In this letter, we propose an efficient grid evolution multiple targets localization framework for off-grid targets. First, a more accurate model, enabling by considering all the grids as random variables to be inferred. Then, problem is formulated joint sparsifying dictionary learning and sparse signal recovery problem. Finally, optimization solved under general of Bayesian (SBL). Different previous SBL based algorithms, adopt hierarchical Laplace distribution prior, rather than Sudent's t...

Received signal strength (RSS) based source localization method is popular due to its simplicity and low cost. However, this highly dependent on the propagation model which not easy be captured in practice. Moreover, most existing works only consider single identical measurement noise scenario, while practice multiple co-channel sources may transmit simultaneously, tends nonuniform. In paper, we study (MSL) problem under unknown nonuniform noise, jointly estimating parametric model....

Source localization plays an indispensable role in many applications. This paper addresses the directional source problem a three-dimensional (3D) wireless sensor network using hybrid received-signal-strength (RSS) and angle-of-arrival (AOA) measurements. Both position transmission orientation of are to be estimated. In considered positioning scenario, angle range measurements respectively corresponding AOA model RSS that integrates Gaussian-shaped radiation pattern. Given is non-convex...

Received-signal-strength (RSS)-based localization has received widespread attention recently. Due to the simple acquisition of RSS measurements, adequate inexpensive sensors in sensor networks are capable providing information needed for positioning multiple target sources. However, few studies have focused on RSS-based directional sources that common reality. Based deduced parametric Optimal Maximum Likelihood (OML) solution, this paper proposes three new grid search-based algorithms,...

We propose a graph-based modulation format identification (MFI) scheme for elastic optical network (EON), which exploits the trajectory information of adjacent received symbols to identify six commonly-used formats signals. A uniform grid is constructed in first quadrant two-dimensional (2D) Stokes plane capture sequence, and then corresponding converted into matrix via graph theory. The eigenvector associated with largest eigenvalue selected as discriminated-feature signal. Subsequently, we...

Multi-source localization based on received signal strength has drawn great interest in wireless sensor networks. However, the shadow fading term caused by obstacles cannot be separated from signal, which leads to severe error location estimate. In this paper, we approximate log-normal sum distribution through Fenton-Wilkinson method formulate a non-convex maximum likelihood (ML) estimator with unknown factor and transmitted power. order overcome difficulty solving problem, propose novel...

Graph learning often boils down to uncovering the hidden structure of data, which has been applied in various fields such as biology, sociology, and environmental studies. However, distributed sensing realistic application gives rise spatiotemporal signals, can be characterized through new tools graph signal processing a time-varying signal. It calls upon development from static studies joint space-time analysis. In this paper, we study problem graphs signals. Based on correlated properties...

Spectrum prediction has recently gained a lot of attention due to its extensive applications in cognitive radio networks. However, most the related research assumed that spectrum occupancy pattern is time-invariant, which limits performance proposed methods for high burst frequency bands, e.g. ISM (Industrial, Scientific, Medical) bands. In order improve accuracy them, this paper first analyzes characteristics collected real WiFi data 2.4GHz band, and shows burstiness band from multiple...

Source localization using RSS (received signal strength) measurements has received considerable attention recently. However, there are few works focusing on the problem of positioning multiple directional sources. To provide a benchmark for evaluating algorithms, this paper derives Cramer-Rao lower bounds (CRLB) estimating locations, orientations, transmit powers, beam widths sources and path loss exponent (PLE) environment. Meanwhile, in order to facilitate computation expression, we also...

Signal processing on graphs extends signal concepts and methodologies from the classical theory to data indexed by general graphs. For a bandlimited graph signal, unknown associated with unsampled vertices can be reconstructed sampled exploiting spatial relationship of signal. In this paper, we propose generalized analytical framework introduce concept diffusion operator which consists local-mean global-bias operator. Then, operator-based iterative algorithm is proposed reconstruct data....

Mining natural associations from high-dimensional spatiotemporal signals plays an important role in various fields including biology, climatology, and financial analysis. However, most existing works have mainly studied time-independent without considering the correlations of that achieve high learning accuracy. This paper aims to learn graphs better reflect underlying data relations by leveraging long- short-term characteristics signals. First, a signal model is presented considers both...

Due to the characteristics of being license-free, ISM (Industrial, Scientific, Medical) bands are frequently utilized by wireless communication systems. However, ubiquitous and unmanaged IEEE 802.11 systems deployed in may bring non-negligible interference emergency communications. This paper considers suppressing transmission 2. 4GHz band for protecting public safety scenarios. To this end, two kinds suppression frames that comply with protocol analyzed, which respectively borrow ideas...

Sparsity based methods have gained its popularity in two-dimensional (2D) direction-of-arrival (DOA) estimation recent years. However, these suffer from the off-grid problem, and also need additional angle pairing process, which results degraded performance when applied practice. In this paper, to address problems, a novel effective method named grid adaptive sparse Bayesian learning (GASBL) is proposed for 2D-DOA with L-shape array perspective of learning. Specifically, an DOA model enable...

Received signal strength (RSS) measurement based source localization is highly dependent on the propagation model. However, such model not easy to be captured in practical applications. In this paper, we address multiple sources (MSL) problem while jointly estimating parametric Specifically, as being parameterized by unknown locations and parameters. Then, reformulated a joint sparsifying dictionary learning (PSDL) sparse recovery (SSR) problem. Finally, solved under framework of Bayesian...

We address the problem of learning hidden graph structure from spatiotemporal signals which are prevalent in distributed sensor networks. Based on a space-time representation model that takes into account correlated properties dynamic evolution, we formulate as regularized multi-convex optimization problem. A correlation-aware and differential smoothness-based method (CADS) is proposed, simultaneously estimates time correlation each vertex refines under smoothness prior. The proposed...

Localization based on received signal strength (RSS) has drawn great interest in the wireless sensor network (WSN). In this paper, we investigate 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 adopted to optimize multiple sources problem. Moreover, exploit a sparse recovery weighted average of candidates (SR-WAC) set up an...