A method for selecting auxiliary channels in reduced-dimension space-time adaptive processing (stap) is proposed for airborne multiple-input multiple-output radar. The auxiliary channel selection of the proposed appro...
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A method for selecting auxiliary channels in reduced-dimension space-time adaptive processing (stap) is proposed for airborne multiple-input multiple-output radar. The auxiliary channel selection of the proposed approach is data dependent. Based on maximum cross-correlation energy metric, the significance of each spatial-Doppler channel is evaluated, and the auxiliary channels are selected step-by-step through utilising iteration. For the sake of achieving better performance as much as possible, the proposed approach will select two auxiliary channels at the first step, and select one channel at the next each step. Due to that the explicit physical meaning is very important for a stap algorithm, the physical meaning of the maximum cross-correlation energy metric is discussed, and the fact that the local optimal output signal-to-interference-noise (SINR) performance can be assured by the maximum cross-correlation energy metric is proved theoretically. The simulations demonstrated that the output SINR loss of the proposed approach is about -1.9 dB when only two auxiliary channels are selected. Consequently, the proposed approach can reduce the requirement of the sample support dramatically. This will be more obvious advantage for the practical application in heterogeneous clutter environments where the number of secondary samples is extremely limited.
In this study, two novel joint iterative optimisation space-time adaptive processing (stap) algorithms are proposed based on L-1-regularised for airborne radar. Both of them are implemented in generalised sidelobe can...
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In this study, two novel joint iterative optimisation space-time adaptive processing (stap) algorithms are proposed based on L-1-regularised for airborne radar. Both of them are implemented in generalised sidelobe canceller (GSC) architecture. When the rank of the interference covariance matrix is far fewer than the system degrees of freedom, the stap filter weight will be sparse. In this case, a sparse constraint is further imposed to the minimum variance criterion of GSC. To solve this optimisation problem, a joint iterative recursive least squares (RLS) algorithm (L-1-JI-RLS) and a joint iterative least mean square (LMS) algorithm (L-1-JI-LMS), both of which are based on L-1-regularised, are proposed. The L-1-JI-RLS algorithm achieves the minimum output power by adjusting the penalty parameter and filter weight adaptively, while the L-1-JI-LMS algorithm does it by adjusting the penalty parameter, step and filter weight adaptively. The computational complexity of the L-1-JI-LMS algorithm is far lower than the conventional and some sparse stap algorithms. Monte Carlo experiments validate that both the L-1-JI-RLS and L-1-JI-LMS algorithms outperform other L-1-based and reduced-rank stap algorithms, such as a faster convergence rate, improved output signal-to-interference-plus-noise-ratio and target detection performance.
In this Letter, the author proposes a novel space-time adaptive processing (stap) algorithm by enforcing sparse constraint on the beam-Doppler patterns for clutter mitigation when the number of training data is limite...
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In this Letter, the author proposes a novel space-time adaptive processing (stap) algorithm by enforcing sparse constraint on the beam-Doppler patterns for clutter mitigation when the number of training data is limited. By exploiting the sparsity of the beam-Doppler patterns of the stap filter, the proposed algorithm formulates the filter design as a mixed l(2)-norm and l(1)-norm minimisation problem. Moreover, the proposed algorithm develops an adaptive approach to update the regularisation parameter. Simulation results illustrate that the proposed algorithm outperforms the traditional stap algorithms in a limited sample support.
The traditional parallel processing methods of stap(Space-Time Adaptive Processing) schedules the algorithm to different processors of specific hardware system based on coarse-grained task division to improve the calc...
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The traditional parallel processing methods of stap(Space-Time Adaptive Processing) schedules the algorithm to different processors of specific hardware system based on coarse-grained task division to improve the calculation throughput of the system by pipeline processing between processorsBut there are two disadvantagesFirstly, coral-granularity division hinders the parallelism of the algorithmSecondly, the traditional processing method only takes affects on specific hardware systemThis paper puts forward a new parallel processing method based on fine-grained task scheduling, which consists of three steps as follows: Establishing fine-grained task model of stap algorithm in the form of DAG(Direct Acyclic Graph);Describing different target hardware systems by uniform topology model;Scheduling task model to processors in the topology model in fine-grained task mannerThe experiment result shows that the parallel method achieves a favorable speedup, and more flexible adaptation to different stap applications.
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