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DOA estimation for local scattered CDMA signals by particle swarm optimization.

Chang JC - Sensors (Basel) (2012)

Bottom Line: For conventional spectral searching estimators with local scattering, the searching complexity and estimating accuracy strictly depend on the number of search grids used during the search.In this paper, a modified PSO is presented to reduce the required search grids for the conventional spectral searching estimator with the effects of local scattering.Finally, several computer simulations are provided for illustration and comparison.

View Article: PubMed Central - PubMed

Affiliation: Department of Information Technology, Ling Tung University, Taichung 408, Taiwan. changjc@mail.ltu.edu.tw

ABSTRACT
This paper deals with the direction-of-arrival (DOA) estimation of local scattered code-division multiple access (CDMA) signals based on a particle swarm optimization (PSO) search. For conventional spectral searching estimators with local scattering, the searching complexity and estimating accuracy strictly depend on the number of search grids used during the search. In order to obtain high-resolution and accurate DOA estimation, a smaller grid size is needed. This is time consuming and it is unclear how to determine the required number of search grids. In this paper, a modified PSO is presented to reduce the required search grids for the conventional spectral searching estimator with the effects of local scattering. Finally, several computer simulations are provided for illustration and comparison.

No MeSH data available.


The required number of calculating fitness function (B) versus number of particles.
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f4-sensors-12-03228: The required number of calculating fitness function (B) versus number of particles.

Mentions: Comparison results with other estimators, including the GMUSIC, HPSO-GMUSIC and APSO-GMUSIC with μ = 100 to DOA estimation error are presented. Figure 3 depicts the convergence in terms of DOA RMSE versus the number of iterations. As a result, the HPSO-GMUSIC requires more iterations to achieve convergence. Note that the proposed APSO-GMUSIC achieves fast convergence with the selected parameters, which means that it needs less iterations to approach the desired global extreme. Figure 4 shows the required number of calculating fitness function (B) versus number of particles. For the number of particles in the population, more particles may increase success in searching for optima due to sampling state space more thoroughly. However, more particles require more evaluation cost. The HPSO-GMUSIC needs more particles to approach the desired global extreme. It is confirmed that the proposed PSO-based searching approaches can reduce the computational complexity of the GMUSIC due to the searching process. As expected, this figure also provides a great improvement over the convergence rate on optimization problems. In fact, additional adaptive multiple inertia weight operation can improve the searching speed and RMSE performance further. Figure 5 presents the RMSE of DOA estimation versus varying angular spreads. We note that the subspace-based techniques show serious degradation when faced with local scatters. Local scattering may be viewed as a form of model error and gives rise to the perturbation of the noise subspace. Again, these figures show that the proposed APSO-GMUSIC method yields significantly superior performance over the other methods in the presence of local scatters. For comparison, the result of GA-GMUSIC estimator is also provided. The same parameters of GA-GMUSIC estimator are used in [17]. Figure 6 shows the RMSE versus different SNR of the desired user for angular spreads 2Δp = 1°. For the low SNR case, all of methods may produce highly biased estimates. Clearly, with the compatible searching resolution, the APSO-GMUSIC can save the required number of searching grids and improve the RMSE performance, as compared with the other estimators. The GA-GMUSIC has a poor performance than HPSO-GMUSIC and APSO-GMUSIC. It is well known that premature convergence degrades the performance of GA and reduces the search ability [18]. In addition, it has been shown that the performance of the PSO algorithm with linearly decreasing inertia weight has the ability to quickly converge, the PSO may lack global search ability at the end of a run and may fail to find the required optima in cases when the problem to be solved is too complicated and complex [19]. But to some extent, this can be overcome by employing the proposed adaptive multiple strategy for adjusting the inertia weight. Finally, in Figure 7, we compare the RMSE performance against the number of active user P, given SNR = 20 dBW and angular spread 2Δp = 1°. Basically, the RMSE is increased quite steadily with the increase of P. It can be observed that the APSO-GMUSIC obtain more performance improvement when the number of users P is reasonably increasing. Among them, the proposed APSO method achieves the lowest RMSE.


