http://hypatia.teiath.gr/xmlui/bitstream/handle/11400/10827/1-s2.0-S1051200412001741-main.pdf;sequence=1

Optimal decentralized Kalman filter and Lainiotis filter

The Joint Probabilistic Data Association (JPDA) solves single sensor multi-target tracking in clutter, but it cannot be used directly in multi-sensor multi-target tracking (MMT) and has high computational complexity with the number of targets and the number of returns. This paper presents a hybrid method to implement MMT by combing Maximum Likelihood Estimation (MLE) with Adaptive Neuro-Fuzzy Inference System (ANFIS). The MLE is applied to classify the same source observations at one time into the same set, then the cheap JPDA(CJPDA) approach is used to calculate the data association probability, and ANFIS is used to realize the MMT. The computer simulations indicate that this scheme achieves MMT perfectly with higher precision and easy realization.

/pdf/a-hybrid-multi-sensor-multi-target-tracking-scheme-with-mle-1ednumnv6z.pdf

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

A method to implement the optimal distributed Lainiotis filter is proposed. The method is based on the a-priori determination of the optimal distribution of the measurements in parallel processors, in the sense of minimizing the computation time. The resulting optimal Lainiotis filter presents high parallelism speedup. This is very important due to the fact that, in most real-time applications, it is essential to obtain the estimate in the shortest possible time. Mathematics Subject Classification: 93E11, 93C55, 68Q10, 68Q25

/pdf/optimal-distributed-lainiotis-filter-1859ntvjyi.pdf

Optimal Distributed Lainiotis Filter

The multisensor estimation problem is considered in this paper. New distributed algorithms, which are able to locally process the information and which deliver identical results to those generated by their centralized counterparts are presented. The algorithms can be used to provide robust and computationally efficient solutions to the multisensor estimation problem. 
The proposed distributed algorithms are theoretically interesting and computationally attractive.

/pdf/multisensor-estimation-new-distributed-algorithms-s7b9zod9ad.pdf

K. N. Plataniotis

Papers

Multisensor estimation: New distributed algorithms