Simplified qnar agc systems for adaptive signal detection

Abstract

In order to detect a deterministic signal in nonstationary noise with slow power fluctuations and obtain a constant false alarm rate, an automatic gain control (AGC) system is generally used at the input of the matched filter. But this AGC system does not differentiate between signal and noise. Thus for strong signal-to-noise ratio at the input the amplitude of the output signal is attenuated. In this dissertion, we consider an AGC system to regulate the noise power independently of the signal, that is, the solution in a broad sense which is called Quasi Noise Alone Reference (QNAR). We present two simplified methods to achieve an QNAR estimation of the noise power. Each estimator of the methods is equipped with a test element enabling it to decide whether the observed noise is contaminated by the signal or not and then adapts the value of the estimator accordingly. The first method is based on a threshold comparison scheme for testing the signal contamination of noise and the second method is the use of an adaptive threshold followed by binary integration which is analyzed to detect the signals. By computer simulations the performance of AGC systems applying the above estimation method is compared with that of the conventional QNAR estimator in the presence of constant, pseudorandom and chirp signals. Experiments are performed to detects a line target in the waterbath and defects in the Tissue-Equivalent Phantom, by combining the proposed method. these simulations and experiments show considerable performance improvement due to the introduction of the simplified QNAR AGC circuits in the detection system. Furthermore these algorithm reduce remarkably the computational requirement for realization of the QNAR estimation of noise power and are amenable to hardware implementation for certain real-time signal processing problems.