Multistatic Passive Radar Research Articles

LTE-based passive multistatic radar for high-speed railway network surveillance: design and preliminary results

AbstractWith the aim of performing perimeter surveillance of high-speed railway networks, this paper presents the design of a passive multistatic radar system based on the use of Long-Term Evolution (LTE) downlink signals as the illumination of opportunity. Taking into account the specifications and standard of the LTE system, the ambiguity function of measured downlink signals is analyzed in terms of range and Doppler resolution, ambiguities, and sidelobe level. The deployment of the proposed passive radar is flexible and scalable, and it is based on multichannel software defined radio receivers that obtain the reference and surveillance signals by means of digital beamforming. The signal processing and data fusion are based, respectively, on the delay-Doppler cross-correlation with the reconstructed reference signals and a two-stage tracking at sensor and central level. Finally, the performance of the proposed system is estimated in terms of its maximum detection range and simulation results of the detection of moving targets are presented, demonstrating its technical feasibility for the short-range detection of pedestrians, vehicles, and small drones.

Moving target imaging in multistatic passive radar

This study presents the concept of multistatic passive images. Theoretical considerations are illustrated by the results obtained by imaging a small general aviation plane using DVB-T signals. These results are compared with simulated images derived from the same scenarios in real-life tests. The results described here show that it is possible to obtain ISAR images of aerial targets using multistatic passive radars based on DVB-T signals. Such images could be next applied for non-cooperative target recognition.

Sequential Multi-Sensor JPDA for Target Tracking in Passive Multi-Static Radar With Range and Doppler Measurements

Target tracking in passive multi-static radar (PMSR) with bistatic range and Doppler frequency measurements from multiple transmit–receive pairs is gaining increasing interest. For the data association problem in this scenario, the parallel architecture of a multi-sensor joint probabilistic data association (P-MSJPDA) filter has been significantly investigated. As an alternative architecture, the sequential MSJPDA (S-MSJPDA) is rarely discussed in PMSR. In this paper, we evaluate the behaviors of S-MSJPDA in PMSR target tracking with bistatic range and Doppler frequency measurements. A comprehensive comparison between the S-MSJPDA and the P-MSJPDA in PMSR is provided. It can be found from the analysis that S-MSJPDA outperforms its parallel counterpart in terms of computational efficiency, given an acceptable degradation in position accuracy. The S-MSJPDA is further applied to an experimental passive multi-static radar for aircrafts tracking. The real data results obtained are rather close to the true trajectories of the targets. This demonstrates that the S-MSJPDA has great potentials in PMSR target tracking.

Calibrating the Transmitter and Receiver Location Errors for Moving Target Localization in Multistatic Passive Radar

The presence of transmitter and receiver location uncertainties has been known to remarkably deteriorate the target localization accuracy in multi-static passive radar (MPR) system. This paper explores the use of calibration targets, the positions and velocities of which are known to the MPR system, to counter the loss in target localization accuracy arising from the transmitter/receiver location uncertainties. We firstly evaluate the Cramer-Rao lower bound (CRLB) for bistatic range (BR) and bistatic range rate (BRR)-based target localization in the presence of calibration targets, which analytically indicates the potential of calibration targets in enhancing localization accuracy. After that, a novel closed-form solution is proposed for target localization using the BR and BRR measurements from the unknown target as well as the additional BR and BRR measurements from the calibration targets. The proposed solution includes two processing steps, referred to as calibration step and localization step respectively. The calibration step is devoted to refine the inaccurate transmitter and receiver locations using the BR and BRR measurements from the calibration targets, and then the localization step is devoted to determine the target position and velocity based on the refined transmitter/receiver locations and the BR/BRR measurements from the unknown target. The accuracy of proposed solution is shown analytically to accomplish the CRLB under sufficiently small BR/BRR measurement noises and transmitter/receiver location errors. Simulation results verify the effectiveness and superiority of the proposed solution over existing algorithms.

