Search

Target tracking in wireless sensor networks is traditionally achieved by localization and tracking (LAT), where the sensors are first localized, and in a later stage the target is tracked. This approach is sub-optimal since the sensor-target observations are not used to refine the position estimates of the sensors. In contrast, simultaneous localization and tracking (SLAT) uses these observations to track the target while simultaneously localizing the sensors. In this paper, we propose a novel centralized SLAT method based on real-time nonparametric belief propagation, which has nearly the same complexity and the same communication cost as LAT, and can provide both sensors' and target's estimated distributions in non-Gaussian form., COOPLOC

In target tracking applications where many sensors must have a common view of the target’s state, distributed particle filtering with belief consensus is an attractive solution. It allows for a fully distributed, scalable solution, guarantees exact consensus in connected networks, and convergences fast for network with high connectivity. However, for medium access control, high connectivity is detrimental, possibly leading to a different convergence/performance trade-off. We study the delay/performance trade-off of distributed particle filtering with belief consensus in the presence of time division medium access control. We found that for small networks, (i) the impact of max-consensus should be accounted for; (ii) a simple schedule combined with a large communication range gives the best delay/performance trade-off., COOPLOC

Low density parity check (LDPC) codes can be decoded with a variety of decoding algorithms, offering a trade-off in terms of complexity, latency, and performance. We describe seven distinct LDPC decoders and provide a performance comparison for a practical regular LDPC code. Our simulations indicate that the best performance/latency trade-off is achieved by one version ofthe reweighted max-product decoder. When latency is not an issue, the traditional sum-product decoder yields the best performance.

In target tracking applications where many sensors must have a common view of the target’s state, distributed particle filtering with belief consensus is an attractive solution. It allows for a fully distributed, scalable solution, guarantees exact consensus in connected networks, and convergences fast for network with high connectivity. However, for medium access control, high connectivity is detrimental, possibly leading to a different convergence/performance trade-off. We study the delay/performance trade-off of distributed particle filtering with belief consensus in the presence of time division medium access control. We found that for small networks, (i) the impact of max-consensus should be accounted for; (ii) a simple schedule combined with a large communication range gives the best delay/performance trade-off., COOPLOC

Low density parity check (LDPC) codes can be decoded with a variety of decoding algorithms, offering a trade-off in terms of complexity, latency, and performance. We describe seven distinct LDPC decoders and provide a performance comparison for a practical regular LDPC code. Our simulations indicate that the best performance/latency trade-off is achieved by one version ofthe reweighted max-product decoder. When latency is not an issue, the traditional sum-product decoder yields the best performance.

In target tracking applications where many sensors must have a common view of the target’s state, distributed particle filtering with belief consensus is an attractive solution. It allows for a fully distributed, scalable solution, guarantees exact consensus in connected networks, and convergences fast for network with high connectivity. However, for medium access control, high connectivity is detrimental, possibly leading to a different convergence/performance trade-off. We study the delay/performance trade-off of distributed particle filtering with belief consensus in the presence of time division medium access control. We found that for small networks, (i) the impact of max-consensus should be accounted for; (ii) a simple schedule combined with a large communication range gives the best delay/performance trade-off., COOPLOC

In target tracking applications where many sensors must have a common view of the target’s state, distributed particle filtering with belief consensus is an attractive solution. It allows for a fully distributed, scalable solution, guarantees exact consensus in connected networks, and convergences fast for network with high connectivity. However, for medium access control, high connectivity is detrimental, possibly leading to a different convergence/performance trade-off. We study the delay/performance trade-off of distributed particle filtering with belief consensus in the presence of time division medium access control. We found that for small networks, (i) the impact of max-consensus should be accounted for; (ii) a simple schedule combined with a large communication range gives the best delay/performance trade-off., COOPLOC

Low density parity check (LDPC) codes can be decoded with a variety of decoding algorithms, offering a trade-off in terms of complexity, latency, and performance. We describe seven distinct LDPC decoders and provide a performance comparison for a practical regular LDPC code. Our simulations indicate that the best performance/latency trade-off is achieved by one version ofthe reweighted max-product decoder. When latency is not an issue, the traditional sum-product decoder yields the best performance.

Low density parity check (LDPC) codes can be decoded with a variety of decoding algorithms, offering a trade-off in terms of complexity, latency, and performance. We describe seven distinct LDPC decoders and provide a performance comparison for a practical regular LDPC code. Our simulations indicate that the best performance/latency trade-off is achieved by one version ofthe reweighted max-product decoder. When latency is not an issue, the traditional sum-product decoder yields the best performance.

In target tracking applications where many sensors must have a common view of the target’s state, distributed particle filtering with belief consensus is an attractive solution. It allows for a fully distributed, scalable solution, guarantees exact consensus in connected networks, and convergences fast for network with high connectivity. However, for medium access control, high connectivity is detrimental, possibly leading to a different convergence/performance trade-off. We study the delay/performance trade-off of distributed particle filtering with belief consensus in the presence of time division medium access control. We found that for small networks, (i) the impact of max-consensus should be accounted for; (ii) a simple schedule combined with a large communication range gives the best delay/performance trade-off., COOPLOC

Low density parity check (LDPC) codes can be decoded with a variety of decoding algorithms, offering a trade-off in terms of complexity, latency, and performance. We describe seven distinct LDPC decoders and provide a performance comparison for a practical regular LDPC code. Our simulations indicate that the best performance/latency trade-off is achieved by one version ofthe reweighted max-product decoder. When latency is not an issue, the traditional sum-product decoder yields the best performance.