Global Navigation Satellite System-based passive radar has been applied in the detection of moving targets. However, the low signal power of GNSS on the earth's surface limits the application of this technology for the long-range or low-observable target detection. Increasing the observation time can effectively improve the detection capability. But the target motion involves the range cell migration (RCM) and the Doppler frequency migration (DFM) over the long observation time, which results in the integration gain loss and lower the detection performance. This paper proposes a new hybrid coherent and noncoherent integration method named as the keystone transform and Lv's distribution. The proposed method can not only compensate the RCM and the DFM but also provide coherent and noncoherent integration gains to increase the signal-to-noise ratio. The simulated results and the field trial results demonstrate that the detection performance of the proposed method is superior to the other two known moving target detection methods. And the analysis of the computational complexity shows that the proposed method and the other two methods are in the same order of ().

In the field of maritime surveillance, the global navigation satellite system (GNSS)-based passive radar has proven its potential for moving target detection (MTD), localization and velocity estimation. The next stage is to investigate the possibility of obtaining the radar image of the moving ship for target recognition. However, the limited signal power budget of GNSS prevents the conventional inverse synthetic aperture radar technique that is based on target rotational motion and short observation time for GNSS-based passive radar imaging moving target. In this article, a two-stage imaging processing method relying on the target translational motion over a long observation time is proposed. The first stage confirms the presence of the target by a long-time MTD processing technique. In the second stage, based on the analysis of the Doppler history of the target signal in the slow-time domain, short-time Fourier transform and modified random sample consensus are combined to robustly estimate target velocity with reduced computation complexity. To obtain the focused bistatic image, azimuth compression is conducted by using the estimated target velocity. Finally, an image fusion operation is implemented to combine the bistatic images achievable from multiple satellites so that the multistatic image with high quality can be created. The effectiveness of the proposed method is confirmed by the real experimental results of three cargo ships illuminated by several satellites.

By exploiting the spatial diversity of global navigation satellite system based passive multistatic radar, this article proposes a novel joint detection and localization method for maritime moving ships. First, for each bistatic baseline, the weak target energy is concentrated in the range-Doppler map over the long integration time with a motion compensation technique that needs to search the unknown Doppler frequency rate value. Then, the range-Doppler maps pertaining to all bistatic baselines are projected into the Cartesian planes that represent the surveyed area. The projection processing needs to search the unknown X- and Y-axis components of the target velocity vector. A quadratic equation is derived to describe the relationship between the Doppler frequency rate and target velocity vector. Therefore, one of the velocity vector components does not require to be searched, leading to improved computational efficiency. Finally, multistatic integration is performed to combine all Cartesian planes via an objective function, which cannot only focus the target energy for joint detection and localization but also exclude the invalid solution induced by the quadratic equation. Numerical results against the simulations and real experiments demonstrate that the proposed method has close detection, positioning, and velocity estimation capabilities to the existing method, but with a much higher computational efficiency.

Global navigation satellite system (GNSS)-based passive radar suffers from short operational range and low range resolution problems, due to the weak signal strength and relatively narrow signal bandwidths of GNSS signals. To increase range resolution, this letter proposes a joint target detection and range resolution improvement method. First, the long-time integration technique is performed to ensure adequate integration gain for the target response out of background noise. Then, the target detector is exploited to obtain the target response and its peak signal-to-noise ratio (PSNR). After that, the incremental Wiener filter is implemented to efficiently enhance range resolution by using the reciprocal of PSNR as the regularization parameter. The field trial results confirm the effectiveness of the proposed method and indicate that the proposed method can not only provide range resolution enhancement performance better than the truncated singular value decomposition (TSVD) method and the Tikhonov method but also has a higher computational efficiency.

Current studies of global navigation satellite systems (GNSS)-based bistatic synthetic aperture radar (GNSS-SAR) is focused on static objects on land. However, moving target imaging is also very significant for modern SAR systems. Imaging a moving target has two main problems. One is the unknown range cell migration; the other is the motion parameter estimation, such as the target’s velocity. This paper proposes a moving target imaging formation algorithm for GNSS-SAR. First, an approximate bistatic range history is derived to describe the phase variation of the target signal along the azimuth time. Then, a keystone transform is employed to correct the range cell migration. To address the motion parameter estimation, a chirp rate estimation method based on short-time Fourier transform and random sample consensus is proposed with high processing efficiency and robust estimation errors in low signal-to-noise ratio scenes. The estimated chirp rate can calculate the target’s velocity. Finally, azimuth compression derivation is performed to accomplish GNSS-SAR imaging. A maritime experimental campaign is conducted to validate the effectiveness of the proposed algorithm. The two cargo ships in the SAR images have good accordance with the ground truth in terms of the target-to-receiver vertical distances along the range and the ships’ length along the cross-range.

Global Navigation Satellite System (GNSS) reflected signals have been used to detect moving targets through long-time integration processing. However, mismatches arise between the target’s actual motion and the assumed motion model over a long integration time, degrading detection performance. To address this problem, this paper proposes an improved Track-before-Detect (TbD) method characterized by a two-stage architecture. In the first stage, current Long-Time Hybrid Integration (LTHI) techniques are employed to correct the range and Doppler migrations caused by the target’s motion, thereby concentrating the target energy within an individual scan duration. In the second stage, plot lists extracted from multiple scans are recursively processed to further accumulate target energy by using the target’s kinematic constraints across different scans. Finally, target energy is enhanced sufficiently for reliable detection, while also enabling the acquisition of the target’s motion parameters over all scans. Compared to the existing Dynamic Programming (DP)-TbD method, the proposed method can exploit the characteristics of the stack of integrated range and Doppler maps produced by the LTHI techniques to improve algorithm execution efficiency without sacrificing detection performance and parameter estimation accuracy. The results from the simulations and field trials confirm the effectiveness of the proposed method in detecting maneuvering and non-maneuvering targets. Meanwhile, the proposed method achieves detection performance and motion parameter estimation errors comparable to the existing DP-TbD method, while significantly lowering the computational time.