lso, a value of B = 2 seems to give best performance. Suppose that we again use the metric of (1.228) but now the jammer state estimate is provided by means external to the decoder. In particular, assume that the probability of error in Z is described by the false alarm and missed detection probabilities PFA=Pr{Z = Z} when Z = 0 PMD = Pr{Z # Z} when Z = 1 (1.233) i.e., the probability of estimating the state of the jammer depends on the state of the jammer. Typically, we would want to choose PMD PEA since Other Coding Metrics for Pulse Jamming 1.000 0.800 SOFT UNKNOWN 0.600 0.400 0.200 SOFT KNOWN 0.000 0.000 0.200 0.400 0.600 0.800 1.000 Figure 1.22. Channel parameter VS. p for system with estimate based on signal level and with EJN1 = 5 db and No/N1 = 1.0. (Reprinted from [35].) the effect on the metric of missing a jammed channel symbol is much more severe than that produced by assuming the jammer is present when indeed he is not. For this scenario, the channel parameter D has been shown to be given by [35] + (1 - + p(1 - (1.234) which is illustrated in Figure 1.23 versus p and PEA for PMD = 10-8, EJN1 = 5 dB, and No/N1 = 1.0. Finally, it should be obvious that one could employ a metric that combines the averages of clamping the decoder input level with a jammer state esti- mate provided by external means. Such metrics have been considered in [35] and their performance analyzed by the general analysis techniques of Chapter 4, Part 1. In conclusion, we leave the reader with the thought that while many other metrics for the pulse jamming channel with additive background noise are theoretically possible, from a practical standpoint, one wants to select a metric that is easy to implement and robust in the sense that the Coherent Direct-Sequence Systems Figure 1.23. Channel parameter versus p and PEA for PMD==10-8, = EJN1 = 5 dB, and No/N1 = 1.0. (Reprinted from [35].) worst case jammer does not do much more harm than the baseline con- stant power jammer. 1.9 DISCUSSION With DS/BPSK signals we have shown that any ja