mmer (no jammer state information). (Reprinted from [35].) Coherent Direct-Sequence Systems Es/N/ (dB) Figure 1.20. Channel parameter versus p and signal-to-noise ratio for best clamp- ing level. (Reprinted from [35].) it fixes k at some value between .01 and 1.0, but the jammer, on the other hand, is intelligent and assumed to know k, in which case, he chooses p to maximize D. For this scenario, Figure 1.21 illustrates D versus EJN1 and k. Clearly, much larger values of D result for this case as compared with those in Figure 1.20. We now return to a consideration of soft decision decoding metrics with jammer state information that derive their benefit from the fact that the con- tribution to the metric in those time intervals where the jammer is absent is very heavily weighted compared to that in the intervals where the jammer is known to be present. Up until now, we have assumed that knowledge of the presence or absence of the jammer during a given time interval was per- fect. With, for example, additive background noise, the estimation of Z, the jammer state parameter, will not be perfect. We now examine how to suit- ably modify the soft decision decoding metric and the impact on its perfor- mance as a result of having imperfect jammer state information. Es/N/ (dB) Figure 1.21. Channel parameter versus signal-to-noise ratio and clamping level for worst case jammer. (Reprinted from [35].) Other Coding Metrics for Pulse Jamming DS/BPSK transmission over a channel with background additive white Gaussian noise of power spectral density No and a pulse jammer with para- meters N1 = J/W and p, is equivalent to sending a BPSK signal over an addi- tive white Gaussian noise channel with noise spectral density (1.225) if Z = 1 where Z is again the jammer state parameter with probabilities as in (1.218). If perfect jammer state information were possible, then the metric of (1.217) would be used with ((1) = 1/1 (1.226) Since, as mentioned above, the background noise perfect jammer state information is not possible, then th