Abstract
A novel robust particle filtering algorithm is proposed for updating both the waveform and noise parameter for tracking accuracy simultaneously and adaptively. The approach is a significant step for cognitive radar towards more robust tracking in random dynamic systems with unknown statistics. Meanwhile, as an intelligent sensor, it would be most desirable for cognitive radar to develop the application of a traditional filter to be adaptive and to expand the adaptation to a wider scope. In this paper, after analysis of the Bayesian bounds and the corresponding cost function design, we propose the cognitive radar tracking method based on a particle filter by completely reconstructing the propagation and the update process with a cognitive structure. Moreover, we develop the cost-reference particle filter based on optimizing the cost function design according to the complicated system or environment with unknown statistics. With this method, the update of the estimation cost and variance arrives at the approximate optimization, and the estimation error can be more adjacent to corresponding low bounds. Simulations about the tracking implementation in unknown noise are utilized to demonstrate the superiority of the proposed algorithm to the existing methods in traditional radar.
Highlights
We assume that the process noise and measurement noise is independent and temporally white in this case, with the mixture Gaussian pdf as Equations (53)–(55) show, but the given methods are mismatched with the dynamic systems and models
We assume that the process noise and measurement noise is independent and temporally white in this case, with the mixture (a) Gaussian pdf as Equation (55)–(57) show, (b) but the given methods are mismatched with the dynamic systems and models
The cognitive CRPF (CCRPF) for cognitive radar is proposed as a significant step toward random dynamic systems with unknown statistics
Summary
Cognitive radar can basically be defined as: an intelligent radar system of hardware and software in which the transmit and receive parameters (i.e., pulse length, pulse repetition frequency (PRF), modulation, power, frequency, and polarization) are selected, in real-time, and use adaptation between the information extracted from the sensor/processor and the design and transmission of subsequent waveforms, in response to the observed scene to optimize the performance of a given application. The problem of target tracking in cognitive radar system has received considerable attention. It is well known that most of the state-space dynamical systems are nonlinear or non-Gaussian. Despite the performance decline of extended Kalman filter (EKF) and unscented Kalman filter (UKF) in highly nonlinear problems, and that general analytical solutions are intractable in nonlinear or non-Gaussian systems, solutions continue to emerge from different viewpoints. Most of the methods rely on the assumption that the noise has known statistics, or they require accurate mathematical representation of the dynamics of the system evolution; otherwise, it is almost impossible to directly approximate the true distribution. The same situation occurs in cognitive radar systems. We briefly review various existing methods to cognitive radar tracking problem that involve particle filter (PF) in some relevant manner
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