Distributed Fusion Receding Horizon Filtering in Linear Stochastic Systems

Abstract

This paper presents a distributed receding horizon filtering algorithm for multisensor continuous-time linear stochastic systems. Distributed fusion with a weighted sum structure is applied to local receding horizon Kalman filters having different horizon lengths. The fusion estimate of the state of a dynamic system represents the optimal linear fusion by weighting matrices under the minimum mean square error criterion. The key contribution of this paper lies in the derivation of the differential equations for determining the error cross-covariances between the local receding horizon Kalman filters. The subsequent application of the proposed distributed filter to a linear dynamic system within a multisensor environment demonstrates its effectiveness.

Distributed mixed continuous‐discrete receding horizon filter for multisensory uncertain active suspension systems with measurement delays

“…. , N ; α = β) and fusion equations (35) and (36) completely establish the new mixed CD FRHF. In the particular case for mixed multisensory systems without time delays the proposed FRHF (35), (36) coincides with the known results [37].…”

“…, N ; α = β) and fusion equations (35) and (36) completely establish the new mixed CD FRHF. In the particular case for mixed multisensory systems without time delays the proposed FRHF (35), (36) coincides with the known results [37]. In early publications as has already mentioned we developed discrete versions of the FRHF for different types of discrete-time system models with/without time delays, with equal and non-equal horizon lengths, with single/multiple sensors and so on.…”

“…As we see at α = β fusion formulas (35) and (36) depend on time-update P (αα) (τ ) ≡ P (αα) (s = τ ) and measurement update P (αα) (t k ) ≡ P (αα) (s = t k ) local error auto-covariances which are known from (32) and (33), respectively. However, at α = β the covariances P (αβ) in (36) represent time-update and measurement update cross-covariances…”

“…. W (N ) ] are calculated using (36), and finally, the distributed fusion receding horizon estimatex FRHF (τ ), t k−1 < τ ≤ t k is completely implemented by (35).…”

A Hybrid Multi-sensor Multi-target Tracking Scheme with MLE and ANFIS

“…With the developments of technology, a single sensor system cannot meet needs, and multi-sensor multi-target tracking [4][5][6] has become a hot research. For multi-sensor fusion multi-target tracking systems, the two most important issues are needed to be firstly solved, one is the matching problem of different sensors on the same objective measurements, that is to say, to identify which measurement from the different sensors is derived from the same target (homologous division) and then fusion of multiple measurements from the same target to produce the equivalent joint measurement of the same target; two is data association problem of the same sensor scans measured at different time, that is to say, the correct matching association problems of measurement and measurement, measurement and tracking.…”