ers to the fact that the sampled received signal plus noise (after down conversion) is modelled as an autore- gressive (AR) process, thus allowing identification of the instantaneous fre- quency of this signal by an algorithm developed by El-Ghouroury and Gupta [7].3 This algorithm, which is incorporated in the frequency identifi- cation processor of Figure 3.13, inputs the received signal samples to a lin- ear adaptive filter whose coefficients are updated each sample by a stochastic gradient technique [10] in accordance with the changing statistics of the input signal. The remainder of the frequency identification processor is the fre- quency identification algorithm [7] which determines the frequency estimate from the signal spectrum, which in turn is related to the filter coefficients through an all-pole model [10]. Note, unlike its PN counterpart (the RASE technique), the ASEAT seeks to achieve acquisition times on the order of the number of stages (n1) of the LFSR as opposed to the length of the code (2n which is characteristic of active correlation serial search techniques. The ability to accomplish this depends quite heavily on the input signal-to-noise ratio, which in turn depends on the nature of the noise interference. Typically, the ASEAT works well in a broadband Gaussian noise environment even for signal-to- noise ratios down to -15 dB [6]; but, like the RASE technique, it is highly vulnerable to narrowband interference. Here the worst case offender is a Superscript(3)In [8], an ASEAT is proposed where the frequency identification processor employs a modi- fication of the spectral estimation algorithms of [9] which uses partial-correlation coefficients to identify the received signal frequency in each hop interval. DELAY IN-LOCK (n,-1)Th DETECTOR FREQUENCY SYNTHESIZER ACQUISITION FREQUENCY RECEIVED LOGIC IDENTIFICATION FH SIGNAL PROCESSOR (e.g.,MSB SELECTION) PN CODE FEEDBACK LOGIC TRACK AND MODULO 2 ADDER CLOCK LOAD/TRACK LOGIC TO TRACKING LOOP TRACK Figure 3.13. An autoregressive spectral estimati