Received Signal Strength Model Research Articles

Using recovered radio transmitters to estimate positioning error and a generalized Monte Carlo simulation to incorporate error into animal telemetry analysis

BackgroundMobile radio tracking is an important tool in fisheries research and management. Yet, the accuracy of location estimates can be highly variable across studies and within a given dataset. While some methods are available to deal with error, they generally assume a static value for error across all detections. We provide a novel method for making detection-specific error estimates using detections of recovered transmitters (i.e., mortalities or tag expulsion). These data are used to establish the relationship between received signal strength (RSS) and positional error, which can then be used to predict positional error of detections for fish at large. We then show how detection-specific estimates can be integrated into a Monte Carlo framework to analyze movement in ways robust to spatial uncertainty.ResultsIn a telemetry study in a large river (~ 90 m), we recovered 22 transmitters to estimate and model positional error. Error averaged 94 m (range = 1–727 m) for transmitters tracked by researchers on foot using a Yagi antenna, and 200 m (range = 1–1141 m) for transmitters tracked from vehicles using an omnidirectional whip antenna. Transmitters located near roads were tracked more accurately with both methods. Received signal strength was a strong predictor of positional error (r2 = 0.86, ground tracking; 0.65, tracking from truck) and was thus used to make detection-specific estimates of error for detections of fish at large. Monte Carlo analysis for a binary movement classification revealed that only 18% of location estimates could be confidently assigned to movement (p < 0.05); the remainder were associated with stasis or movement that was within the range of positional error. Ignoring positional error led to positive bias of up to 1300% in individual movement estimates and varied seasonally—it was highest when fish were inactive and lowest when fish were most active.ConclusionUsing recovered transmitters and RSS models to estimate telemetry error is a viable alternative to staged ‘dummy transmitter’ trials and assuming error is a constant. Our proposed approaches to incorporate detection-specific error estimates into analysis are broadly applicable and can ‘make the most’ out of highly accurate detections while also cautiously extracting spatial information from less-accurate detections.

Unsupervised Learning in RSS-Based DFLT Using an EM Algorithm

Received signal strength (RSS) changes of static wireless nodes can be used for device-free localization and tracking (DFLT). Most RSS-based DFLT systems require access to calibration data, either RSS measurements from a time period when the area was not occupied by people, or measurements while a person stands in known locations. Such calibration periods can be very expensive in terms of time and effort, making system deployment and maintenance challenging. This paper develops an Expectation-Maximization (EM) algorithm based on Gaussian smoothing for estimating the unknown RSS model parameters, liberating the system from supervised training and calibration periods. To fully use the EM algorithm’s potential, a novel localization-and-tracking system is presented to estimate a target’s arbitrary trajectory. To demonstrate the effectiveness of the proposed approach, it is shown that: (i) the system requires no calibration period; (ii) the EM algorithm improves the accuracy of existing DFLT methods; (iii) it is computationally very efficient; and (iv) the system outperforms a state-of-the-art adaptive DFLT system in terms of tracking accuracy.

Directional source localization based on RSS-AOA combined measurements

Source localization plays an indispensable role in many applications. This paper addresses the directional source localization problem in a three-dimensional (3D) wireless sensor network using hybrid received-signal-strength (RSS) and angle-of-arrival (AOA) measurements. Both the position and transmission orientation of the source are to be estimated. In the considered positioning scenario, the angle and range measurements are respectively corresponding to the AOA model and RSS model that integrates the Gaussian-shaped radiation pattern. Given that the localization problem is non-convex and the unknown parameters therein are coupled together, this paper adopts the second-order cone relaxation and alternating optimization techniques in the proposed estimation algorithm. Moreover, to provide a performance benchmark for any localization method, the corresponding Cramer-Rao lower bounds (CRLB) of estimating the unknown position and transmission orientation of the source are derived. Numerical and simulation results demonstrate that the presented algorithm effectively resolves the problem, and its estimation performance is close to the CRLB for the localization with the hybrid measurements.

Simulation Tool for the Analysis of Cooperative Localization Algorithms for Wireless Sensor Networks.

Within the context of the Internet of Things (IoT) and the Location of Things (LoT) service, this paper presents an interactive tool to quantitatively analyze the performance of cooperative localization techniques for wireless sensor networks (WSNs). In these types of algorithms, nodes help each other determine their location based on some signal metrics such as time of arrival (TOA), received signal strength (RSS), or a fusion of them. The developed tool is intended to provide researchers and designers a fast way to measure the performance of localization algorithms considering specific network topologies. Using TOA or RSS models, the Crámer-Rao lower bound (CRLB) has been implemented within the tool. This lower bound can be used as a benchmark for testing a particular algorithm for specific channel characteristics and WSN topology, which allows determination if the necessary accuracy for a specific application is possible. Furthermore, the tool allows us to consider independent characteristics for each node in the WSN. This feature allows the avoidance of the typical “disk graph model,” which is usually applied to test cooperative localization algorithms. The tool allows us to run Monte-Carlo simulations and generate statistical reports. A set of basic illustrative examples are described comparing the performance of different localization algorithms and showing the capabilities of the presented tool.

