“We can help you find that needle in the haystack.”
If you are looking for a small signal in a high or strongly cluttered background, even a signal to noise of 1 (or less), we can help.
- Do you have more data than anyone could possibly want, or a highly clutter-ridden background for your signal, yet no way to extract the specific result you need?
- Do you need a confidence assessment of the identification you have made, i.e., how good is your result?
- Maybe you have extremely limited information and urgently need to predict an answer?
If you know what you are looking for, our team of experts can help you find that elusive result you are seeking. We will work with you to develop sensors, algorithms, or a combination of the two, to use your existing understanding of your problem, with our proven sensor and signal processing technologies and methods to extract data accurately.
The data environment can be widely varied, including:
- High noise levels that bury the signal of interest
- Highly cluttered signal conditions
- Big data sets, with your critical information buried within
- Low or sparse signals – a “picket fence” instead of a clean spectrum?
- Signals with low resolution, few or missing data points, or other challenges
The type of signal detection could be, but is not limited to, the following:
- Low signal to noise sensing (SNR of 1 or less)
- Sparse signal discovery
- Sparse signal identification
This application can be applied to any dataset with existing models, but here are examples:
- EO/IR signature detection
- Radioactive isotope identification
- Chemical signature analysis
- Applied statistical analysis
- Facial attribute recognition
- Behavioral or communication patterns
Using Bayesian or similar applied statistical and analytical techniques, integrated with a hardware and/or software solution, signal processing tools, and engineering design methods, we will provide the most efficient and effective solution needed to solve your problem. We will work with you to prototype hardware and software solutions to achieve an extremely high probability of detection or identification, with confidence, using a very small set of data points or massive data set.