High Speed Dim Air Target Detection Using Airborne Radar under Clutter and Jamming Effects

The challenging potential problems associated with using airborne radar in detection of high Speed Maneuvering Dim Target (HSMDT) are the highly noise, jamming and clutter effects. The problem is not only how to remove clutter and jamming as well as the range migration and Doppler ambiguity estimation problems due to high relative speed between the targets and airborne radar. Some of the recently published works ignored the range migration problems, while the others ignored the Doppler ambiguity estimation. In this paper a new hybrid technique using Optimum Space Time Adaptive Processing (OSTAP), Second Order Keystone Transform (SOKT), and the Improved Fractional Radon Transform (IFrRT) was proposed. The OSTAP was applied as anti-jamming and clutter rejection method, the SOKT corrects the range curvature and part of the range walk, then the IFrRT estimates the target’ radial acceleration and corrects the residual range walk. The simulation demonstrates the validity and effectiveness of the proposed technique, and its advantages over the previous researches by comparing its probability of detection with the traditional methods. The new approach increases the probability of detection, and also overcomes the limitation of Doppler frequency ambiguity

Adaptive Radar Detection of Dim Moving Targets in Presence of Range Migration

This paper addresses adaptive radar detection of dim moving targets. To circumvent range migration, the detection problem is formulated as a multiple hypothesis test and solved applying model order selection rules which allow to estimate the "position" of the target within the CPI and eventually detect it. The performance analysis proves the effectiveness of the proposed approach also in comparison to existing alternatives.Comment: 5 pages, 2 figures, submitted to IEEE Signal Processing Letter

A Fast Algorithm of Generalized Radon-Fourier Transform for Weak Maneuvering Target Detection

The generalized Radon-Fourier transform (GRFT) has been proposed to detect radar weak maneuvering targets by realizing coherent integration via jointly searching in motion parameter space. Two main drawbacks of GRFT are the heavy computational burden and the blind speed side lobes (BSSL) which will cause serious false alarms. The BSSL learning-based particle swarm optimization (BPSO) has been proposed before to reduce the computational burden of GRFT and solve the BSSL problem simultaneously. However, the BPSO suffers from an apparent loss in detection performance compared with GRFT. In this paper, a fast implementation algorithm of GRFT using the BSSL learning-based modified wind-driven optimization (BMWDO) is proposed. In the BMWDO, the BSSL learning procedure is also used to deal with the BSSL phenomenon. Besides, the MWDO adjusts the coefficients in WDO with Levy distribution and uniform distribution, and it outperforms PSO in a noisy environment. Compared with BPSO, the proposed method can achieve better detection performance with a similar computational cost. Several numerical experiments are also provided to demonstrate the effectiveness of the proposed method

Multi-Beam Associated Coherent Integration Algorithm for Weak Target Detection

Weak target detection is a great challenging in radar field. To detect the weak targets with beam migration, a novel tri-dimensional time model (i.e. fast time, slow time, and beam time) and a novel tri-dimensional signal model which based on the time model are set up firstly. Then, according to the presented models, we propose two multi-beam associated (MBA) coherent integration algorithms based on time-shared multi-beam (TSMB) and space-shared multi-beam (SSMB), respectively. The two proposed algorithms could both eliminate beam migration via associating multi-beam and realize coherent integration via discrete Fourier transform. According to different beam scanning modes, the subsequent analyses show that the MBA coherent integration algorithm based on SSMB (MBACIA-SSMB) may have a better detection performance than that based on TSMB (MBACIA-TSMB). Moreover, the capabilities to estimate the target’s radial velocity and tangency velocity are analyzed. Finally, some numerical experiments are given to verify the performances of MBACIA-TSMB and MBACIA-SSMB

A Weak Target Detection Algorithm IAR-STFT Based on Correlated K-distribution Sea Clutter Model

The detection performance of weak target on sea is affected by the special effects of sea clutter amplitude. Aiming at the time and space correlated of sea clutter, the correlated K-distribution sea clutter model is established by the sphere invariant random process algorithm. To solve the problems of range migration (RM) and Doppler frequency migration (DFM) of moving target in the case of long-time coherent accumulation, a novel integration detection algorithm, improved axis rotation short-time Fourier transform (IAR-STFT) is proposed in this paper, which is based on a generalization of traditional Fourier transform (FT) algorithm and combined with improved axis rotation. IAR-STFT not only can eliminate the RM effect by searching for the target motion parameters, but also can divide the non-stationary echo signal without range migration into several blocks. Each block of signal can be regarded as a stationary signal without DFM and FFT is performed on each signal separately. The signals of each block are accumulated to detect the target in the background of the above sea clutter. Finally, the effectiveness of the algorithm is verified by simulation. The results show that the detection ability of this algorithm is better than that of Radon-fractional Fourier transform, generalized Radon Fourier transform and Radon-Lv's distribution in low SNR environment, e.g., when the SNR is -45dB, the detection ability of this algorithm is about 55%, which is higher than that of Radon-fractional Fourier transform, generalized Radon Fourier transform and Radon-Lv's distribution