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ABSTRACTFor pulse doppler (PD) radar, two tensor structure-based methods are proposed to improve the clutter suppression performance. One is tensor singular value decomposition (TSVD) and the other is tensor robust principal component analysis (TRPCA). Two algorithms both stack the range-Doppler matrices of multiple coherent processing intervals (CPI) into a tensor structure, which helps make the most of the temporal information across CPIs. TSVD utilizes the strong temporal correlation of ground clutter data to estimate the clutter subspace. Meanwhile, TRPCA exactly separates both low-rank clutter component and sparse target components from the time series range-Doppler tensor structure. Experimental results indicate that compared with conventional matrix-based schemes such as singular value decomposition (SVD) or singular value decomposition (RPCA), the proposed methods have significant advantages in keeping the target echo while suppressing the clutter. In addition, the computational complexity of each algorithm is analysed in detail.

Prolonging radar observation time is capable of enhancing noise robustness for weak target detection, whereas the range migration phenomenon induced by the target's high-speed movement will deteriorate the energy accumulation ability of the traditional moving target detection method. This article concerns long-time coherent integration for radar high-speed target and proposes an efficient detection algorithm based upon azimuth resampling. The proposed algorithm can be implemented via azimuth nonuniform fast Fourier transform and further developed into the sparse representation mechanism utilizing 2-D sparse Fourier transform. Furthermore, the dictionary dynamic updating strategy is presented to improve the weak target detectability in the multitarget observation environment. The processed results of both simulated and measured radar data have been displayed to validate the effectiveness of the presented algorithm.

ABSTRACTDealing with range ambiguity is a hot issue in SAR signal processing. One limitation of current algorithms is their poor robustness to noise and the energy of ambiguous targets, which can lead to performance degradation in complex environments. This letter proposes a new algorithm for range ambiguity suppression in SAR. The algorithm utilizes the orthogonality of the transmitted signal to separate the echo signals of the main region and the ambiguous area, and subsequently applies the alternating direction method of multipliers (ADMM) algorithm to perform joint sparse recovery on both areas. Experimental results show that the proposed method can effectively suppress range ambiguity, and it has a good ability to maintain the weak targets and the details of the main region’s image.

ABSTRACTThis letter deals with the range-Doppler image reconstruction problem in co-located multiple input multiple output (MIMO) noise radar, where the waveform diversity is applied not only among different transmit channels but also among different pulse repetition intervals (PRI). The transmit channel diversity and pulse diversity lead to high range-Doppler sidelobes, which would cause the weak target to be overwhelmed by strong target or clutters. In this letter, we propose a sparse recovery-based approach to deal with the range-Doppler image reconstruction problem in collocated MIMO noise radar, by exploiting the 2D-block sparsity of range-Doppler profiles to enhance the range-Doppler image reconstruction performance and its robustness to clutter. Simulation results show that the range-Doppler image reconstruction performance of the proposed method outperforms traditional methods, especially when noise and strong clutter exist.

ABSTRACTThis paper proposes an implementation of non-continuous piecewise nonlinear frequency modulation (N-PNLFM) signals in MIMO radar which are composed of a sequence of N-PNLFM subpulses. The N-PNLFM subpulse can be divided into three segments, the first and third segment are composed of linear frequency modulation (LFM) waveforms. The LFM waveforms in each N-PNLFM subpulse will be orthogonal in one sequence of our new N-PNLFM signal. A non-linear frequency modulation (NLFM) waveform is in the central component of each N-PNLFM subpulse, the bandwidths of each NLFM waveforms are distributed randomly. Each subpulse can be controlled by different variable parameters in both auto-correlation and cross-correlation functions. In order to suppress the high sidelobes in the new N-PNLFM signal and increase the diversity of signals, each subpulse duration is also distributed randomly. Our proposed PNLFM signals are optimized by applying a particle swarm optimization (PSO) algorithm. Comparing with other PNLFM signals, numerous simulations illustrate that our implementation achieves better performance.

