In this paper, the design of low probability of intercept (LPI) radar waveforms considers not only the performance of passive interception systems (PISs), but also radar detection and resolution performance. Waveform design is an important considerations for the LPI ability of radar. Since information theory has a powerful performance-bound description ability from the perspective of information flow, LPI waveforms are designed in this paper within the constraints of the detection performance metrics of radar and PISs, both of which are measured by the Kullback–Leibler divergence, and the resolution performance metric, which is measured by joint entropy. The designed optimization model of LPI waveforms can be solved using the sequential quadratic programming (SQP) method. Simulation results verify that the designed LPI waveforms not only have satisfactory target-detecting and resolution performance, but also have a superior low interception performance against PISs.

To address the difficulty of accurate motion-model prespecification and delayed model switching in maneuvering target tracking, this paper proposes a joint model- and network-driven screened Interacting Multiple Model–Gated Recurrent Unit (IMM-GRU) method. In the filtering stage, multiple network outputs are screened using a smooth local reference trajectory constructed from measurements, providing a physically meaningful criterion for rejecting inconsistent estimates while retaining candidates compatible with local track evolution. For radar low-probability-of-intercept (LPI) operation, a difference-enhanced attention Bidirectional GRU (Bi-GRU) classifier identifies target motion modes during radar-silent intervals and supplies model priors for predictor screening. A screened IMM-GRU predictor then selects candidates using model probability, prediction uncertainty, inter-network disagreement, and local kinematic consistency. Cumulative prediction variance and measurement consistency are used to schedule radar radiation. Simulation results show that the proposed filtering and prediction modules improve tracking and extrapolation accuracy. Under the equal-accuracy LPI criterion, the proposed framework achieves the lowest four-scenario average radiation ratio at every prescribed MRMSE level from 40 to 250 m and is the only method that satisfies the 30 m MRMSE requirement in all four scenarios. These results demonstrate improved maneuvering-target tracking within the examined model and noise ranges while reducing unnecessary radar exposure.