Standoff detection of hydroponic equipment through electromagnetic emissions

Previous studies have demonstrated that hydroponics equipment such as ballasts used by high-pressure sodium (HPS) and metal halide (MH) lamps, especially ageing ones, emit detectable RF signals. It is, however, not yet clear whether such emissions are sufficiently strong or unique to permit standoff detection from vehicles located near or adjacent to buildings or structures that contain such equipment. With support from the Surrey Fire Service, which seeks to develop such techniques to facilitate fire safety inspections, we have sought to resolve this issue using a four-point strategy: 1) Assessment of the nature and strength of RF emissions from typical hydroponics equipment in a controlled environment, e.g., an anechoic chamber, using an Agilent PXA signal analyzer and various receiving antennas covering a range of frequency bands, 2) Assessment of the nature and strength of RF emissions from man-made and natural sources in typical urban and suburban environments using the same test and measurement equipment carried aboard a mobile test van. 3) Development of mobile antenna concepts that are capable of enhancing RF emissions from hydroponics equipment and suppressing RF emissions from other sources. 4) Development of filtering concepts that are capable of enhancing RF emissions from hydroponics equipment and suppressing RF emissions from other sources. Our results suggest that while emissions from new equipment operated in commercial or industrial environments will be difficult to detect due to the combination of very low emissions from the equipment and a high level of background noise, older and poorly maintained ballasts that are more likely to be used under unsafe circumstances are indeed prone to emit more strongly and are more easily detected in low noise residential environments and that the distinctive nature of the signals emitted by ageing ballasts lend themselves to enhanced detection in the presence of noise through suitable signal processing.