Presence Of Gaussian Interference Research Articles

Diffuse Multipath Exploitation for Adaptive Detection of Range Distributed Targets

This paper studies adaptive radar detection of range distributed targets in the presence of Gaussian interference and possible diffuse multipath returns modeled as independent zero-mean complex circular Gaussian random vectors with unknown covariance matrices. For this problem, an adaptive constrained generalized likelihood ratio (ACGLR) test is devised, where in each range cell of the primary data the covariance matrix (due to both multipath and disturbance echoes) is forced to belong to a neighborhood of the secondary data sample covariance. The size of the uncertainty set is determined adaptively employing jointly a union-intersection test and an expectation likelihood (EL)-based estimate. Besides, an adaptive detector based on the complex parameter Rao test criterion is derived. Remarkably, both the two new architectures possess the desired constant false alarm rate (CFAR) property with respect to the disturbance covariance. Finally, their detection performance is assessed and validated via numerical examples.

Distributed Recovery of a Gaussian Source in Interference with Successive Lattice Processing

A scheme for recovery of a signal by distributed listeners in the presence of Gaussian interference is constructed by exhausting an “iterative power reduction” property. An upper bound for the scheme’s achieved mean-squared-error distortion is derived. The strategy exposes a parameter search problem, which, when solved, causes the scheme to outperform others of its kind. Performance of a blocklength-one scheme is simulated and is seen to improve over plain source coding without compression in the presence of many interferers, and experiences less outages over ensembles of channels.

Fluctuating target detection in presence of non Gaussian clutter in OFDM radars

Today, Orthogonal Frequency Division Multiplexing (OFDM) radars are very interested in detection of low Radar Cross Section (RCS) targets. A vast number of research work has been reported about target detection in OFDM radars. But all these papers concern detection in presence of Gaussian interference and also assume that target has a constant scattering coefficient or RCS. In this paper, we present a Generalized Likelihood Ratio (GLR) detector for detecting a moving target in presence of Gaussian interference and evaluate its performance in presence of different types of clutter distribution and target fluctuation. We assume three scenarios for detecting a fluctuating target in the presence of four different distributions of clutter; Rayleigh, Log-normal, Weibull and K-compound. In the first scenario, we consider the RCS's of target are constant subcarrier to subcarrier and pulse to pulse in a burst of pulses while they are varying fast according to Chi-square probability density function (swerling case II) burst to burst. In the second scenario, we assume that the RCS of target varies slowly according to Chi-square probability density function (swerling case I) subcarrier to subcarrier and they are constant pulse to pulse in a burst of pulses while they are varing fast according to Chi-square probability density function burst to burst. And in the third scenario, we assume the scattering coefficients of target are varing fast subcarrier to subcarrier and burst to burst, while they are constant pulse to pulse in a burst of pulses. We provide a few numerical examples to illustrate the detection performance of moving target under conditions of three scenarios, fluctuating models and different distributions of clutter and demonstrate the achieved performance improvement due to the OFDM signaling.

Adaptive detectors with diagonal loading for airborne multi-input multi-output radar

The problem of adaptive target detection for airborne multi-input multi-output (MIMO) radars with space–time receivers in the presence of Gaussian interference (including clutter and noise) is studied. Previous work has assumed the interference covariance matrix to be known. The case with unknown covariance matrix is investigated here. By exploiting the low rank property of clutter subspace, generalised likelihood ratio test detector and adaptive matched filter detector with diagonal loading are suggested to improve the detection performance of MIMO radars in limited secondary data case. The closed-form detection probabilities and false alarm probabilities of the two proposed detectors are derived and numerically evaluated. Theoretical analysis and numerical results show the advantages of the proposed detectors.

Optimized diversity combining with imperfect channel estimation

In a communication system using receive diversity and linear combining in the presence of cochannel interference (CCI), optimum combining (OC) is known to give the best error performance since it maximizes the instantaneous signal-to-interference-plus-noise ratio of the combiner output, and consequently, in the presence of Gaussian interference plus noise, it minimizes the error rate. However, this is based on the assumption that a perfect estimate of the channel is available. Channel estimation methods in reality use some overhead. When the channel is time-invariant, the estimation error decreases with increase in the amount of overhead, like the number of pilot symbols. With the growing need for high data rate applications, the amount of overhead that can be allocated for the estimation of the channel needs to be reduced, and the channel estimation error cannot be ignored. In this situation, replacing the channel by its imperfect estimate in the OC weight vector no longer results in an optimum scheme. We have to find an optimum scheme based on the channel estimation method and the detection criterion, which results in what we call optimized diversity combining (ODC). Here we focus on ODC resulting from a pilot symbol based maximum likelihood (ML) channel estimation method applied to a correlated flat Rayleigh fading channel in the presence of CCI and additive noise. The channel is randomly time-invariant during the reception of pilot and data symbols. The decision rule, which is optimum in the ML sense, is derived using concepts of Gaussian and Wishart statistics. Numerical results show that ODC can perform significantly better than OC with imperfect channnel estimates by appropriate choice of system parameters