Deep learning based wireless localization for indoor navigation

Abstract

Location services, fundamentally, rely on two components: a mapping system and a positioning system. The mapping system provides the physical map of the space, and the positioning system identifies the position within the map. Outdoor location services have thrived over the last couple of decades because of well-established platforms for both these components (e.g. Google Maps for mapping, and GPS for positioning). In contrast, indoor location services haven't caught up because of the lack of reliable mapping and positioning frameworks. Wi-Fi positioning lacks maps and is also prone to environmental errors. In this paper, we present DLoc, a Deep Learning based wireless localization algorithm that can overcome traditional limitations of RF-based localization approaches (like multipath, occlusions, etc.). We augment DLoc with an automated mapping platform, MapFind. MapFind constructs location-tagged maps of the environment and generates training data for DLoc. Together, they allow off-the-shelf Wi-Fi devices like smartphones to access a map of the environment and to estimate their position with respect to that map. During our evaluation, MapFind has collected location estimates of over 105 thousand points under 8 different scenarios with varying furniture positions and people motion across two different spaces covering 2000 sq. Ft. DLoc outperforms stateof-the-art methods in Wi-Fi-based localization by 80% (median & 90 th percentile) across the two different spaces. CCS CONCEPTS • Networks → Location based services; • Computing methodologies → Robotic planning; Supervised learning; • Information systems → Sensor networks.

SSLIDE: Sound Source Localization for Indoors Based on Deep Learning

“…When the sound source is from angle θ and distance d, then Pns(θ, d) have a high value. If we have U and V grid points for θ and d, then we will obtain a likelihood surface with dimension U × V which can indicate the likelihood of the signal in the given θ and d. For reverberation and noise free data, the localization is simply identifying the θ and d that correspond to the maximum likelihood [21]. Due to the reverberation, much of the sound received by the microphones is a result of multipath, which is a complicated function of the different microphone locations relative to the source.…”

“…Now that we have the inputs and targets for performing single point identification, we utilize the network architecture as shown in Fig. 2 and based on encoder-decoder architecture with one encoder and two parallel decoders inspired from [21]. The input to the encoder is the likelihood surface without range compensation indicated as (2).…”

Particle Filtering-Based Indoor Positioning System for Beacon Tag Tracking

“…Like other fingerprinting schemes, AngLoc also needs to collect offline angle data for its database construction. The authors in [21] presented a deep-learning based indoor navigation framework (called DLoc) by an environment mapping approach. DLoc uses a CSI-based AoA and ToF combined method to localize a target.…”

“…From a practical point of view, this mapping function needs to be evaluated by a specific experiment device used for our PFIPS. Based on (21), given an RSSI value, we can obtain a distance by…”

“…16 means more data points collected at the corresponding receiving power level. Based on averaged values of the data, we use a curve fitting function in the Python SciPy package [36] to find proper parameters in (21). The found parameters are n = 1.147 and a = −72.693 for front-facing case shown in Fig.…”