  The recently proposed sequential distributed detector based on
level-triggered sampling operates as simple as the decision fusion techniques
and at the same time performs as well as the data fusion techniques. Hence, it
is well suited for resource-constrained wireless sensor networks. However, in
practical cases where sensors observe discrete-time signals, the random
overshoot above or below the sampling thresholds considerably degrades the
performance of the considered detector. We propose, for systems with stringent
energy constraints, a novel approach to tackle this problem by encoding the
overshoot into the time delay between the sampling time and the transmission
time. Specifically, each sensor computes the local log-likelihood ratio (LLR)
and samples it using level-triggered sampling. Then, it transmits a single
pulse to the fusion center (FC) after a transmission delay that is proportional
to the overshoot, as in pulse position modulation (PPM). The FC, upon receiving
a bit decodes the corresponding overshoot and recovers the transmitted LLR
value. It then updates the approximate global LLR and compares it with two
threshold to either make a decision or to continue the sequential process. We
analyze the asymptotic average detection delay performance of the proposed
scheme. We then apply the proposed sequential scheme to target detection in
wireless sensor networks under the four Swerling fluctuating target models. It
is seen that the proposed sequential distributed detector offers significant
performance advantage over conventional decision fusion techniques.
