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Conventional radar systems often struggle near wind farms, where large moving structures generate erratic echoes that resemble airborne targets. This system addresses that challenge with a smart thresholding mechanism. For each radar “resolution cell” (a segment of monitored space), the system scans Doppler bins and identifies the maximum signal amplitude from non-zero frequency bins. These values are stored in a dedicated memory array and analyzed across multiple radar scans (“dwells”) to generate an adaptive, aggregate threshold. A transition-state delay and configurable tracking sample period stabilize system sensitivity, preventing sudden Doppler anomalies (such as turbine blade movement) from triggering false positives. The system then compares its adaptive threshold against existing fixed thresholds, applying whichever is greater. If a cell corresponds to a known structure, such as a wind turbine (based on a stored radar map), the adaptive threshold is used; otherwise, standard methods apply. The result is a highly flexible system that reduces clutter without sacrificing sensitivity and can be integrated with existing pulse-Doppler radar platforms, including MTI and MTD variants.

The novel Vision-based Approach and Landing System (VALS) provides Advanced Air Mobility (AAM) aircraft with an Alternative Position, Navigation, and Timing (APNT) solution for approach and landing without relying on GPS. VALS operates on multiple images obtained by the aircraft’s video camera as the aircraft performs its descent. In this system, a feature detection technique such as Hough circles and Harris corner detection is used to detect which portions of the image may have landmark features. These image areas are compared with a stored list of known landmarks to determine which features correspond to the known landmarks. The world coordinates of the best matched image landmarks are inputted into a Coplanar Pose from Orthography and Scaling with Iterations (COPOSIT) module to estimate the camera position relative to the landmark points, which yields an estimate of the position and orientation of the aircraft. The estimated aircraft position and orientation are fed into an extended Kalman filter to further refine the estimation of aircraft position, velocity, and orientation. Thus, the aircraft’s position, velocity, and orientation are determined without the use of GPS data or signals. Future work includes feeding the vision-based navigation data into the aircraft’s flight control system to facilitate aircraft landing.