High speed data acquisition systems

DRAP – RAID-based high-speed Digital Data Record And Playback


Need to collect LOTS of digital data/signals really fast?

If a logic analyzer or external hard drive aren’t cutting it, check out DRAP.

For debug and validation of high-data-rate systems

Parameter Capability
Storage Capacity 5+ TB
Aggregate Record Rate 1+ GB/s

Do you develop complex systems such as
radar, communication systems, RF sensors, imaging, or video?

Reduce Your Development Cycle

Enable concurrent off-line analysis, algorithm development, and test.

Debug faster and more methodically

Reconcile real-time algorithms with models and provide repeatable stimulus during algorithm debug and optimization.

Reduce Test Setup Time & Costs

Set up your test environment once, collect a bunch of data, and re-use the data over and over.

Software Framework

The DRAP Software Framework provides baseline functionality so that you can get up and running quickly.  For basic record and playback capabilities with off-the-shelf hardware interfaces, this can be accomplished in less time than it takes to order hardware.

For more customized functionality, you can take advantage of an architecture that incorporates the foundational elements that any DRAP system needs, including: API, data stream conversion, logging, and hardware interface abstraction.


  • LabVIEW-based
  • API service directory – allows client application to perform such functions as start/stop record, start/stop playback, query system status (run, halt, errors), and perform loopback test
  • Data stream to file conversion handling
  • Hardware abstraction layer – handles interface translation for various hardware interfaces, including, but not limited to FlexRIO, HS Serial, DAQmx, and R Series cards
  • Various low-level implementation details required for data buffering, recording, and manipulation of high-speed data


  • Model vs real-time algorithm reconciliation – enables a better understanding of subtle differences between real-time algorithm implementation and off-line models (e.g. fixed vs. floating point, approximation algorithms)

  • Repeatable stimulus – enables faster debugging by providing repeatable input stimulus to see the impact on algorithms during debug, optimization, and regression testing

  • Slow down playback – to assist with synchronization with external equipment or devices


  • In-line anomaly detection – supplementary analysis algorithms can look for anomalies that impact performance, providing more thorough verification
  • Index to points of interest – skip unneeded data during playback to find and fix bugs faster
  • Packet analysis – Route and filter data on record or playback
  • Derivative data sets – create prior to playback for additional scenarios by injecting faults and corner-case scenarios for more thorough verification

Want to chat about your scenario?

Case Studies

C4ISR Case Study:


RF Receiver Case Study: