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This paper examines the target detection problem in multistatic passive radar with one non-cooperative illuminator of opportunity (IO) and multiple distributed receivers. Unlike most existing passive detectors which ignore the residual direct-path interference (DPI), we consider explicitly its effect and develop two new detectors under the conditions of known noise level and, respectively, when it...
This paper considers adaptive signal detection in stochastic homogeneous environments where the disturbance covariance matrix of both test and training signals, R, is assumed to be a random matrix with a priori knowledge of R. Unlike existing detectors assuming a known hyperparameter associated with R, a knowledge-aided detector with the capability of automatic weighting is considered by accounting...
In this paper, we consider the problem of detecting a moving target with a distributed multiple-input multiple-output (MIMO) radar when perfect waveform separations at the local receivers are no longer valid. By formulating a composite hypothesis testing problem with a subspace modeling of the target residual, the generalized likelihood ratio test (GLRT) is developed by deriving the maximum likelihood...
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...
Our previous study addresses moving target detection (MTD) using a distributed multiple-input multiple-output (MIMO) radar in clutter with non-homogeneous power. The developed detector, referred to as the MIMO-GLRT detector, assumes perfect knowledge of the clutter subspace and uses the assumed clutter subspace to construct a projection matrix which is required to compute the test statistic. In this...
We consider the problem of detecting a primary user in a cognitive radio network by employing multiple antennas at the cognitive receiver. In vehicular applications, cognitive radios typically transit regions with differing densities of primary users. Therefore, speed of detection is key, so detection based on a small number of samples is particularly advantageous for vehicular applications. Without...
In this paper, the problem of detecting a multichannel signal in the presence of spatially and temporally colored disturbance is considered. By modeling the disturbance as a multichannel auto-regressive (AR) model and treating the spatial covariance matrix as a random matrix, a parametric generalized likelihood ratio test (P-GLRT) is developed based on a Bayesian framework. The resulting P-GLRT, which...
In this paper, we address the problem of cooperatively detecting a primary user with unknown transmit power among multiple cognitive radio (CR) users with their location information available at the CR base station. A generalized likelihood ratio test (GLRT) is developed at the CR base station to first estimate the transmit power of the primary user and then detect the primary user signal. The maximum...
We consider multichannel signal detection in the presence of spatially and temporally colored disturbance, a problem also known as space-time adaptive processing (STAP) in radars. A number of sample covariance matrix based STAP detectors have been proposed, which often require a lot of training data to ensure convergence. In many practical scenarios, these detectors may suffer significant performance...
The parametric Rao and generalized likelihood ratio test (GLRT) detectors, recently developed by exploiting a multi channel autoregressive (AR) model for the disturbance, has been shown to perform well with very limited or no training data. The AR model order, however, should be estimated by some model order selection technique. Standard non-recursive implementation of the parametric detectors is...
The parametric Rao test for a multichannel adaptive signal detection problem is derived by modeling the disturbance signal as a multichannel autoregressive (AR) process. Interestingly, the parametric Rao test takes a form identical to that of the recently introduced parametric adaptive matched filter (PAMF) detector. The equivalence offers new insights into the performance and implementation of the...
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