DA-PMHT for Multistatic Passive Radar Multitarget Tracking in Dense Clutter Environment

Multistatic passive radar exploits illuminators of opportunity such as radio or television stations for tracking airborne targets. This paper focuses on broadcast signals in a single frequency network modulated according to the digital audio/video broadcasting (DAB/DVB) standards. The main challenge is that one has to solve the measurement-to-illuminator association ambiguity in addition to the conventional measurement-to-target association ambiguity, which increases data association complexity greatly. In this paper, to solve the additional measurement-to-illuminator data association ambiguity, a novel deterministic annealing probabilistic multi-hypothesis tracker based on the extended Kalman filter is proposed. The proposed algorithm works directly in the original three-dimensional Cartesian space, and can efficiently handle a large number of association hypotheses among the targets, measurements, and illuminators in a natural way, also it is computationally attractive. As the angular measurement is not available or often of extremely poor quality, this paper assumes measurements of bistatic range and range rate and does not process bearing information. The simulation results show that the proposed algorithm works reasonably well in dense clutter environment for multistatic passive radar system.

An Approximate Maximum Likelihood Algorithm for Target Localization in Multistatic Passive Radar

This paper addresses the problem of target localization using Bistatic range (BR) measurements in a distributed multistatic passive radar system. The rangebased positioning technique employs multiple transmitterreceiver pairs, which provide separate BR measurements. Based on the Maximum likelihood (ML) function, an efficient algebraic Approximate maximum likelihood (AML) algorithm for single target localization is proposed. The closed-form AML solution has neither initial condition requirements nor convergence difficulty. Simulations are included to compare its performance to that of the CramerRao lower bound (CRLB) and the Two-step Weighted least squares (TS-WLS) algorithm. The proposed method is shown to be able to achieve the CRLB accuracy under Gaussian measurement noise. It is more robust to noise than the TS-WLS method, and presents relative insensitivity to target-sensor geometry.

Cramér–Rao Lower Bound Analysis for Stochastic Model Based Target Parameter Estimation in Multistatic Passive Radar With Direct-Path Interference

This paper addresses the problem of Cramér-Rao lower bound (CRLB) analysis for joint target location and velocity estimation in a multistatic passive radar system comprised of multiple noncooperative illuminators of opportunity (IOs) and multiple geographically separated receivers. Unlike other existing studies, special attention in this paper is paid to a more ubiquitous scenario, in which no reference channel exists in receiver networks. Besides, the situation where the measurements collected at the receivers are contaminated by the interference directly illuminated from the IOs is taken into account. Namely, each receive station simultaneously obtains direct-path interference (DPI) from all the IOs and echo signals reflected by the target. Furthermore, the IO waveform is modeled as a stochastic process in which samples of the unknown IO waveform are treated as a complex Gaussian sequence. Finally, the effects of multipath clutter on the signal model and CRLB are well analyzed. The numerical results are provided to prove that the joint CRLB is not only a function of the signal-to-noise ratio (SNR), DPI-to-noise ratio (DNR), and clutter-to-noise ratio (CNR) but also associated with both IO waveform parameters and relative geographical distribution of the system.

Knowledge-Aided Target Detection for Multistatic Passive Radar

This paper studies the detection problem for multistatic passive radar. We consider scenarios where prior knowledge about the spectrum and peak-to-average ratio (PAR) of the non-cooperative illuminators of opportunity (IOs) is available. We develop several knowledge-aided (KA) detectors within the framework of the generalized likelihood ratio test (GLRT) to exploit such prior knowledge. Particularly, the knowledge about the bandwidth of the transmitted signal is employed to suppress the out-of-band noise, and the knowledge about the PAR constraint is exploited to eliminate the remaining high-power noise. The challenge of unknown spectrum condition is also addressed, where block sparse Bayesian learning (BSBL) is exploited to derive the maximum-likelihood estimates (MLEs) of the unknown, temporally correlated signal. The numerical results indicate that the proposed KA detectors offer significant performance improvements compared with the traditional detectors, which do not exploit such prior information.

