COJOT prides itself on understanding client needs. Where a quick setup and removal is required or where the permanent installation of an antenna on a vehicle is not possible, our magnetic mount antennas are unparalleled.
Here are three of our most popular magnetic mount antennas:
WB460W (420 – 6000 MHz)
WB25512W (25 – 512 MHz)
WB30108W (30-108 MHz)
In a world where drone strikes are a near-daily occurrence, counter-drone measures are becoming increasingly important. At COJOT, we are working tirelessly on our beam steering antennas and proudly introduce two new versions with enhanced counter-UAS antenna functionality.
The new extended dual band SBA2456DB (10W) and high power SBA2456XDB (100W) are now covering the full 2 and 5GHz WiFi frequency bands (2.4 to 2.5 and 5 to 6 GHz), while providing the same excellent performance with up to 24 sharp beams and a minimum of 20-degree beamwidth to cover the full 360-degree horizontal area. The SBA2456 models are equipped with the latest updates of the COJOT SBA product family, including support for fast RS-485/422 and Ethernet interfaces as well as thoroughly environmentally tested mechanics – according to MIL-STD-810.
Fields of applications: Counter-UAS, Counter-Drone, EW, ISR applications
COJOT SBA product family:
C4i Band IV/C: SBA4450B and SBA4450XB
C4i Band III+/L: SBA1327B
5G: SBA3438B-DP
By Johan Fuhri, Product Owner
In the previous blog post we considered some of the ways in which we can express the performance of an DF antenna, and we noted that simply stating the ‘accuracy’ of a DF antenna is not quite that simple. We also noted that there are various algorithms that can be used along with a typical DF antenna, and that the accuracy and sensitivity of DF system is dependent on both the quality of the antenna and the direction-finding algorithm.
In this blog post we will discuss the process of ‘characterizing’ a DF antenna array to generate a table of values which can be used in correlative algorithms. We will also look at a few real-life problems that cause inaccuracies in DF systems, specifically when using basic correlative algorithms.
Correlation is a mathematical and statistical tool that can be used to quantitively express how similar two or more sets of data are to each other. It is basically a fancy way of playing ‘pick the closest match’.
To illustrate the process, we are going to use colour swatches (the type that adorns the walls of the paint department at your favourite hardware chain store) and create a simple direction-finding system. Consider the image below, which is a circle divided into six sectors and a colour assigned to each sector. Let’s suppose we have a DF antenna right in the center of this circle, and the lines/sectors represent the various directions from which a signal can arrive. This, however, is a very special antenna, in the sense that it will tell you the colour of an incoming signal.
If we are clever, we can make sure that we install the antenna in such a way that a colour of our choosing, yellow in this case, is always pointing due north. If we then receive a signal from our antenna (which will be a color, remember), we can compare the color that we received from the antenna with the image above to determine which direction the signal came from! That’s not too hard now, is it?
On a very basic level, this is what correlative DF algorithms do. The DF antenna is spun through 360⁰ and the incoming signal from each direction, on each of the elements on the antenna, is sampled and stored in a table. This table is called the ‘characterization table’ (although different people might name it something else), and it effectively allows a DF system to calculate the exact direction from which a signal arrives by comparing the signal with those stored in the characterization table, just like we did with the colours. The circle indicating which colour is in which direction is effectively our own ‘characterization table’.
Alaris Antennas offer characterization of our DF antennas and systems as a service to our clients. These characterization measurements are most often done at an outdoor reflection range at the National Antenna Test Range just north of Pretoria, which we affectionately simply refer to as ‘Paardefontein’ (https://www.paardefontein.co.za/).
The measurements entail mounting the antenna on one of the turntable towers and setting up a signal source some distance from the tower. The DF antenna is then turned through 360 degrees in 4⁰ steps (depending on client requirements) and a sample captured from each of the elements of the antenna at each step. This data is then compiled into a characterization table which we make available to our clients.
As you might expect though, in real life things are not quite as simple. There are various factors that can influence the accuracy of a DF system in real life scenarios, and in this section we are going to consider just a few of these, using our colour-wheel to illustrate why they cause problems.
