The passive localization using multiple sensors is considered for emitter transmitting pulse signals with unknown start transmission time and period. Time of arrival (TOA) of pulse is estimated at every sensor, and it is difficult for sensors to intercept all pulses during the observation time in practice, as there maybe obstacle on the line of sight or the signal is too weak to be detected, namely incomplete measurements. In this paper, an algorithm based on combination of a new second difference of coherent time delays (SDCTD) measurement and traditional time difference of arrival (TDOA) is proposed to improve localization performance in the presence of two kinds of TOA errors, including the independent estimation errors and common errors among multiple sensors. The Cramer–Rao lower bound for the proposed algorithm is derived, and simulations validate that the performance of the proposed algorithm significantly outperforms existing algorithms, especially in the case of pulse loss.

This paper focuses on passive emitter localization using moving sensors. The increase in observation time is beneficial to improve the localization accuracy, but it could cause deterioration of the relative motion between the emitter and the sensors, especially the nonlinear motion. The common localization algorithms typically have two steps: (1) parameter estimation and (2) position determination, where the parameters are assumed to be constant, and it is not applicable for long observation times. We proposed the time-varying delay-based direct position determination (DPD-TVD) method, regarding the variation in the propagation time delay during the observation time. Using one step, the proposed algorithm can obtain the emitter’s position directly from the received signals by calculating the cost function corresponding to the map grid. By better adapting to highly dynamic scenarios, the proposed algorithm can achieve better localization accuracy than that of constant parameters using one-step or two-step procedures, which is demonstrated by the simulation results.

Radio Frequency Interference (RFI) significantly degrades the quality of spaceborne Synthetic Aperture Radar (SAR) images, and RFI source localization is a crucial component of SAR interference mitigation. Single-station, single-channel SAR, referred to as single-channel SAR, is the most common operational mode of spaceborne SAR. However, studies on RFI source localization for this system are limited, and the localization accuracy remains low. This paper presents a method for locating the ground-based RFI source using spaceborne single-channel SAR echo data. First, matched filtering is employed to estimate the range and azimuth times of the RFI pulse-by-pulse in the SAR echo domain. A non-convex localization model using Pulse Range Difference of Arrival (PRDOA) is established based on the SAR observation geometry. Then, by applying Weighted Least Squares and Semidefinite Relaxation, the localization model is transformed into a convex optimization problem, allowing for the solution of its global optimal solution to achieve RFI source localization. Furthermore, the error analysis on the PRDOA localization model is conducted and the Cramér–Rao Lower Bound is derived. Based on the simulation platform and the SAR level-0 raw data of Gaofen-3, we conduct several verification experiments, with the Pulse Time of Arrival localization selected for comparison. The results demonstrate that the proposed method achieves localization accuracy with a hundred-meter error in azimuth and a kilometer-level total error, with the total localization errors reduced to approximately 1/4 to 1/3 of those of the Pulse Time of Arrival method.

Recently, the passive synthetic aperture (PSA) technique has been used in passive localization to improve the position accuracy of single source by estimating the Doppler parameter of the received signal. However, in the presence of multiple sources, time-frequency aliasing will lead to serious cross-term interference during Doppler signal extraction, resulting in low localization performance. To solve this problem, a spaceborne passive synthetic aperture localization algorithm based on the multiple-stay detector HOUGH transform (MSD-HOUGH) is proposed in this paper. Firstly, an improved convolutional neural network based on the adaptive histogram equalization method (AHE-CNN) is proposed to achieve source number estimation. Then, the PSA Doppler equations are established in the HOUGH domain, which can suppress the cross-term interference of the multiple emitters. Meanwhile, a multiple-stay detector (MSD) is designed to reduce the pseudo-peaks in HOUGH domain. The estimated source number determines when the MSD will be terminated. Finally, a PSA cost function is established based on the estimated Doppler parameter to achieve signal source localization. Experimental results show that compared with other localization methods, the proposed algorithm has a significant improvement for multiple signal source localization.

Synthetic aperture radar (SAR) often receives interference from multiple emitters operating within the same frequency band. These interference signals are densely overlapped in time and frequency domains and strongly coupled with SAR echoes, which substantially increases the difficulty of signal recognition and sorting. In conventional processing chains, recognition and sorting are treated as independent modules with task-specific inputs, resulting in severe information fragmentation and cumulative-error propagation under dense interference conditions. To address these limitations, this paper proposes a unified recognition-sorting network for overlapping SAR interference signals, termed Uni-RSNet. Based on continuous multi-frame time-frequency representations, Uni-RSNet jointly captures high-dimensional intra-pulse features and inter-pulse temporal correlations of signals from the same emitter, enabling simultaneous modulation classification, pulse-parameter estimation, and emitter attribution. This framework extends interference recognition from isolated pulses to entire frames, while simultaneously imposing modulation-aware constraints on the signal sorting process. Furthermore, a statistically consistent contrastive objective is designed to enhance intra-emitter compactness and inter-emitter separability, while a geometric consistency constraint improves the robustness of pulse localization. The unified architecture compresses the conventional serial processing chain and eliminates the need for task-dependent input adjustments. Extensive experiments on both simulated and GaoFen-3 (GF-3) satellite data demonstrate the superior performance of the proposed method under dense interference conditions. The code and datasets are publicly available at: https://github.com/QianyueXue/Uni-RSNet.