A multi-modal event detection system for river and coastal marine monitoring applications

Abstract—This work is investigating the use of a multi-modal sensor network where visual sensors such as cameras and satellite imagers, along with context information can be used to complement and enhance the usefulness of a traditional in-situ sensor network in measuring and tracking some feature of a river or coastal location. This paper focuses on our work in relation to the use of an off the shelf camera as part of a multi-modal sensor network for monitoring a river environment. It outlines our results in relation to the estimation of water level using a visual sensor. It also outlines the benefits of a multi-modal sensor network for marine environmental monitoring and how this can lead to a smarter, more efficient sensing network

Multisensor Poisson Multi-Bernoulli Filter for Joint Target-Sensor State Tracking

In a typical multitarget tracking (MTT) scenario, the sensor state is either assumed known, or tracking is performed in the sensor's (relative) coordinate frame. This assumption does not hold when the sensor, e.g., an automotive radar, is mounted on a vehicle, and the target state should be represented in a global (absolute) coordinate frame. Then it is important to consider the uncertain location of the vehicle on which the sensor is mounted for MTT. In this paper, we present a multisensor low complexity Poisson multi-Bernoulli MTT filter, which jointly tracks the uncertain vehicle state and target states. Measurements collected by different sensors mounted on multiple vehicles with varying location uncertainty are incorporated sequentially based on the arrival of new sensor measurements. In doing so, targets observed from a sensor mounted on a well-localized vehicle reduce the state uncertainty of other poorly localized vehicles, provided that a common non-empty subset of targets is observed. A low complexity filter is obtained by approximations of the joint sensor-feature state density minimizing the Kullback-Leibler divergence (KLD). Results from synthetic as well as experimental measurement data, collected in a vehicle driving scenario, demonstrate the performance benefits of joint vehicle-target state tracking.Comment: 13 pages, 7 figure

Adaptive multi-target tracking in heterogeneous wireless sensor networks

Energy efficient multiple-target tracking is an important application of Wireless Sensor Networks (WSNs). Most prior studies consider tracking multiple tar- gets as an extension of executing a single target tracking algorithm multiple times, and use a single parameter for energy efficiency. We consider various factors such as mul- tiple targets tracked by the sensor, remaining energy of the sensor and relative location of the sensor with respect to a target's motion, in order to decide the tracking state of a sensor in a distributed environment. Further, we explore and identify the effective combination of these parameters to optimize energy usage, depending on specific network conditions. We then propose the Adaptive Multi-Target Tracking (AMTT) algorithm that can recognize the network condition based on local information without centralized coordination, and uses effective parameters to achieve en- ergy efficiency

Real-time 3D human tracking for mobile robots with multisensors

© 2017 IEEE. Acquiring the accurate 3-D position of a target person around a robot provides fundamental and valuable information that is applicable to a wide range of robotic tasks, including home service, navigation and entertainment. This paper presents a real-time robotic 3-D human tracking system which combines a monocular camera with an ultrasonic sensor by the extended Kalman filter (EKF). The proposed system consists of three sub-modules: monocular camera sensor tracking model, ultrasonic sensor tracking model and multi-sensor fusion. An improved visual tracking algorithm is presented to provide partial location estimation (2-D). The algorithm is designed to overcome severe occlusions, scale variation, target missing and achieve robust re-detection. The scale accuracy is further enhanced by the estimated 3-D information. An ultrasonic sensor array is employed to provide the range information from the target person to the robot and Gaussian Process Regression is used for partial location estimation (2-D). EKF is adopted to sequentially process multiple, heterogeneous measurements arriving in an asynchronous order from the vision sensor and the ultrasonic sensor separately. In the experiments, the proposed tracking system is tested in both simulation platform and actual mobile robot for various indoor and outdoor scenes. The experimental results show the superior performance of the 3-D tracking system in terms of both the accuracy and robustness

Localisation of mobile nodes in wireless networks with correlated in time measurement noise.

Wireless sensor networks are an inherent part of decision making, object tracking and location awareness systems. This work is focused on simultaneous localisation of mobile nodes based on received signal strength indicators (RSSIs) with correlated in time measurement noises. Two approaches to deal with the correlated measurement noises are proposed in the framework of auxiliary particle filtering: with a noise augmented state vector and the second approach implements noise decorrelation. The performance of the two proposed multi model auxiliary particle filters (MM AUX-PFs) is validated over simulated and real RSSIs and high localisation accuracy is demonstrated

Biologically inspired, self organizing communication networks.

