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Register a new userMultiple Sources Localization with Sparse Recovery under Log-normal Shadow Fading
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Localization based on received signal strength (RSS) has drawn great interest in the wireless sensor network (WSN). In this paper, we investigate the RSS-based multi-sources localization problem with unknown transmitted power under shadow fading. The log-normal shadowing effect is approximated through Fenton-Wilkinson (F-W) method and maximum likelihood estimation is adopted to optimize the RSS-based multiple sources localization problem. Moreover, we exploit a sparse recovery and weighted average of candidates (SR-WAC) based method to set up an initiation, which can efficiently approach a superior local optimal solution. It is shown from the simulation results that the proposed method has a much higher localization accuracy and outperforms the other
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Received signal strength (RSS) based source localization method is popular due to its simplicity and low cost. However, this method is highly dependent on the propagation model which is not easy to be captured in practice. Moreover, most existing works only consider the single source and the identical measurement noise scenario, while in practice multiple co-channel sources may transmit simultaneously, and the measurement noise tends to be nonuniform. In this paper, we study the multiple co-channel sources localization (MSL) problem under unknown nonuniform noise, while jointly estimating the parametric propagation model. Specifically, we model the MSL problem as being parameterized by the unknown source locations and propagation parameters, and then reformulate it as a joint parametric sparsifying dictionary learning (PSDL) and sparse signal recovery (SSR) problem which is solved under the framework of sparse Bayesian learning with iterative parametric dictionary approximation. Furthermore, multiple snapshot measurements are utilized to improve the localization accuracy, and the Cramer-Rao lower bound (CRLB) is derived to analyze the theoretical estimation error bound. Comparing with the state-of-the-art sparsity-based MSL algorithms as well as CRLB, extensive simulations show the importance of jointly inferring the propagation parameters,and highlight the effectiveness and superiority of the proposed method.
A new family of operators, coined hierarchical measurement operators, is introduced and discussed within the well-known hierarchical sparse recovery framework. Such operator is a composition of block and mixing operations and notably contains the Kronecker product as a special case. Results on their hierarchical restricted isometry property (HiRIP) are derived, generalizing prior work on recovery of hierarchically sparse signals from Kronecker-structured linear measurements. Specifically, these results show that, very surprisingly, sparsity properties of the block and mixing part can be traded against each other. The measurement structure is well-motivated by a massive random access channel design in communication engineering. Numerical evaluation of user detection rates demonstrate the huge benefit of the theoretical framework.
In this paper, we present an acoustic localization system for multiple devices. In contrast to systems which localise a device relative to one or several anchor points, we focus on the joint localisation of several devices relative to each other. We present a prototype of our system on off-the-shelf smartphones. No user interaction is required, the phones emit acoustic pulses according to a precomputed schedule. Using the elapsed time between two times of arrivals (ETOA) method with sample counting, distances between the devices are estimated. These, possibly incomplete, distances are the input to an efficient and robust multi-dimensional scaling algorithm returning a position for each phone. We evaluated our system in real-world scenarios, achieving error margins of 15 cm in an office environment.
Multistage design has been used in a wide range of scientific fields. By allocating sensing resources adaptively, one can effectively eliminate null locations and localize signals with a smaller study budget. We formulate a decision-theoretic framework for simultaneous multi-stage adaptive testing and study how to minimize the total number of measurements while meeting pre-specified constraints on both the false positive rate (FPR) and missed discovery rate (MDR). The new procedure, which effectively pools information across individual tests using a simultaneous multistage adaptive ranking and thresholding (SMART) approach, can achieve precise error rates control and lead to great savings in total study costs. Numerical studies confirm the effectiveness of SMART for FPR and MDR control and show that it achieves substantial power gain over existing methods. The SMART procedure is demonstrated through the analysis of high-throughput screening data and spatial imaging data.
The Internet of Things (IoT) comprises an increasing number of low-power and low-cost devices that autonomously interact with the surrounding environment. As a consequence of their popularity, future IoT deployments will be massive, which demands energy-efficient systems to extend their lifetime and improve the user experience. Radio frequency wireless energy transfer has the potential of powering massive IoT networks, thus eliminating the need for frequent battery replacement by using the so-called power beacons (PBs). In this paper, we provide a framework for minimizing the sum transmit power of the PBs using devices positions information and their current battery state. Our strategy aims to reduce the PBs power consumption and to mitigate the possible impact of the electromagnetic radiation on human health. We also present analytical insights for the case of very distant clusters and evaluate their applicability. Numerical results show that our proposed framework reduces the outage probability as the number of PBs and/or the energy demands increase.
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