LPI radar target detection performance optimization based on joint cognitive frequency transmission and power allocation

In the Low Probability of Intercept (LPI) radar system, in order to optimize the radar target detection performance, the energy consumption should be reduced while improving the target detection performance. Based on the Hidden Markov Model (HMM), this paper proposes an optimization algorithm for target detection performance joint cognitive frequency transmission and power allocation combined with the target Radar Cross Section (RCS) frequency response function prediction. The algorithm uses the target RCS's characteristics with different frequency response functions at different frequencies. The radar transmits a set of broadband pulse signals with different frequencies at the current time to irradiate the target and obtain the echo. The passive receiving system processes the echo to obtain the target RCS frequency response measurement value, and determines it as a priori information. The RCS frequency response function of the target at the subsequent time is predicted by the prior information and HMM, and the complete RCS prediction information of the target is obtained. Finally, using the radar equation, the radar transmitting power is calculated through the maximum frequency response function in the predicted values at different times and its corresponding transmitting frequency. The optimized information is then transmitted to the detection radar to complete the joint cognitive frequency transmission and power allocation. The simulation results show that the method used in this paper can lead to more accurate prediction information than the traditional algorithm, efficiently reduce the target detection transmitting power, and improve the radar target detection and LPI performance, which has a high engineering application value.