Passive Radar Network Research Articles

3-D Target Tracking for Distributed Heterogeneous 2-D–3-D Passive Radar Network

3-D Target Tracking for Distributed Heterogeneous 2-D–3-D Passive Radar Network

Robust Target Localization for Multistatic Passive Radar Networks

Passive multistatic radar networks localize a non-cooperative target exploiting the bistatic measurements associated with signals emitted by several transmitters of opportunity and acquired by multiple receivers. However, in realistic scenarios, it might happen that one or more receivers are damaged and/or under malicious attacks with a consequent degradation of the system performance. This paper proposes a procedure to reveal sensors that return anomalous measurements due to an attack or a failure. Specifically, we first detect such measurements by solving a binary hypothesis test, then we cancel them and estimate the final delays as well as the target position. The performance of the overall architecture is assessed using both synthetic data and real-recorded data.

SkyWatch: A Passive Multistatic Radar Network for the Measurement of Object Position and Velocity

Quantitative three-dimensional (3D) position and velocity estimates obtained by passive radar will assist the Galileo Project in the detection and classification of aerial objects by providing critical measurements of range, location, and kinematics. These parameters will be combined with those derived from the Project’s suite of electromagnetic sensors and used to separate known aerial objects from those exhibiting anomalous kinematics. SkyWatch, a passive multistatic radar system based on commercial broadcast FM radio transmitters of opportunity, is a network of receivers spaced at geographical scales that enables estimation of the 3D position and velocity time series of objects at altitudes up to 80[Formula: see text]km, horizontal distances up to 150[Formula: see text]km, and at velocities to [Formula: see text][Formula: see text]km/s ([Formula: see text][Formula: see text]Mach). The receivers are designed to collect useful data in a variety of environments varying by terrain, transmitter power, relative transmitter distance, adjacent channel strength, etc. In some cases, the direct signal from the transmitter may be large enough to be used as the reference with which the echoes are correlated. In other cases, the direct signal may be weak or absent, in which case a reference is communicated to the receiver from another network node via the internet for echo correlation. Various techniques are discussed specific to the two modes of operation and a hybrid mode. Delay and Doppler data are sent via internet to a central server where triangulation is used to deduce time series of 3D positions and velocities. A multiple receiver (multistatic) radar experiment is undergoing Phase 1 testing, with several receivers placed at various distances around the Harvard–Smithsonian Center for Astrophysics (CfA), to validate full 3D position and velocity recovery. The experimental multistatic system intermittently records raw data for later processing to aid development. The results of the multistatic experiment will inform the design of a compact, economical receiver intended for deployment in a large-scale, mass-deployed mesh network. Such a network would greatly increase the probability of detecting and recording the movements of aerial objects with anomalous kinematics suggestive of Unidentified Aerial Phenomena (UAP).

Learning Resource Allocation in Active-Passive Radar Sensor Networks

Recent advances in Passive Coherent Location (PCL) systems make combined active and passive radar sensor networks very attractive for both military and civilian air surveillance. PCL systems seem promising as cost-effective gap fillers of active radar coverage especially in alpine terrain and also as covert early warning sensors. However, PCL systems are sensitive to changes of Transmitters of Opportunity (ToO). Many approaches for energy-efficient target detection have been proposed for active radar sensor networks. However, energy-efficiency and topology optimization of combined active-passive radar sensor networks in realistic scenarios have been poorly studied until today. We here propose an unsupervised learning approach for topology optimization and energy-efficient detection in combined active-passive radar sensor networks. The interdependence of active and passive sensors in the network and the given target scenario is naturally accounted for by our approach. Optimal power budget and detection sectors of active radars and the most useful ToOs for each PCL sensor are simultaneously learned over time. This is a critical contribution for minimizing the need for active radar power budget and PCL computational resources. The power budget of active radars is minimized in a way that the added value of PCL sensors is fully exploited. We also demonstrate how our approach dynamically relearns to achieve robust performance when changes in the ToO of PCL sensors occur. We test our approach in a simulation suite for active-passive radar sensor networks using real-world air surveillance data and ToOs under real-world topographical conditions.

