The technology disclosed herein generally relates to methods and apparatus for detecting and classifying repetitive signals.
A receiver system is any system configured to receive energy waves and process these energy waves to identify desired information carried in the energy waves. As used herein, an “energy wave” is a disturbance that propagates through at least one medium while carrying energy. For examples, energy waves may comprise electromagnetic waves, radio waves, microwaves, sound waves or ultrasound waves.
Typically, a receiver system includes a transducer and a receiver. A transducer may be any device configured to convert one type of energy into another type of energy. The transducers used in a receiver system are typically configured to receive energy waves and convert these energy waves into an electrical signal. An antenna is one example of a transducer. A receiver processes the electrical signal generated by a transducer to obtain desired information from the electrical signal. The desired information includes information about signals carried in the energy waves.
Oftentimes, energy waves are used to carry repetitive signals. A repetitive signal is a signal that has a time period over which some aspect of the signal repeats. Repetitive signals are used in timing operations, synchronization operations, radar operations, sonar operations, and other suitable operations. For example, the characteristics of a repetitive signal may be used to synchronize two or more devices.
Electronic warfare (EW) systems are receive-only systems that have front-end receivers that produce pulse descriptor words (PDWs) for each radar pulse they detect. They are unlike radar systems in that they do not naturally produce range and they must handle unknown signals rather than look for reflected versions of their transmitted signals. The digital versions of these receivers are typically designed as a filter bank; within each filter channel, radar pulses are separated from other coincident signals and have their noise decreased by the relative filter bandwidth compared to the total input bandwidth. These pulses with their increased signal-to-noise ratio (SNR) are processed to estimate PDW elements such as pulse width, frequency, time of arrival, bandwidth, and amplitude. While channelizers have many advantages, they also have key disadvantages such as large size, weight and power due to the multipliers and adders required for very large filter banks that operate continuously whether a signal is present or not. In addition, signals that do not match the bandwidth and frequency of each filter in the filter bank are processed sub-optimally or split across filter channels, resulting in missed, false and inaccurate PDWs.
If a channelizer is not used, the two main processing tasks of noise reduction and signal separation must be done using different methods. Noise reduction can be done using denoising techniques which model signals via adaptive recursive equations that enhance the structure of signals and consequently reduce the unstructured signal noise. Signal separation requires constructing on-the-fly real-time automated matched filter construction. These filters are constructed and changed in real time to find new signals, track them and hold during difficult SNR conditions. Under these dynamic conditions, the pulse parameters must also be measured at the output of a tracking filter.
Systems are known in which PDWs are generated based on respective blind source separated signals output by tracking filters of a blind source separation system. Each PDW may contain data representative of signal characteristics of interest derived from a singular pulse of blind source separated signal, such as phase modulation parameters, frequency, bandwidth, time of arrival, time of departure, pulse width, pulse amplitude, pulse repetition interval, and/or angle of arrival.
In some systems, the generation of PDWs includes a process for phase modulation estimation of incoming radar. Some previous solutions for phase modulation estimation of radar signals typically use Fourier transform methods, which are complicated in hardware implementations and typically require full-bandwidth sampling of the phase-modulated signal. Alternatively, the signal can be mixed down to baseband and then Fourier methods can be used at the baseband bandwidth. This has two disadvantages. One is that this method creates noise mixing products that reduce the accuracy of the parameter estimates. Another is that the sampling rate of the estimation must still operate at the baseband bandwidth, while the phase modulation is very simple and can be inferred more directly at even lower samples rates. In addition, solutions that use Fourier transforms are difficult to make in a streaming form implementable by means of a field-programmable gated array (FPGA) or an application-specific integrated circuit (ASIC). Also, other solutions are quite often not easily designed for sub-sampling implementations.
It would be desirable to provide enhanced systems and methods for detecting and estimating parameters of a phase-modulated signal in a continuous manner.