Search

Bayesian filtering solutions that are developed under the assumption of heavy-tailed uncertainties are more robust to outliers than the standard Gaussian ones. In this work, we consider robust nonlinear Bayesian filtering in the presence of multivariate t-distributed process and measurement noises. We develop a robust stochastic integration filter (RSIF) based on stochastic spherical-radial integration rule that achieves asymptotically exact evaluations of multivariate t-weighted integrals of nonlinear functions that arise in nonlinear Bayesian filtering framework. The superiority of the proposed scheme is demonstrated by comparing its performance against the cubature Kalman filter (CKF), a robust CKF, and the standard SIF in a representative example concerning bearings-only target tracking.

In this work, we address the target tracking problem for a coordinate-decoupled Markovian jump-mean-acceleration based maneuvering mobility model. A novel low-complexity alternative to the conventional interacting multiple model (IMM) filter is proposed for this class of mobility models. The proposed tracking algorithm utilizes a bank of interacting filters where the interactions are limited to the mixing of the mean estimates, and it exploits a fixed off-line computed Kalman gain matrix for the entire filter bank. Consequently, the proposed filter does not require matrix inversions during on-line operation which significantly reduces its complexity. Simulation results show that the performance of the low-complexity proposed scheme remains comparable to that of the traditional (highly-complex) IMM filter. Furthermore, we derive analytical expressions that iteratively evaluate the transient and steady-state performance of the proposed scheme, and establish the conditions that ensure the stability of the proposed filter. The analytical findings are in close accordance with the simulated results.

In this paper, we design and analyze a Newton-like blind equalization algorithm for the APSK system. Specifically, we exploit the principle of minimum entropy deconvolution and derive a blind equalization cost function for APSK signals and optimize it using Newton?s method. We study and evaluate the steady-state excess mean square error performance of the proposed algorithm using the concept of energy conservation. Numerical results depict a significant performance enhancement for the proposed scheme over well established blind equalization algorithms. Further, the analytical excess mean square error of the proposed algorithm is verified with computer simulations and is found to be in good conformation. HighlightsFormulation of a Newton-like blind equalization algorithm for APSK system.Discussion on stationary points of the cost in joint impulse response domain.Discussion on equalizer's tracking performance in non-stationary environment.Evaluation of moments in closed-form using Nuttal Q-function.

A fractional-norm constrained blind adaptive algorithm is presented for sparse channel equalisation. In essence, the algorithm improves on the minimisation of the constant modulus criteria by adding a sparsity-inducing lp-norm penalty (0 < p ≤ 1). The simulation results demonstrate that the proposed equaliser exploits the inherent channel sparsity effectively and exhibits faster convergence compared with its counterparts.

Exploiting the error variance relation, this letter discusses the evaluation of excess mean square error (EMSE) of constant modulus equalizers in a noise-free non-stationary environment. The EMSE analyses are presented for two equalizers - the CMA22 and the βCMA. Simulation results show a close match in theoretical and simulated errors for both algorithms.

This paper presents the simulation study of “Host based autonomous Mobile Address Translation” using NCTUns simulator. It is a network layer, end-to-end vertical handover solution, based upon modification of “Mobile IP with address Translation”. Vertical handover approaches generally require new network elements, a new layer in TCP/IP stack, or fixing a protocol at a particular layer. To enhance handover experience, recent approaches focus on reducing signalling, localizing the registration, creating hierarchies, using proxy, preparing handover in advance, predicting target network, or exploiting multicasting and path extension techniques. These approaches, however, demand change in the network infrastructure to support mobility and limit the scope of mobility. Despite end-to-end signalling, the Host based autonomous Mobile Address Translation scheme ensures minimum service disruption and distinctly allows global mobility of the mobile node without requiring any modification in the network. We have simulated the mobility of a multi-interface mobile node in a heterogeneous network environment composed of WiFi (IEEE802.11a, IEEE802.11b) and WiMAX (IEEE802.16e) access networks. Performance of the scheme is evaluated taking into account wide range of end-to-end delays between mobile node and the correspondent node, various speeds of the mobile node and different packet loss rates of the network. Based on our detailed simulation study, it has been observed that this scheme offers reduced service disruption time, packet loss and packet latency. The service disruption time is found to be significantly low (typically in the range of 10 ms) compared to that of Mobile IP (which is in the order of 100 ms); this makes this new scheme perfectly suitable for real time applications. Low service disruption time consequently reduces the packet loss by manyfold and the packet latency remains unaffected during and after handover due to translation of address at the source. The results suggest that this protocol is a viable vertical handover solution due to its simplicity, scalability, low overhead and ready deployability.

