If a signal has failed screening, further analysis will allow a better understanding of that signal. For example, digitally modulated signals contain periodic information such as time slot duration, symbol rates, chip rates, and hopping frequencies. Using a signal correlation density (SCD) algorithm, the periodic characteristic unique to a particular signal can be extracted, measured, and compared to a reference in order to classify a particular signal of interest. Depending upon the depth of the analysis, a measurement system employing an SCD algorithm can further distinguish between different signal types within a signal family. The end result is a digital fingerprint of the signal.

An SCD algorithm is a measure of “power” at various frequency offsets within a signal. The cyclic frequency is the distance a signal component is from the center frequency of the signal (Fig. 5). Signal components are also called tones or sine waves. It is possible to statically measure the match of the cyclic components of a digitally modulated signal to a particular wireless communications standard. The percent difference between the expected frequency offset and the measured frequency offset is called the alpha error and is measured in percent. Low alpha error values are considered a good match between an unknown signal and the standard’s representation of that signal.

The amplitude of the tones, or cyclic components, can be measured statically against expected values with a coherence match measurement. A coherence match is the ratio of differences between expected and actual SCD amplitude values at given alpha offsets, or a measure of power at a given frequency within the signal. A coherence match function makes it possible to measure how well the internal rates of specific digital signals conform to expected values.

Once a signal has been identified as a threat or as questionable, often the next task is to locate the signal emitter. For this task, a broadband spectrum analyzer designed for signal hunting is very useful. This spectrum analyzer should include various signal hunting tools, such as a signal strength meter, a spectrogram, and most important, the ability to map signals onboard to facilitate analysis of the more difficult signal hunting tasks.

A sensitive spectrum analyzer with a signal strength meter is one of the first tools required for signal hunting. The meter produces a tone available by speaker or headphones that varies in pitch with the strength of the signal. Using such an instrument is a field-proven method for operators to search for a signal while following the surroundings and not a spectrum-analyzer display screen. Such a tool allows operators to correlate where their antenna is pointing with tones. They can simply drive or walk around while listening to the tone, looking for the higher pitch. Unfortunately, some signals are more difficult to find. Conditions of low signal strength, multipath effects, or other signal distortion, can require more advanced techniques. In such cases, mapping the signals can be very useful and will help to untangle the complicated situations.

The first type of mapping involves traces or measurements recorded on the map at the operator’s current Global Position System (GPS) derived location. At the same time the signal is recorded, an operator can draw a vector, shown in red, to document the bearing of the signal (Fig. 6). This is a quick way to resolve complexities caused by reflections or obstructions. Once data collection is complete, the traces can be reviewed and the data analyzed without any need to return to a vehicle, office, or laboratory. An alternative form of mapping uses a GPS-driven spectrum analyzer to automatically map and record signals as the operator moves around. In this case, the color of the icons indicates when a signal approaches an operator- defined limit. This is a quick way to find signals when the RF issues are complex.

A third form of signal mapping is useful inside buildings where GPS will not work. In this mode, signals are recorded on a building floor plan automatically as the operator walks around the facility. This method borrows a technique from drive testers, but asking for a start and end point of the walk and automatically placing measurements in between. Any of these mapping techniques provide tools to collect, analyze, and document data in the field.

Of course, not every signal can be identified in the field. New signals, modified signals, signals from damaged equipment, and sometimes unknown or unusual signals exist in every part of the world. Since modified- from-standard signals are likely suspects in the signal hunting game, it may be worthwhile to capture a signal sample for further analysis in a laboratory. Given sufficient acquisition memory, a spectrum analyzer can capture a frame or more of most modern digital signals. If captured in the right format, the data can be analyzed offline by software tools such as the personal computer (PC) version of a real-time spectrum analyzer (Fig. 7) or by using analytical tools such as Matlab software from The Mathworks.

As RF communications systems evolve to the use of newer and more efficient techniques, many of which involve advanced digital modulation formats, sorting out suspect signals from legitimate communications signals becomes more and more difficult. Separating illegitimate RF signals from legitimate signals is the first step. And since signals vary with time, signal logging is often necessary for this task. Logging can be triggered by a time interval or by signal mask violations. Logging in a spectrum analyzer’s spectrogram mode allows rapid indexing of traces and provides a quick way to spot intermittent or weak signals.

Once the signal has been spotted, it can be judged against a set of parameters. Power levels, frequency, channelization, shape, location, and timing are all significant. Dynamic behavior, such as bursting or hopping is also significant. It is also possible to look inside a signal to better understand the signal’s internal rate structure using an SCD measurement. This measurement checks the signal’s internal data rates, which are difficult to disguise and likely to be affected if the signal is not what it seems.

If a suspicious signal has failed all these tests, it is likely time to locate the signal. A broadband spectrum analyzer with signal hunting tools, as well as integrated mapping, can help in this task, whether the signal hunt is taking place inside or out-of-doors. Finally, if a signal really is new, it may be wise to capture a sample of the signal for later analysis. Saving the unknown signal for categorization can help save time during future signal hunting episodes. Off-the-shelf software, as well as a number of development packages, is available to help with this task.