Novel sensor selection strategy for LPI based on an improved IMMPF tracking method

Abstract

Sensor platforms with active sensing equipment such as radar may betray their existence, by emitting energy that will be intercepted by enemy surveillance sensors. The radar with less emission has more excellent performance of the low probability of intercept (LPI). In order to reduce the emission times of the radar, a novel sensor selection strategy based on an improved interacting multiple model particle lter (IMMPF) tracking method is presented. Firstly the IMMPF tracking method is improved by increasing the weight of the particle which is close to the system state and updating the model probability of every particle. Then a sensor selection approach for LPI takes use of both the target's maneuverability and the state's uncertainty to decide the radar's radiation time. The radar will work only when the target's maneuverability and the state's uncertainty exceed the control capability of the passive sensors. Tracking accuracy and LPI performance are demonstrated in the Monte Carlo simulations.

Novel Power Control Scheme for Target Tracking in Radar Network with Passive Cooperation

“…Technically speaking, low transmit power, short dwell time, large revisit interval, and ultra-low side lobe antenna will lead to better LPI performance. Thus, the problem of LPI based radar resource scheduling in traditional monostatic radars has been extensively studied from various perspectives [20][21][22][23][24][25][26] [26]. In [24], a novel radar energy control algorithm based on an improved interacting multiple model particle filtering (IMMPF) was formulated for LPI performance enhancement, which controlled the radiation time and power of radar according to the target's range and radar cross section (RCS) with a given probability of target detection.…”

“…In [24], a novel radar energy control algorithm based on an improved interacting multiple model particle filtering (IMMPF) was formulated for LPI performance enhancement, which controlled the radiation time and power of radar according to the target's range and radar cross section (RCS) with a given probability of target detection. Later, Zhang et al [25] developed an optimal sensor selection strategy based on passive sensor cooperation, and it was shown that the proposed algorithm can decrease the radar radiation times with excellent target tracking accuracy. Chen et al extended the results of the previous research and presented a radar radiation control strategy for multiple aircraft platforms based on time difference of arrival (TDOA) cooperation [26], which utilized the comparison of covariance and the predefined threshold to control the radiation state [27].…”

LPI Optimization Framework for Radar Network Based on Minimum Mean-Square Error Estimation

“…Shi et al [17] proposed an LPI-based resource management algorithm for target tracking in a distributed radar network. Zhang et al [18] investigated a sensor selection strategy to reduce the emission times of the radar based on an improved interacting multiple model particle filter (IMMPF) tracking method. Generally, the above works provide us an opportunity to deal with the LPI optimization problem of a radar network system.…”

Low probability of intercept-based adaptive radar waveform optimization in signal-dependent clutter for joint radar and cellular communication systems

“…Thus, extensive research has been conducted in LPI optimization from various perspectives [15–28]. In [18], the authors propose an optimal sensor selection strategy based on passive sensor cooperation, where the results are extended in [20] and a time difference of arrival cooperation based radar radiation control in multiple aircraft platforms is proposed. Shi et al address the LPI optimization schemes in radar networks for the first time [21–24], and it has been demonstrated that radar network systems with multiple transmitters and multichannel receivers can provide significant LPI performance advantages over traditional monostatic radar systems.…”