Direction Finding for Multiple Incoming Signals

Using Time Modulated Array

5/25/2017

Dr. Joseph Ernst

Virginia Tech / Hume Center

New Proposal Status Report Final Report

Long Term Goal(s)

There is one major and one minor goal for this research project. The major goal is to determine

methods of detecting and estimation the angle of arrival (AoA) for multiple incoming signals.

The minor goal is to develop a technique for overcoming the issues with the estimation of a

signals AoA sign, with respect to bore sight.

Background for Long Term Goals

This research is a continuation of time modulated array direction finding (TMADF) project "Cost

Effective GNU Radio Direction Finding" [1]. The previous work studied a TMADF method that

focused on using the TMA harmonics to estimate the AoA [2]. During that research two future

research paths were found, that are being focused on now. The major goal is to continue use

the TMADF harmonic AoA estimation method but apply it to multiple incoming signals

simultaneously in real time. The minor goal of research is to address the issue with determining

if the incoming signal AoA is arriving from the left or right of boresight, or has a negative or

positive sign to the AoA.

The minor goal of this research was found due to the similarities in the returns between and

negative and positive AoA signal. Due to the similarities in the received signals, the research

focused on methods that find differences or produce differences. The first technique that was

applied was a principal component analysis (PCA). A PCA allows for the principal components of

a data set to be compared in variance. The components are constructed into a descending

order of variance. This PCA would show non-obvious differences in the positive and negative

AoA incoming signals. The components for this PCA where 20 high order cumulants. Figure 1

shows a grouping of plots that compare multiple components of the PCA against each other to

find possible differences between the negative and positive AoA signals. The plots start with

comparing the two highest variance components of the data and descend into comparing

smaller variance components. Unfortunately, there were no differences in the components that

could be found. The negative AoA signal results (red) overlap the positive signal results (blue) in

every plot. With these results, it was determined that a difference would have to be added to

the received signal to allow for a better AoA sign estimation.

Figure 1 PCA analysis that compares 20 different dimensions of AoA data. The red crosses are negative AoA signals and the blue crosses are the positive

In TMA the easiest way to introduce difference between the elements is to adjust the percentage of

time that an element is on, compared to a normal switching period. Due to the conservation of energy

between the time and frequency domain, any changes in the percentage of time an element is on, or

the switch waveform pattern, leads to an amplitude change in the frequency domain. There are several

TMADF papers that use the switch waveform changes to produce wanted beam steering in a TMA

system [3-6]. The issue with steering the main beam in the current system architecture is that

there are only two elements in the array. The scanning range of the main beam is limited and

the beam width is wide. Figure 2 shows collection of pattern slices for the two element TMA

with different changes to the switching waveform symmetry. The main beam position has a

scanning range of about -20 to 20 degrees. This does not allow for the signals with higher AoA

to be accurately changed for estimation. The better use of the array steering seems to be in the

pattern nulls. The pattern nulls can scan the full range of possible AoA and should produce a

more definite difference to the received pattern due to the narrow beam width that it has.

Figure 2 Radiation pattern cuts for different changes in the switch waveform offset.

A new system architecture is needed to produce the offsets in the element switching

waveform. Due the complexity in controlling the timing offset in the switching waveform some

sort of controller is needed that can output quick changes to the RF switch TTL line. A Raspberry

Pi 3 is currently being used as the control for the switch waveform. Figure 3 show the changes

in the system architecture that were initial presented in [1].

Figure 3 Current TMA DF system architecture

The major goal of this research is based on the question on how well the TMADF estimation

method works when the received signals are in close frequency proximity. When there is only

one signal producing harmonics the RF switch method works well [1]. The issue arises when the

received signals are in close frequency proximity and there is interference to the signal

harmonic amplitudes, which would cause estimation errors.

After initial simulations it has been found the more difficult task when multiple signals are

present is determining the number of signals present and their center frequencies. A method is

needed for differentiating the unneeded harmonics and noise from fundamental and first

harmonics of each signal, or a way of identifying if a collection or peak values is a signal. Figure

4 shows a base band received scenario with three signals present. The three signals have carrier

frequencies that are spaced 5 MHz apart and all have the same AoA. Visually it is easy to tell

that three signals are present, but doing so in real time and automatically is difficult due to

similarities in the harmonics and not having anything else to go on but the harmonic

amplitudes.

Figure 4 Baseband received signal with three incoming signals. The three signals have carrier frequencies spaced 5 MHz apart and the same AoA. The fundamental and first harmonics of each signal is marked in the plot.

Several methods have been implemented, but the current method that is working the best is a

combination of harmonic signal convolutions. The only prior knowledge about the signals is that

they will have harmonics and that the normalized harmonic levels follow a known algorithm.