Composed Resource Optimization for Multitarget Tracking in Active and Passive Radar Network

In this article, a composed resource optimization (CRO) scheme is developed for an active and passive radar network engaged in multiple target tracking (MTT). The motivation of the CRO scheme is to collaboratively optimize the transmit resources of active radars, as well as the receiving processing resources of passive radars, to improve the overall MTT performance. We utilize the predicted conditional Cramér–Rao lower bound to evaluate the impact of allocation strategies on tracking performance and formulate the CRO as a mixed-integer nonlinear program problem since the adaptable parameters w.r.t. the target selection process are in binary form. To solve the problem, we propose an alternating direction method of multiplier-based algorithm. This algorithm transforms the original problem into an equality constrained problem by introducing two auxiliary vectors. In such a case, the CRO problem can be tackled by alternately solving several simple subproblems. Specifically, the subproblem w.r.t. the resource vector is convex, and the subproblems w.r.t. the auxiliary vectors are separable. Simulation results demonstrate that the proposed CRO scheme outperforms the traditional allocation schemes in terms of MTT performance. In addition, the performance of the CRO scheme is close to the optimal performance provided by the exhaustive method, but the computation load of the CRO scheme is lower than that of the exhaustive method. Finally, physical interpretations are presented to support our conclusions.

Convex Optimization-Based Power Allocation Strategies for Target Localization in Distributed Hybrid Non-Coherent Active-Passive Radar Networks

To more effectively exploit the omnipresent public non-cooperative illuminators of opportunity (IOs) to improve the low probability of intercept (LPI) performance, a power allocation strategy is developed for target localization in distributed hybrid non-coherent active-passive radar networks. The core of the strategy is to optimize each active radar power allocation to minimize the total intercepted power for the hostile intercept receiver in view of power constraints, while achieving the predetermined collaborative target localization accuracy with the assistance of public non-cooperative IOs. Considering the timing uncertainty of public non-cooperative IOs, the localization performance is determined by joint CramrRao lower bound (CRLB) of the target position and the time-of-arrival (TOA) of the direct-path in a distributed passive radar network. Then, the Bayesian CRLB (BCRLB) is derived to evaluate collaborative target localization accuracy, adopted as the optimization criterion for the power allocation strategy. Furthermore, the optimization problem of the power allocation strategy is strictly proved to be a convex optimization, which implies that the global optimal lower bound can be easily obtained. By introducing a semi-definite matrix, the optimization problem is subsequently converted to semi-definite programming (SDP), resolved by the exiting classical algorithm directly. Finally, the simulation results verify the theoretical analysis and the LPI performance of the proposed strategy which outperforms existing strategies.

Directional sensitivity of track initialisation of a multistatic Doppler‐only passive radar network

A multistatic passive radar network originally presented in Ptak et al. for long-distance aircraft tracking is sensitive to track initialisation errors. When these errors exceed 50 km, target tracking often diverges. Initialisation can be improved by even coarse directional information. The impact of binary directional information was studied, which allows flights to be assigned to start from east or west of the multistatic network. Substantial improvement to the performance of flight tracking is observed on reference data based on Automatic Dependent Surveillance–Broadcast information.

Barrier Coverage Quality Improvement for AI-Based Passive Bistatic Radar Networks

Artificial Intelligent based smart Internet of Things(AI-based IoTs) refers to a network that has sensing data analysis function and network automatic management functions such as self-diagnosis and self-healing function. Among them, self-healing function is a critical problem in an AI-based Passive Bistatic Radar Sensor Network(PBRSN). Much different from active bistatic radar, transmitters in PBRSN are not deployed by network designer and may be out of work due to some irresistible reasons, then the coverage quality of a protected region cannot be satisfied. Therefore, how to localize barrier gap accurately and mend barrier gap quickly are the most important issues in a PBRSN. In this paper, we study the problem of how to automatically localize barrier gap and compute the locations of new receivers for coverage quality improvement in a PBRSN with an AI-based smart node, where the network was providing <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${K}$ </tex-math></inline-formula> barriers coverage for a protected region by adopting receivers optimal deployment strategy. First, we design an algorithm to localize all barrier gaps, and then an algorithm called Deployment Line between Sub-barriers Algorithm (DLSA) is designed to compute the locations of new receivers inside gap region with the objective of minimizing the total number of new receivers. Finally, extensive simulations are conducted to validate the correctness and effectiveness of our proposed algorithms. The simulation results show that our gap finding algorithm can localize all barrier gaps accurately. While the DLSA can mend a barrier gap with fewer receivers if there is at least one working transmitter in this barrier.

