Fully Coherent Fusion of BeiDou-Based Passive Multistatic Radar for Weak Target Detection

Passive bistatic radar (PBR) exploits illuminators of opportunity such as frequency-modulated (FM) radio and cellular base stations, providing low-cost and resilient sensing. Reliable detection requires a clean reference signal; however, target echoes may leak into the reference channel due to wide antenna beamwidth, geometry, or target motion. Such contamination generates ghost peaks in the range-Doppler plane that share the true target's Doppler but are shifted in range, making them difficult to separate and severely degrading detection. This paper analyzes the mechanism of ghost-peak formation from a matched-filter perspective and proposes a suppression strategy that combines multi-frame consistency analysis with an anchor-based masking operation. Unlike reconstruction-based methods, the proposed approach applies lightweight post-detection processing to selectively remove spurious responses while preserving genuine targets. Simulation studies using synthesized data derived from measured FM broadcasts demonstrate that the method effectively suppresses ghost peaks across different contamination scenarios, reduces false tracks, and maintains reliable detection with low computational cost. These results confirm the practicality and efficiency of the proposed approach for mitigating target-induced contamination in passive radar.

Target Detection for Distributed Hybrid Active–Passive Radars

A widely linear RLS-STAP algorithm based on airborne passive radar with non-convex pseudo-norms

Passive bistatic radar experiences serious clutter echo interference problems; the target echo is submerged by the sidelobes of the strong clutter echoes. Extensive cancellation algorithm is an efficient method for clutter cancellation, but it requires high-order matrix inversion which poses a great challenge to the existing hardware performance and is even impossible to achieve. Aiming at this problem, a fast clutter cancellation method based on the extensive cancellation algorithm is proposed in this paper. In this novel method, the high-order clutter delay matrix is divided into several low-order matrices, and at the same time, multiple sub-matrices are utilized for clutter cancellation simultaneously, which significantly reduces the computational complexity. Simulation results and applications on real data illustrate that the proposed method ensures the clutter cancellation performance while reducing the computational complexity in the passive bistatic radar system.

MFS: A Motion Feature Separation Model for UAV Detection Under Passive Radar

Super-resolution multiple-parameter estimation for the OFDM-based passive radar with ULA using unitary parallel factor direct method

Clutter Suppression Algorithm via Covariance Matrix Reconstruction With Airborne Passive Radar

Fast Extensive Cancellation Algorithm in Passive Radar Signal Processing

Comprehensive maritime domain awareness is crucial for navigation safety, traffic management, and security surveillance. In the context of an increasingly complex modern electromagnetic environment, the disadvantages of traditional active single-station radars, such as their high cost and susceptibility to interference, have started to surface. Due to their unique advantages, such as low cost, environmental sustainability (by reusing existing signals), and resilience in congested spectral environments, non-cooperative passive multistatic radar (PMR) systems have gained significant interest in maritime monitoring. This paper presents the research background of non-cooperative passive multistatic radar systems, performs a fundamental analysis of the detection performance of multistatic radar systems, and suggests an optimization method for the transceiver configuration of non-cooperative passive multistatic radar systems based on geometric coverage theory and a signal-to-noise ratio model. A multi-objective optimization model is developed, considering both detection coverage and positioning error, and is solved using the Non-dominated Sorting Genetic Algorithm II (NSGA-II). The optimization aims to find the optimal receiver location relative to a fixed configuration of four transmitters, representing common maritime traffic patterns. According to the simulation results, the multi-target genetic algorithm can be utilized to optimize the receiver position under the S-band radar settings used in this work. Compared to a random placement baseline, this can reduce the positioning error by about 8.9% and extend the detection range by about 15.8%. Furthermore, for the specific four-transmitter configuration and S-band radar parameters considered in this study, it is found that the best detection performance is more likely to be obtained when the receiver is placed within 15 km of the transmitters’ geometric center.

In passive radar, the multiple model probability hypothesis density (MM-PHD) filter has demonstrated robust capability in tracking multi-maneuvering targets. Nevertheless, non-uniform clutter in practical scenarios causes misestimation of component weights, thereby generating false targets. To solve the false targets problem, a feature-matching MM-PHD (FM-MM-GM-PHD) algorithm for passive radar tracking is proposed in this paper. First, the measurement likelihood function was refined by leveraging target radar cross-section (RCS) and Doppler features to assist in suppressing false targets and reduce clutter interference. Additionally, the proposed algorithm incorporated adaptive component pruning and absorption processes to enhance tracking accuracy. Finally, a missed-alarm correction mechanism was introduced to compensate for measurement losses. Simulations of the passive radar results validated the findings that the proposed algorithm outperformed the traditional MM-PHD filter in both tracking accuracy and cardinality estimation. This superiority was particularly pronounced in non-uniform clutter environments under low detection probabilities.

Effective means are urgently needed to identify non-cooperative targets intruding on airport clearance zones for the safety of low-altitude flights. Passive radars are an ideal means of low-altitude airspace surveillance for their low costs in terms of hardware and operation. However, non-ideal signals transmitted by third-party illuminators challenge feature extraction and target recognition in such radars. To tackle this problem, we propose a light-weight recognition-before-tracking method based on a beam constraint for passive radars. Under the background of sparse targets, the proposed method utilizes the continuity of target motion to identify the same target from the same array beam. Then, with its peaks detected in range-Doppler maps, a feature vector based on the biased radar cross-section is constructed for recognition. Meanwhile, to use the local scattering characteristics of targets for dynamic recognition, we introduce a parameter named normalized bistatic velocity to characterize the attitude of the target relative to the receiving station. With the proposed light-weight metric, the similarity of feature vectors between the unknown target and standard targets is measured to determine the target type. The feasibility and effectiveness of the proposed method are validated by the simulated and measured data.

