Output signal-to-interference-plus-noise ratio (SINR) can be increased by extending the dwell time or coherent processing interval (CPI) over which target signal energy is integrated. However, over a long dwell time, nonlinear phase components in the slow-time signal limit the effectiveness of traditional Fourier-based processing methods to coherently integrate signal energy; thus, signal energy is often coherently integrated on a sub-CPI basis. This paper investigates a new extended dwell temporal signal processing algorithm that utilizes the signal energy in adjacent sub-CPIs in a unique way to improve the detection of weak, slow-moving targets. In many cases, the extended dwell algorithm more than doubles the probability of detection of low radial velocity targets over conventional noncoherent integration.
Improvements in detecting weak targets from a small radar platform must come through increased temporal integration, i.e., extending the time over which target samples are coherently integrated. Conventional single-channel radar assumes a linear-phase signal model that is only accurate over a short dwell time for typical target motion. Over an extended dwell, the target signal includes multiple nonlinear phase components, each of whose effects become significant at different times during the dwell. An algorithm is presented that develops a multiphase signal model in multiple stages based on these times. A modification of the proposed approach improves the signal model for the most challenging targets. When used as the detection filter, the multiphase signal model yields near optimal performance over an extended dwell time for a wide range of target parameters. Typical improvement in output signal-to-noise ratio (SNR) for a 500 ms dwell is 12−13 dB over conventional processing.
Temporal weights used in element-space pre-Doppler STAP ideally maximize the magnitude and frequency coverage of the pre-Doppler temporal output signal. Maximizing the magnitude of the temporal output signal is critical for detection of weak targets, while broad frequency coverage is essential for detection of low radial velocity targets. Traditional pre-Doppler STAP uses binomial filter coefficients, which act as a high-pass filter, for the temporal weights. A comparison of the traditional weighting method and an alternative method, which uses linear prediction to determine the temporal weights, shows that weights determined with linear prediction often contribute to a higher output SINR than do binomial temporal weights. In addition, linear prediction based temporal weights are determined adaptively from the data which offers a significant advantage in flexibility over the traditional method whose weights are based on a specific collection geometry.
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