Non Parametric Distributed Inference in Sensor Networks Using Box Particles Messages

This paper deals with the problem of inference in distributed systems where the probability model is stored in a distributed fashion. Graphical models provide powerful tools for modeling this kind of problems. Inspired by the box particle filter which combines interval analysis with particle filtering to solve temporal inference problems, this paper introduces a belief propagation-like message-passing algorithm that uses bounded error methods to solve the inference problem defined on an arbitrary graphical model. We show the theoretic derivation of the novel algorithm and we test its performance on the problem of calibration in wireless sensor networks. That is the positioning of a number of randomly deployed sensors, according to some reference defined by a set of anchor nodes for which the positions are known a priori. The new algorithm, while achieving a better or similar performance, offers impressive reduction of the information circulating in the network and the needed computation times

Performance Analysis of Angle of Arrival Algorithms Applied to Radiofrequency Interference Direction Finding

Radiofrequency (RF) interference threatens the functionality of systems that increasingly underpin the daily function of modern society. In recent years there have been multiple incidents of intentional RF spectrum denial using terrestrial interference sources. Because RF based systems are used in safety-of-life applications in both military and civilian contexts, there is need for systems that can quickly locate these interference sources. In order to meet this need, the Air Force Research Laboratory Weapons Directorate is sponsoring the following research to support systems that will be able to quickly geolocate RF interferers using passive angle-of-arrival estimation to triangulate interference sources. This research studies the performance of angle-of arrival (AoA) estimation algorithms for an existing uniform linear antenna array. Four algorithms are presented, they are phase-shift beamforming, Capon or Minimum Variance Distortionless Response (MVDR) beamforming, the Multiple Signal Identification and Classification (MUSIC) algorithm, and one instantiation of a Maximum Likelihood Estimation (MLE) algorithm. A modeling and simulation environment using MATLAB™ is developed and the performance of each algorithm is simulated as implemented on a uniform linear array. Performance is characterized under various non-ideal conditions

Signal Subspace Processing in the Beam Space of a True Time Delay Beamformer Bank

A number of techniques for Radio Frequency (RF) source location for wide bandwidth signals have been described that utilize coherent signal subspace processing, but often suffer from limitations such as the requirement for preliminary source location estimation, the need to apply the technique iteratively, computational expense or others. This dissertation examines a method that performs subspace processing of the data from a bank of true time delay beamformers. The spatial diversity of the beamformer bank alleviates the need for a preliminary estimate while simultaneously reducing the dimensionality of subsequent signal subspace processing resulting in computational efficiency. The pointing direction of the true time delay beams is independent of frequency, which results in a mapping from element space to beam space that is wide bandwidth in nature. This dissertation reviews previous methods, introduces the present method, presents simulation results that demonstrate the assertions, discusses an analysis of performance in relation to the Cramer-Rao Lower Bound (CRLB) with various levels of noise in the system, and discusses computational efficiency. One limitation of the method is that in practice it may be appropriate for systems that can tolerate a limited field of view. The application of Electronic Intelligence is one such application. This application is discussed as one that is appropriate for a method exhibiting high resolution of very wide bandwidth closely spaced sources and often does not require a wide field of view. In relation to system applications, this dissertation also discusses practical employment of the novel method in terms of antenna elements, arrays, platforms, engagement geometries, and other parameters. The true time delay beam space method is shown through modeling and simulation to be capable of resolving closely spaced very wideband sources over a relevant field of view in a single algorithmic pass, requiring no course preliminary estimation, and exhibiting low computational expense superior to many previous wideband coherent integration techniques

An Analysis of Radio-Frequency Geolocation Techniques for Satellite Systems Design

This research 1) evaluates the effectiveness of CubeSat radio-frequency geolocation and 2) analyzes the sensitivity of different RF algorithms to system parameters. A MATLAB simulation is developed to assess geolocation accuracy for variable system designs and techniques (AOA, TDOA, T/FDOA). An unconstrained maximum likelihood estimator (MLE) and three different digital elevation models (DEM) are utilized as the surface of the Earth constraint to improve geolocation accuracy. The results presented show the effectiveness of the MLE and DEM techniques, the sensitivity of AOA, TDOA, and T/FDOA algorithms, and the system level performance of a CubeSat geolocation cluster in a 500km circular orbit