Lei Yu

Passive radar (PR) based on existing spaceborne illuminators has significant advantages including emission‐free operation, low cost and high security. It can detect the target independently or serve as a complementary sensor to active radars. This paper investigates the moving target detection under the low earth orbit navigation augmentation (LEO‐NA) signal‐based PR (LNAS‐PR) system, which utilises the LEO satellite with navigation augmentation capability as the illuminator. However, the motion of the LEO satellite illuminator, receiver and target makes the geometric structure of the LNAS‐PR detection scenario constantly change. During the long‐time observation, the radar's observation angle varies with time, then, the target's radar cross‐section (RCS) will exhibit a moderate fluctuation. Therefore, this paper studies the detection for moderately fluctuating Rayleigh targets under the LNAS‐PR system. By jointly considering the target's motion and electromagnetic scattering characteristics, this paper proposes a novel detection algorithm based on quadratic phase compensation (QPC), Keystone transform (KT) and hybrid integration (HI), which is named as QPC‐KT‐HI algorithm. First, the range cell migration (RCM) and Doppler frequency migration (DFM) caused by target motion are corrected through QPC and KT. Moreover, utilising the target's scattering characteristics, the HI is performed to accumulate the target energy, thereby achieving target detection. Finally, the effectiveness and advantage of the QPC‐KT‐HI algorithm are verified by three simulation experiments.

In addressing the degradation of range resolution and the coupling of target range and angle in the coherent frequency diverse array (FDA), which impedes the full utilization of the two-dimensional degrees of freedom for target indication, this paper proposes an array transmission mode called nonlinear time-variant phase diverse FDA (NTPD-FDA). This mode aims to controllably enhance range resolution and indicate target position without coupling and ambiguity, all without increasing system complexity. NTPD-FDA incorporates a nonlinear time-variant phase, comprising chirp diverse and constant phase diverse, into the transmitted signals of each element in coherent FDA. This technique adjusts the power spectrum of the aggregated transmitted signal to match a bandwidth-adjustable Gaussian function, achieving enhanced controllable range resolution while avoiding signal-to-noise ratio (SNR) loss and maintaining a $-$ 25dB range sidelobe level (RSL). Based on the varying chirp rates of transmitted signals in NTPD-FDA and the optimal energy concentration characteristics of the fractional Fourier transform (FRFT) for chirp signals, this paper proposes a signal processing method. This method involves extracting time-frequency points related to the target signals in the fractional Fourier domain (FRFD) and constructing steering vectors that includes range and angle, thus enabling joint estimation of target range and angle. The Cramér-Rao lower bounds (CRB) for range and angle estimation errors were derived as a performance guarantee for this signal processing method. Theoretical analysis demonstrates that NTPD-FDA is more favorable for achieving unambiguous range estimation and offers higher accuracy in range and angle estimation compared to FDA-MIMO. Numerical results validate the promised advantages of NTPD-FDA.

Micro-Doppler (m-D) effect, which is induced by the rotation of rotor blades, provides a distinctive characteristic for the identification of micro unmanned aerial vehicles (UAVs) in hovering. Actually, the rotation frequencies of rotors are always unstable to keep the hovering balance in breezy conditions. In this case, m-D signals generated by different blades are severely super-imposed, resulting in the difficulty for estimating rotation frequencies of different components by Time-frequency (TF) analysis. To address this issue, a complex Bayesian compressive sensing algorithm using modified Laplace priors (CBCS-MLP) is proposed. Firstly, the sinusoidal frequency-modulated echoes are decomposed by Bessel function basis and all m-D components are separated. Then, a dictionary matrix is established which converts multi-frequency estimation problem into a sparse signal reconstruction task. Subsequently, Bayesian compressive sensing algorithm is extended to the complex domain by using modified Laplace prior. Simultaneously, marginal integration is employed to eliminate the impact of measurement noise variance. Finally, the sparse solution can accurately indicate multiple rotation frequencies. The effectiveness of the proposed algorithm is verified by simulations and field experiments of UAV.

The challenge of accurately locating multi-target in complex environments has become increasingly critical as radar technology evolves. Traditional phased array radars utilize onedimensional measurement methods for range and angle estimation separately, resulting in the inability to efficiently distinguish between similar targets, which affects the ability to identify and track targets. Conventional approaches to mitigate range ambiguity, such as frequency staggering and pulse discarding, complicate the system and may degrade signal integrity. In contrast, frequency diverse array-multiple input multiple output (FDA-MIMO) radar utilizes fixed frequency offsets to create two-dimensional beams, thereby increasing the degrees of freedom for target detection. However, FDA-MIMO radar still encounters limitations in resolving range ambiguity. This article proposes a model for resolving range ambiguity through random logarithmic frequency offset, supported by both theoretical and experimental analyzes. Applicable to FDAMIMO radars with random logarithmic frequency offset, we propose two enhanced subspace-based two-dimensional algorithms for joint range-angle estimation, which demonstrate improved performance under low signal-to-noise ratio (low-SNR) conditions. The results indicate that our designed algorithms significantly enhance estimation accuracy and resolution, facilitating more effective multitarget detection in challenging environments.

