Florian Meyer

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—We introduce the framework of cooperative simultaneous localization and tracking (CoSLAT), which provides a consistent combination of cooperative self-localization (CSL) and distributed target tracking (DTT) in sensor networks without a fusion center. CoSLAT extends simultaneous localization and tracking (SLAT) in that it uses also intersensor(More)
—Localization and synchronization in wireless networks are strongly related when they are based on internode time measurements. We leverage this relation by presenting a message passing algorithm for cooperative simultaneous localization and synchronization (CoSLAS). The proposed algorithm jointly estimates the locations and clock parameters of the network(More)
—We present a factor graph framework and a particle-based belief propagation algorithm for distributed cooperative simultaneous localization and synchronization (CoSLAS) in decentralized sensor networks. The proposed algorithm jointly estimates the locations and clock parameters of the network nodes in a fully decentralized fashion. This estimation is based(More)
We propose low-complexity detectors for large MIMO systems with BPSK or QAM constellations. These detectors work at the bit level and consist of three stages. In the first stage, maximum likelihood decisions on certain bits are made in an efficient way. In the second stage, soft values for the remaining bits are calculated. In the third stage, these(More)
The recently introduced framework of cooperative simultaneous localization and tracking (CoSLAT) combines Bayesian cooperative agent self-localization with distributed target tracking. The original CoSLAT algorithm suffers from high computation and communication costs because it uses a particle-based message representation. Here, we propose an advanced(More)
—We introduce a distributed, cooperative framework and method for Bayesian estimation and control in decentralized agent networks. Our framework combines joint estimation of time-varying global and local states with information-seeking control optimizing the behavior of the agents. It is suited to nonlinear and non-Gaussian problems and, in particular, to(More)
The sigma point (SP) filter, also known as unscented Kalman filter, is an attractive alternative to the extended Kalman filter and the particle filter. Here, we extend the SP filter to nonsequential Bayesian inference corresponding to loopy factor graphs. We propose sigma point belief propagation (SPBP) as a low-complexity approximation of the belief(More)
We propose a method for multisensor-multitarget tracking with excellent scalability in the number of targets (which is assumed known), the number of sensors, and the number of measurements per sensor. Our method employs belief propagation based on a “detailed” factor graph that involves both target-related and measurement-related association(More)
We propose a Bayesian method for distributed sequential localization of mobile networks composed of both cooperative agents and noncooperative objects. Our method provides a consistent combination of cooperative self-localization (CS) and distributed tracking (DT). Multiple mobile agents and objects are localized and tracked using measurements between(More)