A Novel Approach of Passive Localization for Indoor Positioning

A review on uncertainty quantification of shadowing reconstruction and signal measurements in Radio Tomographic Imaging

In radio-frequency (RF)-based device-free localization (DFL), the number of sensors acting as RF transmitters and receivers is crucial for accuracy and system costs. Two promising approaches for DFL have been identified in the past: radio tomographic imaging (RTI) and multi-static radar (MSR). RTI in its basic version requires many sensors for high accuracy, which increases the cost. In this paper, we show how RTI benefits from multipath propagation. By evaluating the direct and echo paths, we increase the coverage of the target area, and by utilizing UWB signals, the RTI system is less susceptible to multipath propagation. MSR maps reflections that occur within the target area to reflectors such as persons or other objects. MSR does not require that the person is located near a signal path. Both suggested methods exploit ultra-wideband (UWB) channel impulse response (CIR) measurements. CIR measurements and the modeling of multipath effects either increase the accuracy or reduce the required number of sensors for localization with RTI. We created a test setup and measure UWB CIRs at different positions with a commercially available off-the-shelf UWB radio chip, the Decawave DW1000. We compare the localization results of RTI, multipath-assisted (MA)-RTI, and MSR and investigate a combined approach. We show that RTI is improved by the analysis of multipath propagation; furthermore, MA-RTI results in a better performance compared to MSR: with 50% of all cases, the localization error is better than 0.82 m and in 80% of all cases 1.34 m. The combined approach results in the best localization result with 0.64 m in 50% of all cases.

A radio tomographic imaging (RTI) system uses the received signal strength (RSS) measured by RF sensors in a static wireless network to localize people in the deployment area, without having them to carry or wear an electronic device. This paper addresses the fact that small-scale changes in the position and orientation of the antenna of each RF sensor can dramatically affect imaging and localization performance of an RTI system. However, the best placement for a sensor is unknown at the time of deployment. Improving performance in a deployed RTI system requires the deployer to iteratively guess-and-retest, i.e., pick a sensor to move and then re-run a calibration experiment to determine if the localization performance had improved or degraded. We present an RTI system of servo-nodes, RF sensors equipped with servo motors which autonomously dial it in, i.e., change position and orientation to optimize the RSS on links of the network. By doing so, the localization accuracy of the RTI system is quickly improved, without requiring any calibration experiment from the deployer. Experiments conducted in three indoor environments demonstrate that the servo-nodes system reduces localization error on average by 32% compared to a standard RTI system composed of static RF sensors.

Sparsity promoting decentralized learning strategies for radio tomographic imaging using consensus based ADMM approach

One of the most recently developed wireless technologies is Radio Tomographic Imaging (RTI). RTI employs a wireless sensor network that produces images of the change in the electromagnetic field of a monitored area using Received Signal Strength (RSS) measurements. This allows the tracking of device-free objects such as humans and cars. This paper is the first to propose and validate a simulation model that simulates RSS measurements for arbitrary RTI networks, based on the ZigBee communication protocol. The simulation model allows the specification of an RTI network from the ground up, including node positions, network size and geometry and RSS measurement processing. Furthermore, this paper demonstrates the implementation of the simulation model to an enhancement of a recently proposed RTI system, which acts as a roadside surveillance system. The enhancement includes three newly proposed techniques, namely a new weight matrix calculation method, a new node spacing setup and a new vehicle detection method. The simulation results indicate that it is possible to detect both one or two family sized cars simultaneously. Using techniques that reduce RSS variance due to multipath effects and the newly proposed methods, simulated vehicle detection performance is demonstrated to be between 95% and 100%.

