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If you google ``fully adapted particle filters'' you will find a lot more material. The authors have considered four different and all relevant application examples. The experimental section shows that the iFDM seems to work and that it can provide interesting results. The only comparison provided is against the FFBS-type algorithm, which we know will perform worse due to its construction. I know that it is a lot of work to implement other solutions to the problem, but if one were to do so it would probably provide an even better understanding of the performance of the model and it would be interesting to see the performance of existing solution to these problems. For example, for the multitarget tracking example, the simplest solution to this problem would probably be to use an extended Kalman filter together with nearest neighbour data association. Since your targets are very well separated I would expect this solution to perform quite well. It would be interesting to compare your performance against this simple standard solution. I have not worked with the cocktail party problem and the multiuser detection problems, but for the power disaggregation problem there are interesting solutions available, see for example the following NIPS paper (which is gaining some influence): Kolter, J. Z.; Batra, S.; and Ng, A. Y. Energy disaggregation via discriminative sparse coding.