Ground maneuvering target detection is a hot topic in the applications of synthetic aperture radar (SAR), whereas its focusing performance is severely deteriorated by range migration and Doppler frequency migration during a long integration time. This paper proposes a novel method to image the target and estimate its parameters via performing two independent 2-dimensional (2-D) searches after a parameter separation operation. In order to improve the search speed, we set the limited search ranges and propose local mapping sparse Fourier transform (LMSFT) to replace fast Fourier transform (FFT). Compared with the traditional algorithms, the proposed method can realize fast coherent integration of multiple maneuvering targets via compensating the high-order range migration and Doppler frequency migration. In addition, the proposed method is stable under noise. Several simulation results have validated the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Doppler beam sharpening (DBS) is an effective technique to improve the azimuth resolution of airborne pulse Doppler radar in its air-to-ground mode. This article describes new developments of DBS in [...]

ABSTRACTThis letter proposes a novel structured compressive sensing (CS) imaging method in random stepped frequency (RSF) synthetic aperture radar (SAR) for better imaging performance in the presence of strong additive noise. In this method, the multiple measurement vectors (MMV) model is established in range-Doppler domain with range migration taken into consideration and a modified sparse recovery algorithm is derived to synthesize the one-dimension range profile. Traditional cross-range focusing algorithm is then performed to obtain a well-focused image. The proposed method makes full use of the coherent information among different bursts, which may further illuminate the influence of additive noise and decrease the number of transmitted pulses needed in one burst. The simulated results demonstrate the validity of the proposed method.

With a long synthetic aperture time, the energy of the ground moving target will be defocused due to the effect of range migration (RM) and Doppler frequency migration (DFM). This paper combines Radon transform and the modified Lv's distribution (MLVD), then proposes RMLVD to eliminate RM and estimate the motion parameters of target. It is validated that the proposed method can estimate the equivalent slant acceleration and jerk simultaneously. Besides, the proposed method can realize coherent integration with a low computational load and eliminate the Doppler ambiguity effect. The simulation results show that the proposed method has an excellent detection performance in the multiple targets or low signal-to-noise ratio (SNR) environment. [ABSTRACT FROM AUTHOR]

The detection of maneuvering targets is a challenging work for radar system. During a long integration time, the target’s complex motion will cause severe range migration and Doppler frequency migration, which greatly degrade the energy integration performance. In order to obtain a well-focused result of the maneuvering target, this paper proposes a novel and fast algorithm without any parameter searching process. In our algorithm, a parameter separation (PS) operation is conducted to isolate the target’s acceleration from other motion parameters. Then, the second-order keystone transform (SOKT) is utilized to correct range curvature and relocate the target’s energy into one range cell. Lastly, the target’s energy is integrated along the slow time axis via performing the non-uniform sparse Fourier transform (NUSFT), which is a fast implementation of Fourier transform (FT) for the input signal with unequally sampled points and frequency domain sparse representation. According to the simulation results, the proposed PS–SOKT–NUSFT method can realize multiple targets detection due to its good ability to suppress cross terms. In addition, the runtime of PS–SOKT–NUSFT (106.23 s) is much shorter than the PS–acceleration search (AS) method (2168.67 s).

Long-time coherent integration is conducive to improving the radar detection performance for weak moving targets, whereas range migration (RM) and Doppler frequency migration (DFM) might occur when the target has high speed and high maneuverability. Although a variety of methods have been proposed to correct RM and DFM, they are widely based on the target's radial motion model, which is expressed as a finite order polynomial with respect to slow time. However, when an offset exists between the target's moving direction and radar sight line, model mismatch will occur and deteriorate the integration performance. In this paper, the accurate echo model for the moving target with arbitrary moving direction is established, then the modified Radon Fourier transform (MRFT) algorithm is proposed to realize coherent integration via removing the complex RM and DFM. Moreover, the generalized form of MRFT, i.e., modified generalized RFT (MGRFT), is introduced to focus the target with high maneuverability. Both simulation and real data processed results have been provided to validate the effectiveness of the proposed method. According to the coherent integration results for the target with oblique maneuvering motion, the signal-to-noise ratio (SNR) gain of the proposed method is about 4.5 dB higher than that of the existing GRFT method with the same computational complexity. [ABSTRACT FROM AUTHOR]