Feature extraction algorithm of precession target based on image length and Doppler broadening

Abstract

In space defense, utilizing the micromotion features to distinguish real targets from interfering targets and decoys is effective. Due to the imaging of the high-speed precession target by microwave radar consisting of isolated scattering centers, there are many difficulties in using inverse synthetic aperture radar (ISAR) images for feature extraction. On the other hand, the inverse synthetic aperture ladar (ISAL) image is relatively continuous because of the short wavelength of laser, and the image sequence contains information about the variation in image length and Doppler width caused by target precession, which can be used for inverse motion parameters. By establishing an observation model of the precession target and performing image processing on the obtained ISAL image at different times, the image length sequence and Doppler width sequence can be obtained. Using the ellipse fitting method to process the obtained sequence, the precession parameters of the target can be obtained. The algorithm does not require prior information such as the radius and speed of the target motion, effectively improving the practicability of the algorithm. Finally, the effectiveness of the algorithm is verified by experimental results, and the error is controlled within 2%.

Knee Bending Motion Extraction Algorithm for Ball Motion Image Using Vision Sensor

“…It can be seen from Figure 6 that the distance error of the algorithm in literature [2] is about 20 pixels, the distance error of the algorithm in literature [3] is about 33 pixels, and the distance error of the algorithm in literature [4] is about 55 pixels, which is the highest among the six algorithms; the distance error of the algorithm in literature [5] is about 40 pixels. The distance error of the algorithm in literature [6] is about 23 pixels, while the distance error of the algorithm in this paper is about 10 pixels.…”

“…It can be seen from Table 1 that for different ball motions, compared with the literature algorithm, the algorithm in this paper can effectively shorten the time required to extract the knee bending action, and the calculation process has high efficiency. For example, for volleyball, the time before the algorithm is applied is 11.59 s. In literature [2], the time consumption after the algorithm is applied is 6.14 s; in literature [3], the time consumption after the algorithm is applied is 6.66 s; in literature [4], the time consumption after the algorithm is applied is 7.42 s; in literature [5], the time consumption after the algorithm is applied is 7.51 s; and in literature [6], the time consumption after the algorithm is applied is 5.83 s. Compared with these algorithms, the time consumption of the algorithm in this paper is only 2.65 s Literature [2] Literature [3] Literature [4] Literature [5] Literature [6] This paper Literature [2] Literature [3] Literature [4] Literature [5] Literature [6] This paper 8…”

“…The pixel light intensity of the frame becomes the pixel light intensity of the new original state after output. Make a logical judgment on the pixel light intensity of subsequent frames according to the above steps, and the change amplitude of pixel light intensity is ΔIðx, yÞ, as shown in Equation (2).…”

“…Basketball Literature [2] 15.79 9.84 Literature [3] 8.43 Literature [4] 10.47 Literature [5] 11.63 Literature [6] 7.21 This paper 2.89…”

“…In the existing research, many scholars have made relevant research. Literature [2] proposed a motion extraction algorithm based on image length and Doppler broadening. This algorithm can extract motion, but the accuracy still needs to be further improved.…”