Zhuoling Xiao

Lightweight map matching for indoor localisation using conditional random fields

Indoor tracking and navigation is a fundamental need for pervasive and context-aware smartphone applications. Although indoor maps are becoming increasingly available, there is no practical and reliable indoor map matching solution available at present. We present MapCraft, a novel, robust and responsive technique that is extremely computationally efficient (running in under 10 ms on an Android smartphone), does not require training in different sites, and tracks well even when presented with very noisy sensor data. Key to our approach is expressing the tracking problem as a conditional random field (CRF), a technique which has had great success in areas such as natural language processing, but has yet to be considered for indoor tracking. Unlike directed graphical models like Hidden Markov Models, CRFs capture arbitrary constraints that express how well observations support state transitions, given map constraints. Extensive experiments in multiple sites show how MapCraft outperforms state-of-the art approaches, demonstrating excellent tracking error and accurate reconstruction of tortuous trajectories with zero training effort. As proof of its robustness, we also demonstrate how it is able to accurately track the position of a user from accelerometer and magnetometer measurements only (i.e. gyro- and WiFi-free). We believe that such an energy-efficient approach will enable always-on background localisation, enabling a new era of location-aware applications to be developed.

Non-Line-of-Sight Identification and Mitigation Using Received Signal Strength

Indoor wireless systems often operate under non-line-of-sight (NLOS) conditions that can cause ranging errors for location-based applications. As such, these applications could benefit greatly from NLOS identification and mitigation techniques. These techniques have been primarily investigated for ultra-wide band (UWB) systems, but little attention has been paid to WiFi systems, which are far more prevalent in practice. In this study, we address the NLOS identification and mitigation problems using multiple received signal strength (RSS) measurements from WiFi signals. Key to our approach is exploiting several statistical features of the RSS time series, which are shown to be particularly effective. We develop and compare two algorithms based on machine learning and a third based on hypothesis testing to separate LOS/NLOS measurements. Extensive experiments in various indoor environments show that our techniques can distinguish between LOS/NLOS conditions with an accuracy of around 95%. Furthermore, the presented techniques improve distance estimation accuracy by 60% as compared to state-of-the-art NLOS mitigation techniques. Finally, improvements in distance estimation accuracy of 50% are achieved even without environment-specific training data, demonstrating the practicality of our approach to real world implementations.

Distortion Rejecting Magneto-Inductive Three-Dimensional Localization (MagLoc)

Localization is a research area that, due to its overarching importance as an enabler for higher level services, has attracted a vast amount of research and commercial interest. For the most part, it can be claimed that GPS provides an unparalleled solution for outdoor tracking and navigation. However, the same cannot yet be said about positioning in GPS-denied or challenged environments, such as indoor environments, where obstructions such as floors and walls heavily attenuate or reflect high-frequency radio signals. This has led to a plethora of competing solutions targeted toward a particular application scenario, yielding a fragmented solution landscape. In this paper, we present a fresh approach to 3-D positioning based on the use of very low frequency (kHz) magneto-inductive (MI) fields. The most important property of MI positioning is that obstacles such as walls, floors, and people that heavily impact the performance of competing approaches are largely “transparent” to the quasi-static magnetic fields. MI has a number of challenges to robust operation that distort positions, including the presence of ferrous materials and sensitivity to user rotation. Through signal processing and sensor fusion across multiple system layers, we show how we can overcome these challenges. We showcase its highly accurate 3-D positioning in a number of environments, with positioning accuracy below 0.8 m even in heavily distorted areas.

Robust pedestrian dead reckoning (R-PDR) for arbitrary mobile device placement

Pedestrian dead reckoning, especially on smart-phones, is likely to play an increasingly important role in indoor tracking and navigation, due to its low cost and ability to work without any additional infrastructure. A challenge however, is that positioning, both in terms of step detection and heading estimation, must be accurate and reliable, even when the use of the device is so varied in terms of placement (e.g. handheld or in a pocket) or orientation (e.g holding the device in either portrait or landscape mode). Furthermore, the placement can vary over time as a user performs different tasks, such as making a call or carrying the device in a bag. A second challenge is to be able to distinguish between a true step and other periodic motion such as swinging an arm or tapping when the placement and orientation of the device is unknown. If this is not done correctly, then the PDR system typically overestimates the number of steps taken, leading to a significant long term error. We present a fresh approach, robust PDR (R-PDR), based on exploiting how bipedal motion impacts acquired sensor waveforms. Rather than attempting to recognize different placements through sensor data, we instead simply determine whether the motion of one or both legs impact the measurements. In addition, we formulate a set of techniques to accurately estimate the device orientation, which allows us to very accurately (typically over 99%) reject false positives. We demonstrate that regardless of device placement, we are able to detect the number of steps taken with >99.4% accuracy. R-PDR thus addresses the two main limitations facing existing PDR techniques.

On assessing the accuracy of positioning systems in indoor environments

As industrial and academic communities become increasingly interested in Indoor Positioning Systems (IPSs), a plethora of technologies are gaining maturity and competing for adoption in the global smartphone market. In the near future, we expect busy places, such as schools, airports, hospitals and large businesses, to be outfitted with multiple IPS infrastructures, which need to coexist, collaborate and / or compete for users. In this paper, we examine the novel problem of estimating the accuracy of co-located positioning systems, and selecting which one to use where. This is challenging because 1) we do not possess knowledge of the ground truth, which makes it difficult to empirically estimate the accuracy of an indoor positioning system; and 2) the accuracy reported by a positioning system is not always a faithful representation of the real accuracy. In order to address these challenges, we model the process of a user moving in an indoor environment as a Hidden Markov Model (HMM), and augment the model to take into account vector (instead of scalar) observations, and prior knowledge about user mobility drawn from personal electronic calendars. We then propose an extension of the Baum-Welch algorithm to learn the parameters of the augmented HMM. The proposed HMM-based approach to learning the accuracy of indoor positioning systems is validated and tested against competing approaches in several real-world indoor settings.

