### Table 2. Effect of variation of local estimator 1 and local estimator 2 window sizes on classification accuracy (number of feature vectors = 11).

2007

"... In PAGE 6: ... Window sizes of local estimators 1 and 2 are varied and classification is performed. Table2 shows the classification accuracy for varying combinations of local estimator 1 and local estimator 2 for the number of feature vectors kept as 11. Figures 3(a)to(h) show classification accuracy versus window size of local estimator 2 with fixed values of local estimator 1.... ..."

### Table 1. Effect of number of feature vectors on classification accuracy with window sizes of local estimators 1 and 2 being 13 and 15 respectively.

2007

### Table II. Optimal traverse parameters H20849convergence angle and leg lengthH20850 estimated under various scenarios for maintaining one percent localization accuracy.

2006

### Table 1. Pseudo-code for localization estimation in a static frame- work. The number of cycles C is equal to 5 on average which is sufficient for efficient accuracy and helps for high computing speed.

### Table 4. Summary of existing localization systems. Accuracy as reported in [11].

2005

"... In PAGE 10: ... Centroid DV Hop Amorphous APIT Accuracy Fair Good Good Good Node Density gt;10 gt;8 gt;8 gt;6 Anchors Heard gt;10 gt;8 gt;8 gt;10 ANR gt;0 gt;0 gt;0 gt;3 DOI Good Good Fair Good GPSError Good Good Fair Good Overhead Smallest Largest Large Small Table 3. Comparison of range free schemes Table4 gives a global view of localization techniques classified by achievable accuracy and the type of location estimation used for various technologies.... ..."

Cited by 3

### Table 4: Classi cation accuracies in percentage of the metrics SF2 with Na ve , Kernel and cross-validated estimator with di erent localities. Bold means a signi cative (p lt; 0:05) di erence between the locally restricted and unrestricted metrics.

1999

Cited by 8

### Table 1: Localization error as a function of node density (d = 40).

2006

"... In PAGE 7: ...2 Node Density In order to evaluate the e ect of node density on predic- tion accuracy, we vary sensor range r. Table1 indicates that the localization algorithm is able to estimate the location of the moved nodes with high degree of accuracy over all the... ..."

Cited by 6

### Table 2. Image network configuration under assumption of rover localization accuracy (1 %) and two sites

2005

"... In PAGE 3: ... Based on estimations of the range measurement error and azimuth measurement error and the error propagation principle, we estimated the rover localization error under different network configurations. Table2 lists configurations of the image network that meet the desired one percent localization accuracy for different camera settings. The angle and length parameters in the table are the optimal convergence angle and traverse length, respectively.... In PAGE 4: ...Based on Table2 , we can draw the following conclusions for rover localization using two sites. (1) Rover localization error varies with the traverse length, number and distribution of tie points (landmarks), and camera system (stereo base, focal length, etc.... ..."

Cited by 3

### Table 2. Image network configuration under assumption of rover localization accuracy (1 %) and two sites

2005

"... In PAGE 3: ... Based on estimations of the range measurement error and azimuth measurement error and the error propagation principle, we estimated the rover localization error under different network configurations. Table2 lists configurations of the image network that meet the desired one percent localization accuracy for different camera settings. The angle and length parameters in the table are the optimal convergence angle and traverse length, respectively.... In PAGE 4: ...Based on Table2 , we can draw the following conclusions for rover localization using two sites. (1) Rover localization error varies with the traverse length, number and distribution of tie points (landmarks), and camera system (stereo base, focal length, etc.... ..."

Cited by 3

### Table 3 lists the comparison between various range free schemes. Tian He et al. experiment with several parameters such as node density (ND), anchors heard (AH), anchor to node range ratio (ANR), anchor percentage (AP), degree of irregularity (DOI), GPS error and placement of node and anchors. Recently research on localization is focused on incorporating the mobility model. Although mobility makes the analysis more difficult, more accuracy is obtained. In [15], Lingxuan Hu etal. use a sequential Monte Carlo Localization method and argues that they exploited mobility to improve accuracy and preci- sion of localization. Probabilistic techniques, such as Markov modeling, Kalman filtering and Bayesian analysis can also be used to determine the absolute loca- tion of a mobile node [18].

2005

"... In PAGE 10: ... Table3 . Comparison of range free schemes Table 4 gives a global view of localization techniques classified by achievable accuracy and the type of location estimation used for various technologies.... ..."

Cited by 3