**Nicholas B. Pulsone and Charles M. Rader**
**MIT Lincoln Laboratory**
**Lexington, MA**
**Email: pulsone@ll.mit.edu**

**Abstract**** **
Research in the area
of signal detection in the presence of unknown interference has resulted
in a number of adaptive detection algorithms. Examples of such algorithms
include the Adaptive Matched Filter (AMF), the Generalized Likelihood Ratio
Test (GLRT), and the Adaptive Coherence Estimator (ACE). Each of these
algorithms results in a tradeoff between detection performance for matched
signals and rejection performance for mismatch signals. For example, AMF
has better matched signal detection characteristics than ACE, but ACE has
better mismatched signal rejection capabilities. This paper introduces
a new detection algorithm which we call Adaptive Beamformer Orthogonal
Rejection Test (ABORT). Our test decides if an observation contains a multidimensional
signal belonging to one subspace or if it contains a multidimensional signal
belonging to an orthogonal subspace when unknown complex Gaussian noise
is present. In our analysis we use a statistical hypothesis framework to
develop a generalized likelihood ratio decision rule. We evaluate the performance
of this decision rule in both the matched and mismatched signal cases.
Our results show that in the matched signal case, ABORT’s detection performance
exceeds that of ACE and is comparable to AMF and GLRT. In the mismatched
signal case, ABORT’s discrimination capability is better than AMF and GLRT,
but not as good as ACE’s.