In this letter, we adaptively optimize the equalizer output energy to obtain a joint blind equalization and carrier-phase recovery solution. The resulting (multimodulus) update algorithm possesses a particular zero-memory Bussgang-type nonlinearity. We provide evidence of good performance, in comparison to existing adaptive methods, like RCA, MMA and CMA, through computer simulations for higher-order quadrature amplitude modulation signalling on symbol- and fractionally-spaced channels.

By generalizing and modifying some existing cost-functions, we present two new, generic and efficient multimodulus families of blind equalization algorithms for use in higher-order quadrature amplitude modulation based digital communication systems. Proposed algorithms are shown to be capable of blindly equalizing and recovering carrier-phase at convergence speed much faster than existing counterparts on certain QAM sizes. We show that particular examples of the proposed cost-functions include a number of existing algorithms. We also provide detailed dynamic convergence analysis which is found in good conformation with those obtained from Monte-Carlo experiments.

A constant modulus algorithm is presented for blind equalization of complex-valued communication channels. The proposed algorithm is obtained by solving a novel deterministic optimization criterion which comprises the minimization of a priori as well as a posteriori dispersion error, leading to an update equation having a particular zero-memory continuous Bussgang-type nonlinearity. We also derive a stochastic bound for the range of step-sizes for a generic Bussgang-type constant modulus algorithm. The theoretical result is validated through computer simulations.

In the field of blind source separation, Jacobi-like diagonalization-based approaches constitute an important tool for independent component analysis (ICA). Recently, simultaneous diagonalization of cumulant matrices of third- and fourth-order has been studied by a number of authors. In this work, we present an optimal parametrized composition of these cumulants that puts two classical contrasts, namely, the cumulant-based ICA and the weighted fourth-order contrast in a common framework. It is shown that the optimal weight parameter depends on the a priori statistical knowledge of the original mixing sources. Following the same spirit of the ICA algorithm, we derive the analytical solution for the case of two sources. Finally, a number of computer simulations have been performed to illustrate the behaviour of the Jacobi-like iterations for the maximization of the proposed parametrized contrast.

This letter presents two new blind equalization algorithms that are specifically designed for cross-QAM signals. Proposed algorithms minimize the dispersion of the equalizer output with respect to single or multiple cross-shaped zero error contour(s). Simulations and analysis demonstrate the good performance of the proposed algorithms

A new multimodulus algorithm is presented for blind equalization of complex-valued communication channels. The proposed algorithm is obtained by solving a novel deterministic optimization criterion that constituted the dispersion minimization of a priori as well as a posteriori quantities, leading to an update equation having a particular zero-memory continuous nonlinearity. Analyses of automatic phase-recovery and interference cancellation capabilities of the proposed algorithm are provided

We employ minimum entropy deconvolution principle for blind equalization of complex digital signals. In essence, we maximize the output energy, constrain the equalizer output not to exceed certain level and obtain an adaptive solution capable of opening a closed-eye with the recovery of true energy of signal. We provide evidence of good performance in comparison to existing adaptive methods, like CMA and its variants, through simulations.

Presented is a non-data-aided (NDA) signal-to-noise ratio (SNR) estimation method for amplitude-phase shift-keying (APSK) in the presence of additive white Gaussian noise over a quasi-static channel. The proposed estimator is based on a rank discrimination test and uses a look-up table for SNR estimation. The performance of the proposed estimator is evaluated for 8APSK and 16APSK. It is shown through Monte-Carlo simulations that the proposed estimator outperforms existing NDA moment-based SNR estimators in terms of normalised mean-square error.