Hybrid TOA/RSS Range-Based Localization with Self-Calibration in Asynchronous Wireless Networks

The paper addresses the problem of localization based on hybrid received signal strength (RSS) and time of arrival (TOA) measurements, in the presence of synchronization errors among all the nodes in a wireless network, and assuming all parameters are unknown. In most existing schemes, in fact, knowledge of the model parameters is postulated to reduce the high dimensionality of the cost functions involved in the position estimation process. However, such parameters depend on the operational wireless context, and change over time due to the presence of dynamic obstacles and other modification of the environment. Therefore, they should be adaptively estimated “on the field”, with a procedure that must be as simple as possible in order to suit multiple real-time re-calibrations, even in low-cost applications, without requiring human intervention. Unfortunately, the joint maximum likelihood (ML) position estimator for this problem does not admit a closed-form solution, and numerical optimization is practically unfeasible due to the large number of nuisance parameters. To circumvent such issues, a novel two-step algorithm with reduced complexity is proposed: A first calibration phase exploits nodes in known positions to estimate the unknown RSS and TOA model parameters; then, in a second localization step, an hybrid TOA/RSS range estimator is combined with an iterative least-squares procedure to finally estimate the unknown target position. The results show that the proposed hybrid TOA/RSS localization approach outperformed state-of-the-art competitors and, remarkably, achieved almost the same accuracy of the joint ML benchmark but with a significantly lower computational cost.

Unknown RSSD-Based Localization CRLB Analysis With Semidefinite Programming

Passive source localization is an important research area with numerous applications in mobile communications and signal processing. This paper presents an analysis of energy-based localization performance with unknown transmit power using received signal strength (RSS) and RSS difference (RSSD) models. These models are widely used because of their low cost and simple implementation. Previous studies were based on the assumption that the source node transmit power is known, which is not practical in many situations. The Cramer–Rao lower bound for the RSS and RSSD models with correlated noise is derived as a performance benchmark for the mean squared error (MSE) of the location and transmit power estimation. It is shown that the MSE can be factored into two independent terms corresponding to the geometric distribution of the sensors and the channel parameters including the noise variance. The effect of the sensor and source node positions on the location accuracy and the MSE is derived via the geometric dilution of precision (GDOP). The GDOP is then used to evaluate the effect of the joint estimation of unknown power and source location on the performance. Lower bounds on the GDOP are derived to obtain the minimum MSE. The RSSD solution is obtained by formulating the nonlinear and nonconvex objective functions into a convex optimization problem through relaxation and semidefinite programming. Simulation results are presented with the source inside or outside the sensor network.

Simultaneous Positioning and Orientating for Visible Light Communications: Algorithm Design and Performance Analysis

Visible light communication (VLC) based simultaneous positioning and orientating (SPAO), using received signal strength (RSS) measurements, is studied in this paper. RSS-based SPAO for VLCs of great challenge as it is essentially a non-convex optimization problem due to the nonlinear RSS model. To address this non-convexity challenge, a novel particle-assisted stochastic search (PASS) algorithm is proposed. The proposed PASS-based SPAO scheme does not require the knowledge of the height of receiver, the perfect alignment of transceiver orientations or inertial measurements. This is a huge technical improvement over the existing VLC localization solutions. The algorithmic convergence is established to justify the proposed PASS algorithm. In addition, a closed-form Cramer–Rao lower bound (CRLB) on localization error is derived and analyzed to gain insights into how the VLC-based SPAO performance is related to system configurations. It is shown that the receiver's position and orientation accuracy is linear with signal-to-noise ratio and direction information. In addition, the position accuracy decays with six powers of the transceiver distance, while the orientation accuracy decays with four powers of the transceiver distance. Finally, simulation results verify the performance gain of the proposed PASS algorithm for VLC-based SPAO.

