Tracking closely spaced multiple sources via spectral-estimation techniques

Modern spectral-estimation techniques have achieved a level of performance that attracts interest in applications area such as the tracking of multiple spatial sources. In addition to the original "superresolution' capability, these techniques offer an apparent 'absence of sidelobes' characteristic and some reasonable solutions to the difficult radar coherent-source problem that involves a phase-dependent SNR (signal-to-noise ratio) penalty. This report reviews the situation briefly, and it discusses a few of the techniques that have been found useful, including natural or synthetic doppler shifts, non-Toeplitz forward-backward subaperture-shift processing, and recent eigenvalue/eigenvector analysis algorithms. The techniques are applied to multiple-source situations that include mixtures of coherent and noncoherent sources of unequal strengths, with either an 8-or a 12-element linear-array sampling aperture. The first test case involves the estimation of six sources, two of which are 95% correlated. The second test case involves a tracking-simulation display example of four moving sources: three are -10dB coherent sources 95% correlated, and the other is a strong 20-dB noncoherent source. These test cases demonstrate the remarkable improvements obtained with the recent estimation techniques, and they point to the possibilities for real-world applications.