We address the indoor tracking problem by combining an Impulse Radio-Ultra-Wideband handset with an ankle-mounted Inertial Measurement Unit embedding an accelerometer and a gyroscope. The latter unit makes possible the detection of the stance phases to overcome velocity drifts. Regarding radiolocation, a time-of-arrival estimator adapted to energy-based receivers is applied to mitigate the effects of dense multipath profiles. A novel quality factor associated with this estimator is also provided as a function of the received signal-to-noise ratio, enabling us to identify outliers corresponding to obstructed radio links and to scale the covariance matrix of radiolocation measurements. Finally, both radio and inertial subsystems are loosely-coupled into one single navigation solution relying on a specific extended Kalman filter. In the proposed fusion strategy, processed inertial data control the filter state prediction whereas Combined Time Differences Of Arrival are formed as input observations. These combinations offer low computational complexity as well as a unique filter structure over time, even after removing outliers. Experimental results obtained in a representatively harsh indoor environment emphasize the complementarity of the two technologies and the relevance of the chosen fusion method while operating with low-cost, noncollocated, asynchronous, and heterogeneous sensors. 1. Introduction For the last past years, new location and tracking (LT) needs have been gradually introduced into a wide variety of applications, such as security, health care, rescue, logistics; or house automation. A growing interest has been more particularly expressed in location-dependent indoor services, which require seamless pedestrian navigation capabilities in harsh environments where satellite-based solutions cannot operate. In this context, alternative technologies are currently under investigation, based on for example, location-enabled wireless networks [1, 2]. On their own, most of modern wireless networks can indeed retrieve the positions of mobile radio devices relative to the known position of reference anchors or base stations (BS). The radiolocation functionality simply relies on the measurement of radio metrics, which depend on the distance traveled in the air by transmitted signals. For instance, when a mobile radio device is synchronized with a BS (e.g., using n-way ranging protocol transactions [3]), range information can be derived from the time of arrival (TOA) of the received signal. Several TOA-based range measurements collected (with respect to
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