lustrated in Figure 1.1, which was originally suggested in [14]. A more detailed discussion of the various forms of the structures themselves and how they fit into the overall acquisition scheme will follow shortly. DETECTOR STRUCTURE COHERENT NON-COHERENT BAYES NEYMAN-PEARSON OTHER LOW DECISION RATE HIGH DECISION RATE (ACTIVE) (PASSIVE) (MATCHED FILTER) Presidentiat VARIABLE DWELL TIME FIXED DWELL TIME (SEQUENTIAL DETECTION) MULTIPLE DWELL TIME SINGLE DWELL TIME VERIFICATION WITH FULL PERIOD PARTIAL PERIOD VERIFICATION WITH IMMEDIATE REJECTION IMMEDIATE REJECTION CORRELATION CORRELATION Figure 1.1. Structure of detectors for acquisition purposes (reprinted from [38]). Receivers Historical Survey Not unlike the classification of detector types, the classification of acqui- sition schemes according to search strategy follows a tree structure [14] (see Figure 1.2) where many of the categories produce the next level of the tree by a simple dichotomy. Perhaps the simplest, at least conceptually, of the search techniques on the first level of the hierarchy is the maximum-likeli- hood algorithm. In its strictest form, the algorithm requires that the input PN signal be correlated with all possible code positions (or perhaps frac- tional code positions) of the local PN code replica. The correlations are assumed to be performed in parallel and as such the corresponding detec- tor outputs all pertain to the identical observation of received signal plus noise. The correct PN alignment is chosen as that local code phase position which produced the maximum detector output. In a less strict version, the maximum-likelihood algorithm4 can be implemented in a serial fashion (see Figure 1.3). Here the input PN signal is serially correlated with all possible code positions (or perhaps again fractional code positions) of the local PN code replica and the corresponding detector outputs stored. At the end of the test, the correct PN alignment is again chosen as that local code phase position which produced the maximum detector output.