An Adaptive System for Signal Search with Compressed Data

Technology

The Inventors set forth a statistical framework, where a decision vector is obtained from a received sample vector through a non-linear transformation and a linear compression. The search is then performed by selecting an element of the vector through a decision rule. The system design is divided parts corresponding to the three sections of the scheme: (i) non-linear transformation; (ii) linear compression; and (iii) decision rule. The non-linear transformation improves the performance under different operating conditions. The linear compression is used to reduce the search computational complexity.

If the non-linear transformation is a monotonic function, the probability distribution functions of the variables at the output of the compression operation can be easily derived from the probability distribution functions of the variables at the input of the compression operation. The decision rule is designed by defining an objective function related to a performance metric, which can be written in terms of probability of selecting each element of the decision vector.

Problem Addressed

Many applications in signal processing require the search for a signal in a received waveform. A system for signal search usually processes the received samples through a non-linear receiver and makes a decision based on a decision rule. This is designed to achieve a performance goal in terms of detection rate or false-alarm rate. This search can be made through soft-decision or hard-decision approaches that typically yield suboptimal solutions for reducing complexity. The Inventors propose a new scheme for an adaptive system that performs signal search on compressed data which yields more optimal solutions. Their system is designed to solve an optimization problem whose objective function (i) is adaptive according to the variability of the operating environment and (ii) has low-complexity computation. 

Advantages

• Generalized system with respect to the measured variables (e.g., frequency, time and space)
• System is adaptable to the operating environment (e.g., through use of channel state information)
• Decision processes are low complexity compared to traditional methods