Flying ad-hoc networks （FANET） using directional transmissions face challenges during network initialization due to frequent topology changes and fluctuations in link quality. To address these issues， a sensing-assisted rapid neighbor discovery method is proposed. The main idea of this method is to use a sensing mechanism to obtain neighbor node information， which accelerates network efficiency. In addition， this paper describes the adoption of a new integrated sensing and communication waveform， known as multiple-input multiple-output （MIMO） orthogonal time-frequency space （OTFS），which combats the Doppler effect in fast-changing channels and improves link quality. For FANET scenarios， a multi-target detection technique based on MIMO-OTFS integrated sensing and communication waveforms is studied at the physical layer. This physical layer sensing scheme is then mapped to the upper layer network， and a sensing-assisted efficient neighbor discovery algorithm is designed. Additionally， a integrated sensing and communication mechanism for collaborative discovery of multi-point is proposed. This mechanism indirectly senses and discovers potential targets by interacting with sensing information and neighbor discovery tables among neighboring nodes. The proposed mechanism enhances the initial networking efficiency of FANET. Simulation results demonstrate that the proposed scheme outperforms pure communication protocols. This leads to a significant reduction in the initial networking time of FANET， an increase in target sensing accuracy， and an overall improvement in network performance.

The high-precision radar target & simultaneous pulsed polarization measuring method requires high orthogonality of transmitting waveform. However， in practical applications， it is often difficult to transmit precise pulsed polarization measuring waveforms that meet the requirements of time width， bandwidth， and high orthogonality at the same time. A high-precision simultaneous pulsed polarization measuring method based on oblique projection filtering is proposed. Due to the nature of no orthogonality of the linear subspaces by oblique projection filtering algorithm， this method can obtain high-precision pulsed polarization measuring results with non-ideal orthogonal waveforms. Simulation results show that compared with the conventional simultaneous pulsed polarization measuring methods based on matched filtering， this method can reduce the amplitude and phase measurement errors of target polarization parameters and greatly improve the precision of simultaneous pulsed polarization measurement.

Aiming at the problem that existing deinterleaving algorithms' accuracy and efficiency decrease in a complex electromagnetic environment， a dynamic histogram deinterleaving method is proposed based on correlated pulse pairs. The process firstly performs fixed box length statistics on the difference of TOA（time of arrival）. It merges adjacent boxes higher than the detection threshold to obtain dynamic histogram statistics and estimate the potential PRI（pulse repetition interval）. By analyzing correlation pulse pairs corresponding to the potential PRI， the false PRI is eliminated， and the amount of jitter of the real PRI is estimated. Finally， the pulse sequence search is completed according to the PRI parameters to realize the deinterleaving of radar signals. Simulation experiments show that the method can achieve 95% accuracy in deinterleaving multiple complex PRI modulated signals under a lower depth and the technique can effectively deinterleave radar signals in a complex electromagnetic environment.

Reconnaissance area planning is the primary task for communications countermeasure reconnaissance of smart drone swarms. The ability to properly plan the reconnaissance area is critical to the accomplishment of the communication countermeasure reconnaissance task. By researching the reconnaissance area planning allocation and dynamic adjustment process， combining the combat principles of communication countermeasures and relevant military experience， the behavior rules， judgment rules and strategy rules for reconnaissance area planning of communication countermeasures drone swarms are collated and summarized， and the use of the rules is suggested and specific application scenarios are conceived.