A fpga-based mwc compression sampling wideband digital receiver pdw forming method

Abstract

The invention provides an MWC compressed sampling wideband digital receiver PDW forming method based on an FPGA, and belongs to the field of electronic countermeasures in information and communicationengineering. Firstly, a signal is transferred to a baseband by a mixing module, a baseband compressed sampling signal is obtained by a low pass filter, and then deceleration extraction is performed to greatly reduce the data size so as to facilitate hardware implementation. The extracted signal is input to a CORDIC module to extract amplitude and a phase, sub-band frequency is measured by using aphase difference frequency measurement method, and a sub-band where the signal is located is calculated by a frequency calculation module in a multichannel signal parallel channelization mode so as to obtain absolute carrier frequency of the signal. Pulse extraction is performed by using a threshold judgment method, and a pulse width is calculated from a signal arrival time and a signal extinction time extracted from pulses. By adoption of the MWC compressed sampling wideband digital receiver PDW forming method provided by the invention, the feasibility of physical implementation of PWD dataforming of a digital receiver based on a MWC compressed sampling structure is verified, and a theoretical and hardware implementation foundation is laid for subsequent implementation of the FPGA basedon the overall system of the novel receiver.

Description

technical field [0001] The invention belongs to the field of electronic countermeasures in information and communication engineering. Specifically, it uses FPGA to realize a new type of digital receiver based on MWC compressed sampling, and performs sub-Nyquist sampling on broadband intermediate frequency signals and generates corresponding PDW methods. The generated PDW is used for sorting and identification of subsequent electronic reconnaissance. Background technique [0002] For low probability of intercept (LPI) signals (including linear frequency modulation (LFM) signals, phase encoding (PSK) signals, frequency encoding (FSK) signals, etc.) Multiple sub-signals with different carrier frequency positions and different bandwidths, and even their carrier frequency positions and bandwidths are time-varying. In the traditional uniform channelization structure, the sub-band division is blind, and the sub-band bandwidth cannot be changed after the sub-band division is determ...

AI Technical Summary

Problems solved by technology

In the traditional uniform channelization structure, the sub-band division is blind, and the sub-band bandwidth cannot be changed after the sub-band division is determined. Therefore, cross-channel problems will occur when receiving signals, resulting in complex digital signal processing. Increasing the sub-band bandwidth will greatly reduce the sensitivity of the receiver and reduce the effect of improving the signal-to-noise ratio

[0003] Judging from the current literature on the study of new channelization structures, most of the research is on how to use non-uniform channelization structures to solve the problems of uniform channelization structures, but this channel division is always fixed and not adaptive. ability

Images