Application of imaging S-lidars: functional and diagnostic capabilities for remote air pollution detection

激光雷达 遥感 计算机科学 测距 环境科学 航程(航空) 参数统计 电信 材料科学 地质学 数学 统计 复合材料
作者
Ravil R. Agishev
出处
期刊:Optical Engineering [SPIE]
卷期号:60 (08) 被引量:4
标识
DOI:10.1117/1.oe.60.8.084104
摘要

Imaging S-lidars have proven themselves in recent years as a new class of laser sensors for remote environmental monitoring and an alternative to traditional atmospheric lidars. Providing range-resolvable remote monitoring, these lidars use low-power CW lasers and advanced nanophotonics technologies to enable compact and cost-effective technological solutions. As a topical application, we have explored the potential of S-lidars to detect atmospheric pollution. We presented a generalized system structure adapted for such application field focusing on approaches to provide the necessary spatial selectivity. By adapting the universal lidar equation to S-lidar features, we have used a dimensionless parametric approach to provide a generalized description of this class of remote sensors. The possible wide variability of the ambient optical weather in the visible and near-infrared ranges was taken into account. It was shown how to apply the Q-criterion of spatial selectivity, we introduced for accounting the S-lidars specificity, to predict the borders of the operation range that can actually be covered by the sensor for reliable gaseous pollution detection. We have demonstrated how to estimate the possible narrowing of the range of concentration sensitivity with increasing requirements for spatial selectivity. The proposed methodology for analyzing the functional and diagnostic capabilities of S-lidars shows the presence of both undoubted advantages and some specific limitations of the achievable range of detectable gas concentrations. Following this methodology, it is possible to improve the validity of design solutions in a variety of applications of this promising class of lidars.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
1秒前
烟雨梦兮发布了新的文献求助10
1秒前
2秒前
2秒前
echo发布了新的文献求助30
2秒前
2秒前
3秒前
可爱的函函应助随风沙ZYX采纳,获得10
3秒前
传奇3应助耍酷的徐坤采纳,获得10
3秒前
4秒前
脑洞疼应助热心的寻冬采纳,获得10
4秒前
某某完成签到,获得积分10
5秒前
5秒前
Xx丶发布了新的文献求助10
5秒前
LIU发布了新的文献求助10
5秒前
科研通AI2S应助大意的谷冬采纳,获得10
6秒前
德古完成签到,获得积分10
6秒前
乔宇发布了新的文献求助10
7秒前
科研通AI6.1应助yyuu采纳,获得10
7秒前
7秒前
8秒前
smooth8发布了新的文献求助20
8秒前
墨维晟发布了新的文献求助10
8秒前
9秒前
心心完成签到,获得积分10
9秒前
pkinglu完成签到,获得积分10
9秒前
隐形曼青应助Leeee采纳,获得10
10秒前
隐形曼青应助TYH采纳,获得10
10秒前
领导范儿应助不搭采纳,获得10
10秒前
任从蓉完成签到,获得积分10
10秒前
mr.pork完成签到,获得积分20
12秒前
舒一一发布了新的文献求助10
12秒前
lin完成签到 ,获得积分10
12秒前
cc发布了新的文献求助10
13秒前
无花果应助忧伤的冰薇采纳,获得10
13秒前
科研通AI6.4应助迅速寻琴采纳,获得10
14秒前
14秒前
wan完成签到 ,获得积分10
14秒前
聪明蛋挞完成签到,获得积分10
15秒前
15秒前
高分求助中
Overcoming Stigma and Bias in Obesity Management 1200
Signals, Systems, and Signal Processing 610
Software that combines deep learning,3D reconstruction and CFD to analyze the state of carotid arteries from ultrasound imaging 500
Bounds for Statistical Estimation in Semiparametric Models 500
Forced degradation and stability indicating LC method for Letrozole: A stress testing guide 500
Ideology and Meaning-Making under the Putin Regime 450
Adhesion Science: Principles & Practice 400
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 物理 内科学 复合材料 催化作用 物理化学 光电子学 电极 细胞生物学 基因 无机化学
热门帖子
关注 科研通微信公众号,转发送积分 6492937
求助须知:如何正确求助?哪些是违规求助? 8290508
关于积分的说明 17691208
捐赠科研通 5585086
什么是DOI,文献DOI怎么找? 2915526
邀请新用户注册赠送积分活动 1892599
关于科研通互助平台的介绍 1750900