4.3 Results
4.3.2 Type II: Blizzard Tail Study
In all Type I experiments the in-situ measurements allowed for easier localisation of the peaks corresponding to air/snow and snow/ice interfaces in the radar return. This a priori knowledge of where to look for a snow/ice peak in the radar signal may have forced a conclusion that the radar was operational. To eliminate this possibility, RAASTI was drawn along a 50 m transect over a snow mound that had formed behind an island during a blizzard. At every meter, the radar signature was captured and snow thickness data measuredin-situwith a ruler.
Figure 4.10 presents these data as a stacked spectrogram, along the x-axis are two radar returns for each meter, and along the y-axis are the FFT range bins (proportional to snow thickness). A single refractive index of 1.35 is used to convert the free-space distance to snow depth, and the red line representsin-situmeasured depth. A simple peak-picking algorithm was written in MATLAB to detect the air/snow and snow/ice peaks in the radar data based on their relative power levels. A correlation of 0.92 is found between the radar derived andin-situmeasured depths.
The blizzard tail data shows a strong power return at the air/snow interface, and a considerably weaker return at the snow/ice interface. As previously mentioned, this is likely accounted for by the wetness and possible salinity of the snow pack5. However, it also poses the question of whether or not the full signal bandwidth manages to penetrate the snow pack, be reected from the snow/ice interface, and reach the receiver above the noiseoor of the radar. Both penetration and reception of the full transmitted bandwidth is necessary to achieve the expected vertical range resolution.
5Wetness of the snow was observed, but neither the wetness nor the salt content of the snow
Figure 4.10: The radar view of the blizzard tail, created by stacking radar returns recorded every meter. The red line is an example pick of the snow/ice interface.
Usable Bandwidth
To conrm that the received bandwidth of the radar contains data from the air/snow and snow/ice interface reections, the return signal was split into smaller bandwidths and the spectrograms compared. This test validated the employment of the whole bandwidth of the radar in resolution calculations.
In future experiments, careful calibration of the radar transmit power across the full bandwidth would make it possible to use the returns from separate frequency bands to study the penetration depth of the signal into snow pack. This would allow for estimation of snow pack, since snow wetness strongly affects the power level of the return signals.
Figure 4.11 plots the radar return when the complete transmitted bandwidth is processed, while
gures 4.12, 4.13, and 4.14 show the same data but processing only 2.2 - 4.1; 4.1 - 5.9; 5.9 - 7.8 GHz of the bandwidth respectively. Comparing the layers present in thegures demonstrates that indeed the full bandwidth of the radar contains information from the air/snow and snow/ice reections. Additionally, as has previously been reported by Marshall et al. [2008b], higher
4.3. RESULTS 81
frequencies are more sensitive to layering within the snow pack. In this case, when the bands are individually studied, the reections from the layers are strong enough to be clearly observed.
Figure 4.11: Radar view of the end of the blizzard tail: full bandwidth. The snow/ice interface is clearly visible.
4.4 Summary
This chapter presents the results of six sled-based experiments that demonstrate two important features of RAASTI’s operating capacity. Firstly, they demonstrate a sufcient level of agreement between measured and estimated snow thickness data. Secondly, they show the penetration of the full transmitted bandwidth into the snow pack, a measure that is important for range resolution estimation. Exact correspondence between the in-situ measured and radar estimated snow thickness results, however, should not be expected. The two methods cannot be expected to achieve one-to-one correspondence. The loss of power level in the returned signals was greater than theoretically anticipated (using the Fresnel formula). This observation is likely explained by snow wetness and possible salinity, neither of which were measured in these experiments but should be studied in future work.
These sled-based results also highlight the need for improved understanding of the interaction of the 2 - 8 GHz radiation (at 150 - 37.5 mm wavelength) with snow pack which is of comparable depth to the wavelengths. The questions to be answered are: if snow is capable of displaying a thin-lm effect, whereby destructive interference will occur at wavelengths matching the snow thickness, what is the effect on the chirp waveform? Is it strong enough to be detected? How do icy layers which are usually <37.5 mm in thickness affect the signal? Is this a strong enough signature to be detected?
4.4. SUMMARY 83
Figure 4.12: Radar view of the end of the blizzard tail: 2.2 - 4.1 GHz.
Figure 4.13: Radar view of the end of the blizzard tail: 4.1 - 5.9 GHz.
85
Chapter
5
Development of a Non-linearity Correcting
Algorithm
In FMCW radar as the frequency generator sweeps from 2 - 8 GHz during radar operation, systematic and random phase errors present in the generator degrade the linearity integrity of the sweep (Griffiths, 1991). The radar data collected from a helicopter-borne platform is found to be highly corrupted by this noise, which causes spreading of the detected difference frequency for each target. Figure 5.1 provides one example of this, a spectrogram of radar data collected over sea ice from a helicopter, where a loss in distinction of the reections from the air/snow and snow/ice interfaces can be seen. The similarity and repeated features in this spectrogram lead to the conclusion that they are a product of a systematic error source. It is impossible to identify the air/snow and snow/ice reection lines with ne resolution as the frequency spread causes a range ambiguity over at least 2 m. Consequently, before any attempts can be made to extract the air/snow and snow/ice interface information this error must be corrected.
This chapter presents a description and results of the application of a non-linearity correcting algorithm developed to assist in the analysis and information extraction from the radar signal. Firstly, a description is provided of the nature of the error in the radar signal. Secondly, using this information a model of the error is presented which naturally leads to an algorithm for correcting these errors. Finally, the results of the application of the correction method to raw radar data are presented and discussed.
5.1 Problem Description
The bandwidth of a radar system is dened as: BW = fH −fL (Harmuth, 1981), i.e. the
difference between the highest and lowest frequencies accepted by the receiver. This bandwidth sets the theoretical limit to the achievable range resolution of a radar, related to the bandwidth by:
Figure 5.1: Spectrogram of raw radar data illustrating the ≈2 m smearing in the IF. The identication of the air/snow and snow/ice interfaces is impossible.
By nature of the processing involved in FMCW radar, this bandwidth is provided by a linear sweep (also referred to as alinear chirp) of frequencies between fLand fH (see section 2.1). Unlike
pulse or step frequency radar architectures, it is the degree of linearity of this sweep that affects how closely the actual resolution approaches the theoretical. For the best possible performance, all the frequencies within the bandwidth must be present, and the transition from one to the other must be in a strict linear fashion.
As the frequency generator sweeps from fL to fH, a large non-linearity is imparted on the
transmitted signal. This signal is subsequently reected from the medium, received by the radar, and mixed with the transmitted signal. The mixing of the two non-linear chirps is what consequently leads to “smearing” observed in the radar data. This smearing makes it difcult to accurately determine the location of the air/snow and snow/ice interfaces. Figure 5.2 compares the idealised linear transmitted frequency as a function of time in order to achieve the theoretical range resolution, with the actual frequency non-linearities imparted on the linear sweep (as derived from the radar during operation, see section 5.3.3).