Given its superior information acquisition capability, Polarimetric Synthetic Aperture Radar has been widely used in maritime target extraction and detection. However, due to the interference of complex imaging environment and the require of various-scale ship extraction, the capability of high-precision PolSAR ship extraction is still highly constrained. To this end, we propose a translation difference-based framework for complete ship extraction. A multi-sequence data generating strategy is firstly proposed, which acquires the master and slave data through data translation, offering a series space to fully highlight the difference information. Based on the generated multi-sequence data, a revised polarization ratio extraction operator is proposed by incorporating the optimal polarization state and depolarization effect, which can simultaneously achieve the purpose of target extraction, clutter cancellation, and interference rejection. Finally, in order to visualizes the scattering differences of different regions, we put forward a translation matrix representation diagram, which can display the parts where positive and negative change direction and trend of the constructed operator. Experiments carried out with real PolSAR data demonstrate that the proposed method can, on the one hand, effectively suppress interference and detect small-scale ship targets, and possess considerable receiving operating characteristic curve. On the other hand, it is able to extract the content of ship target completely with favorable target clutter ratio. Moreover, the directional sensitivity of the extraction operator and the potential of translation-based multi-sequence data in contour extraction is also demonstrated.

In the realm of bearings-only target tracking, the pseudo-linear Kalman filter (PLKF) has garnered significant interest, due to its low computational demands and robust stability. However, the interrelation between the measurement matrix and noise introduces bias into the PLKF’s target state estimation. To address this issue, we introduce a fusion unbiased PLKF (FUBKF) algorithm. This algorithm initiates with a global pseudo-linear treatment of the measurement equation, subsequently isolating the noise within the measurement matrix. By employing the unscented Kalman filter (UKF), the algorithm achieves precise estimation of the measurement matrix, thereby mitigating the estimation error stemming from the correlation between the measurement matrix and noise. Simulation outcomes demonstrate that the proposed algorithm substantially enhances tracking accuracy and sustains high stability in both 2D and 3D bearings-only target tracking scenarios, encompassing both non-maneuvering and maneuvering conditions.

With respect to the detection and recognition of an Artificial Modulation Target (AMT) with different modulated types, the state-of-the-art methods generally suffer the deficiencies of overfitting and insufficient generalization of existing neural network solutions. To address these problems, this paper proposes a multi-scale amplitude-phase feature discrimination method for AMTs in SAR images. First, a multi-type modulated AMT Dataset is generated (AMT Detection and Modulation Type Recognition Dataset, ADMTR Dataset), wherein the factors of jamming position, jamming-to-signal ratio (JSR), and the modulated parameter are considered to enhance the generalization. Second, a Multi-Input Multi-Output Fusion Wavelet Neural Network (MIMOFWTNN) is established, which not only uses the amplitude information of the scene but also adequately makes use of the phase and high-frequency information. This empowers us to detect the AMT in a higher dimensional feature space such that the type recognition can be implemented with more certainty. Analysis and discussions conducted on comparison experiments and ablation experiments demonstrate that the proposed network can achieve an average accuracy of 96.96% on the cross-validation set and a correct rate of 99.0% on the completely independent test set, which outperforms the compared methods.

In a multi-point source system, increasing the jamming power can expand the distribution area of the equivalent radiation center, but significantly increases the system exposure risk. Therefore, in order to achieve an optimal balance between the two, this paper proposes a joint optimization method for jamming power and an array of multi-point source systems. First, based on determining the spatial geometric relationship between the triplet antenna and the target, the distribution law of the equivalent radiation center of the triplet antenna under the condition of the target echo is derived. Second, by introducing the angle factor, the jamming power and equivalent radiation center distribution area are combined to construct the joint optimization model of jamming power and array in omnidirectional and non-omnidirectional situations. Third, based on the non-dominated sorting whale optimization algorithm (NSWOA), an adaptive inertia weight based on the cosine function and logistic chaotic map is introduced to obtain the optimal arrangement. The experimental results show that in the omnidirectional case, when the average jamming-to-signal ratio is 13.83 dB, the equilateral triangle array can achieve the goal of protecting the target while avoiding the exposure of the triplet antenna position. In the non-omnidirectional case, when the average jamming-to-signal ratio is 13.90 dB, the equilateral triangle array can achieve the optimal balance between the jamming power and the area of the distribution area of the equivalent radiation center, and control the distribution of the equivalent radiation center to strictly meet the preset angular domain constraints. Furthermore, the optimal JSR value was reduced by an average of 1.14 dB compared with that of the conventional selection scheme.

In a multi-point source jamming system (MPSJ), the antenna phase error is an intrinsic issue that seriously affects the distribution of synthetic false target (SFT). In order to make the position of SFT locate within the constraint range in the presence of phase error, this paper proposes a controllable SFT generation method for MPSJ systems. First, through establishing the spatial geometric relationship between the triplet antenna and the target, the distribution law of the SFT of the MPSJ system is derived. Second, by introducing the constraint jamming function, the SFT generation model of MPSJ system is constructed. Third, an adaptive golden whale optimization algorithm with adaptive variable inertia weight, golden sine strategy and chaotic map is developed to solve the model. These derive the amplitude and phase parameters, which ensures that the generated SFTs constantly distribute within the constraint range. Simulation experiments show that in the case of 7° phase error, the generated SFTs corresponding to all amplitude and phase parameter is position-controllable. When the radius of the constraint range is reduced to the baseline length of the MPSJ system, the SFT position corresponding to more than 10% of the amplitude and phase parameters is outside the constraint range.