Accurate identification of radar operation modes is the important premise of threat level assessment and interference decision. But parameters overlap of PDW between different radar working modes seriously affects the recognition accuracy. A discrete process neural network (DPNN) based on particle swarm optimization (PSO) training is proposed to realize radar working modes recognition. Firstly, radar syntactic modeling method is proposed to extract radar phrases as operation modes character description. Then, the appropriate DPNN structure is built to and trained via PSO. Finally, radar working mode recognition of unknown radar phrases is realized by the finished DPNN. Different from traditional machine learning method based on single sampling of radar signals, this method achieve recognition according to accumulation of radar pulse sequence, and make the best use of time series change law of radar signals. The simulation results show that compared with traditional machine learning method, such as LSSVM, BPNN, working modes recognition rate of the novel method increases significantly under the condition of serious parameter overlap.

This paper investigates the formation joint resource scheduling problem from the perspective of cooperative jamming against radar systems. First, the formation survivability is redefined based on the task requirements. Then, a hierarchical adaptive scheduling strategy solution framework is constructed for state prediction and detection fusion of the networked radar system. Considering the scene constraints, an Improved Adaptive Parameter Evolution Marine Predators Algorithm is designed as an optimizer and embedded in the proposed framework to jointly optimize the platform beam allocation and jamming mode selection. Based on the original algorithm, real number random coding is used to perform dimensional conversion of decision variables, an adaptive parameter evolution mechanism is designed to reduce the dependence on algorithm parameters, and an adaptive selection mechanism for dominant strategies and a search intensity control strategy are proposed to help decision-makers explore the optimal resource scheduling strategy quickly and accurately. Finally, considering the formation maneuvering behavior and incomplete information, the proposed method is compared with existing base strategies in different typical scenarios. It is proved that the proposed strategy can fully exploit the limited jamming resources and maximize the survivability of the formation in radar system cooperative jamming scenarios, demonstrating superior jamming performance and shorter decision time.

The degradation properties of Indium phosphide hetero-junction bipolar transistors (InP HBTs) under proton irradiation are studied and modelled using a compact model for pre-irradiation, post-irradiation, and post-annealing. The variation rates of the model parameters, such as the base–emitter saturation current (I_SE) and ideality factor in the ideal region (N_E) in the forward Gummel characteristics, the zero-biased capacitance (C_je) and the grading factor (M_jer) in the BE junction capacitance, and the transit time parameter in the base region (T_fb), are analysed to delve into the degradation mechanism induced by proton irradiation. The displacement damage, induced by proton irradiation in the space charge region of the base–emitter junction and in the quasi-neutral bulk base region, is found to be responsible for the decrease in current gain and cut-off frequency. After annealing, the variation rates of the parameters decrease significantly compared to post-irradiation. This suggests that the recombination of unstable defects leads to a slight recovery in the degradation characteristics of InP HBTs after a period of annealing.

The single-event effects (SEEs) of frequency divider circuits and the radiation tolerance of the hardened circuit are studied in this paper. Based on the experimental results of SEEs in InP HBTs, a transient current model for sensitive transistors is established, taking into account the influence of factors such as laser energy, base-collector junction voltage, and radiation position. Moreover, the SEEs of the (2:1) static frequency divider circuit with the InP DHBT process are simulated under different laser energies by adding the transient current model at sensitive nodes. The effect of the time relationship between the pulsed laser and clock signal are discussed. Changes in differential output voltage and the degradation mechanism of unhardened circuits are analyzed, which are mainly attributed to the cross-coupling effect between the transistors in the differential pair. Furthermore, the inverted output is directly connected to the input, leading to a feedback loop and causing significant logic upsets. Finally, an effective hardened method is proposed to provide redundancy and mitigate the impacts of SEEs on the divider. The simulation results demonstrate a notable improvement in the radiation tolerance of the divider.

The peak sidelobe level (PSL) of an orthogonal waveform set directly governs the range and azimuth ambiguities of synthetic aperture radar (SAR) images and is therefore a key figure of merit for the imaging quality of multiple-input multiple-output SAR (MIMO-SAR). Minimizing the PSL is an NP-hard problem with a non-differentiable objective, and existing approaches often suffer from high computational cost and limited scalability while achieving only suboptimal PSL suppression. We therefore propose a loss function built around the 1.5-entmax sparse transform, which is simultaneously differentiable, sparse, and adaptive. Analysis of its differentiability and derivation of its analytical gradient allow us to recast the NP-hard problem as a differentiable optimization problem that can be solved by well-established algorithms. Owing to the sparsity of the transform, the gradient is concentrated on the high-energy sidelobes, while the gradient contribution from low-energy sidelobes becomes exactly zero, removing the gradient noise contributed by low-energy sidelobes and yielding a lower PSL. To overcome the scale sensitivity of the 1.5-entmax function, the upper bound on the sidelobe magnitude is used to normalize the input, which makes the threshold adaptive and removes the need for additional loss-function hyperparameter tuning across waveform-design tasks of different sizes. To further improve computational efficiency, we combine gradient descent with a deep learning framework: the waveform phases are treated as the learnable parameters of a neural network-like model, thereby yielding a back-propagation-based optimization framework with graphics processing unit (GPU) parallelism. The algorithm is implemented for parallel execution on GPU, and the gradient is computed efficiently through the network’s automatic differentiation in conjunction with a custom analytical-gradient operator, leading to a substantial increase in computational speed. Without the need for the manual tuning of loss-function hyperparameters, the proposed algorithm achieves the lowest PSL across waveform sets of various sizes and reduces computation time by approximately two orders of magnitude in large-scale settings.

To address the limited reconnaissance perspective and susceptibility to noise interference of single-platform approaches in multifunction radar (MFR) working mode recognition, this paper proposes a fusion recognition model based on bidirectional long short-term memory networks (BiLSTM) and Dempster–Shafer (DS) evidence theory. The model first employs a BiLSTM network to extract deep temporal features from radar phrase sequences observed by a single platform, thereby obtaining the state probability distribution for each platform. The probability distributions from multiple platforms are then treated as independent evidence and fused at the decision level using DS evidence theory to enable accurate recognition of radar working modes. Experiments construct a multi-platform co-simulation dataset based on the operational process of a typical “Mercury” radar and compare the proposed method with various algorithms, including the hidden Markov model–Viterbi (HMM-Viterbi), recurrent neural network (RNN), and long short-term memory (LSTM) algorithms. The results show that the proposed model achieves excellent recognition performance under varying noise and missing-data rates. Under the extreme condition of both a 25% noise rate and a 25% missing rate, the recognition accuracy still reaches 95.64%, which is 3.82 percentage points higher than the 91.82% achieved by the conventional HMM-Viterbi algorithm, validating the model’s high accuracy and strong robustness in complex electromagnetic environments.