Optimal Receiver Placement for K -barrier Coverage in Passive Bistatic Radar Sensor Networks

The improvement of coverage quality in the construction of multiple-barrier coverage is a critical problem in a wireless sensor network. In this article, we investigate the K -barrier coverage construction problem in passive bistatic radar sensor networks. In contrast to traditional bistatic radar networks, the transmitters in a passive bistatic radar network are predeployed and noncooperative. To construct K barriers, we need to deploy receivers that couple with predeployed transmitters to build continuous barriers. In this work, we focus on the minimum number of receivers problem of constructing K -barrier coverage, where the minimum number of receivers is based on the predeployed transmitters. To handle this problem, we first investigate the optimal placement of receivers between adjacent transmitters for a sub-barrier formation and then determine the optimal placement of receivers for the one-barrier construction. For multiple-barrier coverage construction, we introduce a weighted transmitter graph (WTG) to describe the relation among different transmitters, where the weight in the graph is the minimum number of receivers needed for these two transmitters for a sub-barrier formation. Based on WTG, the minimum receivers problem changes to a problem of how to find K -disjoint paths with the minimum total weight in the graph. For large-scale networks, we also propose two efficient heuristic algorithms to solve the corresponding problem. Finally, we conduct extensive experiments to validate the correctness and the efficiency of the proposed algorithms.

Closed-Form Localization Method for Moving Target in Passive Multistatic Radar Network

Utilizing the bistatic range (BR) and bistatic range rate (BRR) obtained from time delay difference and Doppler shift difference, a moving target can be localized with the passive multistatic radar (PMR) network. This paper presents a two-stage closed-form method for target localization in PMR. Different from the conventional two-stage weighted least squares (TSWLS) methods, the proposed method employs the weighted spherical-interpolation method to obtain an initial estimate in the first stage and reduces the error in the initial estimate with the deviation refinement in the second stage. Theoretical analysis through mean-square error (MSE) shows the proposed method approaches the Cramér-Rao lower bound (CRLB) accuracy under the assumption of mild measurement noises. Simulation results validate the analytical results. The proposed method is also shown to achieve the accuracy improvement in target velocity estimate at relatively higher noise levels.

Development of a Low-Cost Multistatic Passive Weather Radar Network

Practical and accurate estimation of 3-D wind fields is an ongoing challenge in radar meteorology. Multistatic (single transmitter/multiple receivers) radar architectures offer a cost-effective solution for obtaining the multiple Doppler measurements necessary to achieve such estimates. Existing multistatic weather radars, while less costly than comparable monostatic networks, have been constrained by the need for specialized equipment and software to perform frequency and pulse timing synchronization with the transmitting radar. This article describes the implementation of a passive radar network that performs pulse timing and carrier frequency synchronization through measurements of the sidelobe radiation of the transmitting radar. This makes it possible for the receiver modules to be constructed with minimal size and cost, and it allows for their use in coordination with any in-band transmitter capable of recording time-stamped pointing angle information. A prototype network consisting of two passive receivers has been constructed in the Oklahoma City, OK, USA. Weather observations collected using the radiation from a WSR-88D have been used to validate the accuracy of velocity measurements obtained through this technique.

Evolution of Netted Radar Systems

The netted radar systems are known for being able to provide higher target detection probabilities, more accurate measurements, and better multi-target tracking performance than the monostatic/bistatic radar systems by exploiting information fusion processing. Simple radar networks made of multiple independent monostatic radars with decentralized processing have been around for several decades. In contrast, sophisticated radar networks with centralized data fusion only emerged during the last few years thanks to the development in high-speed digital processing and precise time/phase synchronization. Although the advanced radar networks offer the potential ability to detect stealth targets and low probability of intercept radar signal, there are many technical challenges to be solved before they could evolve from concepts in the research paper to implementable systems in the real-world. By presenting the various target detection and parameter estimation approaches adopted by the distributed MIMO radar, the passive radar network, the hybrid active-passive radar network, and the MIMO synthetic aperture radar (SAR), we lay out the foundation for the future research to be conducted to solve the key issues limiting the practical performance of these advanced radar networks.

