Auxiliary-vector Detection on Measured Radar Data

Derived from statistical conditional optimization criteria, the auxiliary-vector (AV) detection algorithm starts from the target vector and adding non-orthogonal auxiliary vector components generates an infinite sequence of tests that converges to the ideal matched filter (MF) processor for any positive definite input autocorrelation matrix. When the input autocorrelation matrix is replaced by a conventional sample-average estimate, the algorithm effectively generates a sequence of estimators of the ideal matched filter that offer exceptional bias/covariance balance for any given finite-size observation data record. In this work, the AV algorithm is evaluated on collected airborne phased-array radar data from the MCARM program and is seen to outperform in probability of detection (for any given false alarm rate) all known and tested adaptive detectors (for example AMF, generalized likelihood ratio test, the multistage Wiener filter algorithm, etc.).