Multi Detector Fusion of Dynamic TOA Estimation using Kalman Filter

In this paper, we propose fusion of dynamic TOA (time of arrival) from multiple non-coherent detectors like energy detectors operating at sub-Nyquist rate through Kalman filtering. We also show that by using multiple of these energy detectors, we can achieve the performance of a digital matched filter implementation in the AWGN (additive white Gaussian noise) setting. We derive analytical expression for number of energy detectors needed to achieve the matched filter performance. We demonstrate in simulation the validity of our analytical approach. Results indicate that number of energy detectors needed will be high at low SNRs and converge to a constant number as the SNR increases. We also study the performance of the strategy proposed using IEEE 802.15.4a CM1 channel model and show in simulation that two sub-Nyquist detectors are sufficient to match the performance of digital matched filter

Hybrid UWB-Inertial TDoA-based Target Tracking with Concentrated Anchors

In this paper, hybrid radio/inertial mobile target tracking for accurate and smooth path estimation is considered. The proposed tracking approach builds upon an Ultra WideBand (UWB)-based positioning algorithm, based on the Linear Hyperbolic Positioning System (LinHPS), with Time Difference of Arrival (TDoA) processing and anchors concentrated on a single hotspot at the center of the environment where the target moves. First, we design an Adaptive Radio-based Extended Kalman Filter (AREKF), which does not require a priori statistical knowledge of the noise in the target movement model and estimates the measurement noise covariance, at each sampling time, according to a proper LookUp Table (LUT). In order to improve the performance of AREKF, we incorporate inertial data collected from the target and propose three “hybrid” radio/inertial algorithms, denoted as Hybrid Inertial Measurement Unit (IMU)-aided Radio-based EKF (HIREKF), Hybrid Noisy Control EKF (HNCEKF), and Hybrid Control EKF (HCEKF). Our results on experimentally acquired paths show that the proposed algorithms achieve an average instantaneous position estimation error on the order of a few centimeters. Moreover, the minimum target path length estimation error, obtained with HCEKF, is on the order of 6% and 1% for two paths with lengths equal to approximately 17 m and 46 m, respectively

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

A Self-organizing Hybrid Sensor System With Distributed Data Fusion For Intruder Tracking And Surveillance

A wireless sensor network is a network of distributed nodes each equipped with its own sensors, computational resources and transceivers. These sensors are designed to be able to sense specific phenomenon over a large geographic area and communicate this information to the user. Most sensor networks are designed to be stand-alone systems that can operate without user intervention for long periods of time. While the use of wireless sensor networks have been demonstrated in various military and commercial applications, their full potential has not been realized primarily due to the lack of efficient methods to self organize and cover the entire area of interest. Techniques currently available focus solely on homogeneous wireless sensor networks either in terms of static networks or mobile networks and suffers from device specific inadequacies such as lack of coverage, power and fault tolerance. Failing nodes result in coverage loss and breakage in communication connectivity and hence there is a pressing need for a fault tolerant system to allow replacing of the failed nodes. In this dissertation, a unique hybrid sensor network is demonstrated that includes a host of mobile sensor platforms. It is shown that the coverage area of the static sensor network can be improved by self-organizing the mobile sensor platforms to allow interaction with the static sensor nodes and thereby increase the coverage area. The performance of the hybrid sensor network is analyzed for a set of N mobile sensors to determine and optimize parameters such as the position of the mobile nodes for maximum coverage of the sensing area without loss of signal between the mobile sensors, static nodes and the central control station. A novel approach to tracking dynamic targets is also presented. Unlike other tracking methods that are based on computationally complex methods, the strategy adopted in this work is based on a computationally simple but effective technique of received signal strength indicator measurements. The algorithms developed in this dissertation are based on a number of reasonable assumptions that are easily verified in a densely distributed sensor network and require simple computations that efficiently tracks the target in the sensor field. False alarm rate, probability of detection and latency are computed and compared with other published techniques. The performance analysis of the tracking system is done on an experimental testbed and also through simulation and the improvement in accuracy over other methods is demonstrated

Composite Disturbance Filtering: A Novel State Estimation Scheme for Systems With Multi-Source, Heterogeneous, and Isomeric Disturbances

State estimation has long been a fundamental problem in signal processing and control areas. The main challenge is to design filters with ability to reject or attenuate various disturbances. With the arrival of big data era, the disturbances of complicated systems are physically multi-source, mathematically heterogenous, affecting the system dynamics via isomeric (additive, multiplicative and recessive) channels, and deeply coupled with each other. In traditional filtering schemes, the multi-source heterogenous disturbances are usually simplified as a lumped one so that the "single" disturbance can be either rejected or attenuated. Since the pioneering work in 2012, a novel state estimation methodology called {\it composite disturbance filtering} (CDF) has been proposed, which deals with the multi-source, heterogenous, and isomeric disturbances based on their specific characteristics. With the CDF, enhanced anti-disturbance capability can be achieved via refined quantification, effective separation, and simultaneous rejection and attenuation of the disturbances. In this paper, an overview of the CDF scheme is provided, which includes the basic principle, general design procedure, application scenarios (e.g. alignment, localization and navigation), and future research directions. In summary, it is expected that the CDF offers an effective tool for state estimation, especially in the presence of multi-source heterogeneous disturbances

Human mobility monitoring in very low resolution visual sensor network

This paper proposes an automated system for monitoring mobility patterns using a network of very low resolution visual sensors (30 30 pixels). The use of very low resolution sensors reduces privacy concern, cost, computation requirement and power consumption. The core of our proposed system is a robust people tracker that uses low resolution videos provided by the visual sensor network. The distributed processing architecture of our tracking system allows all image processing tasks to be done on the digital signal controller in each visual sensor. In this paper, we experimentally show that reliable tracking of people is possible using very low resolution imagery. We also compare the performance of our tracker against a state-of-the-art tracking method and show that our method outperforms. Moreover, the mobility statistics of tracks such as total distance traveled and average speed derived from trajectories are compared with those derived from ground truth given by Ultra-Wide Band sensors. The results of this comparison show that the trajectories from our system are accurate enough to obtain useful mobility statistics