SBIR-STTR Award

Wideband digitizers produce copious data samples that need considerable interface bandwidth to transport and distribute real-time data to heterogeneous compute resources performing signal analysis. The initial signal analysis component, signal detection for example, will indicate that a signal of interest (SOI) is present, but further actions are required to produce detailed signal analysis results. This need to perform subsequent processing necessitates the recovery of wideband data samples associated with the detected signal before, during and after its presence. The task of managing and distributing the high data volume of wideband systems is addressed by a focused subsystem that time indexes and stores the incoming data samples and later recovers data segments corresponding to SOI activity. The simplistic solution of continuous data storage requires either an expensive bulk storage solution, which fills up very quickly, or an immediate data reduction via channelization and processing, resulting in the storage of only a portion of the total data bandwidth. Neither of these solutions enable the timely recovery of the raw data samples that provide the maximum information content and the most flexibility for subsequent processing. In contrast, an embedded circular buffer approach enables a wideband detection system to cue on specific events and direct data to additional processing specific to the signals and events of interest. An efficiently managed FPGA based buffering subsystem can be tailored to the targeted signal parameters, scenarios, and environments while having reasonable cost, hardware, and power demands. Expedition Technology proposes the development of a Forensic Memory System (FMS) that will accept wideband digitized input, place the samples in an indexed time-tagged memory, and retrieve blocks of time/frequency/bandwidth data segments corresponding to signals of interest. The solution is an embedded real-time approach that leverages field programmable gate array (FPGA) technology to provide high performance interfaces, access to bulk memory, and management of the data buffering. For example, when a sensor system observing RF spectrum detects a particular event in the wideband context (through scanning or other detection), the Forensic Memory System FPGA receives the detection parameters, retrieves the wideband samples, filters, channelizes the samples for the desired processing, and forwards this significantly reduced quantity of data to the appropriate processing resource.

Benefit:
In general, the implementation of FMS supports the real-time needs of high-performance electromagnetic warfare (EW) systems with electromagnetic attack (EA), electromagnetic warfare support (ES), and electromagnetic protection (EP) missions. For all these systems providing sensor results with low latency is necessary for accurate situational awareness, targeting, self-protection, surveillance, and response generation. Direct Radio Frequency Memory (DRFM) EA systems benefit from both low latency analysis and rapidly isolating the raw samples associated with a SOI. This system would directly support the SURE1 system by providing a key component necessary for deployment. With the increased sophistication of communication and radar transmitters, wideband systems need a mechanism to access sparse information from wideband data streams and this system enables this ability and allows the systems to reach back in time. The prosed implementation is modular and flexible enough to be used with a wide range of existing and deployed wideband radios. It bridges the gap between wideband and narrow band spectral analysis systems.

Keywords:
digital data transmission, digital data transmission, First-in-first-out memory, time delay, adaptive signals, channel/time domain synchronization, Electro-magnetic support systems

The Forensic Memory System (FMS) addresses a critical need for wideband sensor systems. Wideband digitizers produce copious data samples that need considerable interface bandwidth to transport and distribute real-time data to heterogeneous compute resources performing signal analysis. The initial signal analysis component, signal detection for example, will indicate a signal of interest (SOI) is present, but further actions are required to produce detailed signal analysis results. The need to perform subsequent processing necessitates the recovery of wideband data samples associated with the detected signal before, during and after its presence. The task of managing and distributing the high data volume of wideband systems is addressed by the focused FMS subsystem that time indexes and stores the incoming data samples and later recovers data segments corresponding to SOI activity. An embedded circular buffer approach enables a wideband detection system to cue on specific events and direct data to additional processing specific to the signals and events of interest. An efficiently managed FPGA based buffering subsystem can be tailored to the targeted signal parameters, scenarios, and environments while having reasonable cost, hardware, and power demands. The FMS system utilizes High Bandwidth Memory (HBM) to provide extremely high data rates, and the FPGA packaging allows the insertion into existing hardware servers. FMS is unique in that it can offer data recovery as a service to multiple downstream consumers and it separates the detection and data recovery functions. Multiple detection functions can operate on a wideband or derived data stream, but they dont need to duplicate the buffering and data management functions. In the past capabilities like FMS existed as an element of stove-piped systems where a rigid service is not exposed to third party DSP utilities. In addition, FMS adds the flexibility of supporting a range of data rates, data structures, and dimensions. For example, FMS could store raw samples or samples that have been produced by a Polyphase Channelizer. FMS has a significant impact in reducing the downstream processing requirements in that only data samples corresponding to signal activity are processed. In a sparse wideband signal environment there can be significant reductions in the required hardware capacity for the mission. FMS will be a service that can be incorporated into an existing sensor server-based systems by adding an FPGA card and using the services APIs provided.

Benefit:
The FMS technology is applicable to any data system where particular segments of stored data need to be recovered based on detection criteria. The recovered data provides efficient support for more in-depth processing or storage. FMS provides a data reduction based on detected activity, and it does it very quickly. The performance of FMS supports real-time wide-band radar and communication sensors for SIGINT and EW missions. Specific EA functions like DRFMs benefit from the low latency of the FMS solution. Since FMS is being developed as a subsystem incorporating both hardware and interfaces, it can be added to existing server-based sensor systems as a supplemental service. The concept of operations for FMS is for it to supplement existing systems as a service. Existing sensors are improved by the addition of FMS because the system can revisit the data samples that contain the detected signal, there is no loss of the original data; the processing resources can be tailored to the signal density with prioritized signals or bands; and FMS has the performance to support both COMINT and ELINT missions. FMS is designed to integrate easily into existing systems that use a services architecture and can support a wide range of data formats. Operational performance improvements in signal analysis and formulating EA response should result from the addition of FMS to systems. Fundamentally these improvements result from exploiting the access to data that had been lost in the past. FMS has a significant impact in reducing the downstream processing requirements in that only data samples corresponding to signal activity are processed. In a sparse wideband signal environment there can be significant reductions in the required hardware capacity for the mission.

Keywords:
High Bandwidth Memory, DRFM, Aurora FPGA, SURE1, ADR, wideband, Channelizer, Radar