Fast synchronisation of an Earth station to a switching satellite

A coarse-search-synchronisation technique is described for switched time-division multiple-access systems that uses a coded search signal. It is shown that this method achieves coarse search synchronisation much faster than the burst-synchronisation method described by Rapuano and Shimasaki.     

Adaptive radar detection

In the letter a new adaptive procedure for the detection of a radar target in clutter is described. The procedure is based on the development of linear models for the two hypotheses H0 and H1. Under hypothesis H0, there is no target present, and the received signal is modelled as a regressive process. Under hypothesis H1, a target is present and the received signal is modelled as an autoregressive process. Results based on actual radar data are included to confirm the validity of this new approach, and comparison is made with the previously published innovations-based detection algorithm.     

Signal detection in a nonstationary environment reformulated as anadaptive pattern classification problem

Concerns the improved detection of a nonstationary target signal in a nonstationary background. Ways to deal with the issue of nonstationarity are discussed, starting with Loeve's probabilistic theory of stationarity processes (1946, 1963). Three important tools emerge: the dynamic spectrum, the Wigner-Ville distribution as an instantaneous estimate of the dynamic spectrum and the Loeve spectrum. Procedures for the estimation of these spectra are described, and their applications are demonstrated using real-life radar data. Time, an essential dimension of learning, appears explicitly in the dynamic spectrum and Wigner-Ville distribution and implicitly in the Loeve spectrum. In each case, the 1D time series is transformed into a 2D image where the presence of nonstationarity is displayed in a more visible manner than in the original time series. This sets the stage for reformulating the signal detection problem as an adaptive pattern classification whereby we can exploit the learning property of neural nets. Hence, we describe a novel learning strategy for distinguishing between the different classes of received signals, such as 1) there is no target signal present in the received signal; 2) the target signal is weak; and 3) the target signal is strong. We present a case study based on real-life radar data. The case study demonstrates that the adaptive approach described is superior to the classical approach     

Modified Kalman filtering

A modified Kalman filtering algorithm is described. The key point of the new algorithm is a model mismatch function, which accounts for deviation of the model from the ideal condition of orthogonality between the innovations process and past observations     

Stochastic Differential Equation Theory Applied to Wireless Channels

Modeling wireless channels is essential to wireless communication systems. An autoregressive (AR) process of order one for wireless channel has long been assumed, but without a rigorous mathematical/physical basis. In this paper, we derive a first-order stochastic AR model for a flat stationary wireless channel, which comes from stochastic differential equation (SDE) theory concerning the nature of multipath fading channels. The resulting AR model describes more of the origin of multipath fading channels than previous AR models, and it can efficiently model and generate Rayleigh-distributed stationary fading channels. The Markovian property of the AR model is inherited through the SDE approach.     

Uncovering nonlinear dynamics-the case study of sea clutter

Nonlinear dynamics are basic to the characterization of many physical phenomena encountered in practice. Typically, we are given a time series of some observable(s) and the requirement is to uncover the underlying dynamics responsible for generating the time series. This problem becomes particularly challenging when the process and measurement equations of the dynamics are both nonlinear and noisy. Such a problem is exemplified by the case study of sea clutter which refers to radar backscatter from an ocean surface. After setting the stage for this case study, the paper presents tutorial reviews of: (1) the classical models of sea clutter based on the compound K distribution and (2) the application of chaos theory to sea clutter. Experimental results are presented that cast doubts on chaos as a possible nonlinear dynamical mechanism for the generation of sea clutter. Most importantly, experimental results show that on timescales smaller than a few seconds, sea clutter is very well described as a complex autoregressive process of order four or five. On larger timescales, gravity or swell waves cause this process to be modulated in both amplitude and frequency. It is shown that the amount of frequency modulation is correlated with the nonlinearity of the clutter signal. The dynamical model is an important step forward from the classical statistical approaches, but it is in its early stages of development     

Detection and estimation using an adaptive rational function filter

Proposes a new nonlinear adaptive filter structure based on rational functions. There are several advantages to the use of this filter. First, it is a universal approximator and a good extrapolator. Second, it ran be trained by a linear adaptive algorithm, which makes it suitable for real-time adaptive signal processing. Third, it has a best approximation for a specified function. To demonstrate its utility as a tool for solving adaptive signal processing problems, the authors apply the adaptive rational function filter to the problem of estimation and detection. The estimation problem pertains to the direction of arrival (DOA) estimation problem in array signal processing. For the detection problem, the authors consider the detection of a weak radar target (a small piece of ice) in an ocean environment     

Neural network approaches to image compression

This paper presents a tutor a overview of neural networks as signal processing tools for image compression. They are well suited to the problem of image compression due to their massively parallel and distributed architecture. Their characteristics are analogous to some of the features of our own visual system, which allow us to process visual information with much ease. For example, multilayer perceptions can be used as nonlinear predictors in differential pulse-code modulation (DPCM). Such predictors have been shown to increase the predictive gain relative to a linear predictor. Another active area of research is in the application of Hebbian learning to the extraction of principal components, which are the basis vectors for the optimal linear Karhunen-Loeve transform (KLT). These learning algorithms are iterative, have some computational advantages over standard eigendecomposition techniques, and can be made to adapt to changes in the input signal. Yet another model, the self-organizing feature map (SOFM), has been used with a great deal of success in the design of codebooks for vector quantization (VQ). The resulting codebooks are less sensitive to initial conditions than the standard LBG algorithm, and the topological ordering of the entries can be exploited to further increasing the coding efficiency and reducing the computational complexity