Qinghua Gao

Toward Robust Indoor Localization Based on Bayesian Filter Using Chirp-Spread-Spectrum Ranging

It is a challenging problem to realize robust localization in complex indoor environments where non-line-of-sight (NLOS) occurs due to reflection and diffraction. To solve this problem, a localization algorithm under the Bayesian framework is proposed in this paper. We adopt the 802.15.4a chirp-spread-spectrum ranging hardware to measure the distances between the mobile node and the anchor nodes, and realize the location estimation by incorporating the range measurements into the localization algorithm. We propose a novel joint-state estimation localization algorithm which adopts a Markov model for NLOS state estimation and a particle filter for location state estimation. For utilizing the positive effect of the NLOS measurements while restraining their negative effect, we present a scheme to build the feasible region of the particles based on the NLOS and line-of-sight (LOS) measurements and calculate the particle weight based only on the LOS measurements. The results of the indoor experiment demonstrate the effectiveness of our approach.

Robust Device-Free Wireless Localization Based on Differential RSS Measurements

As an emerging technique with a promising application prospect, the device-free localization (DFL) technique has drawn considerable attention due to its ability of realizing wireless localization without the need of equipping the target with any device. The DFL technique detects the shadowed links and realizes localization with the received signal strength (RSS) measurements of these links. However, one major disadvantage of the DFL technique is that the RSS signal is too sensitive, and a slight variation of the environment will cause the variation of RSS measurements, which incurs the misjudgment of shadowed links and degradation of localization performance. To solve this problem, a robust DFL scheme based on differential RSS is proposed. The scheme utilizes the novel differential RSS to judge whether a link is shadowed, which not only eliminates the need of acquiring reference RSS measurements but also overcomes the negative effect incurred by the environment. Meanwhile, an outlier detection scheme is presented to filter out the outlier links that are far away from the target. We present the observation model of the shadowed links and incorporate it into the particle filter framework to realize location estimation robustly. Experimental results demonstrate the outstanding performance of the proposed scheme.

Device-Free Localization With Multidimensional Wireless Link Information

As an emerging technique with promising application prospects, device-free localization (DFL) could estimate the location of target within the deployment area of wireless networks (WNs) while eliminating the need to equip the target with a wireless device. However, one major disadvantage of this technique is that it needs several wireless links travelling through the deployment area to guarantee good performance. To overcome this problem, a novel multidimensional wireless-link-information-based DFL scheme is proposed. Different from a traditional DFL scheme that scans wireless links sequentially with one frequency and one transmission power level, the proposed scheme makes full use of multiple frequencies and multiple transmission power levels to enrich the link measurement information. Meanwhile, motivated by the fact that the location information of the target is not only sparse but also changes slowly and continuously over time, we present a novel recursive compressive sensing algorithm to reconstruct the location information from undersampled measurements. The experimental results demonstrate the outstanding performance of the proposed scheme.

Lightweight Robust Device-Free Localization in Wireless Networks

Due to its ability of realizing localization without the need of equipping the target with a wireless device, the device-free wireless localization technique has become a crucial technique for many security and military applications. However, there still lacks an efficient scheme which could achieve robust location estimation performance with low computational cost. To solve this problem, we propose a lightweight robust Bayesian grid approach (BGA) in this paper. The BGA utilizes not only the observation information of the shadowed links, but also the prior information involved in the previous estimations and the constraint information involved in the non-shadowed links, which ensure its robust performance. Meanwhile, the BGA can be carried out with a series of lightweight grid multiplication and addition operations, which eliminates the complex matrix inversion computation involved in the traditional algorithm. The experimental results demonstrate that BGA could achieve a mean tracking error of 0.155 m with a running time of only 1.5 ms.

Device-free localisation with wireless networks based on compressive sensing

A compressive sensing-based approach to solve the problem of tracking targets in the deployment area of the wireless networks without the need of equipping the target with a wireless device has been proposed. We present a dynamic statistical model for relating the change of the received signal strength between the node pairs to the spatial location of the target. On the basis of the model, the problem is formulated as a sparse signal reconstruction problem, and we propose a novel Bayesian greedy matching pursuit (BGMP) algorithm to tackle the signal reconstruction problem even from a small set of measurements. The BGMP iteratively seeks the contribution of each pixel for multi-times to compensate for the inaccuracy of the measurement matrix, and builds the enumeration region based on the past estimations to speed up the algorithm and improve its reconstruction performance simultaneously. Experimental results demonstrate the effectiveness of our approach and confirm that the BGMP algorithm could achieve satisfactory localisation and tracking results.

