Electronic Intelligence Receiver (ELINT) is an important component in electronic warfare (EW) and layer sensing. The information it provides by constant surveillance can be used to detect, track and classify signals across the electromagnetic spectrum. The proper identification and reaction to the threat can avoid disaster and assure spectrum dominance for Air Force systems.

Probability Density Function of PRC CS Scheme Receiver

To meet the challenges in today's and tomorrow's EW environment where signals are increasingly sophisticated, more dynamic, and more crowded in radio frequency (RF) spectrum than before, digital wideband receivers are being developed. The current state-of the-art for digital receiver bandwidth coverage is reaching multi-GHz. The conventional wideband digital ELINT receiver design is based on the Nyquist information theory, whereby, bandwidth coverage is limited by the Nyquist sampling rate. Performance, therefore, depends on current high-speed ADC technology and computation hardware such as field-programmable gate arrays (FPGA).

While these technologies are advancing, the progress may not keep up with the demand of ELINT requirements driven by next generation cognitive and broadband EW capability needs. One potential leap-ahead is to apply compressed sensing (CS) to ELINT receiver development.

CS technique has received significant attention since the fundamental theory was proven in 2006. That research indicated that the band limited by the Nyquist sampling rate is too restrictive. It is particularly true when the signal is sparse, i.e., the signal can be represented by only a few significant components in an orthogonal basis. Under the sparse condition, one can devise a sampling scheme that takes a much-reduced data rate compared to a Nyquist sampling rate and still covers the same bandwidth. The sampling scheme usually involves modulating the incoming signal and sampling data after summing the modulated signal over an interval of several chips. The Nyquist waveform can be reconstructed using the reduced sampling if the measurement matrix representing the sampling scheme has the restrictive isometric property (RIP).

Probability Density Function of NUS CS Scheme Receiver

This study investigates two measurement matrices that exhibit high probability of RIP. One is the pseudorandom chip (PRC) and the other is non-uniform sampling (NUS). The PRC's measurement matrix element is a pseudorandom bit of +1 and -1 with equal probability. The scheme was proposed for image processing using a single pixel camera, where the field of view was masked by a grid structure of a random +1 and -1 pattern.

The NUS scheme was proposed and fabricated by the Defense Advanced Research Projects Agency's (DARPA) analog-to-information (A-to-I) program. In this scheme, an internal Nyquist rate (i.e., chip rate of modulation) of 4.8 GHz is applied. The sampling time is a pseudorandom integer number within a certain range of time grid determined by the internal Nyquist rate. The average sampling rate is about 300 MHz and the average down sampling rate is about 16.

Many reconstruction algorithms have been proposed to restore the original Nyquist waveform of sparse signal from the reduced sampling data set. These reconstruction algorithms have a range of complexity and accuracy. Orthogonal matching pursuit (OMP) was applied to assess the sensitivity of the receiver. OMP is a restoration algorithm developed at an early stage that locates and calculates the signal's component iteratively. There are two major computation steps in every iteration. One is to correlate the residual measurement data with the measurement basis to locate and update the signal component. The other is to update the signal's components using a least square fit scheme.

This work was done by Lihyeh Liou and David Lin of the Air Force Research Laboratory and Ethan Lin and Chien-In Chen of Wright State University. AFRL-0248

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This article first appeared in the October, 2017 issue of Aerospace & Defense Technology Magazine.

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