5G mmWave communication is useful for positioning due to the geometric connection between the propagation channel and the propagation environment. Channel estimation methods can exploit the resulting sparsity to estimate parameters (delay and angles) of each propagation path, which in turn can be exploited for positioning and mapping. When paths exhibit significant spread in either angle or delay, these methods break down or lead to significant biases. We present a novel tensor-based method for channel estimation that allows estimation of mmWave channel parameters in a non-parametric form. The method is able to accurately estimate the channel, even in the absence of a specular component. This in turn enables positioning and mapping using only diffuse multipath. Simulation results are provided to demonstrate the efficacy of the proposed approach.

We propose a search-free beamspace tensor-ESPRIT algorithm for millimeter wave MIMO channel estimation. It is a multidimensional generalization of beamspace-ESPRIT method by exploiting the multiple invariance structure of the measurements. Geometry-based channel model is considered to contain the channel sparsity feature. In our framework, an alternating least squares problem is solved for low rank tensor decomposition and the multidimensional parameters are automatically associated. The performance of the proposed algorithm is evaluated by considering different transformation schemes.

Multipath-assisted positioning makes use of specular multipath components (MPCs), whose parameters are geometrically related to the positions of the transceiver nodes. Diffuse scattering from rough surfaces affects the observed specular reflections in the angular and delay domains. Based on the effective roughness approach, the angular delay power spectrum can be calculated as a function of location parameters, which-in a next step-could be useful to accurately characterize the position-related information of MPCs. The calculated power spectra follow reported characteristics of stochastic multipath models, i.e. Gaussian shape in the angular domain and an exponential shape in the delay domain. The resulting angular and delay spreads are in an equivalent range to values reported in literature.

Setting up indoor localization systems is often excessively time-consuming and labor-intensive, because of the high amount of anchors to be carefully deployed or the burdensome collection of fingerprints. In this paper, we present SALMA, a novel low-cost UWB-based indoor localization system that makes use of only one anchor and that does neither require prior calibration nor training. By using only a crude floor plan and by exploiting multipath reflections, SALMA can accurately determine the position of a mobile tag using a single anchor, hence minimizing the infrastructure costs, as well as the setup time. We implement SALMA on off-the-shelf UWB devices based on the Decawave DW1000 transceiver and show that, by making use of multiple directional antennas, SALMA can also resolve ambiguities due to overlapping multipath components. An experimental evaluation in an office environment with clear line-of-sight has shown that 90% of the position estimates obtained using SALMA exhibit less than 20 cm error, with a median below 8 cm. We further study the performance of SALMA in the presence of obstructed line-of-sight conditions, moving objects and furniture, as well as in highly dynamic environments with several people moving around, showing that the system can sustain decimeter-level accuracy with a worst-case average error below 34 cm.

Accurate indoor radio positioning requires high-resolution measurements to either utilize or mitigate the impact of multipath propagation. This high resolution can be achieved using large signal-bandwidth, leading to superior time resolution and / or multiple antennas, leading to additional angle resolution. To facilitate multiple antennas, phase-coherent measurements are typically necessary. In this work we propose to employ non-phase-coherent measurements obtained from directional antennas for accurate single-anchor indoor positioning. The derived algorithm exploits beam patterns to jointly estimate multipath amplitudes to be used in maximum likelihood position estimation. Our evaluations based on measured and computer generated data demonstrate only a minor degradation in comparison to a phase-coherent processing scheme.

Highly accurate location information is a key facilitator to stimulate future services for the commercial and public sectors. Positioning and tracking of absolute positions of wireless nodes usually requires information provided from technical infrastructure, e.g., satellites or fixed anchor nodes, whose maintenance is costly and whose limited operating coverage narrows the positioning service. In this paper, we present an algorithm aimed at the tracking of absolute positions without using information from fixed anchors, odometers, or inertial measurement units. We perform radio channel measurements, in order to exploit position-related information contained in multipath components (MPCs). Tracking of the absolute node positions is enabled by the estimation of MPC parameters followed by the association of these parameters to a floorplan. To account for uncertainties in the floorplan and for propagation effects like diffraction and penetration, we recursively update the provided floorplan using the measured MPC parameters. We demonstrate the ability to localize two agent nodes without the employment of further infrastructure, using data from ultra-wideband channel measurements. Furthermore, we show the potential performance gain if also one fixed anchor is available, and we validate the algorithm for a range of different signal bandwidths and a number of nodes.

