The invention discloses a target
point cloud enhancement and curved
surface fitting method for sensing integration, which solves the problems of high requirement on computing power and no fidelity in the prior art, and realizes high-precision and high-integrity three-dimensional real-time reconstruction of a complex
urban environment under the condition of not depending on training data. The method comprises the following steps: acquiring original
frequency domain data, extracting corresponding
time domain channel response vectors, and stacking the
time domain channel response vectors to obtain an echo matrix; performing distance dimension detection and
spatial spectrum estimation according to the echo matrix to obtain an original sparse
point cloud set and the
signal amplitude of each point; performing weighted condensation
processing based on
signal amplitude on the original sparse
point cloud set to obtain a denoised point cloud skeleton; performing local
surface fitting and smooth projection on the point cloud skeleton based on a moving least square method to obtain a smooth point cloud; and performing iterative edge splitting interpolation under spatial constraint on the smooth point cloud until the point cloud density meets a preset condition, and outputting the enhanced dense point cloud.