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A common technique to deploy linear prediction to nonstationary signals is time segmentation and local analysis. Variations of a process within such a segment cause inaccuracies. In this paper, we model the temporal changes of linear prediction coefficients (LPCs) as a Fourier series. We obtain a compact description of the vocal tract model limited by the predictor order and the maximum Doppler frequency. Filter stability is guaranteed by all-pass filtering, deploying the human ear's insensitivity to absolute phase. The periodicity constraint induced by the Fourier series is counteracted by oversampling in the Doppler domain. With this approach, the number of coefficients required for the vocal tract modeling is significantly reduced compared to a LPC system with block-wise adaptation while exceeding its prediction gain.As a by-product it is found that the Doppler frequency of the vocal tract is in the order of 10 Hz. A generalization of the algorithm to an auto-regressive moving average model with time-correlated filter coefficients is straight forward.

A wideband radio location system for 3D applications with high precision is introduced. For different test scenarios, the system is evaluated with respect to precision, resolution, and reproducibility. Based on ultra wideband direct sequence spread spectrum transmission at 24GHz, the radio location system shows high robustness against interference as well as suppression of the effects of reflections. Distance measurements are processed in a two-stage Kalman filter allowing object tracking with millimeter accuracy even in highly reflective scenarios.

Abstract-The convergence of iterative decoding schemes utilizing belief propagation is considered. A quantitative bound for the output L-values of a Turbo decoder is given that only depends on the received word and thus is independent from the decoder iterations. Further, it is shown that the exponential increase of the L-values in each iteration within an LDPC decoder is limited by the degree of the variable nodes.

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