Optimized 2D CA-CFAR for Drone-Mounted Radar Signal Processing Using Integral Image Algorithm
Buried survivor detection in the post-disaster environment by employing radar as sensor is an appealing approach. However, the implementation in the real field is challenging especially for large observation missions. Mounting the radar on the flying drone is the most promising solution. In this case, since the limitations of drones such as low computer specification and limited power resources, an efficient radar data processing is crucially required. Hence, this paper study about the implementation of the integral image technique to optimize the computation of the signal processing step of ultra-wideband impulse radar signatures. The evaluation was held on the single board computer mounted on the developed multisensory drone. The results confirm that the developed method can relatively reduce the data processing time.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
CFARNet: Learning-Based High-Resolution Multi-Target Detection for Rainbow Beam Radar
Millimeter-wave (mmWave) OFDM radar equipped with rainbow beamforming, enabled by joint phase-time arrays (JPTAs), provides wide-angle coverage and is well-suited for fast real-time target detection and tracking. However…
Computational EfficiencyCFAR-Based Interference Mitigation for FMCW Automotive Radar Systems
In this paper, constant false alarm rate (CFAR) detector-based approaches are proposed for interference mitigation of Frequency modulated continuous wave (FMCW) radars. The proposed methods exploit the fact that after de…
Unfolding Target Detection with State Space Model
Target detection is a fundamental task in radar sensing, serving as the precursor to any further processing for various applications. Numerous detection algorithms have been proposed. Classical methods based on signal pr…
modelDesign of Customized Adaptive Radar Detectors in the CFAR Feature Plane
The paper addresses the design of adaptive radar detectors having desired behavior, in Gaussian disturbance with unknown statistics. Specifically, given detection probability specifications for chosen signal-to-noise rat…
KAN-powered large-target detection for automotive radar
This paper presents a novel radar signal detection pipeline focused on detecting large targets such as cars and SUVs. Traditional methods, such as Ordered-Statistic Constant False Alarm Rate (OS-CFAR), commonly used in a…