Time-of-Flight-Based Radio Tomography for Device Free Localization

Due to its ability of realizing device free localization with wireless networks, the radio tomography becomes a promising technique that draws considerable attention. Traditional radio tomography makes use of the received signal strength (RSS) of wireless links to realize location estimation. However, the RSS measurement is particularly sensitive to noise. Inspired by the fact that similar to the RSS, the time-of-flight (TOF) measurement also changes significantly when some objects shadow the wireless link, and the fact that compared with the RSS, the TOF measurement is robust to noise, a novel TOF-based radio tomography is proposed in this paper. With the TOF measurements of the shadowed links as observation information, a modified particle filter algorithm which utilizes the compressive sensing technique to produce the importance distribution of the particle set is proposed, so as to realize localization and tracking with under-sampled measurements by making full use of the space-domain sparse and time-domain gradually changed feature of the location information. The experiments with the 802.15.4a chirp spread spectrum ranging hardware are presented to confirm the proposed scheme.

Device-Free Wireless Localization and Activity Recognition: A Deep Learning Approach

Device-free wireless localization and activity recognition (DFLAR) is a new technique, which could estimate the location and activity of a target by analyzing its shadowing effect on surrounding wireless links. This technique neither requires the target to be equipped with any device nor involves privacy concerns, which makes it an attractive and promising technique for many emerging smart applications. The key question of DFLAR is how to characterize the influence of the target on wireless signals. Existing work generally utilizes statistical features extracted from wireless signals, such as mean and variance in the time domain and energy as well as entropy in the frequency domain, to characterize the influence of the target. However, a feature suitable for distinguishing some activities or gestures may perform poorly when it is used to recognize other activities or gestures. Therefore, one has to manually design handcraft features for a specific application. Inspired by its excellent performance in extracting universal and discriminative features, in this paper, we propose a deep learning approach for realizing DFLAR. Specifically, we design a sparse autoencoder network to automatically learn discriminative features from the wireless signals and merge the learned features into a softmax-regression-based machine learning framework to realize location, activity, and gesture recognition simultaneously. Extensive experiments performed in a clutter indoor laboratory and an apartment with eight wireless nodes demonstrate that the DFLAR system using the learned features could achieve 0.85 or higher accuracy, which is better than the systems utilizing traditional handcraft features.

Differential radio map-based robust indoor localization

While wireless local area network-based indoor localization is attractive, the problems concerning how to capture the signal-propagating character in the complex dynamic environment and how to accommodate the receiver gain difference of different mobile devices are challenging. In this article, we solve these problems by modeling them as common mode noise and develop a localization algorithm based on a novel differential radio map approach. We propose a differential operation to improve the performance of the radio map module, where the location is estimated according to the difference of received signal strength (RSS) instead of RSS itself. The particle filter algorithm is adopted to realize the target localization and tracking task. Furthermore, to calculate the particle weight at arbitrary locations, we propose a local linearization technique to realize continuous interpolation of the radio map. The indoor experiment results demonstrate the effectiveness and robustness of our approach.

A Method to Prolong the Lifetime of Wireless Sensor Network

Power management is an important technique to prolong the lifetime of wireless sensor network(WSN). A radio-triggered based wake-up circuit is proposed to control the activation and shutting down of the wireless sensor node, and thus eliminates energy wasting wake-up periods. The radio-triggered wake-up circuit is optimized to achieve maximum sensitivity by characterizing both the impedance transformation network and the rectifier circuit. The simulation and measurement results show that the circuit can produce a DC output of 220mV with the received power as low as -27.7dBm.

Toward Accurate Device-Free Wireless Localization With a Saddle Surface Model

Device-free wireless localization (DFL) is a technique that can locate a target by analyzing its shadowing effect on wireless links, which causes the variation of link measurements, while removing the requirement of equipping the target with a device. It can provide fundamental data for pervasive computing, smart environment, and traffic surveillance applications. The observation model, which represents the relationship between wireless link measurement and target location, is very important for DFL, since it characterizes the shadowing effect of the target on wireless links and, therefore, determines the performance of the DFL system. In this paper, inspired by measurement results, we propose a saddle surface (SaS) model to describe the shadowing effect. The SaS model characterizes the elaborate information within the spatial impact area and provides more useful observation information for the location estimation algorithm. We incorporate the SaS model into the particle filter framework for location estimation. Extensive experiments in indoor and outdoor scenarios are carried out to evaluate the performance of the proposed schemes. The tracking errors of 0.78 and 0.21 m in the given two scenarios demonstrate the better performance of the proposed SaS model compared with existing models.