Abstract
In the field of airborne radar resource management, one of the most accessible solutions to enhance the power of modern radar system is to save target tracking time as much as possible. During the target tracking process, it is effective to utilize the prior knowledge of the radar cross section (RCS) of the target to save airborne radar resource. Since the target RCS is sensitive to frequency and attitude angle, it is difficult to predict the target RCS accurately. This paper proposes an effective way to save airborne radar time resource in the target tracking process by changing radiation frequency of airborne radar to tolerate certain RCS fluctuation. Firstly, based on the tolerable fluctuation range of the target RCS, this paper designs an effective search algorithm to minimize the frequency set while maximizing the average RCS within the limited fluctuation range. Secondly, the detection probability prediction phase is renewed by taking the RCS fluctuation into account in order to reduce the radar dwell time. Finally, by using interactive multiple model Kalman filter (IMMKF) algorithm, a target tracking procedure with the minimum dwell time prediction method is proposed. Simulation results show that the proposed method is effective. As for target tracking simulation of three different trajectories, the proposed method can save at best 81.35% more dwell time than the fixed frequency method.
Highlights
As for radar resource management, power and time are two fundamental resources which determine the search space, detection range, and the number of targets that could be tracked simultaneously
Sun et al took multiple-input and multiple-output (MIMO) radar into account, assuming that the bistatic radar cross section (RCS) of target between an emitter and a receiver of MIMO radar could be predicted in real time to guide the radar to change radiation parameter; the simulations depicted that the tracking performance can be improved if a large number of well-separated antennas were used [3]
We depict that the proposed method based on limited RCS fluctuations can be beneficial to the applications in radar target detection and target tracking
Summary
As for radar resource management, power and time are two fundamental resources which determine the search space, detection range, and the number of targets that could be tracked simultaneously. Zhang and Tian assumed that the radar power and time resources could be changed based on radar network and the strength of clutter and proposed an optimal resource allocation model to save the total power and time resources of radar network [12] They extended the power allocation of radar network based on sensor selection algorithm into multiple-target tracking scenario with the constraint of predetermined mutual information [13]. The purpose of this paper is to reduce radar dwell time during target tracking process using the target RCS prediction method and improve radar detection probability. The prediction step of radar detection probability is applied into the interactive multiple model Kalman filter (IMMKF) algorithm to minimize radar dwell time under the same tracking performance constraint
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