# State-of-the-Art Mobile Radiation Detection Systems for Different Scenarios
## 4 Mobile Radiation Detection Systems
### Recent Advancements in Radiation Detection and Source Search Algorithms  
[PERSON] et al. [90] reviewed the detection algorithms for radiation monitoring. According to the methodology, algorithms can be divided into: (i) true counting processing, (ii) spectroscopy processing for ground contamination monitoring, and (iii) plume tracking algorithms. Algorithms for true counting processing must consider corrections as height (e.g., altitude for airborne surveys), solid-angle, background, and detector efficiency (e.g., variation with energy), while, for spectroscopy processing (aids in source localization), one must consider Compton continuum elimination, de-noising, and stripping ratio (gamma spectra). For plume tracking, it is important to record the plume passage events.  
The search of radioactive sources using mobile sensor networks was proposed in References [91; 92], using Poisson Kriging techniques for the spatial distribution of radiation levels and source location, and in References [93], adopting a Bayesian framework and a sequential Monte Carlo for parameters estimation.  
The authors of Reference [94; 95] suggested the use of a set of measurements made by a mobile robot for the autonomous search of hotspots. While [PERSON] et al. [94] used a search strategy based on a partially observable Markov decision process and a Bayesian framework for the source parameter estimation, [PERSON] et al. [95] used a recursive Bayesian estimation to predict the location and source intensity after each measurement. Future work is needed to consider objects attenuation, autonomous search in obstacle environments with multiple sources, and a cooperative search using land robots or a combination of land and air robots.  
A method to integrate the position of a source into a grid map of the environment was developed, using a mobile robot capable of autonomous positioning [96]. The robot is composed by a radiation dosimeter, a LiDAR, and a mobile base with a odometer. Simultaneous localization and mapping (SLAM) technique is used for the grid map construction, and the estimation of the source parameters (position and intensity) is calculated with a Markov chain Monte Carlo algorithm (based on the radiation measurements). Despite the efficiency demonstrated by this method, some other effects must be considered for improved estimation of the source parameters, such as the wall's contribution for radiationscattering, obstacles shielding effects, and background fluctuations. Future research is still needed in obtaining precise maps and in selecting optimal detection path. Since this method is not efficient for distributed sources localization (multiple sources), a way to improve it is by using a gamma camera.  
The learning-based methods have been increasingly used in recent years. The authors of Reference [97] reviewed the machine learning algorithms in nuclear science and engineering highlighting the risks and opportunities of their application. [PERSON] et al. [98] proposed the use of an artificial neural network to identify radioisotopes in natural gamma sources and determine the uncertainty of the corresponding activity. Another promising use of artificial neural network was proposed by [PERSON] et al. [99], to reconstruct the Compton edges of plastic scintillator's spectra for pseudo gamma spectroscopy, even with poor counting statistics.