Thermal imaging methods of environmental observation are often accompanied by the need to quantify the temperature distribution on the object’s surfaces. In such cases, the accuracy of modeling the information conversion processes that occur in thermal imaging systems is essential. All questions concerning the determination of thermal imagers temperature resolution are important. Experimental methods for determining temperature resolution in this sense are quite unambiguous and well-established in practice. And calculation methods are still being refined and are of interest to the scientific community. The article is devoted to the development of practical methods for calculating the thermal imagers temperature resolution. Such methods must be on the hand one accurate enough, and on the other hand - simple enough to be used in design organizations. The definition of the calculations error is also considered. The calculation model is based on the concept of equivalent noise temperature difference NETD as the most general characteristic of energy transformations in thermal imaging observations. The definition of NETD is based on the use of the thermal imager signal transmission function. A simplified version of the calculation method and an example of determining the temperature resolution for a thermal imager with a microbolometric matrix detector are presented. Such thermal imagers currently occupy a significant part of the market and the calculation of the characteristics of the device with a standard specification may be of interest to specialists. The influence of some elements of the mathematical information transformations model on the temperature resolution is shown. For example, as the background temperature increases, the temperature resolution decreases. The analysis of the proposed calculation model allowed us to outline ways to improve (reduce) temperature resolution. A feature of the developed methods is the possibility of their use for different thermal imaging systems, for example, for polarizing thermal imagers.
Unmanned aerial vehicles are very important in everyday life. Their number is increasing every day, as well as the scope of their use. Therefore, it becomes necessary to automate their flight from departure to kindergarten. During automatic flight, drones have certain problems during takeoff, landing and positioning. The problem is that when landing and taking off, it is necessary to ensure high positioning accuracy, which is impossible when using GPS, as it can provide accuracy of only a few meters, which is not enough, and the use of operators is accurate, but this method requires the use of quality cameras with stabilization. These stabilization cameras are very heavy, so the payload of drones is reduced, and they are very expensive (usually more expensive than the drone itself). Also, the use of operators during landing and departure can lead to a catastrophe due to human factors. The task of this article is to create a classification table, analyze landing methods, assess their advantages and disadvantages, give recommendations for the use of the most effective positioning system, as well as the development of new positioning methods. In the course of work modern, and also the most widespread methods of positioning were considered, the critical analysis of robots is made. As a result, it was proposed to classify drone positioning systems that provide reliable takeoff, landing and delivery of goods using digital cameras. This classification includes all combinations of digital cameras and radiation sources that can be located both on the drone and on the landing or cargo delivery area. Examples are given for each combination proposed in the classification. A thorough analysis of the advantages and disadvantages of each configuration of digital cameras and radiation sources is given. Recommendations for choosing the best drone positioning system are provided. The main disadvantages of these systems are the complexity of algorithms, which makes systems more expensive, as well as complicates the creation of the system, which does not preclude the possibility of making a mistake when creating a system. And this can lead to an accident. All DPSs can be classified on group depending on the number of digital cameras, number and shape of reference light sources, locations of digital cameras and location of the light sources. From the point of reliability and economy the best DPS should include one camera on a drone and a minimal set of reference light sources on the ground. The authors suppose that three reference light sources that specify a triangle is the best choice because it makes possible estimation of the distance and angular coordinates of the landing pad.
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