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
要减肥若烟完成签到,获得积分10
刚刚
yuking发布了新的文献求助10
刚刚
深情安青应助璟黎采纳,获得30
1秒前
maplekrito发布了新的文献求助10
1秒前
1秒前
2秒前
3秒前
陌上尘完成签到,获得积分20
5秒前
科研通AI2S应助土豆儿采纳,获得10
6秒前
6秒前
Orange应助vvvvvv采纳,获得10
8秒前
9秒前
Jasper应助村口烫头祁师傅采纳,获得10
10秒前
10秒前
纯真若云完成签到 ,获得积分10
12秒前
12秒前
小二郎应助Yexidong采纳,获得10
13秒前
13秒前
baopan完成签到,获得积分20
13秒前
学医的小陈完成签到,获得积分10
14秒前
科研通AI6.1应助Shuyu采纳,获得10
14秒前
张美发布了新的文献求助10
14秒前
悦耳谷蓝发布了新的文献求助10
15秒前
16秒前
Niki完成签到,获得积分10
16秒前
16秒前
17秒前
看芥里发布了新的文献求助10
17秒前
18秒前
YI123456发布了新的文献求助10
18秒前
daqing1725发布了新的文献求助30
21秒前
棉花摘心完成签到,获得积分10
22秒前
22秒前
24秒前
LXY完成签到,获得积分10
24秒前
酷酷如楠发布了新的文献求助10
24秒前
YI123456完成签到,获得积分10
25秒前
欢呼曼荷完成签到,获得积分10
26秒前
大模型应助未央采纳,获得10
26秒前
28秒前
高分求助中
GL 2 A method for assessing the in-place cleanability of food processing equipment, Fourth Edition, December 2023 3000
Annie Ernaux: De la perte au corps glorieux 600
Writing Systems 500
Understanding Modeling and Simulation of Polymerization Reactions 400
Invited Discussant 63O and 64O 400
A revision of Limenitis helmanni and its related species (Nymphalidae) from Central and South China 400
Direct and Iterative Linear System Solvers 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 6822540
求助须知:如何正确求助?哪些是违规求助? 8535503
关于积分的说明 18168099
捐赠科研通 6157342
什么是DOI,文献DOI怎么找? 3033835
关于科研通互助平台的介绍 2013907
邀请新用户注册赠送积分活动 2010881