The passive radar systems for urban aerial target surveillance highlight the importance of accurately determining the angle of arrival (AOA) of weak target echoes. The AOA information is crucial for locating targets using passive radars, considerably impacting the detection capabilities of the system. Traditionally, research on AOA estimation has focused on algorithms utilizing two-dimensional correlation processing in the delay-Doppler domain. These methods enhance the signal-to-noise ratio of the echo signal, leveraging the accumulated gain from the mutual ambiguity function between the reference signal and monitoring signals and subsequently facilitating angle estimation. However, existing algorithms face notable challenges. For instance, they are particularly prone to the distance migration effect when tracking weak targets moving at high speeds, adversely affecting the accumulation gain and the accuracy of parameter estimations. In addition, the computational requiremens of the mutual ambiguity function are high, complicating real-time implementation. Although certain rapid implementation methods for the mutual ambiguity function can reduce the computational requiremens, they are unsuitable for platforms with limited processing power. Additionally, current algorithms struggle to differentiate between multiple targets within the same range-Doppler unit owing to their inability to refine target distinction along the angle dimension. Considerably, this paper proposes a more efficient algorithm for delay-Doppler angle estimation tailored to high-speed, multitarget scenarios.

The proposed algorithm is divided into three steps. (1) The reference and monitoring signals undergo segmented processing; this division is based on the target movement and the signal parameters of the external radiation source, distinguishing between the fast time within each segment and the slow time across segments. (2) The second step addresses distance migration, which can occur owing to the high-speed movement of the target. Thus, the Keystone transform is used to adjust the time axis of each frequency, effectively correcting the distance migration for high-speed targets. Next, the energy of the target echo signal is aggregated into a singular delay-Doppler unit. The process continues with the detection and extraction of the slow time-sampling signal from the delay unit containing the target echo. This extracted signal forms the basis for converting the problem into one of the angle measurements, focusing on the multifast beat signal within the slow time dimension. (3) The target azimuth and pitch angles are estimated by employing axial virtual shift coherence within a uniform circular array. The multiple signal classification (MUSIC) algorithm is applied to these coherent signals for efficient processing in scenarios involving multiple targets.

The algorithm can distinguish multiple targets in the same delay-Doppler cell. This differentiation is facilitated by the array axial virtual translation method, which improves the capability of the algorithm to process multiple-target signals.

Simulation results have demonstrated the effectiveness of the proposed method for the delay-Doppler processing, particularly its segmented processing combined with the Keystone transform, which corrects the distance migration of the target and greatly reduces the computational complexity. Consequently, the stability and the real-time performance of the algorithm are markedly improved. The algorithm exhibits obvious performance advantages, especially in scenarios characterized by high-speed movements and the presence of multiple targets.