  We propose a linear time-difference-of-arrival (TDOA) measurement model to
improve \textit{distributed} estimation performance for localized target
tracking. We design distributed filters over sparse (possibly large-scale)
communication networks using consensus-based data-fusion techniques. The
proposed distributed and localized tracking protocols considerably reduce the
sensor network's required connectivity and communication rate. We, further,
consider $\kappa$-redundant observability and fault-tolerant design in case of
losing communication links or sensor nodes. We present the minimal conditions
on the remaining sensor network (after link/node removal) such that the
distributed observability is still preserved and, thus, the sensor network can
track the (single) maneuvering target. The motivation is to reduce the
communication load versus the processing load, as the computational units are,
in general, less costly than the communication devices. We evaluate the
tracking performance via simulations in MATLAB.
