Radars are object-detection systems that use radio waves to retrieve some information about targets. Because they make approaches to targets by radio waves, they are called “active sensors”, too. Contrary to such a concept, passive radars never transmit radio waves. This is the reason why they are called “passive”. They retrieve some information by only receiving radio waves which are transmitted by others for the other purposes. Passive radars do not need new radio wave frequencies, and just consist of rather simple and low cost receivers because they do not transmit radio waves.
We, radiowave remote sensing laboratory, are developing environmental measurement techniques using electromagnetic waves. We investigate the possibility of passive radars for environmental measurement and start this study as a step for the passive radars. This is a collaborative work with radiowave remote sensing laboratory, electromagnetic compatibility laboratory, space-time standards laboratory, and smart wireless laboratory.
Senior Researcher Kawamura carrying out adjustment of equipment.
Localized heavy rain in the urban area, which is difficult to observe or predict, is a social issue in these days. We’ve developed phased array weather radars, which can observe localized heavy rain in detail. Accuracy to predict these phenomena will be expected to be increased through studies using these radars in near future. On the other hand, weather radar cannot observe water vapor. Water vapor is an essential parameter for weather forecast because it is a state before rain drop, but it is one of the most difficult physical quantities to measure with remote sensing technique. If we can monitor water vapor around the ground surface with a wide coverage, the accuracy of weather forecast will be increased, and we might be able to predict the localized heavy rain.
Digital terrestrial television receiving antenna
Radio waves are delayed due to water vapor through propagation. Let’s consider an example of receiving TV broadcasting waves at the point 5 km away from the radio tower. If the humidity increases by 1 %, radio waves delay by about 17 ps (17 x 10-12 seconds). The light, which can propagate 300,000 km per second, propagates only 5 mm during this period. Like this, water vapor can be retrieved if we measure a little propagation delay precisely.
Water vapor can be also estimated by using GPS satellites, and this method is already put into use for the weather forecast. Propagation delay of the radio waves from GPS satellites leads to total sum of water vapor through the vertical propagation paths. Basic idea of this study is same as this GPS method, in which propagation delay is used. Characteristics of this study are using horizontal radio wave propagation (not vertical), using broadcasting waves whose SNR*1 is better than that of satellite waves, having a possibility to increase the horizontal resolution by using a lot of receiving points, and so on.
Digital terrestrial television equipment
Figure 1 shows a schematic diagram of a measurement method we propose in this study. We measure the propagation delay as the phase rotation of the radio waves. τA is the phase rotation due to propagation delay measured at the receiving point A. Because we must measure the delay with the precision of picoseconds, we cannot neglect phase noises of local oscillators at radio tower (φT) and at receiving point A (φA). Accordingly, the measured phase variation at the receiving point A becomes τA＋φT＋φA, and τA is buried with φT＋φA, which is much larger than τA. So we propose measuring the delay at the point B, which is located on the line through the tower and the point A. φT can be canceled out by taking difference between measured phase variations at the point A and B. The difference of the phase variations between A and B consists of the difference of propagation delay (τA－τB) and the difference of the phase noise at A and B (φA－φB). φA－φB will be canceled out if we can synchronize the local oscillators at A and B. Finally we can get the difference of propagation delay (τA－τB), which corresponds to the total sum of water vapor between A and B.
Figure 1. A schematic diagram of a measurement method using digital terrestrial broadcasting wave.
Signal-to-noise ratio. An intensity ratio of desired signals to noise, it is often used as a signal intensity index.
Figure 2 shows a photo of a real-time phase variation measurement system for digital terrestrial broadcasting waves. We have developed this system using software defined radio*2 technique. The phase variations of the radio waves of plural TV channels can be measured at the same time with this system using the reference signals (10 MHz and 1PPS) from a local oscillator. We will set up these systems as the receivers at the receiving point A and B in Figure 1 and measure the phase variations. Water vapor will be retrieved if only the local oscillators at A and B are synchronized. Now we are developing this synchronization technique.
Figure 2. A real-time phase variation measurement system for digital terrestrial broadcasting waves.
Figure 3 shows an example of measured results with the system shown in Figure 2. Data for about 50 minutes measured at a single location is shown. Top and middle panels show the phase variations of channel 21 and 22 respectively. These data are equivalent to “the measured phase variation at the receiving point A (τA＋φT＋φA)” in Figure 1. We used a GPS controlled quarts resonator as the local oscillator of the receiving system at this time. Each TV station, on the other hand, uses rubidium as the local oscillator. Assuming the phase noise of the quarts resonator at receiver, rubidium of channel 21 and 22 asφA, φ21ch, and φ22ch, data in the top and middle panel are equivalent to “τA＋φ21ch＋φA” and “τA＋φ22ch＋φA” respectively. Since the phase noise of rubidium is much smaller than that of quarts resonator (φA ≫ φ21ch ～ φ22ch), the shape of the both variations are quite similar (almost equivalent to the phase variation of the quarts resonator). The difference of both variations is shown in the bottom panel. The amplitude of the variation becomes small. This variation is correspond to the phase noise difference of the rubidium of channel 21 and 22 (φ21ch－φ22ch).
Figure 3. An example of measured results.
Data for about 50 minutes measured at a single location is shown.
*2 Software-defined radio
A technique to perform the major portion of control and signal processing on software. It can adapt to a wide variety of wireless communication systems without modifying the hardware, and thus its low cost and versatility are increasingly attracting interest in recent years.
Now we are planning demonstration experiments to estimate water vapor using two receiving stations. After that, we want to expand it to multi-point measurement system (see Figure 4). We are also investigating miniaturizing the measuring system (Figure 2), which is quite important for multi-point measurement. Routine observation of the horizontal distribution of water vapor and its assimilation*3 to the numerical prediction model are expected to increase the accuracy of weather forecast including localized heavy rain.
Figure 4. Image of multi-point water vapor measurement system.
Out target is the routine observation of the horizontal distribution of water vapor
with hundreds of receivers.
We will investigate new targets other than water vapor or new radio waves other than the digital broadcasting waves in near future, and we want to expand into the passive radar technique for environmental observations.
Data assimilation. In the current weather forecast, the future status is calculated in the numerical model using observed information (current status). This numerical model, which includes various physical mechanisms, is called a numerical prediction model, and incorporating observed data to this model is called data assimilation.
Yoshihisa IrimajiriRemote Sensing Laboratory
Shoichiro KojimaRemote Sensing Laboratory
Aoi NakamizoSpace Environment Laboratory
Maya MizunoElectromagnetic Compatibility Laboratory
Koki WakunamiElectromagnetic Applications Laboratory
Makoto AokiRemote Sensing Laboratory
Seiji KawamuraRemote Sensing Laboratory
Miho FujiedaSpace-Time Standards Laboratory
Kensuke SasakiElectromagnetic Compatibility Laboratory