Detection of Unresolved Targets for Wideband Monopulse Radar.

Sensors (Basel)

School of Information and Communication Engineering, University of Electronic Science and Technology of China, No.2006, Xiyuan Avenue, West Hi-tech Zone, Chengdu 611731, China.

Published: March 2019

Detecting unresolved targets is very important for radars in their target tracking phase. For wideband radars, the unresolved target detection algorithm should be fast and adaptive to different bandwidths. To meet the requirements, a detection algorithm for wideband monopulse radars is proposed, which can detect unresolved targets for each range profile sampling points. The algorithm introduces the Gaussian mixture model and uses a priori information to achieve high performance while keeping a low computational load, adaptive to different bandwidths. A comparison between the proposed algorithm and the latest unresolved target detection algorithm Joint Multiple Bin Processing Generalized Likelihood Ratio Test (JMBP GLRT) is carried out by simulation. On Rayleigh distributed echoes, the detection probability of the proposed algorithm is at most 0.5456 higher than the JMBP GLRT for different signal-to-noise ratios (SNRs), while the computation time of the proposed algorithm is no more than two 10,000ths of the JMBP GLRT computation time. On bimodal distributed echoes, the detection probability of the proposed algorithm is at most 0.7933 higher than the JMBP GLRT for different angular separations of two unresolved targets, while the computation time of the proposed algorithm is no more than one 10,000th of the JMBP GLRT computation time. To evaluate the performance of the proposed algorithm in a real wideband radar, an experiment on field test measured data was carried out, in which the proposed algorithm was compared with Blair GLRT. The results show that the proposed algorithm produces a higher detection probability and lower false alarm rate, and completes detections on a range profile within 0.22 ms.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6427684PMC
http://dx.doi.org/10.3390/s19051084DOI Listing

Publication Analysis

Top Keywords

proposed algorithm
32
jmbp glrt
20
unresolved targets
16
computation time
16
algorithm
12
detection algorithm
12
detection probability
12
proposed
9
wideband monopulse
8
unresolved target
8

Similar Publications

Background: Kidney tumors, common in the urinary system, have widely varying survival rates post-surgery. Current prognostic methods rely on invasive biopsies, highlighting the need for non-invasive, accurate prediction models to assist in clinical decision-making.

Purpose: This study aimed to construct a K-means clustering algorithm enhanced by Transformer-based feature transformation to predict the overall survival rate of patients after kidney tumor resection and provide an interpretability analysis of the model to assist in clinical decision-making.

View Article and Find Full Text PDF

TNIP1 Impacts Prognosis by Modulating the Immune Microenvironment in BRCA.

Biochem Genet

January 2025

Department of Rheumatology and Immunology, Jingmen People's Hospital, JingChu University of Technology Affiliated Jingmen People's Hospital, No.39 Xiangshan Road Dongbao Zone, Jingmen, 448000, China.

Breast invasive carcinoma (BRCA) affects women worldwide, and despite advancements in diagnosis, prevention, and treatment, outcomes remain suboptimal. TNIP1, a novel target involved in multiple immune signaling pathways, influences tumor development and survival. However, the connection between BRCA and TNIP1 remains unclear.

View Article and Find Full Text PDF

Air conditioning systems are widely used to provide thermal comfort in hot and humid regions, but they also consume a large amount of energy. Therefore, accurate and reliable load demand forecasting is essential for energy management and optimization in air conditioning systems. Within the current paper, a novel model on the basis of machine learning has been presented for dynamic optimal load demand forecasting in air conditioning systems.

View Article and Find Full Text PDF

With the increasing intelligence and diversification of communication interference in recent years, communication interference resource scheduling has received more attention. However, the existing interference scenario models have been developed mostly for remote high-power interference with a fixed number of jamming devices without considering power constraints. In addition, there have been fewer scenario models for short-range distributed communication interference with a variable number of jamming devices and power constraints.

View Article and Find Full Text PDF

The Sharp-van der Heijde score (SvH) is crucial for assessing joint damage in rheumatoid arthritis (RA) through radiographic images. However, manual scoring is time-consuming and subject to variability. This study proposes a multistage deep learning model to predict the Overall Sharp Score (OSS) from hand X-ray images.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!