The complexity of combat environment makes it more and more difficult for traditional monostatic radar to track targets continuously. The radar network technology can effectively improve the overall tracking performance. Therefore, in the distributed radar network scenario, a joint sensor selection, bandwidth and dwell time resource optimal allocation algorithm is proposed for multi-target tracking. At first, the local state is estimated and corrected by the Covariance Intersection (CI) fusion strategy. Then the algorithm aims to minimize the predicted Bayesian Cramer-Rao Lower Bound (BCRLB) of the worst target tracking mean square error. And a joint radar node selection, bandwidth and dwell time resource optimization model is established for multi-target tracking in distributed radar network. The radar node is selected with BCRLB as the metric criterion first. Then the optimal model after sensor selection is carried out by using the cyclic minimum algorithm and the minimax algorithm. Simulation experiments show that compared with the uniform resource allocation and single-resource optimal allocation algorithms, the proposed algorithm can effectively improve the multi-target tracking accuracy under the limited resources.
It is well known that long time coherent integration (LTCI) can
effectively improve the radar detection ability of manoeuvring weak
targets, since a considerable signal-to-noise ratio (SNR) improvement
can be achieved [1]. However, for most existing LTCI algorithms
[2-5], there is a common assumption that the observed target is of
the single motion stage (i.e., the motion parameters of targets are
uniform) during the coherent processing interval (CPI). However, with
the advancement of manoeuvrability and the increasement of CPI, the
observed target might be of multiple motion stages. In this case, the
above-mentioned LTCI algorithms will not be effective any more. The
specific LTCI algorithms developed for manoeuvring weak target with
multiple motion stages are relatively few. In [6], a short-time
generalized radon-Fourier transform (STGRFT) based LTCI algorithm is
proposed to remove range migration (RM) and Doppler frequency migration
(DFM) effects and estimate the stage-changing point. Similar as
GRFT[5], STGRFT can be able to obtain an excellent SNR gain through
multi-dimension parametric searching. In [7], a reference signal is
introduced to compensate the motion parameters change (MPC) effect
between different motion stages, and then GRFT is utilized to achieve
the coherent integration during the CPI. However, the computational load
of these algorithms is quite high, since the key procedure is based on
the multi-dimension parametric searching. This may deteriorate the
engineering practicability of these algorithms.
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