High-energy cosmic rays and gamma quanta colliding with the upper atmosphere produce cascades of secondary particles known as extensive air showers (EASs). These showers can be detected and recorded using a variety of telescopes such as imaging atmospheric Cherenkov telescopes (IACTs), arrays of wide-angle integrating air detectors or water detectors; some experiments such as TAIGA [1] and LHAASO [2] combine several telescope types. The data from these observations can be used to identify the primary particle type and estimate its parameters such as energy and direction. In this paper, we estimate the EAS direction which is of interest because it can identify the gamma radiation source and is important in estimating the energy of the primary particle. Highly accurate shower direction estimates can be obtained from the timing measurements of multiple detectors spread over a large area such as TAIGA HiSCORE [3], LHAASO, or HAWC [4]. We use simulated data from TAIGA HiSCORE which is a non-imaging array of wide field-of-view integrating air Cherenkov detector stations. We use artificial neural networks (ANNs) to obtain shower direction estimates. Convolutional neural networks seem like a natural choice for the problem since the HiSCORE stations are positioned on a grid. However, the previous work using this approach [5, 6] produced estimates that were significantly less accurate than previously developed methods, e.g.

A reliable, accurate, and affordable positioning service is highly required in wireless networks. In this paper, the novel Message Passing Hybrid Localization (MPHL) algorithm is proposed to solve the problem of cooperative distributed localization using distance and direction estimates. This hybrid approach combines two sensing modalities to reduce the uncertainty in localizing the network nodes. A statistical model is formulated for the problem, and approximate minimum mean square error (MMSE) estimates of the node locations are computed. The proposed MPHL is a distributed algorithm based on belief propagation (BP) and Markov chain Monte Carlo (MCMC) sampling. It improves the identifiability of the localization problem and reduces its sensitivity to the anchor node geometry, compared to distance-only or direction-only localization techniques. For example, the unknown location of a node can be found if it has only a single neighbor; and a whole network can be localized using only a single anchor node. Numerical results are presented showing that the average localization error is significantly reduced in almost every simulation scenario, about 50% in most cases, compared to the competing algorithms.

Sound localization by barn owls is commonly modeled as a matching procedure where localization cues derived from auditory inputs are compared to stored templates. While the matching models can explain properties of neural responses, no model explains how the owl resolves spatial ambiguity in the localization cues to produce accurate localization near the center of gaze. Here, we examine two models for the barn owl's sound localization behavior. First, we consider a maximum likelihood estimator in order to further evaluate the cue matching model. Second, we consider a maximum a posteriori estimator to test if a Bayesian model with a prior that emphasizes directions near the center of gaze can reproduce the owl's localization behavior. We show that the maximum likelihood estimator can not reproduce the owl's behavior, while the maximum a posteriori estimator is able to match the behavior. This result suggests that the standard cue matching model will not be sufficient to explain sound localization behavior in the barn owl. The Bayesian model provides a new framework for analyzing sound localization in the barn owl and leads to predictions about the owl's localization behavior.