Object detection method based on improved radon transform and multi-frame joint processing

Abstract

The embodiment of the invention provides a target detection method based on improved Radon transformation and multi-frame jointed processing to suppress clutters and eliminate false tracks so as to further realize effective detection of low speed weak targets. The method comprises the following steps: clutter suppression is carried out on radar echoes through a filter to form data corresponding to a first time-distance image; the data corresponding to the first time-distance image is combined to form data corresponding to a second time-distance image; the data corresponding to the second time-distance image is processed to obtain data corresponding to a joint time-distance image; N times of double sampling is carried out on the obtained joint time-distance image; smooth processing is carried out on the data corresponding to N sub-images to obtain S <->; the S <-> is subjected to Randon transformation; an amplitude threshold is set; an angle offset amount threshold is set; a peak point corresponding to a target movement track is detected in a transformation domain matrix; and then a movement track is determined according a theta coordinate corresponding to the peak point of the target movement track.

Description

technical field [0001] The invention belongs to the technical field of signal processing, and in particular relates to a target detection method based on improved Radon transform and multi-frame joint processing, which can be used for detection of small low-altitude, slow-velocity targets in radar. Background technique [0002] For modern radars, especially warning radars, short-range indicator radars and battlefield surveillance radars, detection and tracking are the most basic tasks. However, with the continuous development of electronic countermeasures technology, the continuous emergence of new targets and more challenging target detection tasks also put forward higher requirements for the development of radar technology. Effective detection of low-altitude, slow-moving, weak and small targets ("low, slow, small" targets) is one of the challenges faced by radars in the new era. [0003] Low-altitude, slow-moving and weak targets have the characteristics of "good control...

AI Technical Summary

Problems solved by technology

On the one hand, the clutter background is complex, and the energy of ground clutter, sea clutter, and meteorological clutter generated by the ground, sea surface, and weather environment such as clouds and rain is generally much larger than the energy of the echo signal, and the target is often captured by noise and clutter. In addition, the RCS of targets such as drones, sea-skimming missiles, and stealth aircraft is very small, which further reduces the echo amplitude of the target. These targets are mixed with the spectrum of ground clutter in the frequency domain. Traditionally, relying on The performance of energy detection and methods relying on frequency domain detection has declined, so it is almost impossible to detect targets on a single frame echo image, and even if a target is detected, it is often accompanied by a large number of false targets, which cannot meet the required detection probability and False alarm probability; on the other hand, for low-resolution radar, due to the very low moving speed of the target, the target usually does not move in range units in the time-distance image of a single frame, and the trajectory of the target and the trajectory of the clutter are different. Appears as a continuous straight line, making it difficult to distinguish objects from clutter

The above two aspects make it extremely difficult to detect low-altitude, slow-moving and weak targets. Therefore, how to design filters according to the characteristics of clutter to suppress clutter and eliminate a large number of false tracks generated during the detection process to achieve effective target detection is an important task. Key Points and Difficulties of Invention Research

Images