In our colour wheel example, we divided the circle into 12 sections, each representing an angle of 30⁰. It is thus (nearly) impossible for us to determine the direction of an incoming signal to anything more accurate than about 30⁰. Note that the signal to noise ratio, or the strength of the electric field, or the amount of noise has very little influence in the accuracy we can achieve, since we are fundamentally limited by the number of colours we have in our characterization table.
We can significantly increase our accuracy by dividing the circle into 36 smaller sectors, giving us many more options to compare our incoming signal to! We can now determine the direction of our incoming signal to within a 10⁰ sector.
The original orange signal we received is most similar to the third sector, indicating that our signal arrived from a direction of 25⁰±5⁰.
The same is true for DF systems in practice as well. When the antenna is characterized, the angle steps, or angle resolution, can be freely chosen by the person doing the measurement. While it is tempting to choose super small steps (let’s measure a sample every 0.1⁰), this implies that the DF processor has do deal with a table that has 3600 entries, which can potentially slow things down to a crawl when limited processing power is available. In practice, steps of between 2⁰ and 5⁰ are typically used, depending on the specific implementation of the client.
It is also worth mentioning that certain more sophisticated algorithms can achieve resolutions smaller than the characterization table resolution by employing all kinds of clever mathematical techniques. It is the equivalent of being able to look at a greenish-yellow signal, and inferring that it must be somewhere between the green and yellow sectors, and thus giving a better guess than simply choosing the green or yellow sectors purely by similarity. The details of these ‘super-resolution techniques’ are, however, often part of the proprietary magic that system integrators implement as their own competitive advantage.
Up to now we assumed that the incoming signal that we are evaluating is perfectly clear, and that our accuracy is only limited by the resolution of the table against which we can compare our incoming signal. In antenna terms this is called the ‘large signal accuracy’ of the antenna, as it assumes that no noise is present in the measurements.
Real life signals, however, rarely qualify as large signals and often has noise superimposed on the signal of interest. Let’s look at an example in our colour wheel example.
Adding noise to the signal makes it harder and harder to find the colour that best matches the characterization table. If there is noise present on the signal, invariably the DF processor will look at the ‘colours’ it can choose from and make mistakes. At best it can tell you that the signal belongs to one of these four sectors, and this reduces the RMS accuracy of the overall system.
Adding even more noise, at some point it becomes practically impossible for the DF processor to make any sense of the incoming signal, and it may start taking ‘random’ guesses. These wild guesses can be in any direction whatsoever, and are called appropriately called ‘wild bearings’.
The main source of noise in a DF system is the internal electronic components of the system. Every amplifier, transistor and resistor contribute a bit of noise, and all this noise is added to the incoming signal before the DF processor has the opportunity to compare this signal to the characterization table. We can thus see that it is important to ensure that the received signal is as strong as possible while keeping the noise as small as possible to get a good signal-to-noise ratio.
The design of the DF antenna, specifically the gain of the antenna elements, directly contributes to keeping the SNR as high as possible by ensuring that the incoming signal is converted to a large as possible voltage for the receiver to work with. For a more detailed discussion on this topic, refer to the previous blog post in this series.
In certain scenarios it might be possible for a signal to bounce off objects, typically large buildings, and the signal that arrives at the DF antenna may end up being a combination of copies of the same signal, arriving from different directions.
These multipath signals, which we will illustrate as a mix of two colours, are very difficult for DF processors to deal with, and is very likely to a reduction in accuracy at best, or wild bearings at worst.
Using our colour wheel example, we can see that a pure green signal leaves the source that we are trying to find, but along the way a signal bouncing off a building also makes its way into the DF antenna. The ‘colour’ making its way into the DF processor is mostly green, with a dash of blue mixed in. This mixed signal will certainly cause problems for most simple correlative algorithms, as the received signal does not match any of the signals in the correlation table, causing inaccuracies and wild bearings.
Having said that, there are a few clever algorithms, like MUSIC, which can actually use these strange signals to its advantage and can figure out that there are multiple paths in play. How these clever algorithms work is beyond the scope of this blog post (and the authors current intellectual grasp…).
This blog post attempted to explain the principle behind most correlative algorithms while trying to stay away from the complicated maths that make it work. We also looked at a few real-world effects that influence the accuracy of DF systems in general, and specifically the mechanism that causes problems in correlative algorithms. Most of these inaccuracies are caused by external factors, i.e. they are not so much caused by the design of the DF antenna, but by the environments in which they are employed.
