A bayesian approach to adaptive detection in nonhomogeneous environments

We consider the adaptive detection of a signal of interest embedded in colored noise, when the environment is nonhomogeneous, i.e., when the training samples used for adaptation do not share the same covariance matrix as the vector under test. A Bayesian framework is proposed where the covariance matrices of the primary and the secondary data are assumed to be random, with some appropriate joint distribution. The prior distributions of these matrices require a rough knowledge about the environment. This provides a flexible, yet simple, knowledge-aided model where the degree of nonhomogeneity can be tuned through some scalar variables. Within this framework, an approximate generalized likelihood ratio test is formulated. Accordingly, two Bayesian versions of the adaptive matched filter are presented, where the conventional maximum likelihood estimate of the primary data covariance matrix is replaced either by its minimum mean-square error estimate or by its maximum a posteriori estimate. Two detectors require generating samples distributed according to the joint posterior distribution of primary and secondary data covariance matrices. This is achieved through the use of a Gibbs sampling strategy. Numerical simulations illustrate the performances of these detectors, and compare them with those of the conventional adaptive matched filter

Knowledge-aided bayesian detection in heterogeneous environments

We address the problem of detecting a signal of interest in the presence of noise with unknown covariance matrix, using a set of training samples. We consider a situation where the environment is not homogeneous, i.e., when the covariance matrices of the primary and the secondary data are different. A knowledge-aided Bayesian framework is proposed, where these covariance matrices are considered as random, and some information about the covariance matrix of the training samples is available. Within this framework, the maximum a priori (MAP) estimate of the primary data covariance matrix is derived. It is shown that it amounts to colored loading of the sample covariance matrix of the secondary data. The MAP estimate is in turn used to yield a Bayesian version of the adaptive matched filter. Numerical simulations illustrate the performance of this detector, and compare it with the conventional adaptive matched filter

On adaptive censored CFAR detection

In an automatic radar detection system the received signal in every range resolution cell is compared with a threshold to test for the presence of a target. A Neyman-Pearson type test is used which maximizes the probability of detection for a fixed probability of false alarm. For the simple case where the noise is homogeneous a fixed threshold is chosen to achieve the designed constant false alarm rate (CFAR). In the more realistic case the noise background is non-stationary due to clutter and interference. In this situation, the threshold used for testing a particular cell is usually set adaptively using data from nearby resolution cells. A number of such adaptive schemes have been proposed and these are reviewed and the analysis of some of them extended in this dissertation new adaptive thresholding techniques for use in nonhomogeneous background environments are proposed and analyzed. It is shown that these new schemes under many conditions perform better than the methods described in the literature in terms of achieving lower probabilities of false alarm and higher probabilities of detection. First we analyze the greatest-of, GO and smallest-of, SO-CFAR detectors in time diversity transmission. Time diversity transmission is employed to combat deep fades and the loss of the signal. We then present a comparison of the detection performance and the false alarm regulation of the CA,GO and SO-CFAR detectors. Then we propose and analyze the Automatic Censored Cell Averaging CFAR detector, ACCA-CFAR, which determines whether the test cell is in the clutter or the clear region and selects only those samples that are identically distributed with the noise in the test cell to form the detection threshold. In the presence of two clutter power transitions in the reference window, the ACCA-CFAR detector is shown to achieve robust false alarm regulation performance while none of the detectors in the literature performs well. For multiple target situations we propose and analyze the Adaptive Spiky Interference Rejection detector, ASIR-CFAR, which determines and censors the interfering targets by performing cell-by-cell tests, without a priori knowledge about the number of interfering targets. In addition, the results of the Censored Cell Averaging CFAR detector, CCA-CFAR, are extended for multiple pulse transmission and compared with those of the proposed detector. For multiple target situations in nonhomogeneous clutter the Data Discriminator detector, DD-CFAR, is proposed and analyzed. The DD-CFAR detector performs two passes over the data. In the first pass, the algorithm censors any possible interfering target returns that may be present in the reference cells of the test cell. In the second pass the algorithm determines wheather [sic] the test cell is in the clutter or the clear region and selects only those samples that are identically distributed with the noise in the test cell to form the detection threshold. An analysis of the processing time required by the proposed detector is also presented, and compared with the processing time required by other detectors. Finally we propose and analyze, the Residual Cell Averaging CFAR detector, RCA-CFAR, an adaptive thresholding procedure for Rayleigh envelope distributed signal and noise where noise power residues instead of noise power estimates are processed. The fact that the noise residues become partially correlated to the same degree, if the adjacent samples are identically distributed, enable us to identify non-homogeneities in the clutter power distribution, by simply observing the consistency in the degree of correlation

Multi-Target Detection Capability of Linear Fusion Approach Under Different Swerling Models of Target Fluctuation

