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Impact localization in a floor is complicated due to the dispersion-caused distortion of the generated floor waves. Current localization methods that try to overcome the dispersion problem are computationally expensive, taking in some cases 2 seconds to yield a single footstep location estimate. If an accelerometer sensor network is utilized to localize footsteps, and consequently track an occupant's path, then there is a need for computationally fast algorithms that are able to keep up with the walking (or running) impact frequency; therefore, in this paper, a practical algorithm is proposed for footstep impact localization in an instrumented floor. The proposed algorithm has promising sub-meter localization accuracy and is computationally fast. In addition, the algorithm does not require estimation of floor-dependent parameters, which is an additional advantage since estimating floor-dependent parameters in a floor will have relatively high uncertainty as the floor cannot be treated as an isotropic/homogeneous material. The proposed algorithm is evaluated using simulations and an experiment in an operational smart building.

No abstract

An underfloor accelerometer sensor network can be used to track occupants in an indoor environment using measurements of floor vibration induced by occupant footsteps. To achieve occupant tracking, each footstep impact location must first be estimated. This paper proposes a new energy-based algorithm for footstep impact localization. Compared to existing energy-based algorithms, the new algorithm achieves higher localization accuracy and removes a previously required calibration step (removal of the need to estimate floor-dependent parameters). Furthermore, the algorithm uses a much smaller data sampling rate compared to time of flight/arrival localization methods, which greatly reduces data and data-processing time. The new algorithm is a two-step location estimator: the first step is a coarse location estimate, with the second step as a fine location search through a nonlinear minimization problem. The performance of the proposed algorithm is evaluated using a single occupant walking experiment on an instrumented floor inside an operational smart building. This paper also demonstrates that higher localization accuracy is obtained using an additional Kalman filtering scheme.