Slot-based model for IEEE 802.15.4 MAC with sleep mechanism

In this letter, we develop an exactly slot-based model for IEEE 802.15.4 protocol with sleep mechanism in real-time applications. By explicitly modeling the sleep mechanisms and CSMA/CA mechanism with a precision of slot, we accurately evaluate the performance of the protocol, including energy consumption and throughput. We take into consideration the impacts of several factors, including duty cycle, network traffic and initial backoff exponent. NS-2 simulations show the accuracy of the proposed model.

Indoor Tracking Using Undirected Graphical Models

Indoor tracking and navigation is a fundamental need for pervasive and context-aware smartphone applications. Although indoor maps are becoming increasingly available, there is no practical and reliable indoor map matching solution available at present. We present MapCraft, a novel, robust and responsive technique that is extremely computationally efficient (running in under 10 ms on an Android smartphone), does not require training in different sites, and tracks well even when presented with very noisy sensor data. Key to our approach is expressing the tracking problem as a conditional random field (CRF), a technique which has had great success in areas such as natural language processing. Unlike directed graphical models like Hidden Markov Models, CRFs capture arbitrary constraints that express how well observations support state transitions, given map constraints. In addition, we show how to further improve tracking accuracy, by tuning the parameters of the motion sensing model using an unsupervised EM-style optimization scheme. Extensive experiments in multiple sites show how MapCraft outperforms state-of-the art approaches, demonstrating excellent tracking error and accurate reconstruction of tortuous trajectories with zero training effort. As proof of its robustness, we also demonstrate how it is able to accurately track the position of a user from accelerometer and magnetometer measurements only (i.e., gyroand Wi-Fi-free). We believe that such an energy-efficient approach will enable always-on background localisation, enabling a new era of location-aware applications to be developed.

Robust Indoor Positioning With Lifelong Learning

Inertial tracking and navigation systems have been playing an increasingly important role in indoor tracking and navigation. They have the competitive advantage of leveraging not requiring expensive infrastructure-only existing smart mobile devices with embedded inertial measurement units. When aided with other sources of information, such as radio data from existing WiFi/BLE infrastructure, and environment constraints from floor plans or radio maps, they often report great performance of 0.5-2 m. Given the promising results, what is it that prevents the widespread adoption of this tracking solution? We argue that pedestrian dead reckoning (PDR) techniques are often evaluated in a specific context and are not mature enough to handle variations in user motion, device type, device placement, or environment. They typically use a number of parameters that require careful context-specific tuning, which is labor intensive and requires expert knowledge. In this paper, we propose two novel approaches to address these problems. Our first contribution is a robust PDR algorithm, which is based on general physics principles that underpin human motion and is by design robust to context changes. The second contribution is a novel way of interaction between the PDR and map matching layers based on the principle of lifelong learning. Unlike traditional approaches where information flows unidirectionally from the PDR to the map matching layer, we introduce a feedback loop that can be used to automatically tune the parameters of the PDR algorithm. This is not dissimilar to the way that people improve their navigation skills when they repeatedly visit the same environment. Extensive experiments in multiple sites, with a variety of users, devices, and device placements, show that the combination of a robust PDR with a lifelong learning tracker can achieve submeter accuracy with no user effort for parameter tuning.

Accuracy Estimation for Sensor Systems

In most sensing applications, the measurements generated by sensor networks are noisy and usually annotated with some measure of uncertainty. The question that we address in this paper is how to estimate the accuracy of these uncertain sensor measurements. Existing studies on estimating the accuracy of uncertain measurements in real sensing applications are limited in three ways. First, they tend to be application-specific. Second, they typically employ learning techniques to estimate the parameters of sensor noise models, and ignore alternative state estimation approaches without learning. Third, they do not explore whether exploiting the dynamics of the monitored state can yield significant benefits. We address the above limitations as follows: we define the accuracy estimation problem in a general manner that applies to a broad spectrum of application scenarios. We present a general framework to address this problem, and show that the proposed framework can be implemented in a number of different ways. We evaluate and compare the different implementations in the context of two real sensing scenarios, and discuss how they trade accuracy for computation cost, and how this trade-off largely depends on the users knowledge of the application scenario.

Identification and mitigation of non-line-of-sight conditions using received signal strength

Various applications, such as localisation of persons and objects could benefit greatly from non-line-of-sight (NLOS) identification and mitigation techniques. However, such techniques have been primarily investigated for ultra-wide band (UWB) signals, leaving the area of WiFi signals untouched. In this study, we propose two accurate approaches using only received signal strength (RSS) measurements from WiFi signals to identify NLOS conditions and mitigate the effects. We first explore several features from the RSS which are later demonstrated as very effective in identifying and mitigating NLOS conditions. After that, we develop and compare two major optimization problems based on a machine learning technique and hypothesis testing according to different user requirements and information available. Extensive experiments in various indoor environments have shown that our techniques can not only accurately distinguish between LOS/NLOS conditions, but also mitigate the impact of NLOS conditions as well.