2,897 research outputs found
Distributed Object Tracking Using a Cluster-Based Kalman Filter in Wireless Camera Networks
Local data aggregation is an effective means to save sensor node energy and prolong the lifespan of wireless sensor networks. However, when a sensor network is used to track moving objects, the task of local data aggregation in the network presents a new set of challenges, such as the necessity to estimate, usually in real time, the constantly changing state of the target based on information acquired by the nodes at different time instants. To address these issues, we propose a distributed object tracking system which employs a cluster-based Kalman filter in a network of wireless cameras. When a target is detected, cameras that can observe the same target interact with one another to form a cluster and elect a cluster head. Local measurements of the target acquired by members of the cluster are sent to the cluster head, which then estimates the target position via Kalman filtering and periodically transmits this information to a base station. The underlying clustering protocol allows the current state and uncertainty of the target position to be easily handed off among clusters as the object is being tracked. This allows Kalman filter-based object tracking to be carried out in a distributed manner. An extended Kalman filter is necessary since measurements acquired by the cameras are related to the actual position of the target by nonlinear transformations. In addition, in order to take into consideration the time uncertainty in the measurements acquired by the different cameras, it is necessary to introduce nonlinearity in the system dynamics. Our object tracking protocol requires the transmission of significantly fewer messages than a centralized tracker that naively transmits all of the local measurements to the base station. It is also more accurate than a decentralized tracker that employs linear interpolation for local data aggregation. Besides, the protocol is able to perform real-time estimation because our implementation takes into consideration the sparsit- - y of the matrices involved in the problem. The experimental results show that our distributed object tracking protocol is able to achieve tracking accuracy comparable to the centralized tracking method, while requiring a significantly smaller number of message transmissions in the network
Synchronization and sampling in wireless adaptive signal processing systems
This paper deals with the synchronization in wireless adaptive signal processing systems. Wireless communication offers high flexibility, however, the distributed structure of wireless systems requires the synchronization of the subsystems. The synchronization becomes particularly important if the signal bandwidth is in the kHz range, and it is inevitable in distributed control systems. The demand on the synchronization is presented through the introduction of a wireless active noise control system. In this system wireless sensors (microphones) receive the signal, and the main signal processing algorithm is implemented on a central unit which produces the signal for the actuators (loudspeakers). In spite of the special application, the system has a general structure, so the results are valid for other adaptive systems. First a PLL like algorithm is described for the synchronization of the sampling in a wireless real-time signal processing system, then a higher-level synchronization is introduced for a distrib
uted Fourier-analyzer based noise control system. The effectiveness of the algorithms is demonstrated by measurement results
Efficient Time of Arrival Calculation for Acoustic Source Localization Using Wireless Sensor Networks
Acoustic source localization is a very useful tool in surveillance and tracking applications. Potential exists for ubiquitous presence of acoustic source localization systems. However, due to several significant challenges they are currently limited in their applications. Wireless Sensor Networks (WSN) offer a feasible solution that can allow for large, ever present acoustic localization systems. Some fundamental challenges remain. This thesis presents some ideas for helping solve the challenging problems faced by networked acoustic localization systems. We make use of a low-power WSN designed specifically for distributed acoustic source localization. Our ideas are based on three important observations. First, sounds emanating from a source will be free of reflections at the beginning of the sound. We make use of this observation by selectively processing only the initial parts of a sound to be localized. Second, the significant features of a sound are more robust to various interference sources. We perform key feature recognition such as the locations of significant zero crossings and local peaks. Third, these features which are compressed descriptors, can also be used for distributed pattern matching. For this we perform basic pattern analysis by comparing sampled signals from various nodes in order to determine better Time Of Arrivals (TOA). Our implementation tests these ideas in a predictable test environment. A complete system for general sounds is left for future wor
Efficient Time of Arrival Calculation for Acoustic Source Localization Using Wireless Sensor Networks
