Target position estimation is one of the important research directions in array signal processing. In recent years, the research of target azimuth estimation based on graph signal processing (GSP) has sprung up, which provides new ideas for the Direction of Arrival (DoA) application. In this article, by extending GSP-based DOA to joint azimuth and distance estimation and constructing a fully connected graph signal model, a multi-target joint azimuth and distance estimation method based on GSP is proposed. Firstly, the fully connection graph model is established related to the phase information of a linear array. For the fully connection graph, the Fourier transform method is used to solve the estimated response function, and the one-dimensional estimation of azimuth and distance is completed, respectively. Finally, the azimuth and distance estimation information are combined, and the false points in the merging process are removed by using CLEAN algorithm to complete the two-dimensional estimation of targets. The simulation results show that the proposed method has a smaller mean square error than the Multiple Signal Classification (MUSIC) algorithm in azimuth estimation under the condition of a low signal-to-noise ratio and more accurate response values than the MUSIC algorithm in distance estimation under any signal-to-noise ratio in multi-target estimation.

With the development of radio technology, passive bistatic radar (PBR) will suffer from interferences not only from the base station that is used as the illuminator of opportunity (BS-IoO), but also from the base station with co-frequency or adjacent frequency (BS-CF/AF). It is difficult for clutter cancellation algorithm to suppress all the interferences, especially the interferences from BS-CF/AF. The residual interferences will seriously affect target detection and DOA estimation. To solve this problem, a novel target detection and DOA estimation method for PBR based on compressed sensing sparse reconstruction is proposed. Firstly, clutter cancellation algorithm is used to suppress the interferences from BS-IoO. Secondly, the residual interferences and target echo are separated in spatial domain based on the azimuth sparse reconstruction. Finally, target detection and DOA estimation method are given. The proposed method can achieve not only target detection and DOA estimation in the presence of residual interferences, but also better anti-mainlobe interferences and high-resolution DOA estimation performance. Numerical simulation and experimental results verify the effectiveness of the proposed algorithm.

Target localization is a fundamental problem in array signal processing. The problem of locating near-field targets with multiple-input multiple-output (MIMO) radar has been studied extensively; however, most of the conventional matrix-based approaches suffer from limitations in terms of the representation and exploitation of the multidimensional nature of MIMO radar signals. In this paper, we addressed the problem of localizing multiple targets in the near-field region, aiming at pursuing a solution applicable for multidimensional signal that is able to achieve sufficient accuracy. A tensor-based signal model impinging on a monostatic frequency diverse array multiple-input multiple-output (FDA-MIMO) radar was formulated, and a corresponding tensor decomposition-based localization algorithm (TenDLA) that showcases the connection between the tensor-based analysis and the localization problem was developed. Additionally, a correction procedure to mitigate the estimation deviations on the range and angle was presented, yielding significant improvements in estimation accuracy. Numerical examples demonstrated the validity and effectiveness of the proposed approach, and it was shown that this approach is superior to conventional methods due to its high-resolution estimation accuracy as well as its relatively low computational costs.

In this paper, a novel Doppler shift estimation method for frequency diverse array (FDA) radar based on graph signal processing (GSP) theory is proposed and investigated. First, a well-designed graph signal model for a monostatic linear FDA is formulated. Subsequently, spectral decomposition is conducted on the constructed signal model utilizing graph Fourier transform (GFT) techniques, enabling the extraction of the target’s Doppler shift parameter through spectral peak search. A comprehensive series of simulation experiments demonstrates that the proposed method can achieve the accurate estimation of target parameters even under low signal-to-noise ratio (SNR) conditions. Furthermore, the proposed method exhibits superior performance compared to the MUSIC algorithm, offering enhanced resolution and estimation accuracy. Additionally, the method is highly amenable to parallel processing, significantly reducing the computational burden associated with traditional procedures.

Frequency diverse array (FDA) beams show an “S” shape in space and cannot form a spot beam; thus, they cannot be directly combined with spotlight synthetic aperture radar (SSAR). In this paper, we propose a 2D imaging system emitting multiple repeated subpulses using an FDA and spotlight synthetic aperture radar (MRS-FDA-SSAR). This system carries the FDA on an airborne platform and uses the frequency difference between the array elements to synthesize broadband signals and obtain the distance-direction resolution, and then it uses a synthetic aperture technique to obtain the azimuth-direction resolution. Subsequently, 2D imaging results are obtained using the BP algorithm. A deconvolution algorithm is introduced to address the problem of high target sidelobes in the BP imaging results, which can result in the masking of weak targets. This allows 2D imaging results to be obtained with lower sidelobes. Finally, the MRS-FDA-SSAR model was simulated in experiments to verify its effectiveness.

Frequency diverse array multiple-input multiple-output (FDA-MIMO) radar offers significant advantages in mainlobe deceptive interference suppression, as its transmit steering vector contains both angle and range information, providing additional degrees of freedom beyond the angular dimension. However, conventional FDA-MIMO radar suffers from insufficient angle-range resolution, which limits its ability to suppress interferences located close to the target. Moreover, it lacks robustness under limited snapshots and parameter mismatch conditions. To address these issues, this paper proposes a robust beamforming method based on the FDA-MIMO radar model. A collocated sparse array with a sinusoidal element spacing offset and a logarithmic frequency offset is adopted to enhance beam resolution and resolve the periodic angle-range ambiguity problem. Based on this model, the interference-plus-noise covariance matrix is reconstructed using two-dimensional Capon spatial spectrum, and the steering vector is corrected via a joint objective function that combines MUSIC orthogonality and the flatness of the covariance residual spectrum. Simulation results demonstrate that, under conditions of near-target interferences, random range-angle errors, and frequency offset errors, the proposed method achieves a signal-to-interference-plus-noise ratio (SINR) close to the ideal value, exhibiting excellent mainlobe interference suppression performance and robustness.