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Classical space-time adaptive processing (STAP) detectors are strongly limited when facing highly heterogeneous environments. Indeed, in this case, representative target free data are no longer available. Single dataset algorithms, such as the MLED algorithm, have proved their efficiency in overcoming this problem by only working on primary data. These methods are based on the APES algorithm which removes the useful signal from the covariance matrix. However, a small part of the clutter signal is also removed from the covariance matrix in this operation. Consequently, a degradation of clutter rejection performance is observed. We propose two algorithms that use deterministic aided STAP to overcome this issue of the single dataset APES method. The results on realistic simulated data and real data show that these methods outperform traditional single dataset methods in detection and in clutter rejection.

In the context of radar signal processing, the purpose of space-time adaptive processing (STAP) is to remove ground clutter returns, in order to enhance slow moving target detection. STAP performs two-dimensional space and time adaptive filtering where different space channels are combined at different times [

Classical space-time adaptive processing (STAP) detectors are strongly limited when facing a severe nonstationary environment such as heterogeneous clutter. Indeed, in these cases, representative training data are no longer available. The Maximum Likelihood Estimation Detector (MLED) [

In this paper, we will show how we can overcome this problem by the use of deterministic aided STAP. Moreover, we will extend this method to the Stop-Band APES which greatly reduces the computational workload of the MLED detector.

Section

Consider a radar antenna made of

We make use of a temporal sliding window to work on the temporal dimension; consequently, the estimated covariance matrix

To overcome the previous issues of signal suppression or the none representativeness of secondary data, the MLED detector [

Because the MLED algorithm is a high-resolution method, it requires an oversampling in Doppler frequency, typically by a factor four, to correctly work. Indeed, combining (

Spectral response of regular MLED

In order to explore the use of subspace-based methods, we have to go deeper in the formulation of the MLED detector. Indeed, these methods will only work if the clutter subspace of the covariance matrix

We can demonstrate [

The matrix

The residual clutter-plus-noise covariance matrix is slightly different from the actual covariance matrix

Angle-Doppler map showing the effect of MLED projector for two different Doppler bins.

However, in a situation where the number of Doppler cells is low, we will observe a degradation of the clutter rejection performance of the MLED detector, and this degradation will be even worse for the Stop-Band APES algorithm. This effect is due to the partitioning which is done only in time domain. If spatio-temporal partitioning is employed, only a single bin of the angle-Doppler plane is removed but the computational cost would hugely increase because of the angle-Doppler scanning. We will present in the next section a deterministic processing and, in Section

We will here briefly describe a nonadaptive space-time processing which is the basis of the deterministic aided STAP processing we will introduce in the following section. For a side-mounted antenna, the clutter occupies a one-dimension position in the two-dimensional Doppler-angle domain. The clutter Doppler frequency is a function of the receiving angle as follows:

To illustrate this effect, we compare the non-adaptive processing (^{−1}, radar frequency is

As we can see from Figures

Comparison between sum channel (bold curve, negative speeds), deterministic (bold curve, positive speeds), and adaptive space-time processing (dash curve) on error-free simulated data.

Range-Doppler map of the nonadaptive processing (positive speeds) and the sum channel (negative speeds) on error-free data simulated data.

Range-Doppler map showing the performance of the adaptive processing on error-free data simulated data.

Comparison between sum channel (bold curve, negative speeds), deterministic (bold, positive speeds), and STAP (dash) on realistic data.

Range-Doppler map of the nonadaptive processing (positive speeds) and the sum channel (negative speeds) on realistic data.

Range-Doppler map of the adaptive processing on realistic data.

In GMTI operation, there are two main concerns about heterogeneous environments: clutter heterogeneity (land relief, urban environments) and high-density target area (roads, highways

In the case of Stop-Band APES, where the signal notch is wider, we use an extended projector

In air-to-air situations, the problem is different. The spectral occupation of the mainlobe clutter is much smaller than that of GMTI, whereas clutter sidelobes are much more powerful and have to be cancelled. Moreover, target density is very low, compared to GMTI. As we do not have access to a Doppler-angle relation of the mainlobe clutter, we propose another approach to readd this clutter which is partially removed in the APES-based methods. In air-to-air mode, the mainlobe clutter is pretty homogeneous in the range domain.

We will exploit this property to estimate the matrix

We test the GMTI deterministic aided STAP described in Section

Target position (GMTI data).

Target 1 | Target 2 | Target 3 | |
---|---|---|---|

Speed (m/s) | 3.0 | 5.30 | 5.85 |

Range (number) | 214 | 138 | 149 |

The Doppler-range of the sum channel (Figure

Range-Doppler map showing the sum channel of the RAMSES data.

The classical STAP processing fails to correctly remove the heterogeneous clutter (Figure

Range-Doppler map on RAMSES data showing the performance of classical STAP processing.

Range-Doppler map on RAMSES data showing the performance of the MLED detector.

Range-Doppler map on RAMSES data showing the performance of the Stop-Band detector.

Range-Doppler map on RAMSES data showing the performance of the deterministic aidedc Stop Band detector.

Comparison of classical STAP (dash curve) Stop-Band STAP (dot curve) and deterministic aided Stop-Band (solid curve) on RAMSES data for range gate number 149.

Comparison of classical STAP (dash curve) Stop-Band STAP (dot curve) and deterministic aided Stop-Band (solid curve) on RAMSES data for range gate number 279.

The air-to-air deterministic aided STAP (see Section

Target position (air-to-air).

Targets | Speed (m/s) | Range (km) |
---|---|---|

1 | 50.03 | 58.425 |

2 | 100.22 | 55.425 |

3 | 115.026 | 57.00 |

4 | 185.0265 | 57.30 |

5 | 216.0296 | 59.475 |

The sum channel (Figure

Range-Doppler map of the air-air realistic data showing the sum channel.

Range-Doppler map of the air-air realistic data showing the performance of MLED processing.

Range-Doppler map of the air-air simulated data showing the performance of Stop-Band STAP processing.

Range-Doppler map of the air-air simulated data showing the performance of deterministic aided Stop-Band STAP processing.

On Figure

Comparison of Stop-Band STAP (dot curve) and Deterministic-Aided Stop-Band (solid curve) on air-air data at a distance of 58.5 km.

In this paper, we propose two deterministic aided algorithms both based on the APES method. The first algorithm which relies on the deterministic Doppler-angle relation of the clutter is particularly adapted for GMTI detectors. The results on real data show that it outperforms both classical STAP and APES-based algorithms. The second algorithm, which aims to remove the continuous component of the interference, is on the other hand well adapted to air-to-air modes. In this case, the continuous interference is the main lobe clutter. On realistic simulated data, it totally cancels the mainlobe clutter, whereas classical STAP and traditional APES-based algorithms fail, causing many false alarms.

The authors would like to thank the DGA from the French Ministry of Defense for their support and funding.