In the last post of the series we will discuss a few of the intricacies of DF antenna design and how they can cause inherent inaccuracies (i.e. non-environmental causes) in poor designs. This will be a rare glimpse into the everyday challenges that antenna engineers at Alaris Antennas deal with on a daily basis!
By Johan Fuhri, Product Owner
It is sometimes said that: “If you can’t convince them, confuse them!”. Nowhere is this more apparent than the minefield which is antenna datasheets and brochures.
Direction finding antennas are meant to be instrumental in accurately determining the direction of an incoming signal, that much we all agree on. One would thus think that a graph of accuracy vs frequency would be very obvious and clear measure of how well an antenna will do its job, and you would be very wrong. Welcome to the world of specmanship.
Specmanship is the nefarious use of data to present to the customer what he wants or expects to see, rather than what is true. It is quite possible to present antenna performance figures, like ‘accuracy’ in this case, in a way that looks fantastic on paper, but will fail to live up to the customer expectations in real life applications. This blog post will attempt to delve into the world of DF antenna accuracy to explain how it should be interpreted in the context of real-world applications.
To generate the accuracy plot of a DF antenna, we generate a mathematical model of the antenna, and transmit a hypothetical signal to the antenna from a specific angle of incidence. The antenna (and its associated DF algorithm) will then receive this signal and calculate the angle from which it believes the signal is coming. If we do this for each direction while moving around the antenna, we can calculate an average (or RMS) error, taking into account incoming signals from all directions. This RMS error, plotted over frequency, is the typical ‘accuracy’ plot that you will find in many DF antenna datasheets. Simple enough, right?
Well, not quite. The accuracy with which a DF antenna can estimate the direction of an incoming signal is dependent on a whole bunch of different factors. An accuracy plot without stating the underlying assumptions really provides very little information indeed. Let us consider a few factors that will influence the accuracy of a DF antenna.
The first, and most obvious parameter, is the signal level (or more accurately, the strength of the electric field) of the incoming signal. The incoming E-field needs to be large compared to the noise level, else the antenna/algorithm will have no way of extracting the signal of interest from the background noise.
As an analogy, suppose someone in the room is trying to get your attention at a dinner party. The person will need to shout louder than the average ‘noisiness’ created by all the other people at the party in order for you to differentiate that person’s voice above the others.
When trying to attract someone’s attention at a rock concert, however, you will have to shout much, much louder to get the same effect. Trying to determine your friend’s location by listening for his voice at a rock concert is much harder to do than at a dinner party. The same principle applies in DF systems.
In the example above, we used a hypothetical 8-element DF antenna and calculated the DF accuracy for incoming signals with a Signal-to-Noise ratio (SNR) of 5dB (loud rock concert) and 20dB (dinner party) respectively. As we suspected, when our signal of interest is barely stronger than the noise floor (SNR=5dB), the DF antenna/algorithm will make many estimation errors and will only be accurate to within about 20⁰ or so. The very same antenna, given a nice strong signal (SNR=20dB), is however capable of achieving accuracies of way better than 1⁰.
Antenna manufacturers will sometimes publish accuracy plots like these labeled as “large-signal accuracy”, indicating that the plot assumes zero (or very low) noise levels. This accuracy provides information about the array layout, size and a couple other more esoteric features of the antenna. While this information is extremely useful for the antenna engineer during the design process, it really does not say much about how well the antenna will perform in the real world, mainly because of the following two factors that are cleverly (and sneakily) ‘omitted’.
To address problem 1 from the previous section, the performance of a DF antenna can be expressed in terms of its sensitivity to electric fields. Given that a typical DF antenna, using a correlative algorithm, is capable of sub-one-degree accuracy when the SNR is high, we can choose to calculate the incoming E-field strength that will generate a given SNR, or alternatively, the E-field strength that will produce a given RMS accuracy.
The advantage of using a sensitivity plot as a performance metric for a DF antenna is that it includes the gain of the antenna elements, and thus provides information about how good the individual antenna elements are, in addition to the overall layout of the array. Expressing the performance of a DF antenna in terms of its sensitivity also provides a much clearer indication of how well the antenna is expected to perform in real-life applications. With all other things being equal (same receiver system, same incoming signal), a DF antenna with better sensitivity will achieve better real-life accuracies than an antenna with lower sensitivity. The large-signal accuracy does not provide this straightforward equivalence to real-life performance without significant interpretation.
