The satellite blockage problem, which adversely affects Global Positioning System (GPS) accuracy in urban environments, is addressed in this work. To provide a position solution, there must be at least four satellites within line of sight (LOS) of the receiver (vehicle). However, when satellite blockage occurs, this requirement is not met because most of the sky is obscured by tall buildings, and only a narrow sky sector is exposed to the receiver. Given the short duration of satellite blockages, an efficient solution to this problem can be accomplished through reliable modeling of vehicle motion. In this manner, information regarding vehicle motion can be more precisely obtained in the absence of a sufficient number of measurements. In this paper, a model that uses intrinsic quantities as part of the description of the vehicle path, such as curvature, tangent angle, and tangential speed, is proposed to achieve a more accurate modeling of both the trajectory and kinematics of a land vehicle. The model, which was implemented via an extended Kalman filter, was examined against currently known models during satellite blockage scenarios and demonstrated superior performance.

The commonly used direct-conversion Radio Frequency (RF) chips exhibit an additive 1/f baseband noise around the DC. The 1/f noise level increases as the transistors inside the RF chip become ever smaller. This type of noise mainly affects the low frequencies and can be very significant for small frequency allocations around the DC in Orthogonal Frequency-Division Multiple Access (OFDMA) systems. In this article we present the effects of such 1/f baseband noise on small frequency allocations for a Long Term Evolution (LTE) User Equipment (UE). We proceed to estimate the 1/f baseband noise. Suppression of the 1/f baseband noise is achieved by subtracting the estimated 1/f baseband noise from the received signal. We show that considerable processing gain is achieved when the 1/f baseband noise suppression is applied.

The performance of discrete-time Decision Directed (DD) and Non-Data Aided (NDA) first-order Phase Looked Loops (PLL) for a QPSK modulated signal is evaluated under the disturbance of thermal noise and 1/f2phase noise. A well known continuous-time model is presented that closely approximates the loop¿s behavior. Using the continuous time model, Fokker-Planck techniques are applied to compute the Mean Time to Lose Lock (MTLL) for both PLLs. We optimize the loop filter using the MTLL as the optimization criterion. On the basis of the optimal MTLL, we determine the regions where one PLL outperforms the other. Monte-Carlo simulation results are presented to validate the analytic analysis. For the setting under study, the NDA PLL outperforms the DD PLL for SNR below 4.5dB regardless of the phase noise level. Applicability of the results for a second-order PLL is discussed.

The growing demand for reliable communications leads to the need for very large mean time to lose lock (MTLL) of PLL based synchronization subsystems. These large MTLLs, of the order of months, cannot be simulated or tested in a lab. In this work a systematic approach is given to computing the MTLL of a second order PLL with parasitic delay at low SNR and high phase noise. Computed and simulated results are shown to be in good agreement for values that can be simulated.

We present in this paper a new equalization method with over 8 dB advantage in the residual ISI compared to the classical results presented by Godard and Shalvi/Weinstein (kurtosis criteria). A systematic derivation is presented for obtaining the conditional expectation for f(x/z) that does not rely on the knowledge of the convolutional noise power nor imposes any restrictions on the probability distribution of the (unobserved) input sequence. We use Edgeworth series which is directly related to quasi moments thus to cumulants, and the Laplace integral method. Although the Edgeworth expansion and the Laplace integral method are well known in non-linear optimal filtering theory, they have not yet been used in the field of blind equalization

This paper presents a novel algorithm for precise spotting of plosives. The algorithm is based on a pattern matching technique implemented with margin classifiers, such as support vector machines (SVM). A special hierarchical treatment to overcome the problem of fricative and false silence detection is presented. It uses the loss-based multi-class decisions. Furthermore, a method for smoothing the overall decisions by sequential linear programming is described. The proposed algorithm was tested on the TIMIT corpus, which produced a very high spotting accuracy. The algorithm presented here is applied to plosives detection, but can easily be adapted to any class of phonemes. 1.

A systematic approximation for the mean time to lose lock (MTLL) of a coherent third-order PN-code tracking loop has been derived. Such loops are essential in various spread spectrum systems (Global Positioning System, for example). The computation of the MTLL is based on the singular perturbation method. The application of this method to the coherent delay-locked loop (DLL) yields an approximate expression for the MTLL. Therefore, with the proposed loop model the authors are able to analyze this third-order system at a level that gives a well understanding of the nonlinear loop behavior and the exit phenomenon. The influence of a loop offset due to an acceleration rate (jerk) between transmitter and receiver on the optimal filter parameters is described by comparing MTLL and tracking error performance. As intuitively might be expected it turns out that acceleration rate and code rate are exchangeable in the sense that a lower code rate allows a higher acceleration rate (and vice versa) for the same signal-to-noise ratio in order to maintain the same performance. In a case study, GPS code tracking for objects with high jerk is briefly discussed.>

The classical result of Kramers (1940), originally related to chemical reaction rates, is applied in this letter to a second-order all-pole, coherent code tracking delay-locked loop (DLL). A very simple, explicit expression for the leading order term of the mean time to lose lock (MTLL) is presented. The dependence of the MTLL on the loop tension (offset) due to Doppler shift and code clock mismatch is given, and optimal loop parameters which minimize the MTLL are proposed.>

The performance of various AGC configurations, namely presync, postsync and an adaptive (optimal) AGC are analyzed at and above the threshold for a coherent pseudonoise (PN) second-order code tracking loop (delay lock loop, DLL) used in direct sequence spread spectrum systems. At and near the threshold the criterion of performance is chosen to be the mean time to lose lock (MTLL). Applying a recently presented method, analytic expressions for the loop performance are given, and the threshold degradation caused by Doppler rate is derived. Above the threshold, the "worst case" steady state tracking error is chosen as the loop index of performance. The performance and structure of the "globally-optimal" AGC (at and above the threshold region) is derived, and a simplified, practical and still close to optimum adaptive AGC is presented. This configuration can easily be realized by digitally controlling the AGC parameters.>

The singular perturbation method is used to approximate the mean time to lose lock (MTLL) for a second-order coherent pseudonoise code tracking delay-lock loop (DLL). Approximate expressions for the MTLL are given. The influence of loop offset due to the Doppler rate is studied, and optimal loop parameters (natural frequency and damping factor) which maximize the MTLL are presented. Upper bounds to the maximum allowable Doppler rates for various operating conditions are given.>

New upper and lower bounds for the nonlinear filtering problem are presented. The lower bounds are especially useful in the region of small diffusion coefficients where previously known bounds are inefficient. The upper and lower bounds are shown to be tight. An example demonstrating the tightness of the bounds is presented.>

The minimal mean-square error for the scalar nonlinear filtering problem is considered. Asymptotic lower and upper bounds on the error are derived for the case where the intensity of the observation noise tends to zero.

The system modeldx_{t}=m(x_{t})dt+\sigma_{o}dw_{t}and the observationdy_{t}=h(x_{t})dt+\rho dv_{t}are considered, and a lower bound on the error for the least-square estimate of a function ofx_{t}, sayz(x_{t}), is derived. This bound is then applied to derive a lower bound on the error in estimatingx_{t}in the case where the constant\sigma_{o}in the equation for the system dynamics is replaced by\sigma (x_{t}).

A lower bound on the minimal mean-square error in estimating nonlinear diffusion processes is derived. The bound holds for causal and noncausal filtering.