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Intercepting voice communications among the vast amount of wireless transmissions is a challenging task. The RF spectrum is crowded with many intermittent and short-duration transmissions. This article discusses an improved system architecture for collecting voice signals in the VHF/UHF range with minimum operator attendance. Spectrum monitoring systems look at broad expanses of the frequency range for signals of interest and are sometimes called signal survey or emitter identification systems. They may also have a narrowband mode to further process signal data. Voice signals are a specific type of signal transmission that can be selected in some signal monitoring solutions. The engineers and consultants integrating these systems for deployment need to choose the best equipment for efficiency and productivity.

Some of the challenges in voice interception are the ever-increasing number of wireless transmission devices in addition to the vast number and complexity of transmission protocols. The newest protocols make it difficult to distinguish voice from noise. System architectures should be easily adaptable for the newest devices and protocols.

The wireless transmissions containing voice can be over a broad frequency range and at unknown frequencies. Some signal monitoring systems may sweep the broad spectrum too slowly to find short-duration transmissions. Other systems may be focused on a narrowband. Of course, one solution is to use multiple narrowband receivers, which could significantly increase hardware and operator costs.

Voice signals may not transmit in a consistent manner or at consistent time of day. For this reason, voice-monitoring systems can be tedious to operate when searching for signals of interest. Determining signals of interest from thousands of transmissions often wastes valuable time and resources. An advanced monitoring solution would resolve these issues without the need for constant operator attention.

The complete architecture for a system that is optimized for collecting voice transmissions contains numerous components and subsystems. The front-end hardware and software to identify signals is an important component. Front-end hardware maximizes the probability of intercepting the voice signals. The back-end processing and recording of the voice signals provides critical information about the intercepted voice traffic. However, information on too many extraneous signals wastes valuable resources. The ease of integrating front-end and back-end processing with other subsystems, such as direction finding, should also be considered. When making decisions on the architecture of the solution, the cost of operation, deployment, maintenance and upgradability should be considered along with the tangible costs of equipment and software.

Narrowband receiver architecture

Many existing systems are based upon a narrowband receiver front-end or multiple narrowband receivers, as depicted in Figure 1. In simple systems, an operator manually tunes the receiver according to a pre-determined frequency list or scans a designated range of frequencies. The operator also initiates the recording of signals of interest. The operator may need help from a reviewer to determine if a particular signal should be collected.

Some narrowband receivers can automatically scan a frequency range of interest or a list of specific frequencies. The scan automatically stops when the receiver detects energy over a pre-determined signal level. It still relies on a full-time operator/reviewer to determine if the signal is voice and then initiate recording.

Each narrowband receiver only covers a fraction of the overall frequency range of interest, so signals outside of the narrowband will be missed. To increase the probability of capturing voice traffic, multiple narrowband receivers can be used to cover more of the frequency range of interest as shown in Figure 1. However, complete coverage of a wide frequency range using multiple narrowband receivers becomes cost prohibitive.

In automated systems with multiple narrowband receivers, large amounts of data are collected in a host-processing system for later analysis. These systems connect to the receivers via high-speed interfaces and usually have massive data storage capacities.

The narrowband receiver architecture is a low capital cost solution for simple voice monitoring in a narrow frequency band. The largest drawback to the narrowband receiver approach is the need for operator/reviewer attention to capture the signals of interest. To automate this architecture requires significantly more equipment and resources. Even then, the system created may not maximize the probability of capturing signals of interest because generally only one signal in a narrowband is recorded.

Wideband search architecture

To increase the probability of intercepting voice signals, automatically eliminate extraneous signals and decrease dependency on operators, a wideband search with a narrowband collection system can be deployed. This improved voice intercept architecture, shown in Figure 2, combines high-speed, high-resolution wideband searching with multiple channels of narrowband processing. The wideband search detects candidate voice transmissions in the wideband spectrum, and then “tips” the narrowband voice processing. Confirmed voice signals from the system are recorded to files for offline processing by one or more reviewers. This system architecture should allow monitoring of one or two narrowband channels in real time.

A system with a fast wideband search would be able to measure signals that are present for less than a second. To find small signals close to large signals or to find small signals near to the noise floor requires a high-resolution system. The wideband search system should be able to monitor a 1 GHz spectrum with a 2 kHz resolution bandwidth in 250 milliseconds. Users can discover many more unknown short-duration signals with a wideband, high-speed, high-resolution front end.

