{"title":"Radar Space-Time Domino-Sparse-Pulse Feedback Beampattern Synthesis","authors":"Yifan Wu;Junli Liang;Hing Cheung So;Guiwei Liu;Shengqi Zhu;Mingsai Huan","doi":"10.1109/TSP.2025.3560234","DOIUrl":null,"url":null,"abstract":"In radar space-time adaptive processing (STAP), the sliding window size of the filter is a crucial design parameter that significantly influences system performance. A smaller sliding window size while maintaining performance provides several benefits, particularly in terms of faster response and lower range migration probability on the receiver side. In this paper, we tackle the problem of space-time feedback beampattern synthesis (FBS), which reduces the sliding window size by reducing the required number of pulses while maintaining performance. This is achieved through the formulation of two novel models. Motivated by the domino effect, which creates a chain reaction of falling dominoes when the first one is knocked down, the first formulation introduces a novel domino-group-sparsity (DGS) scheme to achieve domino-sparse-pulses (DSP), and its adaptive version is also provided. While the second formulation minimizes the ratio of maximal sidelobe level to the minimal mainlobe level in the FBS to attain performance comparable to the former but with even fewer pulses. Especially, for the latter, we solve the resultant nested-fractional program by decoupling the fractions layer by layer. Simulation results are provided to demonstrate the effectiveness of the proposed methods.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"2053-2069"},"PeriodicalIF":4.6000,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Signal Processing","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10966214/","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
In radar space-time adaptive processing (STAP), the sliding window size of the filter is a crucial design parameter that significantly influences system performance. A smaller sliding window size while maintaining performance provides several benefits, particularly in terms of faster response and lower range migration probability on the receiver side. In this paper, we tackle the problem of space-time feedback beampattern synthesis (FBS), which reduces the sliding window size by reducing the required number of pulses while maintaining performance. This is achieved through the formulation of two novel models. Motivated by the domino effect, which creates a chain reaction of falling dominoes when the first one is knocked down, the first formulation introduces a novel domino-group-sparsity (DGS) scheme to achieve domino-sparse-pulses (DSP), and its adaptive version is also provided. While the second formulation minimizes the ratio of maximal sidelobe level to the minimal mainlobe level in the FBS to attain performance comparable to the former but with even fewer pulses. Especially, for the latter, we solve the resultant nested-fractional program by decoupling the fractions layer by layer. Simulation results are provided to demonstrate the effectiveness of the proposed methods.
期刊介绍:
The IEEE Transactions on Signal Processing covers novel theory, algorithms, performance analyses and applications of techniques for the processing, understanding, learning, retrieval, mining, and extraction of information from signals. The term “signal” includes, among others, audio, video, speech, image, communication, geophysical, sonar, radar, medical and musical signals. Examples of topics of interest include, but are not limited to, information processing and the theory and application of filtering, coding, transmitting, estimating, detecting, analyzing, recognizing, synthesizing, recording, and reproducing signals.