Sparsity-enabled radio tomographic imaging using quantized received signal strength observations

The increase in demand of detecting obstructions in a wireless medium without attaching any device with the target is well facilitated by the Radio Tomographic Imaging (RTI) system. Even though it is a promising technique it is a cumbersome task to get the exact position and shape of an object due to ill-posed nature of RTI system. Thus vital task is to effectively choose a regularization technique that not only enhances sparsity by reducing noise after detection but also preserves edges of the object with its appropriate shape by using a heuristic weight model. RTI facilitates us with an imaging vector indicating the loss fields created by obstacles in the medium having knowledge of received signal strength(RSS) values and a weight model that assigns weight to the attenuated pixels in a wireless network. This paper addresses the above-mentioned problem by using a fused lasso regularization via ADMM. The second part of the paper extends performance of fused lasso regularization by implementing it incrementally using distributed learning. The performance metrics shows that fused lasso regularization not only reduces the noise level by increasing the sparsity but also retains the sharp features of the object.

Radio tomographic imaging (RTI) is one of the device-free localization (DFL) approaches used to identify obstacles in a wireless network by using the attenuation information of radio waves. The RTI system’s fusion centre collects the received signal strength (RSS) data from all nodes and uses batch estimation to find the spatial loss field (SLF) due to obstacles. The surrounding small noisy pixels in the SLF vector are eliminated by the least absolute shrinkage and selection operator (lasso), which results in improved sparsity in SLF. First-order fused lasso (FL)-based RTI techniques are used for simultaneous improvements in sparsity and structural details of the SLF. However, first-order methods have slower convergence than the second-order methods. Also, this batch FL-based SLF estimation results in high memory requirements. In this letter, a novel second-order fast FL algorithm is proposed to handle such bottlenecks. This algorithm uses a time- and norm-weighted fused lasso (TNWFL) strategy for updating the weights of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -norm through the use of second-order parameters. The effectiveness of the TNWFL estimator is verified through the simulations.

Radio Tomographic Imaging (RTI) finds extensive application in modern day problem. The RTI achieved this using received signal strength (RSS) power and transmitted power by sensor nodes. RTI being an ill-posed inverse problem, requires regularization for proper estimation of spatial loss field(SLF) and able to detect the object. Centralized solution of RTI system requires large communication overheads. This motivates to develop distributed algorithm for RTI. Two novel distributed algorithms using incremental approach are developed in this paper. The first approach is the direct extension of the centralized approach to distributed incremental approach. Second algorithm requires less communication overheads compared to the first one by incorporating data censoring technique. The performance metrics show that the performance of distributed Incremental RTI is comparable to the centralized RTI system. Again the impact of censoring is studied by increasing the censoring ratio , which results in a trade-off between detection performance and computational complexity.

In recent years, Radio frequency (RF) sensor networks have been used to localize people indoor without requiring them to wear invasive electronic devices. These wireless mesh networks, formed by low-power radio transceivers, continuously measure the received signal strength (RSS) of the links. Radio Tomographic Imaging (RTI) is a technique that generates, starting from these RSS measurements, 2D images of the change in the electromagnetic field inside the area covered by the radio transceivers to spot the presence and movements of animates (e.g., people, large animals) or large metallic objects (e.g., cars). Here, we present a RTI system for localizing and tracking people outdoors. Differently than in indoor environments where the RSS does not change significantly with time unless people are found in the monitored area, the outdoor RSS signal is time-variant, e.g., due to rainfall or wind-driven foliage. We present a novel outdoor RTI method that, despite the nonstationary noise introduced in the RSS data by the environment, achieves high localization accuracy and dramatically reduces the energy consumption of the sensing units. Experimental results demonstrate that the system accurately detects and tracks a person in real-time in a large forested area under varying environmental conditions, significantly reducing false positives, localization error and energy consumption compared to state-of-the-art RTI methods.

