2D localization with WiFi passive radar and device-based techniques: an analysis of target measurements accuracy

The aim of the work is to investigate the performance of two localization techniques based on WiFi signals: the WiFi-based passive radar and a device-based technique that exploits the measurement of angle of arrival (AoA) and time difference of arrival. This paper focuses specifically on the accuracy of the AoA measurements. As expected, the results show that for both techniques the AoA accuracy depends on the signal-to-noise ratio also in terms of the number of exploited received signal samples. For the passive radar, very accurate estimates are obtained; however, loss of detections can appear only when the rate of the Access Point packets is strongly reduced. In contrast, device-based estimates accuracy is lower, since it suffers of the limited number of emitted packets when the device is not uploading data. However, it allows localization also of stationary targets, which is impossible for the passive radar. This suggests that the two techniques are complementary and their fusion could provide a sensibly increase performance with respect to the individual techniques

Passive radar based on WiFi transmissions: signal processing schemes and experimental results

Aim of this work is to study innovative techniques and processing strategies for a new passive sensor for short range surveillance. The principle of work of the sensor will be based on the passive radar principle, and WiFi transmissions - which usually provide Internet access within local areas - will be exploited by the passive sensor to detect, localize and classify targets

Passive radar based on WiFi transmissions: signal processing schemes and experimental results

Aim of this work is to study innovative techniques and processing strategies for a new passive sensor for short range surveillance. The principle of work of the sensor will be based on the passive radar principle, and WiFi transmissions - which usually provide Internet access within local areas - will be exploited by the passive sensor to detect, localize and classify targets

Accurate Range-based Indoor Localization Using PSO-Kalman Filter Fusion

Accurate indoor localization often depends on infrastructure support for distance estimation in range-based techniques. One can also trade off accuracy to reduce infrastructure investment by using relative positions of other nodes, as in range-free localization. Even for range-based methods where accurate Ultra-WideBand (UWB) signals are used, non line-of-sight (NLOS) conditions pose significant difficulty in accurate indoor localization. Existing solutions rely on additional measurements from sensors and typically correct the noise using a Kalman filter (KF). Solutions can also be customized to specific environments through extensive profiling. In this work, a range-based indoor localization algorithm called PSO - Kalman Filter Fusion (PKFF) is proposed that minimizes the effects of NLOS on localization error without using additional sensors or profiling. Location estimates from a windowed Particle Swarm Optimization (PSO) and a dynamically adjusted KF are fused based on a weighted variance factor. PKFF achieved a 40% lower 90-percentile root-mean-square localization error (RMSE) over the standard least squares trilateration algorithm at 61 cm compared to 102 cm

Impact of beacon interval on the performance of WiFi-based passive radar against human targets

The capability of WiFi-based passive radar to detect, track and profile human targets in both indoor and outdoor environment has been widely demonstrated. This paper investigates the impact of the Beacon Interval (BI) on the passive radar performance. The results of a dedicated acquisition campaign show that both the detection capability and the localization accuracy progressively degrade as the BI increases due to both the reduction of the received beacons and to the intrinsic undersampling of the target motion. Limit values are suggested for practical applications

RePos : relative position estimation of UHF-RFID tags for item-level localization

Radio frequency identification (RFID) technology brings tremendous applications in location-based services. Specifically, ultra-high frequency (UHF) RFID tag positioning based on phase (difference) of arrival (PoA/PDoA) has won great attention, due to its better positioning accuracy than signal strength-based methods. In most cases, such as logistics, retailing, and smart inventory management, the relative orders of the objects are much more attractive than absolute positions with centimetre-level accuracy. In this paper, a relative positioning (RePos) approach based on inter-tag distance and direction estimation is proposed. In the RePos positioning system, the measured phases are reconstructed based on unwrapping method. Then the distances from antenna to the tags are calculated using the distance differences of pairs of antenna's positions via a least-squares method. The relative relationships of the tags, including relative distances and angles, are obtained based on the geometry information extracted from PDoA. The experimental results show that the RePos RFID positioning system can realize about 0.28-meter ranging accuracy, and distinguish the levels and columns without ambiguity

Design and theoretical analysis of advanced power based positioning in RF system

Accurate locating and tracking of people and resources has become a fundamental requirement for many applications. The global navigation satellite systems (GNSS) is widely used. But its accuracy suffers from signal obstruction by buildings, multipath fading, and disruption due to jamming and spoof. Hence, it is required to supplement GPS with inertial sensors and indoor localization schemes that make use of WiFi APs or beacon nodes. In the GPS-challenging or fault scenario, radio-frequency (RF) infrastructure based localization schemes can be a fallback solution for robust navigation. For the indoor/outdoor transition scenario, we propose hypothesis test based fusion method to integrate multi-modal localization sensors. In the first paper, a ubiquitous tracking using motion and location sensor (UTMLS) is proposed. As a fallback approach, power-based schemes are cost-effective when compared with the existing ToA or AoA schemes. However, traditional power-based positioning methods suffer from low accuracy and are vulnerable to environmental fading. Also, the expected accuracy of power-based localization is not well understood but is needed to derive the hypothesis test for the fusion scheme. Hence, in paper 2-5, we focus on developing more accurate power-based localization schemes. The second paper improves the power-based range estimation accuracy by estimating the LoS component. The ranging error model in fading channel is derived. The third paper introduces the LoS-based positioning method with corresponding theoretical limits and error models. In the fourth and fifth paper, a novel antenna radiation-pattern-aware power-based positioning (ARPAP) system and power contour circle fitting (PCCF) algorithm are proposed to address antenna directivity effect on power-based localization. Overall, a complete LoS signal power based positioning system has been developed that can be included in the fusion scheme --Abstract, page iv

xD-Track: Leveraging Multi-Dimensional Information for Passive Wi-Fi Tracking

We describe the design and implementation of xD-Track, the first practical Wi-Fi based device-free localization system that employs a simultaneous and joint estimation of time-of-flight, angle-of-arrival, angle-of-departure, and Doppler shift to fully characterize the wireless channel between a sender and receiver. Using this full characterization, xD-Track introduces novel methods to measure and isolate the signal path that reflects off a person of interest, allowing it to localize a human with just a single pair of access points, or a single client-access point pair. Searching the multiple dimensions to accomplish the above is highly computationally burdensome, so xD-Track introduces novel methods to prune computational requirements, making our approach suitable for real-time person tracking. We implement xD-Track on the WARP software-defined radio platform and evaluate in a cluttered office environment. Experiments tracking people moving indoors demonstrate a 230% angle-of-arrival accuracy improvement and a 98% end-to-end tracking accuracy improvement over the state of the art localization scheme SpotFi, adapted for device-free localization. The general platform we propose can be easily extended for other applications including gesture recognition and Wi-Fi imaging to significantly improve performance