In evolving radar systems, detection is regarded as a fundamental stage in their receiving end. Consequently, detection performance enhancement of a CFAR variant represents the basic requirement of these systems, since the CFAR strategy plays a key role in automatic detection process. Most existing CFAR variants need to estimate the background level before constructing the detection threshold. In a multi-target state, the existence of spurious targets could cause inaccurate estimation of background level. The occurrence of this effect will result in severely degrading the performance of the CFAR algorithm. Lots of research in the CFAR design have been achieved. However, the gap in the previous works is that there is no CFAR technique that can operate in all or most environmental varieties. To overcome this challenge, the linear fusion (LF) architecture, which can operate with the most environmental and target situations, has been presented

PN Code Acquisition Using Smart Antennas and Adaptive Thresholding for Spread Spectrum Communications;

In this paper, we consider a pseudo-noise (PN) code acquisition for direct sequence spread spectrum communication in a Rayleigh fading multipath channel environment using smart antenna and adaptive thresholding automatic trimmed-mean constant false alarm rate (ATM-CFAR) processing. A smart antenna is an array of antenna elements that can modify the array pattern adaptively to minimize the effect of multiple access interference (MAI) from other users and multipath. PN code acquisition using a fixed threshold may lead to an excessive number of false alarms, and thus, adaptive thresholding ATM-CFAR processing is considered. In addition, since the interference (MAI and multipath) can be considered as outliers, an outlier determiner is embedded to the proposed system based on the interquartile range. This novel approach of combining smart antennas and adaptive thresholding ATMCFAR detection with an outlier determiner proved to be very robust since it resulted in a serious enhancement of the probability of detection

A Novel Variable Index and Excision CFAR Based Ship Detection Method on SAR Imagery

When applying the constant false alarm rate (CFAR) detector to ship detection on synthetic aperture radar (SAR) imagery, multiple interferers such as upwelling, breaking waves, ambiguities, and neighboring ships in a dense traffic area will degrade the probability of detection. In this paper, we propose a novel variable index and excision CFAR (VIE-CFAR) based ship detection method to alleviate the masking effect of multiple interferers. Firstly, we improve the variable index (VI) CFAR with an excision procedure, which censors the multiple interferers from the reference cells. And then, the paper integrates the novel CFAR concept into a ship detection scheme on SAR imagery, which adopts the VIE-CFAR to screen reference cells and the distribution to derive detection threshold. Finally, we analyze the performances of the VIE-CFAR under different environments and validate the proposed method on both ENVISAT and TerraSAR-X SAR data. The results demonstrate that the proposed method outperforms other existing detectors, especially in the presence of multiple interferers

Reduced-Rank STAP Schemes for Airborne Radar Based on Switched Joint Interpolation, Decimation and Filtering Algorithm

In this paper, we propose a reduced-rank space-time adaptive processing (STAP) technique for airborne phased array radar applications. The proposed STAP method performs dimensionality reduction by using a reduced-rank switched joint interpolation, decimation and filtering algorithm (RR-SJIDF). In this scheme, a multiple-processing-branch (MPB) framework, which contains a set of jointly optimized interpolation, decimation and filtering units, is proposed to adaptively process the observations and suppress jammers and clutter. The output is switched to the branch with the best performance according to the minimum variance criterion. In order to design the decimation unit, we present an optimal decimation scheme and a low-complexity decimation scheme. We also develop two adaptive implementations for the proposed scheme, one based on a recursive least squares (RLS) algorithm and the other on a constrained conjugate gradient (CCG) algorithm. The proposed adaptive algorithms are tested with simulated radar data. The simulation results show that the proposed RR-SJIDF STAP schemes with both the RLS and the CCG algorithms converge at a very fast speed and provide a considerable SINR improvement over the state-of-the-art reduced-rank schemes

Adaptive radar in heterogeneous environment

Radar performance in heterogeneous clutter has been a much studied topic. In all the studies so far, various forms of the sample matrix inversion (SMI) technique where used to calculate the weight vector of the processor. In this thesis an eigenanalysis-based technique known as the eigencanceler, is used. Performance of this technique is compared to the performance of the generalized likelihood ration (GLR) processor. This comparison is done using the clutter edge model, in which there is an abrupt change in the clutter power in the reference window. It is shown that the false alarm rate fluctuations, of the cell averaging constant false alarm rate (CA-CFAR) eigencanceler, depend on the number of secondary data vectors used to estimate the covariance matrix. It is also shown that when the estimate of the covariance matrix is poor, the eigencanceler is able to perform where the GLR fails. These two methods are also compared using the range-dependent clutter power model, in which the range clutter power is a Weibull random variable. It is shown that the performance of the eigencanceler depends heavily on the variance of the clutter power random variable. It is again shown that the eigencanceler is able to perform with a low number of range cell samples, where the GLR fails