Name
Abstract | ||
|---|---|---|
In this paper, we investigate the optimal performance of collaborative position location. In particular, we develop a branch-and-bound (BB) solution search strategy, coupled with the reformulation linearization technique (RLT), to solve the maximum likelihood estimation (MLE) problem for collaborative position location, which is in general a nonlinear and nonconvex optimization problem. Compared with existing work which has only approximately solved the MLE problem, our approach is guaranteed to produce the (1 - ε)-optimal solution to the MLE for arbitrarily small ε. With a guaranteed optimal solution to the MLE, we show that for some node geometries in noncollaborative position location, which can be viewed as a special case of collaborative position location, the Cramer-Rao lower bound (CRLB) for an unbiased estimator is no longer a meaningful performance benchmark. We demonstrate that the time-of-arrival (TOA) based MLE is in general a biased estimator and it sometimes has a mean square error (MSE) smaller than the CRLB, and thus can serve as a more practical performance benchmark. Finally, we compare the MLE with some existing position location schemes and demonstrate that it also serves as a good performance benchmark for collaborative position location. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/TWC.2010.01.090869 | IEEE Transactions on Wireless Communications |
Keywords | Field | DocType |
noncollaborative position location,nonconvex optimization problem,existing position location scheme,mle problem,collaborative position location,guaranteed optimal solution,good performance benchmark,practical performance benchmark,optimal performance,meaningful performance benchmark,global positioning system,upper bound,cramer rao lower bound,convergence,couplings,maximum likelihood estimation,noise,distributed algorithms,unbiased estimator,wireless sensor networks,programming,collaboration,maximum likelihood estimate,benchmark testing,mean square error,geometry,branch and bound | Cramér–Rao bound,Convergence (routing),Mathematical optimization,Upper and lower bounds,Mean squared error,Bias of an estimator,Optimization problem,Mathematics,Benchmark (computing),Linearization | Journal |
Volume | Issue | ISSN |
9 | 1 | 1536-1276 |
Citations | PageRank | References |
6 | 0.62 | 22 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tao Jia | 1 | 87 | 9.16 |
| R. M. Buehrer | 2 | 1328 | 133.42 |