## 5 Approaches of Optimal State Estimation Utilizing Filtering
### Real Data Collection from GNSS Receivers and Sensors Fusion
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Ref.** & **Algorithm** & \begin{tabular}{c} **Acovery** \\ **Employed** \\ \end{tabular} & **Accuracy** & \
\begin{tabular}{c} **Geolocation** \\ **Technique** \\ **Used** \\ \end{tabular} & **Field Source** & **Sensor Type** &
\begin{tabular}{c} **No. of Sensors** \\ **Used** \\ \end{tabular} & **Sensors State** & **Emitter Type** & **Emitter State** \\ \hline
[133] & GA & 30.9\% & TDOA & near- field & active & 5 & stationary & active & stationary \\
[95] & GA & \textgreater{}60 m & TDOA & far- field & – & 3 to 5 & stationary & – & stationary \\
[134] & WIS-RW & higher precision & AOA, PDOA & near- field & passive & 1 + 2 reflectors & stationary & passive & stationary \\
[75] & WLA & high accuracy & TDOA, FDA, & near- field & passive & 5 & moving & passive & moving \\
[135] & MoMo & 50 m & TOA, AOA & near, far & passive & 2 & stationary & passive & stationary \\
[136] & CFSRLS & 4.1 & 3.90 & TDOA & near- field & active & 7 & stationary & passive & stationary \\
[137] & MLE & demonstrate & CAF & near- field & – & 2 & moving, & – & stationary \\  & potential gains & & good & TDOA & near, far & passive & 8 & stationary & passive & stationary \\
[138] & CWLS & performance & & RSS & near- field & active & 4 to 9 & stationary & active & stationary \\
[139] & MLE & improve & & & & & & & & \\
[16] & Taylor Series & better accuracy & TDOA, FDA & far- field & passive & 6 & stationary & passive & moving \\
[140] & ICWLS & good & TDOA, GROA & near, far & active & 8 & stationary & active & stationary \\
[141] & WIS & 25 m & TDOA, FDA & near, far & passive & 5 & moving & passive & moving \\
[142] & WIS & high accuracy & TDOA, FDA & far- field & passive & 10 & moving & passive & moving \\
[143] & UKF & high accuracy & TDOA, FDA & far- field & passive & 2 & moving & passive & moving \\
[142] & AEKF & 32.5\%, & TDOA, FDA & far- field & passive & multi-sensor & moving & passive & moving \\  & & 39.0\% & & & & & & & \\  & PDA+ & PDA better than & AOA & far- field & passive & 2 & stationary & passive & moving \\  & EKF (VRF) & NN & & & & & & & \\
[110] & AEKF & 0.15 km & TDOA, FDA & far- field & passive & 4 & moving & passive & moving \\
[145] & MLE+ CMF & 500 m & TDOA / AOA, & far- field & passive & 2 & moving & passive & moving \\
[25] & CKF & 200 m & TDOA / DOA & far- field & passive & 2 & moving & passive & moving \\
[146] & GMM- COKF & superior & & & & & & & \\  & performance & TDOA, FDA & far- field & passive & 2 & moving & passive & moving \\
[21] & GHEF / H\({}_{\alpha}\) & 50\% & TDOA / FDA & far- field & passive & 3 & moving & – & moving \\
[147] & EKE UKF,H\({}_{\alpha}\) & high accuracy & TDOA & near- field & – & 4 & stationary & – & stationary \\
[148] & UKF, H\({}_{\alpha}\) & 5.81\% & – & far- field & passive & multi-sensor & moving & passive & moving \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Summary of golden and target tracking algorithm specifications employed in the state-of-the-art methods. In Figure 35, one can observe different GNSS outlier situations collected during real tests in Montreal, downtown between high buildings. Data were collected and processed a posteriori for INS/GNSS coupled approaches subject to other results and works such as in [149; 150; 151; 152; 153; 154]. Different effects could be identified in such situations with alternative solutions mitigating multi-paths, NLOS biases, GNSS satellite visibility masks due to buildings, measurement interruption, and interference, etc.