Often used in low probability of intercept continuous waveform (CW) emitters, polyphase modulations can have extremely long code lengths (large processing gain), good sidelobe performance and robust Doppler tolerance. This paper presents an efficient algorithm to autonomously extract the polyphase radar modulation parameters from an intercepted waveform using a Wigner-Ville Radon transform. Results show that our method results in a small relative error in the extracted parameters for signal-to-noise ratios as low as - 6dB.

Applications of orthogonal frequency division multiplexing (OFDM) techniques to wireless communications are highly sensitive to time and frequency offsets between the transmitter and the receiver. These can be the effects of Doppler shifts due to relative motion and/or synchronisation errors. We propose an algorithm based on the Bartlett window, which greatly improves the robustness of this class of algorithms and the overall performance in uncertain channels. A simulated example of a mobile wireless communication system with relatively long channel response shows the effectiveness of this approach.

Some confusion frequently arises between random processes that are periodic, those that exhibit cyclostationarity, and others. A simple formulation is presented that makes the distinction clear in terms of two-dimensional spectra of these processes.

An algorithm is presented for smoothing piecewise stationary data from measurements corrupted by additive noise. Its main feature is the combination of Markov random field models, with Kalman filtering techniques and dynamic programming in order to smooth and segment the data within the regions of stationarity without affecting the edges. Applications to one-dimensional and two-dimensional data are given, with particular emphasis on the segmentation of multiregion images. Although application to piecewise constant data are emphasized, the algorithm can be extended to data with regions characterized by textures with which different autoregressive models are associated.< >

An algorithm for multilevel image segmentation is presented. The main feature is that it is based on repetition of a binary segmentation algorithm, in a fashion similar to the binary expansion of an integer number. The binary segmentation considered assumes a Markov Random Field model for the original scene, and an additive i.i.d, noise signal. Simulation rasults are presented, and compared with other filtering algorithms, such as median filtering.

A discrete-time system described by a truncated Volterra series whose input is a Markov chain is considered. A general explicit formula is derived for the mean value of the output process in terms of the transition-probability matrix of the input and of the Volterra kernels.

A new image segmentation algorithm is presented, based on recursive Bayes smoothing of images modeled by Markov random fields and corrupted by independent additive noise. The Bayes smoothing algorithm presented is an extension of a 1-D algorithm to 2-D and it yields the a posteriori distribution and the optimum Bayes estimate of the scene value at each pixel, using the total noisy image data. Computational concerns in 2-D, however, necessitate certain simplifying assumptions on the model and approximations on the implementation of the algorithm. In particular, the scene (noiseless image) is modeled as a Markov mesh random field and the algorithm is applied on (horizontal/vertical) strips of the image. The Bayes smoothing algorithm is applied to segmentation of two level test images and remotely sensed SAR data obtained from SEASAT, yielding remarkably good segmentation results even for very low signal to noise ratios.

This paper presents a new statistical approach to image segmentation. Making use of Gibbs distribution models of Markov random fields a dynamic programming based segmentation algorithm is developed. The algorithm is described in detail and examples are given.

A new image segmentation algorithm is presented, based on recursive Bayes smoothing of images modeled by Markov random fields and corrupted by independent additive noise. The Bayes smoothing algorithm yields the a posteriori distribution of the scene value at each pixel, given the total noisy image, in a recursive way. The a posteriori distribution together with a criterion of optimality then determine a Bayes estimate of the scene. The algorithm presented is an extension of a 1-D Bayes smoothing algorithm to 2-D and it gives the optimum Bayes estimate for the scene value at each pixel. Computational concerns in 2-D, however, necessitate certain simplifying assumptions on the model and approximations on the implementation of the algorithm. In particular, the scene (noiseless image) is modeled as a Markov mesh random field, a special class of Markov random fields, and the Bayes smoothing algorithm is applied on overlapping strips (horizontal/vertical) of the image consisting of several rows (columns). It is assumed that the signal (scene values) vector sequence along the strip is a vector Markov chain. Since signal correlation in one of the dimensions is not fully used along the edges of the strip, estimates are generated only along the middle sections of the strips. The overlapping strips are chosen such that the union of the middle sections of the strips gives the whole image. The Bayes smoothing algorithm presented here is valid for scene random fields consisting of multilevel (discrete) or continuous random variables.