Experimental Evaluation of 79 and 300 GHz Radar Performance in Fire Environments.

This paper presents an experimental study of the propagation of mm-wave/low-THz signals in the frequency ranges of 79 and 300 GHz through fire. Radar performance was investigated in various real scenarios, including fire with strong flame, dense smoke and water vapour. A stereo video camera and a LIDAR were used as a comparison with other common types of sensors. The ability of radars to enable the visibility of objects in fire environments was proven. In all scenarios, the radar signal attenuation was measured, and in the case of steam was compared with theoretical calculations. The analysis of the experimental results allows us to conclude that there are good prospects for millimetre wave and Low Terahertz radar in the field of firefighting imaging equipment.

Underwater Optical Imaging for Automotive Wading.

An underwater imaging system was investigated for automotive use in highly scattered underwater environments. The purpose of the system is the driver's information about hidden obstacles, such as stones, driftwood, open sewer hatches. A comparison of various underwater vision methods was presented by the way they are implemented, the range reached, and the cost of implementation. It has been experimentally shown that a conventional active system can provide a maximum visibility range of up to three light attenuation lengths. In most practical cases of turbid waters during floods, this corresponds to distances of about 1 meter. From the presented analysis it follows that advanced extended range imaging methods allow increasing of the visibility range up to 2 meters.

Mouth-clicks used by blind expert human echolocators - signal description and model based signal synthesis.

Echolocation is the ability to use sound-echoes to infer spatial information about the environment. Some blind people have developed extraordinary proficiency in echolocation using mouth-clicks. The first step of human biosonar is the transmission (mouth click) and subsequent reception of the resultant sound through the ear. Existing head-related transfer function (HRTF) data bases provide descriptions of reception of the resultant sound. For the current report, we collected a large database of click emissions with three blind people expertly trained in echolocation, which allowed us to perform unprecedented analyses. Specifically, the current report provides the first ever description of the spatial distribution (i.e. beam pattern) of human expert echolocation transmissions, as well as spectro-temporal descriptions at a level of detail not available before. Our data show that transmission levels are fairly constant within a 60° cone emanating from the mouth, but levels drop gradually at further angles, more than for speech. In terms of spectro-temporal features, our data show that emissions are consistently very brief (~3ms duration) with peak frequencies 2-4kHz, but with energy also at 10kHz. This differs from previous reports of durations 3-15ms and peak frequencies 2-8kHz, which were based on less detailed measurements. Based on our measurements we propose to model transmissions as sum of monotones modulated by a decaying exponential, with angular attenuation by a modified cardioid. We provide model parameters for each echolocator. These results are a step towards developing computational models of human biosonar. For example, in bats, spatial and spectro-temporal features of emissions have been used to derive and test model based hypotheses about behaviour. The data we present here suggest similar research opportunities within the context of human echolocation. Relatedly, the data are a basis to develop synthetic models of human echolocation that could be virtual (i.e. simulated) or real (i.e. loudspeaker, microphones), and which will help understanding the link between physical principles and human behaviour.

Automotive System for Remote Surface Classification.

In this paper we shall discuss a novel approach to road surface recognition, based on the analysis of backscattered microwave and ultrasonic signals. The novelty of our method is sonar and polarimetric radar data fusion, extraction of features for separate swathes of illuminated surface (segmentation), and using of multi-stage artificial neural network for surface classification. The developed system consists of 24 GHz radar and 40 kHz ultrasonic sensor. The features are extracted from backscattered signals and then the procedures of principal component analysis and supervised classification are applied to feature data. The special attention is paid to multi-stage artificial neural network which allows an overall increase in classification accuracy. The proposed technique was tested for recognition of a large number of real surfaces in different weather conditions with the average accuracy of correct classification of 95%. The obtained results thereby demonstrate that the use of proposed system architecture and statistical methods allow for reliable discrimination of various road surfaces in real conditions.

Comparison of Adaptive Spectral Estimation for Vehicle Speed Measurement with Radar Sensors.

Vehicle speed-over-ground (SoG) radar offers significant advantages over conventional speed measurement systems. Radar sensors enable contactless speed measurement, which is free from wheel slip. One of the key issues in SoG radar is the development of the Doppler shift estimation algorithm. In this paper, we compared two algorithms to estimate a mean Doppler frequency accurately. The first is the center-of-mass algorithm, which based on spectrum center-of-mass estimation with a bandwidth-limiting technique. The second is the cross-correlation algorithm, which is based on a cross-correlation technique by cross-correlating Doppler spectrum with a theoretical Gaussian curve. Analysis shows that both algorithms are computationally efficient and suitable for real-time SoG systems. Our extensive simulated and experimental results show both methods achieved low estimation error between 0.5% and 1.5% for flat road conditions. In terms of reliability, the cross-correlation method shows good performance under low Signal-to-Noise Ratio (SNR) while the center-of-mass method failed in this condition.