Passive radar (PR) relies on receiving signals reflected from targets by other existing noncooperative radiation sources, which are broadly divided into ground- and space-based categories, to achieve target detection and tracking. In the context of space-based PR, this paper proposes a PR using the Iridium satellite signal, which is a low-orbit satellite communication signal with global coverage. With an improved detection range and accredited ambiguity function, a PR using the Iridium satellite signal can address the issues of limited terrestrial coverage for ground-based PR and insufficient receiving power for medium- to high-orbit space-based PR. In addition, to address the problem of multipath clutter broadening caused by the dual movement of low-orbit satellites and the PR receiver, this paper proposes a nonuniform Doppler extraction-enhanced multichannel extensive cancellation algorithm (NuDE-mECA) to suppress multipath clutter broadening while preserving near-field low-speed target detection capabilities. Compared with the traditional multichannel ECA and uniform Doppler extraction multichannel ECA, the NuDE-mECA achieves significantly reduced computational complexity while improving clutter suppression performance and maintaining detection capabilities for low-speed targets. These results provide valuable insights for the lightweight and high-precision design of space-based PR systems.

Drone Detection Using 4G-LTE-Based Passive Radar

Game Theoretic Power Allocation and Antenna Selection for Target Detection in Hybrid Active and Passive MIMO Radar

Target Localization for Distributed Hybrid Active–Passive Radars

A multi-frame hybrid integration method combined with differential evolution for maneuvering target detection with GNSS-based passive radar

The passive bistatic synthetic aperture radar (PBSAR) is recognized as a critical developmental direction for future radar systems. To validate its operational feasibility, we designed a PBSAR system. However, significant measurement errors were observed to degrade imaging quality. Conventional autofocusing algorithms operate under the assumption that measurement errors primarily perturb phase components while exerting negligible influence on signal envelopes. The results from the system demonstrate the invalidity of this assumption, and the performance of conventional autofocusing algorithms severely degrades under enhanced resolution requirements. To address this limitation, we propose a frequency-domain division-based multi-stage autofocusing framework. This approach improves the frequency-dependent characterization of phase errors and incorporates an image sharpness-optimized autofocusing strategy. The estimated phase errors are directly applied for signal-level compensation, yielding refocused imagery with enhanced clarity while achieving an efficiency improvement exceeding 75%. Furthermore, we introduce a ground Cartesian back projection algorithm to adapt it to the PBSAR architecture, significantly improving computational efficiency in autofocusing processing. The integration of the proposed autofocusing algorithm with the accelerated imaging framework achieves an enhancement in autofocusing performance and a computational efficiency improvement by an order of magnitude. Simulations and experimental validations confirm that the proposed methodology exhibits marked advantages in both operational efficiency and focusing performance.

This paper proposes an X-band bandpass filter structure for the Kolchuga passive radar system as an alternative to the Interdigital structure. The proposed structure uses substrate-integrated waveguide (SIW) technology with series-connected resonant frames arranged in a folded form. With the improved structure, the designed filter ensures the insertion loss, reflection coefficient in the pass band, and attenuation slope in the stop band requirements in the X-band for the Kolchuga radar while optimizing the size. Compared with the SIW bandpass filter using the conventional straight IRIS structure, the proposed filter structure reduces the size but achieves equivalent scattering parameters. The simulation results demonstrate the effectiveness of the proposed filter structure in meeting the required criteria and optimizing the size.

This paper proposes a new carrier domain approach to suppress spreading first-order sea clutter in shipborne passive radar systems using Digital Radio Mondiale (DRM) signals as illuminators. The DRM signal is a broadcast signal that operates in the high-frequency (HF) band and employs orthogonal frequency-division multiplexing (OFDM) modulation. In shipborne DRM-based passive radar, sea clutter sidelobes elevate the noise level of the clutter-plus-noise covariance matrix, thereby degrading the target signal-to-interference-plus-noise ratio (SINR) in traditional space–time adaptive processing (STAP). Moreover, the limited number of space–time snapshots in traditional STAP algorithms further degrades clutter suppression performance. By exploiting the multi-carrier characteristics of OFDM, this paper proposes a novel algorithm, termed Space Time Adaptive Processing by Carrier (STAP-C), to enhance clutter suppression performance. The proposed method improves the clutter suppression performance from two aspects. The first is removing the transmitted symbol information from the space–time snapshots, which significantly reduces the effect of the sea clutter sidelobes. The other is using the space–time snapshots obtained from all subcarriers, which substantially increases the number of available snapshots and thereby improves the clutter suppression performance. In addition, we combine the proposed algorithm with the dimensionality reduction algorithm to develop the Joint Domain Localized-Space Time Adaptive Processing by Carrier (JDL-STAP-C) algorithm. JDL-STAP-C algorithm transforms space–time data into the angle–Doppler domain for clutter suppression, which reduces the computational complexity. Simulation results show the effectiveness of the proposed algorithm in providing a high improvement factor (IF) and less computational time.