With many transmissions in the crowded RF spectrum, you need an automated technique to select and record only the signals of interest. Many hours can be spent sorting and identifying signals, only to find they are irrelevant. An advanced voice intercept system would first use thresholds for defining signal levels of interest. When the spectral energy breaks the threshold criteria, it would result in signal detection, as shown in Figure 3. For each signal above the threshold, information is captured and recorded in a real-time database to be used with automated detection tools.

Besides selecting signals using thresholds, alarms and alarm tasks can automate data gathering and more specifically define voice signals of interest. This includes the ability to ignore certain signals, once they are analyzed and determined irrelevant. Voice-monitoring systems generally require a large number of operators, so creating an unattended system using alarms and tasks can save potential man-hours.

In this system architecture, the voice processing is done in a narrowband mode. The search engine is positioned to a 36 MHz span and the multiple narrowband channels are activated for recording voice simultaneously. The narrowband signals are extracted from the wideband datastream using a bank of digital downconverters. Typically, 32 to 92 narrowband channels provide adequate capacity for monitoring voice signals in the 36 MHz span. Narrowband channels can have programmable bandwidths to optimize the captured voice signals.

These narrowband channels substitute for purchasing external hand-off receivers because you can lock one of these channels to a frequency of interest. This integration of hand-off receivers with the voice-processing system makes it much easier to collect the signal data in the file processing system. Most external hand-off receivers provide only analog audio output.

Significant digital signal processing (DSP) horsepower is required for voice detection and analysis. For voice intercepts, DSP tasks would include identifying FM-modulated signals, demodulating the signal, and detecting voice with special processing algorithms. Signal data is formatted and buffered for output to the host PC for recording to disk. Recordings should continue until the end of transmission, user-specified limits are met, or an out-of-range line-of-bearing (LOB) is received from the DF subsystem.

With thousands of potential voice recordings, efficiency of post-processing is important to minimize the number of reviewers. An audio player application, shown in Figure 4, and a real-time audio output would optimize the use of operators and reviewers.

To further reduce the recording of voice transmissions that are not of interest — or find a frequency span that is of interest — operators can listen to one or two narrowband channels in real time. This real-time audio output mode employs the same narrowband-processing infrastructure as used by the voice-processing system, thus reducing the need for separate hand-off receivers. The audio data is streamed to the host workstation, where it is maintained using the computer's sound system.

Reviewers must operate at a pace that is asynchronous to the voice intercept system's recording of files, so valuable information is not lost. Therefore, the review process must be separated from the RF monitoring system. For reviewers to achieve maximum throughput, full-featured audio playback software and superior processing methodology is required.

Software tools should have all the key playback features along with the advanced demodulation features of some radios. For example, a squelch function “skips over” portions of the recording that do not contain voice. With this feature, playback time can be dramatically compressed for many two-sided voice transmissions. This may reduce the amount of time a reviewer would need to listen to a transmission before making a save or delete decision.

With lots of voice recordings, efficient file management becomes another key feature in the overall voice-monitoring solution. The review process should be in a hierarchical structure. Recordings should first be sorted into various file folders or quickly deleted if the recording is not of interest. Multiple reviewers may be needed to monitor a single directory of recordings. All of this reviewing should be occurring while the voice intercept system asynchronously provides more data files for review. Links between the audio player and the voice intercept system would further enhance efficiency by continuing to update an “ignore list” of frequencies used by the system.

Both narrowband receiver and wideband search front-end architectures presented can be integrated with a direction finding (DF) system, so a LOB can be identified on voice transmissions. The LOB information on signals may determine if it is of interest. For example, if the LOB is not from the direction of interest, the voice-processing system would not record the signal data. A system with this and the other previously mentioned capabilities is represented in Figure 5.

The software in these systems should allow sockets interfaces and have application programming interfaces (API) so system developers can customize the user interface or easily add other hardware and software.

Advanced architecture solutions

The advanced voice-collection architecture discussed has been considered in the design of the Agilent E3238S family of signal monitoring solutions. The E3238S signal monitoring system has hardware configured for monitoring signals in the VHF/UHF range. This system has wideband search and narrowband voice processing. Its wideband search capability with high speed and high resolution identifies and determines the unknown, intermittent candidate signals. The optional narrowband voice processing using the proprietary 35688E-VA2 software algorithm to collect information on only the candidate signals that contain voice.

The products that are identified in this article are subject to the export control regulations of the U.S. Departments of State and Commerce. An export license may be required for sale of these products outside of the United States.


Chris DeSalvo is a product marketing engineer for the instrument systems business in Agilent Technologies. He has a BS in Electrical Engineering from the University of Pittsburgh. In his career with Agilent, he has done technical marketing on basic instruments, automotive electronics test systems, mobile handset testers, phase noise systems, and most recently spectrum monitoring solutions.