PhDThe problem of energy-efficient, reliable, accurate and self-organized target tracking in Wireless Sensor Networks (WSNs) is considered for sensor nodes with limited physical resources and abrupt manoeuvring mobile targets. A biologically inspired, adaptive multi-sensor scheme is proposed for collaborative Single Target Tracking (STT) and Multi-Target Tracking (MTT). Behavioural data obtained while tracking the targets including the targets’ previous locations is recorded as metadata to compute the target sampling interval, target importance and local monitoring interval so that tracking continuity and energy-efficiency are improved. The subsequent sensor groups that track the targets are selected proactively according to the information associated with the predicted target location probability such that the overall tracking performance is optimized or nearly-optimized. One sensor node from each of the selected groups is elected as a main node for management operations so that energy efficiency and load balancing are improved. A decision algorithm is proposed to allow the “conflict” nodes that are located in the sensing areas of more than one target at the same time to decide their preferred target according to the target importance and the distance to the target. A tracking recovery mechanism is developed to provide the tracking reliability in the event of target loss. The problem of task mapping and scheduling in WSNs is also considered. A Biological Independent Task Allocation (BITA) algorithm and a Biological Task Mapping and Scheduling (BTMS) algorithm are developed to execute an application using a group of sensor nodes. BITA, BTMS and the functional specialization of the sensor groups in target tracking are all inspired from biological behaviours of differentiation in zygote formation. Simulation results show that compared with other well-known schemes, the proposed tracking, task mapping and scheduling schemes can provide a significant improvement in energy-efficiency and computational time, whilst maintaining acceptable accuracy and seamless tracking, even with abrupt manoeuvring targets.Queen Mary university of London full Scholarshi

Algorithms for multi-modal human movement and behaviour monitoring

This thesis describes investigations into improvements in the field of automated people tracking using multi-modal infrared (IR) and visible image information. The research question posed is; “To what extent can infrared image information be used to improve visible light based human tracking systems?” Automated passive tracking of human subjects is an active research area which has been approached in many ways. Typical approaches include the segmentation of the foreground, the location of humans, model initialisation and subject tracking. Sensor reliability evaluation and fusion methods are also key research areas in multi-modal systems. Shifting illumination and shadows can cause issues with visible images when attempting to extract foreground regions. Images from thermal IR cameras, which use long-wavelength infrared (LWIR) sensors, demonstrate high invariance to illumination. It is shown that thermal IR images often provide superior foreground masks using pixel level statistical extraction techniques in many scenarios. Experiments are performed to determine if cues are present at the data level that may indicate the quality of the sensor as an input. Modality specific measures are proposed as possible indicators of sensor quality (determined by foreground extraction capability). A sensor and application specific method for scene evaluation is proposed, whereby sensor quality is measured at the pixel level. A neuro-fuzzy inference system is trained using the scene quality measures to assess a series of scenes and make a modality decision

A GPS-Less Localization and Mobility Modelling (LMM) System for Wildlife Tracking

Existing wildlife tracking solutions typically use sensor nodes with specialised facilities, such as long-range radio, solar array of cells and Global Positioning System (GPS). This introduces additional manufacturing cost, increased energy and memory consumptions and increased sensor node weight. This paper proposes a novel Localization and Mobility Modelling (LMM) system, that can carry out wildlife tracking by merely using low-cost, lightweight sensor nodes and using short-range peer-to-peer communication facilities only, i.e. without the need for any specialised facilities. This is done by using two computationally simple operations, which are: (i) aggregated data collections from sensor nodes via peer-to-peer communications in a distributed manner, and (ii) estimation of sensor nodes' movement traces using trilateration. The computational load placed on each sensor node is just that of data collection and aggregation, whereas movement traces estimation is carried out on a backend server, separated from the sensor nodes. In the design of the LMM system, we have: (i) carried out an empirical evaluation of different parameter value settings for data collection to develop a Multi-Zone Multi-Hierarchy (MZMH) communication structure, (ii) demonstrated a novel use of an Aggregation based Topology Learning (ATL) protocol for collecting sensor nodes' topology data using peer-to-peer multi-hop communications, and (iii) used a novel Location Estimation (LE) method for estimating sensor nodes' movement traces from the collected topology data. The evaluation results show that the LMM system can accurately estimate sensor nodes' movement traces but with significantly less energy and memory costs, demonstrating its cost-efficiency as compared to the related wildlife tracking solutions. © 2020 IEEE

An Autonomous Sensor System Architecture for Active Flow and Noise Control Feedback

Multi-channel sensor fusion represents a powerful technique to simply and efficiently extract information from complex phenomena. While the technique has traditionally been used for military target tracking and situational awareness, a study has been successfully completed that demonstrates that sensor fusion can be applied equally well to aerodynamic applications. A prototype autonomous hardware processor was successfully designed and used to detect in real-time the two-dimensional flow reattachment location generated by a simple separated-flow wind tunnel model. The success of this demonstration illustrates the feasibility of using autonomous sensor processing architectures to enhance flow control feedback signal generation

Performance Evaluation of Adaptive H-infinity Filter

This study is related to the use of adaptive H-infinity filter  for multi sensor data fusion ( based tracking. AHIF can work efficiently in the presence of uncertainties using sliding window concept. In the present use of , the length of window size is varied to eliminate/minimize the estimation errors and predict almost precise location of a target. Simulation experiments are conducted to evaluate performance of  in comparison with Kalman and H-Infinity filters for mild and evasive maneuvering targets.  Performs better in terms location accuracy and position fit error