The problem of estimating the location of an impact force in a dispersive medium is complicated given the dispersion-related distortion of the generated traveling wave. The problem cannot be solved, with reasonable accuracy, using conventional time difference of arrival (TDOA) techniques. A building floor is an example of a dispersive medium that is being loaded by occupant footsteps. If more accurate localization algorithms are obtained, then they can be used to localize and track occupants in a building using floor vibration sensors measuring the footstep-induced traveling waves. This paper presents the evaluation of a new localization approach, in a simulated aluminum plate (dispersive waveguide), using a network of sensors measuring the plate's vibration. Average signal power is calculated for all the sensors over a fixed time period, and then used to generate a location estimate. Two different location estimation solutions are presented and compared; a constrained least squares solution (CLS), and a non-linear root finding solution generated using the Levenberg-Marquardt (LM) algorithm. A finite element (FE) thin plate model is used as a testbed to evaluate the performance of the developed localization algorithm by estimating the location of virtual hammer impacts acting on the plate. The results encourage further future development.
KEYWORDS: Source localization, Buildings, Smart structures, Machine learning, Vibrometry, Sensing systems, Sensors, Dispersion, Data acquisition, Research management, Signal attenuation, Environmental sensing, Signal to noise ratio
Recent years have shown prolific advancements in smart infrastructures, allowing buildings of the modern world to interact with their occupants. One of the sought-after attributes of smart buildings is the ability to provide unobtrusive, indoor localization of occupants. The ability to locate occupants indoors can provide a broad range of benefits in areas such as security, emergency response, and resource management. Recent research has shown promising results in occupant building localization, although there is still significant room for improvement. This study presents a passive, small-scale localization system using accelerometers placed around the edges of a small area in an active building environment. The area is discretized into a grid of small squares, and vibration measurements are processed using a pattern matching approach that estimates the location of the source. Vibration measurements are produced with ball-drops, hammer-strikes, and footsteps as the sources of the floor excitation. The developed approach uses matched filters based on a reference data set, and the location is classified using a nearest-neighbor search. This approach detects the appropriate location of impact-like sources i.e. the ball-drops and hammer-strikes with a 100% accuracy. However, this accuracy reduces to 56% for footsteps, with the average localization results being within 0.6 m (α = 0.05) from the true source location. While requiring a reference data set can make this method difficult to implement on a large scale, it may be used to provide accurate localization abilities in areas where training data is readily obtainable. This exploratory work seeks to examine the feasibility of the matched filter and nearest neighbor search approach for footstep and event localization in a small, instrumented area within a multi-story building.
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