BEACON: an adaptive set-membership filtering technique with sparse updates

Abstract

Abstract-This paper deals with adaptive solutions to the socalled set-membership filtering (SMF) problem. The SMF methodology involves designing filters by imposing a deterministic constraint on the output error sequence. A set-membership decision feedback equalizer (SM-DFE) for equalization of a communications channel is derived, and connections with the minimum mean square error (MMSE) DFE are established. Further, an adaptive solution to the general SMF problem via a novel optimal bounding ellipsoid (OBE) algorithm called BEACON is presented. This algorithm features sparse updating, wherein it uses about 5-10% of the data to update the parameter estimates without any loss in mean-squared error performance, in comparison with the conventional recursive least-squares (RLS) algorithm. It is shown that the BEACON algorithm can also be derived as a solution to a certain constrained least-squares problem. Simulation results are presented for various adaptive signal processing examples, including estimation of a real communication channel. Further, it is shown that the algorithm can accurately track fast time variations in a nonstationary environment. This improvement is a result of incorporating an explicit test to check if an update is needed at every time instant as well as an optimal datadependent assignment to the updating weights whenever an update is required.

Decentralized set-membership adaptive estimation for clustered sensor networks

“…In 400 iterations, the total number of times an update took place in the EU-SHAPE was 170 for Mu = 1, and 253 for Mu = 2. As with the conventional BEACON [11], increasing γ will reduce the number of updates even further at the expense of an increased steady-state MSE (not shown here). …”

“…The former choice leads to an extension of the so-called DH-OBE algorithm [9], referred to here as MIDH-OBE, which offers better MSE estimation performance. The latter corresponds to an extension of the BEACON algorithm [11], termed MI-BEACON, which provides a better trade off between performance and complexity. Expressions for…”

Multiple Antenna Transmission With Channel State Information: A Low-Rate Feedback Approach

“…On the other hand, set-membership filtering [8], [10], estimates the filter with a bounded criterion. The filter is designed such that the worst-case difference between the filter output and desired output is within a certain pre-specified error bound for most inputs.…”

Distributed parameter estimation with selective cooperation

“…To realize the aforementioned selective update and cooperation strategies, we propose to employ a set-membership adaptive filtering (SMAF) approach, see, e.g., [5][6][7], to solve the underlying estimation problems. Most, if not all, SMAF algorithms feature sparse data-dependent selective parameter updates.…”