Detection of maneuvering small targets has always been an important yet challenging task for radar signal processing. One primary reason is that target variable motions within coherent processing interval generate energy migrations across multiple resolution bins, which severely deteriorate the parameter estimation performance. A coarse-to-fine strategy for the detection of maneuvering small targets is proposed. Integration of small points segmented coherently is performed first, and then an optimal inter-segment integration is utilized to derive the coarse estimation of the chirp rate. Sparse fractional Fourier transform (FrFT) is then employed to refine the coarse estimation at a significantly reduced computational complexity. Simulation results verify the proposed scheme that achieves an efficient and reliable maneuvering target detection with –16dB input signal-to-noise ratio (SNR), while requires no exact a priori knowledge on the motion parameters.

Long-time integration technique is an effective way of improving target detection performance for unmanned aerial vehicle (UAV) in the passive bistatic radar (PBR), while range migration (RM) and Doppler frequency migration (DFM) may have a major effect due to the target maneuverability. This paper proposed an innovative long-time coherent integration approach, regarded as Continuous Radon-matched filtering process (CRMFP), for low-observable UAV target in passive bistatic radar. It not only mitigates the RM by collaborative research in range and velocity dimensions but also compensates the DFM and ensures the coherent integration through the matched filtering process (MFP). Numerical and real-life data following detailed analysis verify that the proposed method can overcome the Doppler mismatch influence and acquire comparable detection performance.

In order to address the problem of high false alarm rate and low probabilities of infrared small target detection in complex low-altitude background, an infrared small target detection method based on improved weighted local contrast is proposed in this paper. First, the ratio information between the target and local background is utilized as an enhancement factor. The local contrast is calculated by incorporating the heterogeneity between the target and local background. Then, a local product weighted method is designed based on the spatial dissimilarity between target and background to further enhance target while suppressing background. Finally, the location of target is obtained by adaptive threshold segmentation. As experimental results demonstrate, the method shows superior performance in several evaluation metrics compared with six existing algorithms on different datasets containing targets such as unmanned aerial vehicles (UAV).

A wireless sensor network mobile target tracking algorithm (ISO-EKF) based on improved snake optimization algorithm (ISO) is proposed to address the difficulty of estimating initial values when using extended Kalman filtering to solve the state of nonlinear mobile target tracking. First, the steps of extended Kalman filtering (EKF) are introduced. Second, the ISO is used to adjust the parameters of the EKF in real time to adapt to the current motion state of the mobile target. Finally, the effectiveness of the algorithm is demonstrated through filtering and tracking using the constant velocity circular motion model (CM). Under the specified conditions, the position and velocity mean square error curves are compared among the snake optimizer (SO)-EKF algorithm, EKF algorithm, and the proposed algorithm. The comparison shows that the proposed algorithm reduces the root mean square error of position by 52% and 41% compared to the SO-EKF algorithm and EKF algorithm, respectively.

Channel equalization plays a pivotal role within the reconstruction phase of passive radar reference signals. In the context of reconstructing digital terrestrial multimedia broadcasting (DTMB) signals for low-slow-small (LSS) target detection, a novel frequency domain block joint equalization algorithm is presented in this article. From the DTMB signal frame structure and channel multipath transmission characteristics, this article adopts a unconventional approach where the delay and frame structure of each DTMB signal frame are reconfigured to create a circular convolution block, facilitating concurrent fast Fourier transform (FFT) calculations. Following equalization, an inverse fast Fourier transform (IFFT)-based joint output and subsequent data reordering are executed to finalize the equalization process for the DTMB signal. Simulation and measured data confirm that this algorithm outperforms conventional techniques by reducing signal errors rate and enhancing real-time processing. In passive radar LSS detection, it effectively suppresses multipath and noise through frequency domain equalization, reducing false alarms and improving the capabilities of weak target detection.

Passive detection of low-slow-small (LSS) targets is easily interfered by direct signal and multipath clutter, and the traditional clutter suppression method has the contradiction between step size and convergence rate. In this paper, a frequency domain clutter suppression algorithm based on sparse adaptive filtering is proposed. The pulse compression operation between the error signal and the input reference signal is added to the cost function as a sparsity constraint, and the criterion for filter weight updating is improved to obtain a purer echo signal. At the same time, the step size and penalty factor are brought into the adaptive iteration process, and the input data is used to drive the adaptive changes of parameters such as step size. The proposed algorithm has a small amount of calculation, which improves the robustness to parameters such as step size, reduces the weight error of the filter and has a good clutter suppression performance.

Underwater monopulse space-time adaptive track-before-detect method, which combines space-time adaptive detector (STAD) and the track-before-detect algorithm based on dynamic programming (DP-TBD), denoted as STAD-DP-TBD, can effectively detect low-speed weak targets. However, due to the complexity and variability of the underwater environment, it is difficult to obtain sufficient secondary data, resulting in a serious decline in the detection and tracking performance，and leading to poor robustness of the algorithm. In this paper, based on the adaptive matched filter (AMF) test and the RAO test, underwater monopulse AMF-DP-TBD algorithm and RAO-DP-TBD algorithm which incorporate persymmetry and symmetric spectrum, denoted as PS-AMF-DP-TBD and PS-RAO-DP-TBD, are proposed and compared with the AMF-DP-TBD algorithm and RAO-DP-TBD algorithm based on persymmetry array, denoted as P-AMF-DP-TBD and P-RAO-DP-TBD. The simulation results show that the four methods can work normally with sufficient secondary data and slightly insufficient secondary data, but when the secondary data is severely insufficient, the P-AMF-DP-TBD and P-RAO-DP-TBD algorithms has failed while the PS-AMF-DP-TBD and PS-RAO-DP-TBD algorithms still have good detection and tracking capabilities.

In the multi-radar networking system, aiming at the problem of locating long-distance targets synergistically with difficulty and low accuracy, a dual-station joint positioning method based on the target measurement error feature complementarity is proposed. For dual-station joint positioning, by constructing the target positioning error distribution model and using the complementarity of spatial measurement errors of the same long-distance target, the area with high probability of target existence can be obtained. Then, based on the target distance information, the midpoint of the intersection between the target positioning sphere and the positioning tangent plane can be solved to acquire the target’s optimal positioning result. The simulation demonstrates that this method greatly improves the positioning accuracy of target in azimuth direction. Compared with the traditional the dynamic weighted fusion (DWF) algorithm and the filter-based dynamic weighted fusion (FBDWF) algorithm, it not only effectively eliminates the influence of systematic error in the azimuth direction, but also has low computational complexity. Furthermore, for the application scenarios of multi-radar collaborative positioning and multi-sensor data compression filtering in centralized information fusion, it is recommended that using radar with higher ranging accuracy and the lengths of baseline between radars are 20–100 km.