Research on Radar Processing Algorithm for Distributed Foreign Object Debris Detection

‍ ‍Weak target detection is one of the key problems in the design of airport foreign object detection systems. Small-sized foreign objects on the runway have weak echo signals and low signal-to-noise ratios， and traditional detection radars have limited detection capabilities. Due to the essential difference between airport road clutter and Gaussian white noise， this paper analyzes the road clutter based on a large number of measured data， and proposes clutter decorrelation constraints. On this basis， it is proposed to apply the distributed system to the foreign object detection radar on the airport pavement. Through the different spatial correlation characteristics of targets and clutter， the coherent accumulation of multi-station radar echoes is expected to improve the signal-to-noise ratio of weak targets. Improve the detection capability of foreign object detection radar in airports. Considering the problem of performance deterioration caused by the foreign objects on the airport pavement being stationary targets and non-ideal quadrature signals under the condition of low signal-to-noise ratio， this paper proposes a “ping-pong” coherent model to make the distributed system suitable for the airport environment and quantitatively analyzes the work. The boundary conditions of the delay estimation parameters and phase estimation parameters of the LSCR gain loss under the process. Aiming at the problem of low parameter estimation accuracy of the traditional peak method under the condition of low signal-to-noise ratio， this paper proposes the guidance information preprocessing technology and conducts Monte Carlo simulation experiments on it. Finally， this paper conducts a simulation experiment based on the measured clutter data. The simulation results show that the signal processing method for the distributed foreign object detection in the airport mentioned in this paper can effectively improve the signal-to-noise ratio of weak foreign objects.