Impact of the Elevation Angle on CYGNSS GNSS-R Bistatic Reflectivity as a Function of Effective Surface Roughness over Land Surfaces

The Earth’s surface bistatic reflectivity Γ L H C P , C y G N S S is experimentally characterized using the novel Global Navigation Satellite Systems Reflectometry (GNSS-R) L-band passive multistatic radar technique from the Cyclone Global Navigation Satellite Systems (CyGNSS) eight-microsatellite constellation. The focus of this study is to evaluate the influence of the GNSS satellites’ elevation angle θ e on Γ L H C P , C y G N S S , as a function of soil moisture content (SMC) and effective surface roughness parameter h . As the average response, the change of the scattering regime at a global scale and considering also vegetated surfaces appears at θ e ≈ 55°. This empirical observation is understood as a change on the dominant scattering term, from incoherent to coherent. Then, the correlation of Γ L H C P , C y G N S S and SMC is evaluated as a function of θ e over specific sparsely vegetated target areas. The smoother the surface, the higher the angular variability of the Pearson correlation coefficients. Over croplands (e.g., Argentinian Pampas), an improved correlation coefficient is achieved over angular ranges where the coherent scattering regime becomes the dominant one. As such, this function depends on the surface roughness. The maximum correlation coefficients are found at different θ e for increasing mean roughness levels: r P a m p a s ≈ 0.78 at θ e ≈ [60,70]°, r I n d i a ≈ 0.72 at θ e ≈ [50,60]°, and r S u d a n ≈ 0.74 at θ e ≈ [30,40]°. SMC retrieval algorithms based on GNSS-R multi-angular information could benefit from these findings, so as to improve the accuracy using single-polarized signals.

Effects of nuisance variables selection on target localisation accuracy in multistatic passive radar

The target localisation problem is considered using the bistatic range measurement in multistatic passive radar. The well-known two-step weighted least-squares algorithm is widely used to locate a single target, in which the nuisance variables are normally introduced in the first step. Whereas the nuisance variables can be categorised as either target–transmitter ranges based or target–receiver ranges based. The influence of nuisance variables selection on target positioning accuracy is investigated in this Letter. Some basic criteria on choosing nuisance variables are presented and validated through simulations.

Ambiguity Function Analysis for OFDM-Based LDACS Passive Multistatic Radar

The ambiguity function analysis is central to investigating the resolution capabilities of a radar system. It is particularly important for passive radar scenarios, where the signals of opportunity have not been originally designed for radar purposes. This paper focuses on studying the achievable resolution capabilities for a noncoherent orthogonal frequency division-multiplexing (OFDM)-based passive multistatic radar for L-band digital aeronautical communication system (LDACS) signals. For this purpose, the ambiguity function obtained when employing the OFDM-based LDACS communications signals as signals of opportunity is studied. A generic bistatic link is considered and closed-form expressions for the expectation of the corresponding ambiguity function when accounting for the different signal contributions are derived. The resulting expressions are then used to evaluate and analyze the overall multistatic radar ambiguity function for two sample radar scenarios. The results highlight the impact of the geometry on the shape of the ambiguity function, allowing therefore for a better understanding of the effect that the geometry has on the range and velocity resolution capabilities of the studied system.

Target detection based on F-test in passive multistatic radar

Passive multistatic radar is of great interest in both civilian and military applications due to its numerous advantages. We focus on the target detection problem in passive multistatic radar consisting of a non-cooperative illuminator of opportunity, and several spatially separated surveillance receivers. Two F-test based detectors are proposed for target detection in this passive multistatic radar. They both exhibit a constant false alarm rate with respect to noise power. We drive closed-form expressions for the probability of false alarm of the proposed detector for the case of equal noise power in each receivers. Simulation results illustrate that the proposed detectors achieve satisfactory detection performance without requirement of knowledge about noise power.

