Pulse Compression Using Spectrum Modification and Window Weighting Techniques
Kuan Lin
Abstract
Kuan Lin
Abstract
In the field of radar, pulse compression is a signal processing technique that allows the transmission of a long pulse which has a bandwidth corresponding to a short pulse. Consequently, it can reconcile the contradiction between long-range target detection and high range resolution. However, the serious sidelobe interference from large targets due to the matched filtering may lead to the masking of nearby smaller targets, hence the detection performance of the radar system will be degraded. In this paper, pulse compression of linear frequency modulated (LFM) signal using both spectrum modification and window weighting (SMWW) techniques is presented and compared with matched filter (MF), mismatched filter (MMF), and reiterative minimum mean-square error (RMMSE) estimation method. The simulation results show that this method can suppress the range sidelobes to the level of noise and still works effectively in the scenario of densely located targets.
OpenAlex reports 3 citations for this work. Citation counts describe recorded attention and do not establish research quality.
A contribution statement is not available in the OpenAlex record.
Method details are not available in the OpenAlex metadata.
Findings are not separately available in the OpenAlex metadata.
Limitations are not available in the OpenAlex metadata.
Application details are not available in the OpenAlex metadata.
In the field of radar, pulse compression is a signal processing technique that allows the transmission of a long pulse which has a bandwidth corresponding to a short pulse. Consequently, it can reconcile the contradiction between long-range target detection and high range resolution. However, the serious sidelobe interference from large targets due to the matched filtering may lead to the masking of nearby smaller targets, hence the detection performance of the radar system will be degraded. In this paper, pulse compression of linear frequency modulated (LFM) signal using both spectrum modification and window weighting (SMWW) techniques is presented and compared with matched filter (MF), mismatched filter (MMF), and reiterative minimum mean-square error (RMMSE) estimation method. The simulation results show that this method can suppress the range sidelobes to the level of noise and still works effectively in the scenario of densely located targets.
Key concepts: Pulse compression, Matched filter, Bandwidth (computing), Radar, Computer science, Weighting, Acoustics, Filter (signal processing)