Regression Based Mobility Estimation Method Using Received Signal Strength

In this paper, estimation of mobility using received signal strength (RSS) is presented. In contrast to standard methods, speed can be estimated without the use of any additional hardware like accelerometer, gyroscope or position estimator. The pattern of mobility can be inferred using any hand-held device such as mobile phone, tablet or smart watch and source of RSS. The strength of Wireless Fidelity (WiFi) signal is considered herein to compute the time-domain features such as mean, minimum, maximum, and autocorrelation of signal strength. The experiments are carried out in different environments like academic area, residential area and in open space. The experimental results indicate that the average accuracy in the estimated speed is 88% using the maximum RSS model with limited data set. The accuracy can be further increased by carrying out the training with a larger dataset. The proposed method is cost-effective and having linear complexity with reasonable accuracy. Additionally, the proposed method is scalable that is the performance is not affected in a multi-smartphones scenario in a WiFi or cellular environment.

Received-Signal-Strength Threshold Optimization Using Gaussian Processes

There is a big trend nowadays to use event-triggered proximity report for indoor positioning. This paper presents a generic received-signal-strength (RSS) threshold optimization framework for generating informative proximity reports. The proposed framework contains five main building blocks, namely the deployment information, RSS model, positioning metric selection, optimization process and management. Among others, we focus on Gaussian process regression (GPR)-based RSS models and positioning metric computation. The optimal RSS threshold is found through minimizing the best achievable localization root-mean-square-error formulated with the aid of fundamental lower bound analysis. Computational complexity is compared for different RSS models and different fundamental lower bounds. The resulting optimal RSS threshold enables enhanced performance of new fashioned low-cost and low-complex proximity report-based positioning algorithms. The proposed framework is validated with real measurements collected in an office area where bluetooth-low-energy (BLE) beacons are deployed.

Heterogeneous Device Tracking With RSS Variation Mitigation Over a Radio Map

This letter presents an extended Kalman filtering algorithm for position tracking with a radio map consisting of received signal strength (RSS) observations by a single device at known positions. The proposed algorithm is based on a time-varying RSS model, introduced in this letter, to track the position of a device and the RSS variation arising from device heterogeneity and environmental changes. The superiority of the proposed algorithm over the existing algorithms is validated with simulations and a real experiment.

A hierarchical RSS model for RF-based device-free localization

One common challenge in device-free localization (DFL) with radio frequency sensor networks is that the variations of received signal strength (RSS) become less sensitive to shadow fading and the performance will be degenerated in a cluttered environment. To address this problem, this paper develops a hierarchical model that contributes to refining the description granularity of RSS variations and enhancing the sensitivity of RSS variations to shadow fading. The novelty is twofold. First, the Exponential-Rayleigh model and the diffraction-based model are integrated to form a two degree-of-freedom parametrization that can capture not only the target-induced multipath interferences but also the diffraction fading contributions. Second, the link dependent characterizations of RSS variations are explored by parameterizing the model coefficients in terms of fade level. The proposed model is experimentally validated in the context of radio tomographic imaging (RTI).

An Exponential-Rayleigh Model for RSS-Based Device-Free Localization and Tracking

A common technical difficulty in device-free localization and tracking (DFLT) with a wireless sensor network is that the change of the received signal strength (RSS) of the link often becomes more unpredictable due to the multipath interferences. This challenge can lead to unsatisfactory or even unstable DFLT performance. This work focuses on developing a new RSS model, called Exponential-Rayleigh (ER) model, for addressing this challenge. Based on data from our extensive experiments, we first develop the ER model of the received signal strength. This model consists of two parts: the large-scale exponential attenuation part and the small-scale Rayleigh enhancement part. The new consideration on using the Rayleigh model is to depict the target-induced multipath components. We then explore the use of the ER model with a particle filter in the context of multi-target localization and tracking. Finally, we experimentally demonstrate that our ER model outperforms the existing models. The experimental results highlight the advantages of using the Rayleigh model in mitigating the multipath interferences thus improving the DFLT performance.

Modeling and Mitigating Noise and Nuisance Parameters in Received Signal Strength Positioning

Localization via received signal strength (RSS) is often employed in cases where the received signal is fairly weak, either due to distance or due to deliberate covert operation or interference avoidance. However, most research on source localization via RSS implicitly assumes that the background noise is negligible, and that parameters of the transmitter and environment are known. Many commercial chipsets provide per-frame RSS measurements obtained when demodulating the signal, which do not include background noise; however, noise can still cause signal outages. In law enforcement, surveillance, and emergency situations, RSS may be obtained more crudely, such as by energy detection, in which case the RSS will include contributions from the background noise as well. This paper proposes new probabilistic RSS models that account for background noise in both types of RSS measurements. We also derive and evaluate maximum likelihood estimators (MLEs) for these new models, as well as for differential RSS, which has hitherto not been rigorously analyzed in the literature. Several of these MLEs are extended to estimate the transmit power and/or path loss if they are unknown. The new models are justified by extensive measured data.