A diagonal growth curve model and some signal-processing applications

We consider a variation of the growth-curve (GC) model, referred to as the diagonal growth-curve (DGC) model, where the steering vectors and waveforms are both known and the complex amplitude matrix is constrained to be diagonal. A closed-form approximate maximum likelihood (AML) estimator for this model is derived based on the maximum likelihood principle. We analyze the statistical properties of this method theoretically and show that the AML estimate is unbiased and asymptotically statistically efficient for a large snapshot number. Via several numerical examples in array signal processing and spectral analysis, we also show that the proposed AML estimator can achieve better estimation accuracy and exhibit greater robustness than the best existing methods.