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Optimized Phase-Based Position Estimation With a Multidimensional Radar on a Hovering UAV
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DOI：10.1109/JSEN.2024.3438833.png)
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Abstract
En 中文
Radars on unmanned aerial vehicles (UAVs) can be used in indoor rescue scenarios to detect missing people based on their motion. The phase of the radar signal provides information about submillimeter changes in distance to the reflecting objects. However, to evaluate the motion of a missing person, the relative position variation in the unmanned vehicle itself must be known and compensated for. To estimate the position variation in the unmanned vehicle, the radar system measures the phase information for multiple static objects in the radar's environment. A model describing how a position change in the radar affects the phase signals is used to build a system of equations with the current position being the solution. The accuracy of the estimated position depends on how accurately the phase measurements represent the model. By analytically decomposing the errors in the measurement model, it is shown that the radar's limited angular resolution induces a significant error. To mitigate this source of error, a weighted least squares (WLS) approach that minimizes the influence of the angular resolution on the position error is derived. By calculating the position error distribution with and without the weighting approach, the benefits of the proposed algorithms are stated. Furthermore, the results are validated using simulations and real-world measurements, showing that the proposed algorithm achieves submillimeter position accuracy.
Keywords:
Ego-motion estimation
radar
unmanned vehicles
weighted least squares (WLS)
Ego-motion estimation
radar
unmanned vehicles
weighted least squares (WLS)
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