Using the known harmonic level algorithm to produce a comparison pattern for convolution

would allow for similar harmonics signals to be identified easier in a peak search setup. The

results of convolving a known harmonic pattern with the received signal pattern can be seen in

figure 5. Figure 5 has four different convolution scenarios and the original pattern all overlaid

on the same plot. There is a convolution with a harmonic signal using a low AoA setup (orange

line), which represents scenarios where the fundamental peak is above the first harmonic

peaks. A high AoA convolution (yellow line) would match scenarios with the fundamental peak

below the first harmonic peaks. The issue with just the low or high AoA harmonic signal

convolution is that it will not work for all scenarios, therefore combination convolutions are

also implemented. The purple line in figure 5 is when both the low and high AoA results are

added together and the green is when the low AoA convolution result is then convolved with

the high AoA harmonic signal (convx2). In the scenario with the signals being spaced 5 MHz the

double harmonic scenario seems to have worked the best. The issue with this test is that most

modern signals have far less frequency spacing between them.

Figure 5 The results of the four convolution methods applied to the received signal in figure 4. The incoming signal fundamental is marked.

A more realistic scenario is to have the carrier frequencies of each signal have a spacing of only

a few hundred KHz. Figure 6 shows the base band receiver scenario when the incoming signals

have a carrier frequency spacing of 500 KHz. Unlike the 5 MHz spacing scenario in figure 4

determining the location of the three signals present is not easily done visually. Not only are the

fundamental peaks hard to determine but the fundamental peaks are also interlaced.

Figure 6 Baseband received signal with three incoming signals. The three signals have carrier frequencies spaced 500 KHz apart he same AoA. The fundamental and first harmonics of each signal is marked in the plot.

Figure 7 show all four convolution scenarios applied to the close frequency proximity situation

in figure 6. Instead of the double convolution scenario, that was successful in figure 5, now the

additive convolution seems to have work better. The additive convolution may have worked

better for several reasons, but it is expected that the resolution of the harmonic convolution

signals used makes a large change into its application to close frequency proximity signals. The

conflicting results show that there is need to do further investigation of a setup that will work

well in all signal proximity situations.

Figure 7 The results of the four convolution methods applied to the received signal in figure 6. The incoming signal fundamental is marked.

Intermediate Term Objectives

The current objectives of the research are to apply the findings that have been done in

simulation to a physical system and conduct outdoor testing. Outdoor testing is currently being

done and once results are obtained a journal paper will be written.

Schedule of Major Steps

This project is towards the end of it research cycle. Currently outdoor physical testing is the

main part of the schedule. Future research may be done, but that is still to be determined.

Dependencies

This research is built upon the S2ERC project "Cost Effective GNU Radio Direction Finding". The

dependency research can be seen in:

A. O'Donnell, W. Clark, J. Ernst and R. McGwier, "Analysis of modulated signals for direction

finding using time modulated arrays," 2016 IEEE Radar Conference (RadarConf), Philadelphia,

PA, 2016, pp. 1-5. doi: 10.1109/RADAR.2016.7485111

Major Risks

Budget

$ 40 k

Staffing

PI: Joseph Ernst

Graduate Student: Alan O’Donnell

Category of Current Stage

The research is in the physical testing phase. Techniques have been determined for both the

major and minor goal and they are being applied in outdoor testing. Outdoor testing is focusing

on the minor goal technique application first and will move to major after successful results are

shown.

Contacts with Affiliates

There has not been any contact outside the sponsors.

Publications and Other Research Products (actual or potential)

The research is currently expected to produce a journal paper. The paper will most likely be

submitted to the “IEEE Antennas and Wireless Propagation” journal.

References

[1] A. O'Donnell, W. Clark, J. Ernst and R. McGwier, "Analysis of modulated signals for direction

finding using time modulated arrays," 2016 IEEE Radar Conference (RadarConf), Philadelphia,

PA, 2016, pp. 1-5. doi: 10.1109/RADAR.2016.7485111

[2] C. He, X. Liang, Z. Li, J. Geng and R. Jin, 'Direction Finding by Time-Modulated Array With

Harmonic Characteristic Analysis', Antennas Wirel. Propag. Lett., vol. 14, pp. 642-645, 2015.

[3] A. Tennant and B. Chambers, 'A Two-Element Time-Modulated Array With Direction-Finding

Properties', Antennas and Wireless Propagation Letters, vol. 6, no. 11, pp. 64-65, 2007.

[4]L. Poli, P. Rocca, G. Oliveri and A. Massa, 'Harmonic Beamforming in Time-Modulated Linear

Arrays', IEEE Trans. Antennas Propagat., vol. 59, no. 7, pp. 2538-2545, 2011.

[5]P. Rocca, Q. Zhu, E. Bekele, S. Yang and A. Massa, '4-D Arrays as Enabling Technology for

Cognitive Radio Systems', IEEE Trans. Antennas Propagat., vol. 62, no. 3, pp. 1102-1116, 2014.

[6]Q. Zhu, S. Yang, R. Yao and Z. Nie, 'Direction Finding Using Multiple Sum and Difference

Patterns in 4D Antenna Arrays', International Journal of Antennas and Propagation, vol. 2014,

pp. 1-12, 2014.