A discrete time filter is constructed where both the observation and signal process have non-linear dynamics with additive white Gaussian noise. Using the reference probably framework a convolution Zakai equation is obtained which updates the unnormalized conditional density. Our work obtains approximate solutions of this equation in terms of Gaussian sum when second order expansions are introd...

Conventional approaches for fixed-point implementation of digital signal processing algorithms require the scaling and word-length (WL) optimization in the algorithm level and the high-level synthesis for functional unit sharing in the architecture level. However, the algorithm-level WL optimization has a few limitations because it can neither utilize the functional unit sharing information for...

In INS/GPS integration, the data fusion algorithm involves properly handling of nonlinear models. Therefore the nonlinear filtering methods have been commonly applied in the INS/GPS integration to estimate the state vector. The most popular and commonly used method is the Extended Kalman Filter (EKF) which approximates the nonlinear state and measurement equations using the first order Taylor s...

Midpoint filter is quite effective in recovering the images confounded by the short-tailed (uniform) noise. It, however, performs poorly in the presence of additive long-tailed (impulse) noise and it does not preserve the edge structures of the image signals. Median smoother discards outliers (impulses) effectively, but it fails to provide adequate smoothing for images corrupted with nonimpulse...

The Extended Kalman Filter (EKF) has long been the de-facto standard for nonlinear state space estimation [11], primarily due to its simplicity, robustness and suitability for realtime implementations. However, an alternative approach has emerged over the last few years, namely the unscented Kalman filter (UKF). This filter claims both higher accuracy and robustness for nonlinear models. Severa...

Digital finite impulse response (FIR) filters play a very important role in digital signal processing (DSP) applications ranging from image and video processing to wireless communication. Digital FIR filter is primarily composed of multipliers, adders and delay elements. Several techniques have been reported in the open literature to implement digital FIR filters using Field Programmable Gate A...

In this work, we propose and experimentally demonstrate a novel low-complexity technique for fiber nonlinearity compensation. We achieved a transmission distance of 2818 km for a 32-GBaud dual-polarization 16QAM signal. For efficient implantation, and to facilitate integration with conventional digital signal processing (DSP) approaches, we independently compensate fiber nonlinearities after li...

This paper presents the methodology for the design of Low pass FIR digital filter using hybrid Differential Evolution (DE) method. It is an evolutionary algorithm that exhibits the features of both the basic DE and HookeJeeves exploratory move to attain the optimal solution. The basic DE and exploratory move have the capability to exploit and explore the search space to design the optimal filte...

In this paper, a nonlinear and/or nonnormal filter is proposed using rejection sampling. Generating random draws of the state-vector directly from the filtering density, the filtering estimate is simply obtained as the arithmetic average of the random draws. In the proposed filter, the random draws are recursively generated at each time. The Monte-Carlo experiments indicate that the proposed no...