 by (2.319) the worst noise jam- mer is again the broadband jammer. Thus with the hard decision uniform channel with no jammer state information the worst noise jammer is again the broadband jammer. 2.7 OTHER CODING METRICS In this section we continue with the general slow fading non-uniform chan- nel where there are Q sub-bands, jammer, noise power distribution given by J, and hopping probability vector p where the channel parameters are a = (a1,a2, ag) and B = (B1,B2, BO). Each coded FH/MFSK chip signal has a Rayleigh fading envelope which is independent from hop to hop and there is white Gaussian receiver noise with parameter No. Up to this point the two extreme types of coding metrics, soft decision and hard decision, were examined. In this section other coding metrics are now considered. The basic approach illustrated here and the various metrics considered can be applied to other types of channels and jammers such as non-fading channels with multitone jamming. Recall that each time an M-ary coded symbol x E {1, 2, M is modu- lated as an FH/MFSK chip signal the receiver dehops the received signal and samples the outputs of M energy detectors. Denote the M-sampled energy detector outputs as e = (e1, e2, em). If the signal hopped into the q-th sub-band, then E has conditional probability density. p(exxq) (2.329) where agE + No + BqJq (2.330) m x x Thus the energy detector outputs are independent central chi-square ran- dom variables of order 2 and their square root values are Rayleigh random variables. When the M energy detector outputs are taken as the channel output, then the maximum-likelihood metric is given as m(e,x/q) defined by (2.309). This Other Coding Metrics is the soft decision ML metric which assumes knowledge of jammer state information represented by J, the jammer noise power distribution. The sub- optimum soft decision energy metric without jammer state information is denoted m(e,x) and is given by (2.279). In practical applications coded systems require a receiver that uses the coding metric i