DOA estimation for local scattered CDMA signals by particle swarm optimization.

Chang JC - Sensors (Basel) (2012)

The required number of calculating fitness function (B) versus number of particles.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3376592&req=5

f4-sensors-12-03228: The required number of calculating fitness function (B) versus number of particles.
Mentions: Comparison results with other estimators, including the GMUSIC, HPSO-GMUSIC and APSO-GMUSIC with μ = 100 to DOA estimation error are presented. Figure 3 depicts the convergence in terms of DOA RMSE versus the number of iterations. As a result, the HPSO-GMUSIC requires more iterations to achieve convergence. Note that the proposed APSO-GMUSIC achieves fast convergence with the selected parameters, which means that it needs less iterations to approach the desired global extreme. Figure 4 shows the required number of calculating fitness function (B) versus number of particles. For the number of particles in the population, more particles may increase success in searching for optima due to sampling state space more thoroughly. However, more particles require more evaluation cost. The HPSO-GMUSIC needs more particles to approach the desired global extreme. It is confirmed that the proposed PSO-based searching approaches can reduce the computational complexity of the GMUSIC due to the searching process. As expected, this figure also provides a great improvement over the convergence rate on optimization problems. In fact, additional adaptive multiple inertia weight operation can improve the searching speed and RMSE performance further. Figure 5 presents the RMSE of DOA estimation versus varying angular spreads. We note that the subspace-based techniques show serious degradation when faced with local scatters. Local scattering may be viewed as a form of model error and gives rise to the perturbation of the noise subspace. Again, these figures show that the proposed APSO-GMUSIC method yields significantly superior performance over the other methods in the presence of local scatters. For comparison, the result of GA-GMUSIC estimator is also provided. The same parameters of GA-GMUSIC estimator are used in [17]. Figure 6 shows the RMSE versus different SNR of the desired user for angular spreads 2Δp = 1°. For the low SNR case, all of methods may produce highly biased estimates. Clearly, with the compatible searching resolution, the APSO-GMUSIC can save the required number of searching grids and improve the RMSE performance, as compared with the other estimators. The GA-GMUSIC has a poor performance than HPSO-GMUSIC and APSO-GMUSIC. It is well known that premature convergence degrades the performance of GA and reduces the search ability [18]. In addition, it has been shown that the performance of the PSO algorithm with linearly decreasing inertia weight has the ability to quickly converge, the PSO may lack global search ability at the end of a run and may fail to find the required optima in cases when the problem to be solved is too complicated and complex [19]. But to some extent, this can be overcome by employing the proposed adaptive multiple strategy for adjusting the inertia weight. Finally, in Figure 7, we compare the RMSE performance against the number of active user P, given SNR = 20 dBW and angular spread 2Δp = 1°. Basically, the RMSE is increased quite steadily with the increase of P. It can be observed that the APSO-GMUSIC obtain more performance improvement when the number of users P is reasonably increasing. Among them, the proposed APSO method achieves the lowest RMSE.

Bottom Line: For conventional spectral searching estimators with local scattering, the searching complexity and estimating accuracy strictly depend on the number of search grids used during the search.In this paper, a modified PSO is presented to reduce the required search grids for the conventional spectral searching estimator with the effects of local scattering.Finally, several computer simulations are provided for illustration and comparison.

View Article: PubMed Central - PubMed

Affiliation: Department of Information Technology, Ling Tung University, Taichung 408, Taiwan. changjc@mail.ltu.edu.tw

ABSTRACT
This paper deals with the direction-of-arrival (DOA) estimation of local scattered code-division multiple access (CDMA) signals based on a particle swarm optimization (PSO) search. For conventional spectral searching estimators with local scattering, the searching complexity and estimating accuracy strictly depend on the number of search grids used during the search. In order to obtain high-resolution and accurate DOA estimation, a smaller grid size is needed. This is time consuming and it is unclear how to determine the required number of search grids. In this paper, a modified PSO is presented to reduce the required search grids for the conventional spectral searching estimator with the effects of local scattering. Finally, several computer simulations are provided for illustration and comparison.

No MeSH data available.