As an example, consider the calculated E-field sensitivity for the same antenna considered in the previous section.
This plot indicates the electric field strength required to achieve a SNR of 15dB (blue line) and the E-field strength required to achieve an RMS error of 2⁰ or better. This plot allows the customer to directly evaluate whether this antenna is appropriate for the type of signals expected in their specific application. Notice however, that these plots also require several assumptions (like the channel bandwidth and noise figure of the system), and these should be stated explicitly.
As is the case for large-signal accuracy, this plot is only valid for correlative algorithms and will differ significantly when used with other types of algorithms.
Given these obvious advantages in using the sensitivity plots as a metric of DF antenna performance, most Alaris Antenna brochures will publish sensitivity plots rather than accuracy plots.
Part 2 of this series will consider the factors that influence the accuracy and sensitivity of DF antennas, while part 3 will delve more deeply into the nuances of characterization measurements as it applies to correlative algorithms.
By Eugene Botha and Kalinka Faul
Humans do audio direction finding to a remarkable precision of less than two degrees. Shortly after the development of radio transmitters and receivers, radio direction finding (RDF or just DF) evolved for much the same reasons as human audio direction finding: firstly, for the location of possible threats and secondly for spatial awareness.
RF direction finding is used in several applications:
RF: Radio Frequency
DF: Direction Finding
RDF: Radio Direction Finding
AOA: Angle of Arrival
The simplest RF direction finding system consists of a directive antenna and a single receiver. The antenna is pointed in different directions while the receiver indicates the received signal strength. Only the magnitude of the signal is used to determine the direction of a transmitter.
The accuracy of this technique is dependent on the width of the antenna radiation pattern. A narrow beam will improve the accuracy but will increase the time spent scanning all possible directions. If the beam is too narrow the target may even be missed especially with intermittent transmission sources. A broad beam will decrease the bearing accuracy.
Scientists use handheld RDF devices to follow and accurately locate wild animals once their general location is known. Radio beacons can be made very small and consume very little power. This is ideal for long duration tracing of even very small animals. The relative signal strength is used to determine the direction of the radio beacon and the absolute signal strength shows some measure of the distance to the radio beacon.
In military applications this RDF is called a Spinning DF system and is commonly used for broad band high frequency DF. These systems are fully motorized and use knowledge of the antenna radiation pattern to improve accuracy.
The benefit of a single receiver RDF system is that they are cost effective, compact and have low power consumption.
The image below shows both a wide and narrow beam as they scan for signals transmitted by the target. The received signal level of both beams can be seen on the left. Note how the signal levels increase the closer the beams are to the direction of the target.
The Doppler DF system uses a single receiver connected to an omni directional antenna that is physically rotated on the circumference of a circle. As the antenna moves toward the radio source, doppler shift will increase the received frequency and the received frequency will decrease as the antenna moves away from the source. The change in frequency (obtained by FM demodulation) is used to determine the direction of the radio source.
The modern approach is to successively sample each antenna in a circular array of antennas, removing the need for any moving parts. This is referred to as Pseudo-Doppler DF.
The image in the left of the figure below shows the circular trajectory of a doppler DF antenna. The received signal is incident from the right.
The received frequency at various positions along the trajectory of the antenna is shown in the right image of the figure. As the antenna moves away from the incident signal, the frequency increases (red graph) and as the antenna moves towards the incident signal, the frequency increases (blue graph). The black curve shows the received frequency response at the position indicated by black circle on the antenna diagram.
The mono-pulse or sum-difference RDF technique uses two antennas. The antennas are connected to a four-port combiner 180° hybrid that generates a sum and difference signal. Such sum and difference patterns are generated by means of closely spaced overlapping radiation patterns at boresight. These signals form sum and difference radiation patterns. The ratio of the sum and difference signals and knowledge of the sum and difference patterns are used to determine the direction of the transmitter. Phase information is used to determine on which side of the sum pattern the transmitter is. An advantage of this system is in its capability to determine the direction of a transmitter after receiving one pulse. Such pulse could be a mere few microseconds. Accuracies of 10meter over a 100Km distance has been reported.
