Modeling and Simulink of Line of Sight Aware Accurate Target Localization
Implementation plan:
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Scenario – 1: (MIMO UWB system)
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Step 1: Initially, we construct a MIMO System with 10 – Antennas, n – Antenna with desired UWB frequency bands with spatial correlation.
Step 2: Next, we implement UWB-MIMO System which includes MIMO Channel Implementation, Data Rate and BER Calculation, Spatial Correlation Impact Study.
Step 3: We perform a highly accurate indoor positioning system, In this process we used ANN, CSO and enhanced AOA algorithm. This process evaluates and compares the performance of the positioning algorithm at multiple signal-to-noise ratio (SNR) points. Using the trained ANN, you can predict the precise position of multiple stations (STAs).
Step 4: Finally, we plot performance for the following metrics:
4.1: Normalized Mean Squared Error (NMSE) vs. Signal-to-Noise Ratio (SNR)
4.2: Bit Error Rate (BER) vs. Signal-to-Noise Ratio (SNR)
4.3: Latency (ms) vs. Signal-to-Noise Ratio (SNR)
4.4: Spectral Efficiency vs. Signal-to-Noise Ratio (SNR)
4.5: BER VS Spatial Correlation
Scenario – 2: (MIMO UWB with TR system)
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Step 1: Initially, we construct a MIMO System with 10 – Antennas, n – Antenna with desired UWB frequency bands with spatial correlation.
Step 2: Next, we implement UWB-MIMO System which includes MIMO Channel Implementation, Data Rate and BER Calculation, Spatial Correlation Impact Study.
Step 3: Then, we design a Time Reversal (TR) filter and incorporate it with the MIMO UWB Model.
Step 4: We perform a highly accurate indoor positioning system, In this process we used ANN, CSO and enhanced AOA algorithm. This process evaluates and compares the performance of the positioning algorithm at multiple signal-to-noise ratio (SNR) points. Using the trained ANN, you can predict the precise position of multiple stations (STAs).
Step 5: Finally, we plot performance for the following metrics:
5.1: Normalized Mean Squared Error (NMSE) vs. Signal-to-Noise Ratio (SNR)
5.2: Bit Error Rate (BER) vs. Signal-to-Noise Ratio (SNR)
5.3: Latency (ms) vs. Signal-to-Noise Ratio (SNR)
5.4: Spectral Efficiency vs. Signal-to-Noise Ratio (SNR)
5.5: BER VS Spatial Correlation
Scenario – 3: (Massive MIMO UWB system)
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Step 1: Initially, we construct a Massive MIMO System with 10 – Antennas, n – Antenna with desired UWB frequency bands with spatial correlation.
Step 2: Next, we implement UWB-MIMO System which includes MIMO Channel Implementation, Data Rate and BER Calculation, Spatial Correlation Impact Study.
Step 3: We perform a highly accurate indoor positioning system, In this process we used ANN, CSO and enhanced AOA algorithm. This process evaluates and compares the performance of the positioning algorithm at multiple signal-to-noise ratio (SNR) points. Using the trained ANN, you can predict the precise position of multiple stations (STAs).
Step 4: Finally, we plot performance for the following metrics:
4.1: Normalized Mean Squared Error (NMSE) vs. Signal-to-Noise Ratio (SNR)
4.2: Bit Error Rate (BER) vs. Signal-to-Noise Ratio (SNR)
4.3: Latency (ms) vs. Signal-to-Noise Ratio (SNR)
4.4: Spectral Efficiency vs. Signal-to-Noise Ratio (SNR)
4.5: BER VS Spatial Correlation
Scenario – 4: (Massive MIMO UWB with TR system)
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Step 1: Initially, we construct a Massive MIMO System with 10 – Antennas, n – Antenna with desired UWB frequency bands with spatial correlation.
Step 2: Next, we implement UWB-MIMO System which includes Massive MIMO Channel Implementation, Data Rate and BER Calculation, Spatial Correlation Impact Study.
Step 3: Then, we design a Time Reversal (TR) filter and incorporate it with the Massive MIMO UWB Model.
Step 4: We perform a highly accurate indoor positioning system, In this process we used ANN, CSO and enhanced AOA algorithm. This process evaluates and compares the performance of the positioning algorithm at multiple signal-to-noise ratio (SNR) points. Using the trained ANN, you can predict the precise position of multiple stations (STAs).
Step 5: Finally, we plot performance for the following metrics:
5.1: Normalized Mean Squared Error (NMSE) vs. Signal-to-Noise Ratio (SNR)
5.2: Bit Error Rate (BER) vs. Signal-to-Noise Ratio (SNR)
5.3: Latency (ms) vs. Signal-to-Noise Ratio (SNR)
5.4: Spectral Efficiency vs. Signal-to-Noise Ratio (SNR)
5.5: BER VS Spatial Correlation
Software Requirements:
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1. Development Tool: Matlab-R2023a or above
2. Operating System: Windows-10 (64-bit)
Note: –
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1. We make a simulation based process only, not a real time process.
2. If the above plan does not satisfy your requirement, please provide the processing details, like the above step-by-step.
3. Please note that this implementation plan does not include any further steps after it is put into implementation.
4. If the above plan satisfies your requirement, please confirm us soon.
