Indoor positioning for smartphone users has received a lot of attention in recent years. While many solutions have been developed, most rely on a need for pre-deployment of infrastructure or collecting ground truth data to train on. In this paper we see what can be done using existing WiFi-infrastructure and Received Signal Strength from these to smartphones, not using any calibration of the signal environment or manually set WiFi positions. We expand on previous work by using a multi-floor model taking into account dampening between floors, and optimize a target function consisting of least squares residuals, to find positions for WiFis and the smartphone measurement locations simultaneously. Pressure sensors are used to do floor estimation. The method was tested inside two multi-story buildings, with 5 stories each, with median errors of smartphone positions of 12.5m and 16.4m and with WiFi median position errors of 7.16m and 19.4m respectively. Correct floor detection was achieved for 96% of all smartphone positions.

In this paper, we propose a novel method for estimating the locations of near- and/or far-field harmonic audio sources impinging on an arbitrary, but calibrated, sensor array. Using a joint pitch and location estimation formed in two steps, we first estimate the fundamental frequencies and complex amplitudes under a sinusoidal model assumption, whereafter the location of each source is found by utilizing both the difference in phase and the relative attenuation of the magnitude estimates. As audio recordings often consist of multi-pitch signals exhibiting some degree of reverberation, where both the number of pitches and the source locations are unknown, we propose to use sparse heuristics to avoid the necessity of detailed a priori assumptions on the spectral and spatial model orders. The method’s performance is evaluated using both simulated and measured audio data, with the former showing that the proposed method achieves near-optimal performance, whereas the latter confirms the method’s feasibility when used with real recordings.

This paper presents a complete characterization and solution to microphone position self-calibration problem for time-of-arrival (TOA) measurements. This is the problem of determining the positions of receivers and transmitters given all receiver-transmitter distances. Such calibration problems arise in application such as calibration of radio antenna networks, audio or ultra-sound arrays and WiFi transmitter arrays. We show for what cases such calibration problems are well-defined and derive efficient and numerically stable algorithms for the minimal TOA based self-calibration problems. The proposed algorithms are non-iterative and require no assumptions on the sensor positions. Experiments on synthetic data show that the minimal solvers are numerically stable and perform well on noisy data. The solvers are also tested on two real datasets with good results.