On optimal track-to-track fusion

Distributed Kalman filtering

“…Perform track fusion optimally for a multiple-sensor system with a specific processing architecture is treated in [295]. Other work cited in Table 2.11 are [338], [15,22,23,24], [16], [17], [19], [21], [20], [71], [72], [73]- [74], [75]- [76], [77], [78]- [79], [124], [126], [260], [261,262], [264,265], [266], [296], [303], [305] and [306]. [266] • Perform track fusion optimally for a multiple-sensor system with a specific processing architecture [295] • Track-to-track fusion for multi-sensor data fusion [296] • Common process noise on the two-sensor fused-track covariance [303] • Track association and track fusion with non-deterministic target dynamics [305] • Comparison of two-sensor tracking methods based on state vector fusion and measurement fusion [306] 2.9.…”

“…In [33], a network is modeled as a Bernoulli random topology and establish necessary and sufficient conditions for mean square sense and almost sure convergence of average consensus when network links fail. Other DKF methods and its applications can be seen in [26], [27], [28], [29], [30], [31], [32], [123], [153], [218], [219] and [220].…”

“…While achieving this approach, many fusion algorithms and filters were derived to combine local estimates local estimates [397][398] [399][400] to prove better efficiency and effectiveness. For example, the state vectors can be fused using weighted covariance [406][407] [408], information matrix [401], and covariance intersection [402] [403].…”

State estimation in a decentralized discrete time LQG control for a multisensor system

“…In fusion nodes a global state estimate is calculated. It may be equivalent to the optimal centralized estimate [4,5,10] or suboptimal [1,2,3].…”

“…In majority papers autonomous systems [1,2,3,6,12] are considered. A control, if introduced, is a known input [5] or depends on local state estimate [7], only.…”