Joint TOA/AOA-based localization in wireless sensor networks

Abstract

Wireless sensor network (WSN) localization has drawn attention in a broad spectrum of applications for security, disaster response, monitoring, surveillance and tactical system. The deployment of a large number of low-cost, low-power and multi-functional sensors is preferred, which provides an exceptional localization performance via cooperative communication.In this paper, we consider a cooperative localization in WSNs consisting of multiple anchor nodes (ANs) equipped with a linear antenna array of M elements, a single-antenna target and a central unit (CU). We focus on a synchronous network and all the channels are supposed to be frequency-flat. Our objective is to evaluate the performance of localization technique for the target's position that jointly exploits both Time-of-Arrival (TOA) and Angle-of-Arrival (AOA) measurements. We derive the Cramér-Rao bound (CRB) for a joint TOA/AOA-based localization. Additionally, for the performance comparison, the CRBs of solely TOA-and AOA-based localizations are also derived. By numerical results, our analysis is verified and it is observed that the joint TOA/AOA-based localization has a superior performance than either TOA-or AOA-based localization.Index Terms-Wireless sensor network, Time-of-Arrival (TOA), Angle-of-Arrival (AOA), Cramér-Rao bound (CRB)

Probabilistic 5G Indoor Positioning Proof of Concept with Outlier Rejection

“…Note that, while Algorithm 1 describes a TDoA approach, the TDoA measurements obtained with (6) clearly directly depend on the measured ToAs. We can therefore use xT,IRLS and w T as initial search point and weights, respectively, for the optimization of the ToA ML function in (2) and (5). Moreover, note that the complexity is quadratic w. r. t. the number of measurements, which is reasonable, since typically only few locators are available in a given area.…”

“…w ∠,K ) for the K locators. Analogous to the outputs of the TDoA algorithm, x∠,IRLS and w ∠ are utilized for the optimization of the AoA ML function in (4) and (5). Note that both IRLS algorithms can also be employed as standalone positioning solutions, but in this work we only use them to improve the ML techniques introduced in Section III by avoiding local maxima caused by outliers.…”

“…For initializing the gradient-based optimization of the joint ToA/AoA ML function given in (5), we combine the initial points xT,IRLS and x∠,IRLS . We assume the two position estimates to be independent measurements with additive Gaussian noise and diagonal covariance matrices.…”

The Power Gain Difference Method Analysis

“…There are several techniques to estimate the target position based on different information available from measurements performed on received radio frequency (RF) signals. This means that RF based localization systems may use a multitude of different techniques, which include the angle of arrival (AoA) [4,5], received signal strength (RSS), time of arrival (ToA), time difference of arrival (TDoA) [6][7][8], Doppler difference (DD) [9,10], or hybrid location methods [11]. While some localization techniques usually come at a low cost but a lower accuracy, others require complex synchronization schemes, which usually make them more expensive [12][13][14][15][16].…”

Energy efficient network localisation using hybrid TOA/AOA measurements

“…In typical range based 2D localisation networks, a minimum of three anchors are required. However, it is challenging in complex environments with multiple obstacles, where the anchor coverage cannot be guaranteed due to the signal attenuation [12]. If the agent is able to extract the AOA from anchors as well as TOA, a minimum of single‐anchor is required, which is an attractive solution to the anchor coverage problem [13].…”