Sharif Digital Repository

Pulse compression is an active research topic in radar systems. A problem in pulse compression is masking of small targets by the range sidelobes of large nearby targets. A new filter structure is introduced here to suppress the sidelobes of radar signals that result from standard matched filtering. The proposed filter is applicable for any type of binary coding signals. Several techniques are used to calculate the filter coefficients such as Wiener filter technique, Lagrange multiplier method, and linear programming (LP) algorithm. Also, a weighting function is utilized to shape the sidelobe energy in an iterative manner that will yield more sidelobe reduction. Comparison of the proposed... 

Signal processing in noise radar is always done by calculating the cross-correlation between transmitted and received signals. Strong echoes of near targets present relatively high sidelobes in the correlation output, so they can conceal weak echoes of weak targets (masking effect). Many classical and modern methods for alleviating this effect have been offered, however, most of them are based on signal processing in the receiver side. In this paper, a new method of waveform design, able to decrease masking effect, is presented. Contrary to the other methods, this method keeps the matched filter structure in receiver unchanged and does not need performing any extra process in the receiver... 

This paper presents efficient denoising and lossy compression schemes for electrocardiogram (ECG) signals based on a modified extended Kalman filter (EKF) structure. We have used a previously introduced two-dimensional EKF structure and modified its governing equations to be extended to a 17-dimensional case. The new EKF structure is used not only for denoising, but also for compression, since it provides estimation for each of the new 15 model parameters. Using these specific parameters, the signal is reconstructed with regard to the dynamical equations of the model. The performances of the proposed method are evaluated using standard denoising and compression efficiency measures. For... 

In this paper, a novel nonlinear joint dynamical model is presented, which is based on a set of coupled ordinary differential equations of motion and a Gaussian mixture model representation of pulsatile cardiovascular (CV) signals. In the proposed framework, the joint interdependences of CV signals are incorporated by assuming a unique angular frequency that controls the limit cycle of the heart rate. Moreover, the time consequence of CV signals is controlled by the same phase parameter that results in the space dimensionality reduction. These joint equations together with linear assignments to observation are further used in the Kalman filter structure for estimation and tracking. Moreover,... 

Any channelized receiver suffers from a phenomenon referred to as the rabbit ear effect. This phenomenon appears when an out-of-band pulse generates a distorted response at the output of the selected filter. In this paper, the rabbit ear effect is analyzed, and a triband filter structure is proposed to identify and suppress the rabbit ear effect. The average likelihood detection strategy is adopted to optimize the parameters of the triband filter, as well as the detector structure. It is shown that although competing methods reject all short-duration pulses, the proposed system preserves all in-band pulses, while eliminating most of the out-of-band pulses (i.e., rabbit ear pulses)