The relative difference in the phase of the signal received by two omni directional antennas spaced a set distance apart can be used to determine direction or angle of arrival of a RF signal. In the omni directional case, the interferometer does not have a way to determine if the signal arrives from the front or the back of the antennas.
As the frequency increases (or electrical separation increases) ambiguities appear as the phase differences wrap. If the antennas are too close (electrical separation very small) then the resulting phase difference will be very small and the system will not be able to determine the AOA. The frequency range of use is thus determined by the separation of the antennas and the noise figure of the receivers.
The image below shows two omni directional antennas with the incident signal circulating around them. The relative phase of the incident signal between the two antennas is shown in the image on the right. This phase difference is used to determine the direction of the incident signal.
An Adcock antenna uses two crossed loop antennas. The bearing of the RF signal is determined using the level of the signals received at each antenna. The method to process the information from a Adcock array is referred to as Watson-Watt. This is the best-known method of radio direction finding.
In a more general application four closely spaced omni directional antennas positioned in a square can be used to form an Adcock array. The opposing antennas are combined using a 180° combiner to form figure-of-8 patterns, which creates a unique set of magnitudes for any bearing direction. A Watson-Watt antenna cannot determine if a signal comes from the front or the back without the use of a third omni directional antenna to resolve the 180° ambiguity.
The figure below shows the typical radiation pattern of an Adcock array. The received signal rotates around the array. The relative amplitude of the signal received by the two crossed loop antennas (or combined omni directional antennas) is shown on the right. The + and – at the top indicates the relative phase of the two crossed loops with respect to the omni. It is this relative phase that resolves the ambiguity.
With the technological improvements in receivers and digital processors, all the information produced by multiple antenna elements can be used to improve the performance of RDF systems. Typically, the bearing is calculated using the phase differences of the signals received at the various antennas in the correlative array. The correlative algorithm compares the phase differences of the incoming RF signals at each antenna to a set of calibration phases stored in the processor to determine the most likely AOA. The correction function correlates the relative phases (and magnitudes for some correlators) of the received signals with the correlation table over all possible angles; the maximum of the correlation function indicates the AOA. The calibration information can be obtained by calculation or by measurement of the antenna.
The most common implementation of a correlative array is to have several omni directional antennas (typically four to nine antennas) in a circular pattern. In this configuration the differences in the phase of the incoming RF signal at each element is used by the correlative algorithm to determine the AOA.
Cutting edge systems can use both magnitude and phase of an arbitrary number of antennas arbitrarily positioned to determine the AOA in 3D, not just in a horizontal plane. The spatial positioning of the antennas in the array is of critical importance to achieve good performance without introducing ambiguities. 3D calibration data (not just in azimuth but also in elevation) with elements spatially positioned in atypical configuration is used to determine the AOA in 3D. In many practical applications the patterns of the antennas will not be omni directional, clever algorithms can incorporate the magnitude information to improve RDF performance.
Even an Adcock antenna can be characterized and used in conjunction with a corelative estimator to improve performance of the Watson-Watt method.
The two critical performance measures for DF systems are accuracy and sensitivity.
Accuracy is the measure of how accurate the bearing direction can be determined. The accuracy of a DF system is dependent on the DF processor, the specific design, the quality of the antenna elements used and the installation environment of the antenna.
Sensitivity is the measure of how well the DF system will perform in the presence of a small signal in a specified noise level. The sensitivity is dependent on the receiver noise, losses in the antenna and even the topology of the antenna elements in the array.
A discussion of these performance measures will be the topic of a follow up blog.
| Antenna | Nominal Gain | 3dB Beamwidth | First-null Beamwidth |
|---|---|---|---|
| Dipole 8-Stack | 8dBi | 9° | 20° |
| Dipole 4-Stack | 5dBi | 18° | 41° |
| 8dBi Antenna | 5dBi Antenna | |
| Antenna @ 4.3° incline | 4.9dBi | 4.05dBi |
| 8dBi Antenna | 5dBi Antenna | |
| Antenna @ 5.71° incline | 2.34dBi | 3.51dBi |
| 8dBi Antenna | 5dBi Antenna | |
| Antenna @ 10° incline | -24.5dBi | 0.92dBi |
