Persymmetric Adaptive Detection of Distributed Targets With Unknown Steering Vectors
Jun Liu, Jiajia Chen, Jiajun Li, Weijian Liu
Abstract
In this paper, we consider the distributed target detection problem with unknown signal signatures in Gaussian noise with unknown covariance matrix. Two adaptive detectors are proposed by using the persymmetry of the noise covariance matrix. We derive analytical expressions for the probabilities of false alarm of the proposed detectors, which indicate their constant false alarm rate properties against the noise covariance matrix. All the theoretical expressions are confirmed by Monte Carlo simulations. Numerical examples demonstrate that the proposed detectors have better detection performance than their counterparts, especially in the case of limited training data.
Topics & Concepts
Covariance matrixConstant false alarm rateFalse alarmDetectorComputer scienceDetection theoryGaussian noiseAlgorithmNoise (video)Monte Carlo methodCovarianceGaussianMatrix (chemical analysis)MathematicsArtificial intelligenceStatisticsTelecommunicationsPhysicsImage (mathematics)Composite materialMaterials scienceQuantum mechanicsRadar Systems and Signal ProcessingDirection-of-Arrival Estimation TechniquesTarget Tracking and Data Fusion in Sensor Networks