This paper presents an experiment using real Long-Term Evolution (LTE) signals to extend positioning from outdoors to indoors. LTE signals are of interest for positioning applications because of their availability indoors, where GNSS signal reception is limited. Different approaches for time of arrival (TOA) extraction are evaluated for their positioning performance, combined with an extended Kalman filter (EKF) for movement tracking. The paper shows that the performance is surprisingly good, with high visibility of cellular signals even in the difficult indoor test environment, and with a positioning error once indoors smaller than 8m in 50% of cases.

Many blind deconvolution algorithms have been designed to extract digital communications signals corrupted by inter-symbol interference. Such algorithms generally fail when applied to signals with impulsive characteristics, such as acoustic signals. In this paper, we provide a theoretical analysis and explanation as to why Bussgang-type algorithms are generally unsuitable for deconvolving impulsive signals. We then propose a novel modification of one such algorithm, the Sato algorithm, to enable it to deconvolve such signals. Sufficient conditions on the source signal to guarantee local stability of the modified Sato algorithm about a deconvolving solution are derived. Computer simulations show the efficiency of the proposed approach as compared to the Shalvi-Weinstein algorithm for deconvolving impulsive signals.

Blind source separation (BSS) is a problem found in many applications related to acoustics or communications. This paper addresses the blind source separation problem for the case where the source signals are non-stationary and the sensors are noisy. To this end, we propose several useful elementary cost functions which can be combined to an overall cost function. The elementary cost functions might have different objectives, such as uncorrelated output signals or power normalization of the output signals. Additionally, the corresponding gradients with respect to the adjustable parameters are given. We discuss the design of an overall cost function and also give a simulation example.

The search for universally applicable nonlinearities in blind signal separation has produced nonlinearities that are optimal for a given distribution, as well as nonlinearities that are most robust against model mismatch. This paper shows yet another justification for the score function, which is in some sense a very robust nonlinearity. It also shows that among the class of parameterizable nonlinearities, the threshold nonlinearity with the threshold as a parameter is able to separate any non-Gaussian distribution, a fact that is also proven in this paper.

It is a known fact that blind algorithms have convergence times of an order of magnitude longer than their nonblind counterparts. However, as shown in this letter, the knowledge of a subset of signals can greatly accelerate the convergence of blind source separation. The convergence behavior of the proposed algorithm is compared with the blind-only case.

A parameterized activation function in the form of an adaptive threshold for a single-layer neural network, which separates a mixture of signals with any distribution (except for Gaussian), is introduced. This activation function is particularly simple to implement, since it neither uses hyperbolic nor polynomial functions, unlike most other nonlinear functions used for blind separation. For some specific distributions, the stable region of the threshold parameter is derived, and optimal values for best separation performance are given. If the threshold parameter is made adaptive during the separation process, the successful separation of signals whose distribution is unknown is demonstrated and compared against other known methods.

A computationally simple nonlinearity in the form of a threshold device for the blind separation of sub-Gaussian signals is derived. Convergence is shown to be robust, fast, and comparable to that of more complex polynomial nonlinearities. Together with the known signum nonlinearity for super-Gaussian distributions, which basically is a threshold device with the threshold set to zero, the general threshold nonlinearity (with an appropriate threshold) can separate any non-Gaussian signals.

A noncoherent Gaussian minimum phase-shift keying (GMSK) detector using differential phase detection combined with the soft-output Viterbi algorithm (SOVA) is presented. This approach overcomes the severe intersymbol interference (ISI) of GMSK signals with low B/sub t/T. Unlike conventional detectors the SOVA produces soft-decision bits resulting in larger coding gains in subsequent convolutional decoders.