Radio tomographic imaging (RTI) is a passive localization method that has important applications in device-free localization (DFL). RTI estimates the location of a target by using the impact of the target entering a region of interest (ROI) on the radio link. However, research on RTI in the field of passive UHF radio frequency identification (RFID) is still insufficient, and there are many issues that need to be addressed. In the RTI method, targets typically appear at the pixel with the highest value in the reconstructed image, displaying the location of the target intuitively by the variation in received signal strength (RSS) as the input. However, multipath effects, noise interference, and the presence of unknown targets in the ROI can lead to the appearance of multiple local maxima in the reconstructed image, making it very difficult to accurately identify the number and location of the targets. We proposed a method to establish feature regions in image reconstruction and introduced the method of link-tracking which uses more robust known link geometric features than RSS measurement values to match the false targets with the real targets one by one. The algorithm traverses all the feature region combinations in a specific order and outputs the localization result, distinguishing targets from false targets and obtaining more accurate results. Experimental results verified the convergence and advantages of the proposed method.

Device-free Localization (DfL) systems offer real-time indoor localization of people without any electronic devices attached on their bodies. The human body influences the radio wave propagation between wireless links and changes the Received Signal Strength (RSS). Wireless Sensor Networks (WSNs) nodes easily measure these RSS changes and appropriate Radio Tomographic Imaging (RTI) algorithms can then process the RSS data and allow human localization. This paper investigates how to choose near-optimal regularization parameter during the regularization process for indoor DfL and describes an experimental indoor DfL setup realized with a Sun SPOT based WSN. The work elaborates on the numerical calculation of the near-optimal regularization parameter by usage of the trade-off curve criterion. The calculated parameter enables conclusive RTI image with sufficient localization precision for eHealth or other ambient-assisted-living applications where the error tolerance is at a scale of several tens of centimeters. The value for the regularization parameter matches the empirical derived value obtained in the authors'previous work.

The device-free localization (DFL) technique for target localization and tracking in a wireless sensor network is important in modern research. Radio tomographic imaging (RTI) is a DFL method that is widely used in today’s image-based localization systems. In the RTI system, spatial loss fields (SLFs) represent the maps that indicate the degree of radio wave attenuation for each spatial location in the WSN due to obstacles. In the real-world RTI model, the data is always perturbed by uncertainty. Therefore, uncertainty in sensor node location leads to uncertainty in the input data of the RTI regression model. To address the sensor location uncertainty problem, this paper proposes a novel stochastic robust approximation (SRA) method for RTI (SRA-RTI). Simulation-based performance analysis shows that the proposed technique is robust against the uncertainty in the sensor node location.

Device-Free Localization (DFL) realized by the Radio Frequency (RF) signal sensing, is an emerging technology to obtain the target’s position without needing the target carrying any electronic devices or tags. Due to its through-wall detectability and privacy protection, the RF-based DFL is becoming a promising research topic in the Location-based Services (LBS) applications based on Internet of Things (IoT). Among the RF-based DFL approaches, Radio Tomographic Imaging (RTI) is a low-cost and novel computational imaging method to reconstruct the target-induced shadowing effect in the RF monitored area and then estimate the target’s position. However, the multipath interference in the RF sensing network often induces the reconstruction degradation in the RTI system and leads to the position misestimation in DFL applications. Addressing this problem, considering that the target’s shadowing may occupy a small spatial range and then express some spatial structure, this aricle focuses on the spatial correlation of the target’s shadowing. Then an RTI reconstruction methods based on the Block Sparse Bayesian Learning is proposed to explore the spatial correlation in the sparse target’s shadowing. Moreover, the actual localization experiments are conducted to validate the spatial correlation in shadowing is an important property to improve the RTI reconstruction performance and DFL accuracy.

Radio Tomographic Imaging (RTI) is an emerging technology for imaging the attenuation caused by physical objects in wireless networks. This paper presents a linear model for using received signal strength (RSS) measurements to obtain images of moving objects. Noise models are investigated based on real measurements of a deployed RTI system. Mean-squared error (MSE) bounds on image accuracy are derived, which are used to calculate the accuracy of an RTI system for a given node geometry. The ill-posedness of RTI is discussed, and Tikhonov regularization is used to derive an image estimator. Experimental results of an RTI experiment with 28 nodes deployed around a 441 square foot area are presented.