Detection and classification of low probability of intercept radar signals using parallel filter arrays and higher order statistics

Abstract

The term Low Probability of Intercept (LPI) is that property of an emitter that because of its low power, wide bandwidth, frequency variability, or other design attributes, makes it difficult to be detected or identified by means of passive intercept devices such as radar warning, electronic support and electronic intelligence receivers. In order to detect LPI radar waveforms new signal processing techniques are required. This thesis first develops a MATLABʼ toolbox to generate important types of LPI waveforms based on frequency and phase modulation. The power spectral density and the periodic ambiguity function are examined for each waveform. These signals are then used to test a novel signal processing technique that detects the waveform parameters and classifies the intercepted signal in various degrees of noise. The technique is based on the use of parallel filter (sub-band) arrays and higher order statistics (third-order cumulant estimator). Each sub-band signal is treated individually and is followed by the third-order estimator in order to suppress any symmetrical noise that might be present. The significance of this technique is that it separates the LPI waveform in small frequency bands, providing a detailed time-frequency description of the unknown signal. Finally, the resulting output matrix is processed by a feature extraction routine to detect the waveform parameters. Identification of the signal is based on the modulation parameters detected.