Joint compressed sensing imaging and phase adjustment via an iterative method for multistatic passive radar

The resolution of the multistatic passive radar imaging system (MPRIS) is poor due to the narrow bandwidth of the signal transmitted by illuminators of opportunity. Moreover, the inaccuracies caused by the inaccurate tracking system or the error position measurement of illuminators or receivers can deteriorate the quality of an image. To improve the performance of an MPRIS, an imaging method based on the tomographic imaging principle is presented. Then the compressed sensing technique is extended to the MPRIS to realize high-resolution imaging. Furthermore, a phase correction technique is developed for compensating for phase errors in an MPRIS. Phase errors can be estimated by iteratively solving an equation that is derived by minimizing the mean recovery error of the reconstructed image based on the principle of fixed-point iteration technique. The technique is nonparametric and can be used to estimate phase errors of any form. The effectiveness and convergence of the technique are confirmed by numerical simulations.

Experimental Research of Multistatic Passive Radar With a Single Antenna for Drone Detection

Digital television signals are attractive illuminations of opportunity for the passive radars in the field of low altitude and slow speed target detection. The digital television standard permits reconstruction of a reference signal using the received signal in surveillance channel, which enables a single-antenna digital television based passive radar (SDPR) processing. This paper investigates the practical feasibility of a multistatic SDPR (MSDPR) for the drone detection. First, the detection range of the SDPR is analyzed in terms of signal processing procedures involving multipath energy, extracted reference signal purity, and receiving antenna. Second, according to the characteristics of the SDPR, the reference signal extraction is analyzed. In addition, considering that the SDPR cannot locate and track the detected target, a novel MSDPR processing method is proposed. The core idea of this method is to use the optimal reference signal extracted from the receiving station with the least interference as the shared reference signal in MSDPR, which can greatly improve the system detection capability. Finally, the small drone detection experiments using the MSDPR are presented. The theoretical considerations are demonstrated using the experimental data.

Maritime Moving Target Localization Using Passive GNSS-Based Multistatic Radar

This paper examines the potential in extracting the instantaneous location of maritime moving targets using a passive multistatic radar with Global Navigation Satellite Systems (GNSS) as illuminators of opportunity and a single receiver. This paper presents a theoretical framework for the localization of a moving target from a set of bistatic range measurements. The algorithm and its predicted accuracy are presented. The localization is achieved by what is essentially a multilateration technique, which can be applied while the transmitting platform is also in motion. The algorithms and the accuracy predictions, as a function of the number of transmitters, have been experimentally confirmed via a dedicated experimental campaign, where two different maritime targets were detected by up to 12 GNSS satellites belonging to different satellite constellations (GPS, GLONASS, and Galileo) simultaneously. To the best of the author’s knowledge, these are the first results of their kind and on this scale not only for GNSS-based passive radar but also for multistatic radar in general.

Target Localization in Multistatic Passive Radar Using SVD Approach for Eliminating the Nuisance Parameters

In this paper, a weighted least squares (WLS) method is presented to find the location of a single target by the use of bistatic range (BR) measurements in multistatic passive radar. The proposed method is based on the idea of eliminating the nuisance parameter. Then, the method employs several WLS minimizations to obtain the target location estimate. The weighting matrix producing a significant improvement in the performance of the method is derived in two different conditions—one of them results in the maximum likelihood estimator (MLE) and the other one leads to the best linear unbiased estimator (BLUE). The accuracy properties of the method are analyzed, and the Cramer-Rao lower bound (CRLB) for target localization accuracy is also derived. In addition, in a two-dimensional space and in the case of localizing with one transmitter and the minimal number of receivers, the different placements of receivers are analytically determined, in each of which the location solution is not unique. Furthermore, from different aspects, the BR-based localization is compared to the hyperbolic localization derived from range difference (RD) measurements.