Effective suppression of mainlobe deceptive jamming targets ensure that radar systems accurately and reliably detect and track true targets in complex electromagnetic environments. For the self-screening jamming scenario, this study proposes a method based on the frequency-diverse array (FDA)-multiple-input multiple-output (MIMO) radar to suppress mainlobe deceptive jamming targets effectively, which consists of a series of steps. In warning stage, a random logarithmic frequency offset is designed to obtain the unambiguous range and angle parameters of targets, and it is proved that under this offset, the range parameter in receiver beampattern of FDA-MIMO radar is unambiguous. In jamming rejection stage, a $M\times 1$ dimensional unambiguous range compensation vector is constructed based on the random logarithmic frequency offset to suppress the jamming targets generated by self-protection jammers. By compensating the transmission steering vector coefficients of targets, the true, mainlobe deceptive, and sidelobe angle-deceptive jamming targets can be distinguished on the combined transmission-reception spatial frequency domain. Finally, to separate the true target signal from the jamming signal at the receiver end, an adaptive filter is designed. Due to the proposed compensation process, the adaptive filter only needs to match the true target and suppress angle-deceptive jamming targets, without the need to suppress mainlobe deceptive jamming targets by setting null steering regions. Additionally, a singular spectrum analysis algorithm is used to select samples to estimate the exact covariance matrix of the interference plus noise. Numerical simulations verify that the proposed method has better robustness and effectiveness than the existing methods in suppressing mainlobe deceptive jamming targets.

This paper intends to address the long-time coherent integration (LTCI) of high maneuvering unmanned aerial vehicle (UAV) in low SNR environment with the idea of sparse reconstruction. Due to the small radar cross section (RCS), high maneuverability and micro-Doppler (m-D) effect, the robust detection of weak UAV target is a challenging problem. Both of the across range unit (ARU) effect and Doppler frequency migration (DFM) will be introduced in UAV returns due to high maneuverability. Furthermore, m-D effect induced by the rotation of rotor blades will also disturb the LTCI performance. To address these problems, a LTCI method called KT-BCS-LSM is proposed for high maneuvering UAV detection. To mitigate the disturbance of m-D effect, the null space pursuit (NSP) with third-order differential operator is constructed to separate the m-D signal from UAV returns. Then, the keystone transform (KT) is applied for range walk correction. Motion parameters estimation, including velocity, acceleration and jerk, is modeled as a sparse representation problem and solved by Bayesian compressive sensing (BCS) with Laplacian scale mixture (LSM) prior. Compared with the searching-based method, like generalized RFT (GRFT), the proposed method has significant higher computational efficiency. Compared with transform-based methods, like fractional Fourier transform (FrFT) and Lv's distribution (LVD), the proposed algorithm is able to compensate the DFM induced by jerk motion and has higher estimation accuracy and noise robustness. The effectiveness of proposed method is validated by numerical simulations and real-measured data of a DJI UAV.

The adjacent cross correlation function (ACCF) is an effective tool to remove the range migration (RM) and reduce the order of Doppler frequency migration (DFM) in the pulsed-radar echoes of maneuvering targets, and thus has been employed in radar target detection and imaging. However, the ACCF is a nonlinear transform, which means that the signal-to-noise ratio (SNR) variation will be introduced by this operation. This letter presents a quantitative analysis of SNR for the ACCF. The frequency-domain derivation and analyses of ACCF are presented. Based on this, the SNR variation of the ACCF is analytically derived. The theoretical derivations and analyses are confirmed by numerical examples.

In this letter, the low peak sidelobe level (PSL) mismatched filter (MSF) designed for coherent frequency diverse array (FDA) radar using phase-coded waveform is proposed. Considering the space-time coupled characteristic of coherent FDA, the proposed MSF incorporates the equivalent transmit beamforming and low sidelobe range compression simultaneously. The minimization of PSL in MSF design is modeled as a second-order cone programming (SOCP) problem and solved by convex optimization. Furthermore, two modified algorithms are proposed to improve the spatial clutter suppression and Doppler tolerance in original framework. The proposed methods will help to expand the applications of coherent FDA radar in wide-covered and high-resolution observation.

This paper intends to address the long-time coherent integration (LTCI) of high maneuvering targets in low signal-to-noise (SNR) environments with high computational efficiency and accuracy. The high-order motion parameters estimation, considering the acceleration and jerk, is modeled as the under-estimated linear regression and the complex-field Bayesian compressive sensing (BCS) algorithm is introduced to resolve the sparse recovery. To correct the high-order range migration (RM) and reduce the order of Doppler frequency migration (DFM), the adjacent cross correlation (ACCF) is applied. An effective method based on non-coherent integration is proposed to extract the self-term from ACCF result. Further, with the proper design of sensing matrix, the acceleration and jerk motion parameters are estimated by sparse reconstruction based on the complex-field BCS. Compared with the traditional methods based on time-frequency transform, like Lv's distribution (LVD), the proposed BCS algorithm is free from the interference of cross-terms and maintains the super-resolution ability, which provides better performance in parameters estimation and multi-targets discrimination. Finally, the numerical experiments validate the advantages of the proposed method in motion parameters estimation, super-resolution ability and computational efficiency.