In this demo, we present a low-cost indoor localization system based on the off-the-shelf ultra-wideband transceiver Decawave DW1000. To obtain an accurate position information, the system makes use of a single anchor and of multipath reflections from walls, hence removing the need of installing a network of anchors or any other additional infrastructure. The procedure of determining the position of a tag can be divided in four consecutive stages. First, the location of virtual anchors is computed by mirroring the anchor position at reflective surfaces. Using two-way ranging, the distance and channel impulse response (CIR) between anchor and tag is obtained. This actual CIR is compared with expected CIRs from possible tag locations using a maximum likelihood approach to estimate the tag's position. Finally, a switchable directional antenna can be exploited to improve the robustness of the system by suppressing undesired, interfering multipath components. By following this procedure, the proposed system can achieve a decimeter accuracy and react to position updates in real-time.

In single-channel speech enhancement the spectral amplitude of the noisy signal is often modified while the noisy spectral phase is directly employed for signal reconstruction. Recently, additional improvement in speech enhancement performance has been reported when the noisy phase is modified. In this work, we propose a Bayesian estimator for phase of harmonics given the noisy speech. The proposed estimator relies on the fundamental frequency and the signal-to-noise ratio at harmonics. Throughout our experiments, we evaluate the performance of the proposed phase enhancement in comparison with the noisy phase, a benchmark and the clean phase as the upper-bound. The proposed method leads to joint improvement in quality and intelligibility at different SNRs and noise types.

Conventional speech enhancement methods typically utilize the noisy phase spectrum for signal reconstruction. This letter presents a novel method to estimate the clean speech phase spectrum, given the noisy speech observation in single-channel speech enhancement. The proposed method relies on the phase decomposition of the instantaneous noisy phase spectrum followed by temporal smoothing in order to reduce the large variance of noisy phase, and consequently reconstructs an enhanced instantaneous phase spectrum for signal reconstruction. The effectiveness of the proposed method is evaluated in two ways: phase enhancement-only and by quantifying the additional improvement on top of the conventional amplitude enhancement scheme where noisy phase is often used in signal reconstruction. The instrumental metrics predict a consistent improvement in perceived speech quality and speech intelligibility when the noisy phase is enhanced using the proposed phase estimation method.

In this paper, we present an overview on the previous and recent methods proposed to estimate a clean spectral phase from a noisy observation in the context of single-channel speech enhancement. The importance of phase estimation in speech enhancement is inspired by the recent reports on its usefulness in finding a phase-sensitive amplitude estimation. We present a comparative study of the recent phase estimation methods and elaborate their limits. We propose a new phase enhancement method relying on phase decomposition and time-frequency smoothing filters. We demonstrate that the proposed time-frequency phase smoothing method successfully reduces the variance of the noisy phase at harmonics. Our results on different speech and noise databases and different signal-to-noise ratios show that in contrast to the existing benchmark methods only the proposed method balances a tradeoff between a joint improvement in perceived quality of 0.2 in PESQ score and speech intelligibility of 2% by phase-only enhancement.

In conventional single-channel speech enhancement, typically the noisy spectral amplitude is modified while the noisy phase is used to reconstruct the enhanced signal. Several recent attempts have shown the effectiveness of utilizing an improved spectral phase for phase-aware speech enhancement and consequently its positive impact on the perceived speech quality. In this paper, we present a harmonic phase estimation method relying on fundamental frequency and signal-to-noise ratio (SNR) information estimated from noisy speech. The proposed method relies on SNR-based time-frequency smoothing of the unwrapped phase obtained from the decomposition of the noisy phase. To incorporate the uncertainty in the estimated phase due to unreliable voicing decision and SNR estimate, we propose a binary hypothesis test assuming speech-present and speech-absent classes representing high and low SNRs. The effectiveness of the proposed phase estimation method is evaluated for both phase-only enhancement of noisy speech and in combination with an amplitude-only enhancement scheme. We show that by enhancing the noisy phase both perceived speech quality as well as speech intelligibility are improved as predicted by the instrumental metrics and justified by subjective listening tests.