Networked collocated multiple-input multiple-output (C-MIMO) radar system shows the good performance in the multi-target tracking (MTT), and the resource allocation strategy can further improve target tracking performance. A joint polarization mode, beam and power allocation strategy (JPBPS) with the radar cross-section (RCS) prediction is proposed to improve the MTT accuracy. The RCS predicted model on polarization mode and observation angle is used to obtain the predicted RCS information. Moreover, to evaluate the global tracking performance, the posterior Cramér-Rao lower bound (PCRLB) based on the covariance intersection (CI) fusion rule is derived, and the optimization model is established. As the optimization model is non-convex, we propose a three-step algorithm to transform the problem into a convex problem by separating the variables step by step. Simulation results show that the proposed JPBPS can effectively improve the global MTT accuracy compared with other strategies.

The proliferation of electronic countermeasure (ECM) technology has presented military radar with unprecedented challenges as it remains the primary method of battlefield situational awareness. In this paper, a joint antenna scheduling and power allocation (JASPA) scheme is put forward for multi-target tracking (MTT) in the distributed multiple-input multiple-output (D-MIMO) radar. Aiming at radar resource scheduling in the presence of range deception jamming (RDJ), the false target discriminator is designed based on the Cramer–Rao lower bound (CRLB) in terms of the spoofing range, and the predicted conditional CRLB (PC-CRLB) plays a role in evaluating tracking accuracy. The JASPA scheme integrates the quality of service (QoS) principle to develop an optimization model based on false target discrimination, with the objective of enhancing both the discrimination probability of false targets and the tracking accuracy of real targets concurrently. Since the optimal variables can be separated in constraints, a four-step optimization cycle (FSOC)-based algorithm is developed to solve the multidimensional non-convex problem. Numerical simulation results are provided to illustrate the effectiveness of the proposed JASPA scheme in dealing with MTT in the RDJ environment.

The simultaneous multi-beam working mode of the collocated multiple-input and multiple-output (MIMO) radar enables the radar to track multiple targets simultaneously. A joint power and bandwidth allocation algorithm in a collocated MIMO radar based on the quality of service (QoS) framework is proposed for the multi-target tracking problem with different threat levels. Firstly, a posterior Cramer–Rao lower bound (PCRLB) concerning the power and bandwidth is derived. In addition, the optimal objective functions of power and bandwidth are designed based on the QoS framework, and the problem is solved using the convex relaxation technique and the cyclical minimization algorithm. The numerical results show that the proposed algorithm has better tracking accuracy and achieves more reasonable resource allocation compared to strategies such as average allocation.

The cognition radar utilizes the dynamic waveform configuration to adapt to the environment to improve tracking performance. A novel tracking algorithm based on waveform selection is proposed to address the maneuvering aircraft tracking problem in clutter. Based on the modified current statistical (MCS) model, the modified probabilistic data association filter (MPDAF) is integrated with the square-root cubature Kalman filter (SCKF) to deal with the nonlinear measurement and the clutter. Additionally, the waveform library is established by applying the fractional Fourier transform (FRFT) to a base waveform, and a direct method is proposed to select the optimal waveform to minimize the posterior state error covariance matrix. The simulation results showed that, compared with the filters with the fixed waveform as well as the state-of-the-art algorithms, the proposed algorithm achieved higher estimation precision and lower track loss percentage while maintaining a low computational burden.

An adaptive constant acceleration (ACA) model is proposed for the maneuvering target tracking problem. Based on the Taylor series expansion of acceleration, we establish the relationship between the Jerk and the velocity as well as the acceleration so that the maneuvering acceleration variance is approximated by the components in the state error covariance matrix. Then, the latter one is connected with the process noise, and the adaptive adjustment of the ACA model is realized. Combining with the strong tracking square-root cubature filter (ST-SCKF) in our previous work, an ACA-ST-SCKF is developed. The simulation results show that the proposed filter possesses better adaptability, tracking accuracy and lower computational complexity compared with the adaptive current statistical (ACS) model-based ST-SCKF, the modified CS (MCS) model-based ST-SCKF, and the IMM-based STF-SCKF.