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With the expected growth of Machine-to-Machine (M2M) communication, new requirements for future communication systems have to be considered. Traffic patterns in M2M communication fundamentally differ from human based communication. Especially packets in M2M are rather small and transmitted sporadically only. Moreover, nodes for M2M communication are often of reduced functionality which makes complex...
In this paper, we develop a unified Bayesian approach that enables the prediction of binary random events and random scalar fields from heterogeneous data collected by mobile sensor networks with different detectors and sensors. The heterogeneous uncertainties such as different false detection rates and measurement noises are taken into account. This proposed unified approach exploits the statistical...
This paper studies the multi-cell cooperative transmission based on interference alignment (IA). A new macro-diversity approach is presented in which each BS not only transmits data to its serving users but also transmits data to the users belonging to the cooperative BSs. By coherently designing the precoding and detection matrices, desired signals from multi-BSs are detected to achieve macro-diversity...
the problem of adaptive detection of spatially distributed targets or targets embedded in no homogeneous clutter with unknown covariance matrix is studied. At first, assume the clutter is complex circular zero-mean Gaussian clutter with an unknown positive definite covariance matrix, and it is independent of the covariance matrix vector under test, the secondary data are assumed to be random, then...
In this paper, we consider an adaptive subspace detector for partially homogeneous environments. In this environment, the clutter covariance matrix (CCM) of secondary data is equal to the CCM of the cell under test (CUT), except for a real constant factor. We also suppose that we have some prior knowledge of the CCM, and the prior knowledge is controlled by the parameters of the statistical distribution...
Detecting the presence of a white Gaussian signal distorted by a noisy time-varying channel is addressed by means of three different detectors. First, the generalized likelihood ratio test (GLRT) is found for the case where the channel has no temporal structure, resulting in the well-known Bartlett's test. Then it is shown that, under the transformation group given by scaling factors, a locally most...
In this paper, an affine invariance feature detection method based on Scale Invariant Feature Transform (SIFT) and Maximally Stable Extremal Regions (MSER) is proposed. Classical SIFT algorithm is not robust to affine deformations, because it is based on DOG detector which extracts circle regions for keypoint location. In order to overcome this disadvantage, DOG detector in conventional SIFT algorithm...
In many statistical signal processing applications, the quality of the estimation of parameters of interest plays an important role. We focus in this paper, on the estimation of the covariance matrix. In the classical Gaussian context, the Sample Covariance Matrix (SCM) is the most often used, since it is the Maximum Likelihood estimate. It is easy to manage and has a lot of well-known statistical...
We consider a decentralized detection problem in a power-constrained wireless sensor networks (WSNs), in which a number of sensor nodes collaborate to detect the presence of a deterministic vector signal. The signal to be detected is assumed known a priori. Each sensor conducts a local linear processing to convert its observations into one or multiple messages. The messages are conveyed to the fusion...
In this paper, we consider the detection of a deterministic signal with an unknown scaling amplitude in the presence of a colored noise, when there is a covariance mismatch between the null and alternative hypotheses. Specifically, we consider a scenario where the target incurs an additional subspace interference that is orthogonal to the target steering vector and only present under the alternative...
This paper presents a novel strategy for dam monitoring by repeat-pass SAR interferometry. The proposed approach couples sub-band / sub-aperture decomposition prior to the GLRT-LQ detector. This method is tested with spaceborne InSAR images provided by the TerraSAR-X satellite.
We study the constant false alarm rate matched subspace detector (CFAR MSD) of a signal observed under additive noise following a complex elliptically symmetric (CES) distribution which include the class of compound-Gaussian (CG) distributions as special cases. We prove that the detector is distribution-free under the null (signal free) hypothesis and derive simple expressions for the probability...
This paper addresses the target detecting problem of the shared-spectrum multistatic radar. All of the transmitters emit the same waveforms in the same spectrum in this radar system. By widely separating the platforms of the shared-spectrum radars and adopting the temporal diversity of transmitter-receiver pairs, the spatial diversity is exploited to improve the detection performance. The signal model...
The generalized coherence (GC) estimate is a well studied statistic for detection of a common but unknown signal on several noisy channels. In this paper, it is shown that the GC detector arises naturally from a Bayesian perspective. Specifically, it is derived as a test of the hypothesis that the signals in the channels are independent Gaussian processes against the hypothesis that the processes...
This paper considers a parametric approach for adaptive multichannel signal detection, where the disturbance is modeled by a multichannel auto-regressive (AR) process. Motivated by the fact that a symmetric antenna geometry usually yields a persymmetric structure on the covariance matrix of disturbance, a new persymmetric AR (PAR) modeling for the disturbance is proposed and, accordingly, a persymmetric...
This paper deals with the problem of covariance matrix estimation for radar signal processing applications. We propose and analyze a class of estimators which do not require any knowledge about the probability distribution of the sample support and exploit the characteristics of the positive definite matrix space. Any estimator of the class is associated with a suitable distance in the considered...
In this paper we analyze sea clutter data recorded by the IPIX-radar and we show that the coherent radar returns can be modelled as a compound-Gaussian process with inverse-Gamma texture. We also investigate the performance of two adaptive detectors by processing real recorded sea clutter data.
The problem of adaptive detection for a multichannel signal in space-time colored compound Gaussian clutter is studied. The speckle component of the clutter is modeled as a multichannel autoregressive (MAR) process. Firstly, assuming that the MAR parameters are known, we derive a parametric generalized likelihood ratio test (PGLRT) against compound Gaussian clutter. By replacing the ideal MAR parameters...
The FRACTA algorithm and its enhancements has been shown to be an effective STAP methodology for the airbore or spaceborne radar configuration in which there exists nonhomogenous clutter, jammer, and dense target clusters. But the FRACTA algorithm is rather complicated and has higher computational requirements, and can't estimate the target parameter accurately. In order to overcome the defects and...
Many standard multivariate detection algorithms used in hyperspectral image analysis (e.g., Mahalanobis distance, matched filter) rely on the inverse covariance matrix, C−1. The inverse covariance matrix has a clear geometrical interpretation as a “whitening” operator that transforms the original measurement coordinates such that the background data distribution is uncorrelated with equal variance...
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