Blind Received Signal Strength Difference Based Source Localization With System Parameter Errors

Lohrasbipeydeh, Hannan; Gulliver, T. Aaron; Amindavar, Hamidreza

doi:10.1109/tsp.2014.2336634

Cited by 50 publications

(18 citation statements)

References 58 publications

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“…The estimate the source position, we should firstly measured the received power of each sensing node from the unknown source. By using the radio propagation path loss model, the average received power i P of the th i sensing node can be written as [11] ( ) ( ) 10 0 dB dB 10 log…”

Section: Signal Modelmentioning

confidence: 99%

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Efficient Kernel Based 3-D Source Localization via Tensor Completion

2019

KSII TIIS

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Source localization in three-dimensional (3-D) wireless sensor networks (WSNs) is becoming a major research focus. Due to the complicated air-ground environments in 3-D positioning, many of the traditional localization methods, such as received signal strength (RSS) may have relatively poor accuracy performance. Benefit from prior learning mechanisms, fingerprinting-based localization methods are less sensitive to complex conditions and can provide relatively accurate localization performance. However, fingerprinting-based methods require training data at each grid point for constructing the fingerprint database, the overhead of which is very high, particularly for 3-D localization. Also, some of measured data may be unavailable due to the interference of a complicated environment. In this paper, we propose an efficient kernel based 3-D localization algorithm via tensor completion. We first exploit the spatial correlation of the RSS data and demonstrate the low rank property of the RSS data matrix. Based on this, a new training scheme is proposed that uses tensor completion to recover the missing data of the fingerprint database. Finally, we propose a kernel based learning technique in the matching phase to improve the sensitivity and accuracy in the final source position estimation. Simulation results show that our new method can effectively eliminate the impairment caused by incomplete sensing data to improve the localization performance.

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Section: Signal Modelmentioning

confidence: 99%

“…Many kinds of the localization methods such like received signal strength (RSS) based algorithms or their improved methods have been investigated which are based on the measured energy data [8]- [11]. Most of these methods were designed in 2-D and can be extended to 3-D cases.…”

Section: Introductionmentioning

confidence: 99%

Efficient Kernel Based 3-D Source Localization via Tensor Completion

2019

KSII TIIS

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“…Let

P_{i j}

denote RSS measurement at BM i from anchor j . Then, we can use a path loss model in [18]:

\begin{matrix} P_{i j} = P_{j} + ς_{i j} (bold-italicϕ) + η_{i j}, & i \in {scriptS}_{U}, j \in {scriptS}_{V} \cup {scriptS}_{A}, \end{matrix}

where

ς_{i j} (ϕ) = - 10 β \log d_{i j}

, β is a distance–power gradient (i.e., a path loss exponent),

d_{i j}

is the actual distance between machines i and j , and

η_{i j}

is noisy power due to measurement errors at BM i and unmodeled variability in the fading channel between machines i and j (for example, shadowing). We assume that

η_{i j}

s are independently distributed zero-mean Gaussian random variables with standard deviation

σ_{i j}

(dB).…”

Section: System Modelmentioning

confidence: 99%

Greedy Successive Anchorization for Localizing Machine Type Communication Devices

Haq

Kim

2016

Sensors

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Localization of machine type communication (MTC) devices is essential for various types of location-based applications. In this paper, we investigate a distributed localization problem in noisy networks, where an estimated position of blind MTC machines (BMs) is obtained by using noisy measurements of distance between BM and anchor machines (AMs). We allow positioned BMs also to work as anchors that are referred to as virtual AMs (VAMs) in this paper. VAMs usually have greater position errors than (original) AMs, and, if used as anchors, the error propagates through the whole network. However, VAMs are necessary, especially when many BMs are distributed in a large area with an insufficient number of AMs. To overcome the error propagation, we propose a greedy successive anchorization process (GSAP). A round of GSAP consists of consecutive two steps. In the first step, a greedy selection of anchors among AMs and VAMs is done by which GSAP considers only those three anchors that possibly pertain to the localization accuracy. In the second step, each BM that can select three anchors in its neighbor determines its location with a proposed distributed localization algorithm. Iterative rounds of GSAP terminate when every BM in the network finds its location. To examine the performance of GSAP, a root mean square error (RMSE) metric is used and the corresponding Cramér–Rao lower bound (CRLB) is provided. By numerical investigation, RMSE performance of GSAP is shown to be better than existing localization methods with and without an anchor selection method and mostly close to the CRLB.

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“…Among them, TDOA-based method places a relatively high demand on the time synchronization of nodes and AOA-based method places a relatively high demand on node hardware for the need of array antenna. However, RSS-based method places a relatively low demand on node hardware and the power dissipation is relatively small, so it best suits the application of WSNs [2] . WSNs-based multi-source localization has attracted the attention of scholars from both China and beyond.…”

Section: Introductionmentioning

confidence: 99%