one node is designated as the final receiver, and the N other nodes act as amplify-and-forward relays, which adapt their phases such that the relayed signals add up constructively at the designated receiver. This yields received SNR scaling linearly with N, while avoiding the linear increase in overhead incurred by a direct approach in which received signals are separately quantized and transmitted for centralized processing. By transforming the task of long-distance distributed receive beamforming into one of local distributed transmit beamforming, we can leverage a scalable one-bit feedback algorithm for phase synchronization. We show that time division between the long-distance and local links eliminates the need for explicit frequency synchronization. We provide an analytical framework, whose results closely match Monte Carlo simulations, to evaluate the impact of phase noise due to relaying delay on the performance of the one-bit feedback algorithm. Experimental results from our prototype implementation on software-defined radios demonstrate the expected gains in received signal strength despite significant oscillator drift, and are consistent with results from our analytical framework.

A probabilistic approach to 3-dimensional mapping is proposed that only uses data gathered by GNSS (Global Navigation Satellite System) devices. To accomplish this, the environment is gridded and a physically motivated sensor model is developed that assigns likelihoods of blockage to satellite signals based on their measured SNR (signal-to-noise ratio). It is then shown that the posterior distribution of the map represents a sparse factor graph on which a low complexity implementation of Loopy Belief Propagation can be used for efficient Bayesian estimation. Experimental results are presented which demonstrate our algorithm's ability to coarsely map in three dimensions a corner of a university campus.

A novel approach is proposed to achieve simultaneous localization and mapping (SLAM) based on the signal-to-noise ratio (SNR) of global navigation satellite system (GNSS) signals. It is assumed that the environment is unknown and that the receiver location measurements (provided by a GNSS receiver) are noisy. The 3D environment map is decomposed into a grid of binary-state cells (occupancy grid) and the receiver locations are approximated by sets of particles. Using a large number of sparsely sampled GNSS SNR measurements and receiver/satellite coordinates (all available from off-the-shelf GNSS receivers), likelihoods of blockage are associated with every receiver-to-satellite beam. The posterior distribution of the map and poses is shown to represent a factor graph, on which Loopy Belief Propagation is used to efficiently estimate the probabilities of each cell being occupied or empty, along with the probability of the particles for each receiver location. Experimental results demonstrate our algorithm's ability to coarsely map (in three dimensions) a corner of a university campus, while also correcting for uncertainties in the location of the GNSS receiver.

Due to frequent non-line-of-sight (NLOS) signal reception, geopositioning using Global Navigation Satellite Systems (GNSS), such as GPS, is unreliable in urban environments, with errors on the order of tens of meters. This poses a major problem for mobile services that benefit from accurate urban localization, such as navigation, geofencing, and hyperlocal advertising applications. Mobile network operators also seek improvements in localization, as government regulators increase handset location accuracy requirements of enhanced 991 service (e911). In our demonstration, we will present the most recent prototype of our urban location improvement technology, called ShadowMaps, which will be shown to accurately track a mobile device in an urban environment, with up to an order of magnitude reduction in GNSS positioning error.

We propose and demonstrate a scalable architecture for distributed receive beamforming. In a receive cluster of N + 1 nodes receiving a message from a distant transmitter, N nodes are designated as amplify-and-forward relays and one node is designated as the receiver. The relay nodes apply a phase shift to their received signal and forward it such that their forwarded signals add up constructively at the receiver, with received SNR scaling linearly with N. This approach transforms a distributed receive beamforming problem on the "long link" from transmitter to receive cluster into a distributed transmit beamforming problem on the "short link" from relays to receiver, so that the number of degrees of freedom on the short link need not scale with N. A key simplification relative to distributed transmit beamforming is that, for stable oscillators, relay frequency synchronization is not required. For oscillators with drift, we provide a simple rule of thumb for when explicit frequency synchronization can be avoided. Explicit timing alignment can also be avoided by exploiting the timing of the message received on the long link. That leaves the problem of phase adjustment at the relays, and we employ an algorithm originally invented for distributed transmit beamforming for this purpose, using one bit (per iteration) feedback from the receiver. Experimental results with software-defined radios (whose oscillators have significant drift) demonstrate that the expected gains in received signal strength are obtained with the proposed architecture.

The main objective of this paper is to develop an efficient method for learning and reproduction of complex trajectories for robot programming by demonstration. Encoding of the demonstrated trajectories is performed with hidden Markov model, and generation of a generalized trajectory is achieved by using the concept of key points. Identification of the key points is based on significant changes in position and velocity in the demonstrated trajectories. The resulting sequences of trajectory key points are temporally aligned using the multidimensional dynamic time warping algorithm, and a generalized trajectory is obtained by smoothing spline interpolation of the clustered key points. The principal advantage of our proposed approach is utilization of the trajectory key points from all demonstrations for generation of a generalized trajectory. In addition, variability of the key points' clusters across the demonstrated set is employed for assigning weighting coefficients, resulting in a generalization procedure which accounts for the relevance of reproduction of different parts of the trajectories. The approach is verified experimentally for trajectories with two different levels of complexity.