Track association and track fusion with nondeterministic target dynamics

Mori, Shintaro; Barker, William H.; Chong, Chee-Yee; Chang, Kuo‐Chu

doi:10.1109/taes.2002.1008994

Cited by 95 publications

(67 citation statements)

References 17 publications

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“…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.…”

Section: Msdf Systemsmentioning

confidence: 99%

Distributed Kalman filtering

Mahmoud¹

2012

Distributed Control and Filtering for Industrial Systems

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Section: Msdf Systemsmentioning

confidence: 99%

Distributed Kalman filtering

Mahmoud¹

2012

Distributed Control and Filtering for Industrial Systems

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“…We specify this construction as follows: Note that (16) where is the Kalman gain matrix. The conditional error covariance is given by (17) where can be constructed from and received information , via the recursion (4) and the representation (18) Note that the last term in (17) is local information at sensor . Manipulation of (1), as outlined in Section III, yields that (19) therefore, can be computed based on and via (4), in turn is calculated via (16).…”

Section: Communication Delaysmentioning

confidence: 99%

“…The lack of conditional independence has in turn motivated development of practically appealing approximate linear filtering approaches wherein current state estimates are optimally fused but their past correlations are ignored. These techniques aim to obtain good tracking performance by judicious choice of fixed combination coefficients [4], [5], [9], [18]. Apart from being suboptimal these techniques also assume a completely connected communication infrastructure that interconnects the sensors.…”

Section: Introductionmentioning

confidence: 99%

Distributed Tracking in Multihop Sensor Networks With Communication Delays

Rahman

Alanyali

Saligrama

2007

IEEE Trans. Signal Process.

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Abstract-We describe distributed tracking of a nonlinear dynamical system via networked sensors. The sensors communicate with each other by means of a multihop protocol over a communication network.We derive in-network processing algorithms to deal with arbitrary network topology and then extend these results to account for communication delays and packet losses. We show that these algorithms are optimal in the linear setting and achieve centralized performance. The proposed techniques differ from existing techniques in two important aspects: a) there is no designated leader/fusion node and each sensor attempts to optimally track the system trajectory based on its local observations and time-dependent information available from other sensors in the network; b) the message computation at each sensor is structurally identical. Consequently, the sensor network can be queried at any time and at any node to obtain optimal estimates for the state of the dynamical system. We then present two multihop protocols-one based on gossip and another token-based-for distributed implementation of the in-network processing techniques. We show several advantages of token-based schemes over gossip protocols: a) message complexity is significantly smaller for achieving the same performance; b) they are well-suited for situations where target and network data aggregation time-scales are comparable; and c) they are well-suited for random geometric graphs with nodes having small communication-connectivity radius-a scenario typical of ad-hoc wireless networks. This is because they can fuse data only from the set of nodes that can be visited in any time period.Index Terms-Ad-hoc wireless networks, energy efficiency, gossip, token-based protocols, link losses and delays, target tracking.

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“…They are concise and powerful in processing uncertainty problems. For the uncertainty problems with imprecise and incomplete information, it can provide an effective and accurate mathematical tool [7][8][9][10][11]. In the framework of fuzzy theories, fuzzy mathematics can describe or express different information, and then establish a relationship between them.…”

Section: Introductionmentioning

confidence: 99%