Acoustic source localization is a very useful tool in surveillance and tracking applications. Potential exists for ubiquitous presence of acoustic source localization systems. However, due to several significant challenges they are currently limited in their applications. Wireless Sensor Networks (WSN) offer a feasible solution that can allow for large, ever present acoustic localization systems. Some fundamental challenges remain. This thesis presents some ideas for helping solve the challenging problems faced by networked acoustic localization systems. We make use of a low-power WSN designed specifically for distributed acoustic source localization. Our ideas are based on three important observations. First, sounds emanating from a source will be free of reflections at the beginning of the sound. We make use of this observation by selectively processing only the initial parts of a sound to be localized. Second, the significant features of a sound are more robust to various interference sources. We perform key feature recognition such as the locations of significant zero crossings and local peaks. Third, these features which are compressed descriptors, can also be used for distributed pattern matching. For this we perform basic pattern analysis by comparing sampled signals from various nodes in order to determine better Time Of Arrivals (TOA). Our implementation tests these ideas in a predictable test environment. A complete system for general sounds is left for future wor
Self-Calibration Methods for Uncontrolled Environments in Sensor Networks: A Reference Survey
Growing progress in sensor technology has constantly expanded the number and
range of low-cost, small, and portable sensors on the market, increasing the
number and type of physical phenomena that can be measured with wirelessly
connected sensors. Large-scale deployments of wireless sensor networks (WSN)
involving hundreds or thousands of devices and limited budgets often constrain
the choice of sensing hardware, which generally has reduced accuracy,
precision, and reliability. Therefore, it is challenging to achieve good data
quality and maintain error-free measurements during the whole system lifetime.
Self-calibration or recalibration in ad hoc sensor networks to preserve data
quality is essential, yet challenging, for several reasons, such as the
existence of random noise and the absence of suitable general models.
Calibration performed in the field, without accurate and controlled
instrumentation, is said to be in an uncontrolled environment. This paper
provides current and fundamental self-calibration approaches and models for
wireless sensor networks in uncontrolled environments
Wireless Software Synchronization of Multiple Distributed Cameras
We present a method for precisely time-synchronizing the capture of image
sequences from a collection of smartphone cameras connected over WiFi. Our
method is entirely software-based, has only modest hardware requirements, and
achieves an accuracy of less than 250 microseconds on unmodified commodity
hardware. It does not use image content and synchronizes cameras prior to
capture. The algorithm operates in two stages. In the first stage, we designate
one device as the leader and synchronize each client device's clock to it by
estimating network delay. Once clocks are synchronized, the second stage
initiates continuous image streaming, estimates the relative phase of image
timestamps between each client and the leader, and shifts the streams into
alignment. We quantitatively validate our results on a multi-camera rig imaging
a high-precision LED array and qualitatively demonstrate significant
improvements to multi-view stereo depth estimation and stitching of dynamic
scenes. We release as open source 'libsoftwaresync', an Android implementation
of our system, to inspire new types of collective capture applications.Comment: Main: 9 pages, 10 figures. Supplemental: 3 pages, 5 figure
Efficient Time of Arrival Calculation for Acoustic Source Localization Using Wireless Sensor Networks
Acoustic source localization is a very useful tool in surveillance and tracking applications. Potential exists for ubiquitous presence of acoustic source localization systems. However, due to several significant challenges they are currently limited in their applications. Wireless Sensor Networks (WSN) offer a feasible solution that can allow for large, ever present acoustic localization systems. Some fundamental challenges remain. This thesis presents some ideas for helping solve the challenging problems faced by networked acoustic localization systems. We make use of a low-power WSN designed specifically for distributed acoustic source localization. Our ideas are based on three important observations. First, sounds emanating from a source will be free of reflections at the beginning of the sound. We make use of this observation by selectively processing only the initial parts of a sound to be localized. Second, the significant features of a sound are more robust to various interference sources. We perform key feature recognition such as the locations of significant zero crossings and local peaks. Third, these features which are compressed descriptors, can also be used for distributed pattern matching. For this we perform basic pattern analysis by comparing sampled signals from various nodes in order to determine better Time Of Arrivals (TOA). Our implementation tests these ideas in a predictable test environment. A complete system for general sounds is left for future wor
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