  Since the groundbreaking work of the Kalman filter in the 1960s, considerable
effort has been devoted to various discrete time filters for dynamic state
estimation, especially including dozens of different types of suboptimal
implementations of the Bayes filters. This has been accompanied by the rapid
development of simulation/approximation theories and technologies. While
admitting the success of filters in many cases, this study investigates the
failure cases when they are in fact ineffective for state estimation. Several
classic models have shown that the straightforward observation-only (O2)
inference that does not need system modeling can perform better (in terms of
both accuracy and computing speed) for estimation than filters. Special
attention has been paid to quantitatively analyze when and why a filter will
not outperform the O2 inference from the information fusion perspective. Thanks
to the rapid development of advanced sensors, the O2 inference is not only
engineering friendly and computationally fast but can also be very accurate and
reliable by fusing the information received from multiple sensors. The
statistical attributes of the multi-sensor O2 inference are analyzed and
demonstrated through simulations. In the situation with limited sensors, the O2
approach can work jointly with existing clutter filtering and data association
algorithms for multi-target tracking in clutter environments. Given an adequate
number of sensors, the O2 approach can employ the multi-sensor data fusion to
deal with clutter and can handle the very general multi-target tracking
scenario with no background information.