GLRT Detector in Single Frequency Multi-static Passive Radar Systems

The target detection problem in multi-static passive radar systems (MS-PRS) is investigated, where multiple transmitters operate at a single frequency and a single multi-antenna radar receiver processes the received signals. In contrast to multi-frequency MS-PRS, the detector design is challenging due to the fact that the detector sees a mixture of transmitted signals which may not be separately treated. We propose the design of generalized likelihood ratio test (GLRT) receivers to optimize the detection performance. The beamformers at the radar receiver are optimized so that the received signals can be seen as statistically independent signals from multiple reference channels and a surveillance channel. The detector optimization problem is accurately approximated with a convex optimization algorithm. It is analytically shown that the performance of the proposed GLRT receiver approaches that of the active radar. Computer simulations verify the analytical results.

Multistatic passive detection with parametric modeling of the IO waveform

This paper examines the target detection problem for a passive multistatic radar employing illuminators of opportunity (IOs), where the receivers are contaminated by non-negligible noise and direct-path interference (DPI). A parametric approach is proposed by modeling the unknown signal transmitted from the IO as an auto-regressive (AR) process whose temporal correlation is jointly estimated and exploited for passive detection. The proposed solution is developed based on the generalized likelihood ratio test principle, which involves non-linear estimation that is solved by using the expectation-maximization (EM) algorithm. We also discuss the initialization of the EM algorithm and the joint adaptive model order estimation for the AR process without using any training signal. In addition, we extend several conventional passive detectors, which were introduced by assuming no DPI is present, to provide them with an ability to handle the DPI problem. A clairvoyant matched filtering (MF) detector is derived as well assuming the knowledge of the IO waveform. Extensive simulation results are presented, using simulated waveforms whose temporal correlation can be easily controlled, as well as practical IO waveforms transmitted by frequency modulation (FM) radio. The results show that the proposed EM-based passive detector outperforms conventional passive detectors due to the exploitation of the waveform correlation.

A Direct-Path Interference Resistant Passive Detector

Passive radar, which detects and tracks targets of interest by using noncooperative illuminators of opportunity (IOs), has become popular since qualified IO sources are widely accessible nowadays. This letter examines the target detection problem for a passive multistatic radar, where the receivers are contaminated by nonnegligible noise and direct-path interference (DPI). The signal transmitted from the IO is treated as a deterministic but unknown process. A generalized likelihood ratio test approach is proposed, where the $\mathcal {H}_1$ estimation problem is solved using an iterative method. A clairvoyant matched filtering detector, which assumes the knowledge of the IO waveform, is provided as well to benchmark performance. Simulation results are presented to show the effectiveness of the proposed detector in the presence of DPI.

Multistatic Detection for Passive Radar With Direct-Path Interference

This paper examines the target detection problem in a multistatic passive radar with one noncooperative illuminator of opportunity (IO) and multiple distributed receivers. Specifically, we consider how to address the direct-path interference (DPI), which refers to the direct transmission from the IO to a receiver, to enhance passive detection performance. The DPI is in general much stronger (by many tens to even over a hundred dB) than the target echo. It is standard for a passive radar to apply some kind of interference cancellation by using, e.g., an adaptive array, to reduce the DPI. However, due to practical limitations of such techniques and the significant difference in strength between the DPI and target signal, the residual DPI after cancellation is often at a nonnegligible level. Unlike most existing passive detectors which ignore such residual DPI, we consider explicitly its effect and develop two new detectors under the conditions when the noise level is known and, respectively, when it is unknown. Another distinction from existing solutions is that the proposed detectors exploit the correlation of the IO waveform for passive detection. The proposed detectors are developed within the generalized likelihood ratio test (GLRT) framework, which involves nonlinear estimation that is solved using the expectation-maximization algorithm. Numerical results are presented to illustrate the performance of the proposed methods and several well-known passive detectors.