Maximum Likelihood and IRLS Based Moving Source Localization with Distributed Sensors

In this article, we consider the problem of estimating the location and velocity of a moving source using a distributed passive radar sensor network. We first derive the maximum likelihood estimator (MLE) using direct sensor observations, when the source signal is unknown and modeled as a deterministic process. Since the MLE obtains the source location and velocity estimates through a search process over the parameter space and is quite computationally intensive, we also developed an efficient algorithm to solve the problem using a two-step approach. The first step finds the time difference of arrival (TDOA) and frequency difference of arrival (FDOA) estimates for each sensor with respect to a reference sensor by using a two-dimensional fast Fourier transform and interpolation, while the second step employs an iterative reweighted least square (IRLS) approach with a varying weighting matrix to determine the source location and velocity. To benchmark the performance of the proposed methods, a constrained Cramer–Rao bound (CRB) for the considered source localization problem is derived. Numerical results show that the IRLS approach has a lower signal-to-noise ratio threshold phenomenon compared with several recent TDOA/FDOA-based methods, especially when the source is considerably farther away from some sensors than others, creating a larger disparity in the quality of sensors observations.

K ‐coverage based receiver placement optimisation in passive radar network

In passive radar network (PRN), the receiver placement optimisation which has the ability to improve the coverage performance attracts much attention recently. In this study, the K-coverage performance is considered since the pre-condition of deghosting in a radar network is that the target has to be covered by K K ≥ 3 bistatic pairs. Thus, a K-coverage-based receiver placement optimisation is formulated, in which a weighted M-centre model and the required radar cross section (RCS) for target detection are involved. Moreover, in order to solve this high dimensional optimisation problem, an iterative method which is based on K-order Voronoi algorithm and neighbourood search is proposed. Finally, the proposed method shows its effectiveness in solving the K-coverage optimisation problem of PRN via simulations. Specially, the probability of K-coverage is up to 99.8% with the optimal placement.

Cramér‐Rao Lower Bounds for Joint Target Parameter Estimation in FM‐Based Distributed Passive Radar Network with Antenna Arrays

AbstractTo avoid the disadvantages of the active radar which utilizes its own transmitter to emit electromagnetic radiations, passive radars use the signals readily available in the environment and can provide superior capabilities of stealth target detection, low probability of intercept, low cost, and robustness. This paper investigates the joint target parameter (delay and Doppler) estimation performance for a frequency modulation (FM)‐based distributed passive radar network (DPRN) system with antenna arrays. The DPRN system consists of multiple FM‐based illuminators of opportunity and multiple radar receivers, which are placed on moving platforms. First, we consider the scenario where the target state parameters are unknown, the maximum likelihood estimator is developed, and the log‐likelihood ratio of the received signal for a complex Gaussian extended target is derived. Then, the Cramér‐Rao lower bounds (CRLBs) on the Cartesian coordinates of target position and velocity are computed for a DPRN system with MT FM‐based transmitters of Q antenna elements and MR multichannel receivers of P antenna elements. Finally, numerical examples demonstrate that grouping the receiving antenna elements into properly sized arrays can reduce estimation errors. It is also shown that the joint CRLB is a function of signal‐to‐noise ratio, the number of receiving antenna elements, the properties of the transmitted FM waveform, and the relative geometry between the target and the DPRN architecture. The analytically closed‐form expressions for CRLB are an important performance metric in that they enable the optimal placement of radar receivers to improve the target parameter estimation performance.

Joint Optimization of Receiver Placement and Illuminator Selection for a Multiband Passive Radar Network.

The performance of a passive radar network can be greatly improved by an optimal radar network structure. Generally, radar network structure optimization consists of two aspects, namely the placement of receivers in suitable places and selection of appropriate illuminators. The present study investigates issues concerning the joint optimization of receiver placement and illuminator selection for a passive radar network. Firstly, the required radar cross section (RCS) for target detection is chosen as the performance metric, and the joint optimization model boils down to the partition p-center problem (PPCP). The PPCP is then solved by a proposed bisection algorithm. The key of the bisection algorithm lies in solving the partition set covering problem (PSCP), which can be solved by a hybrid algorithm developed by coupling the convex optimization with the greedy dropping algorithm. In the end, the performance of the proposed algorithm is validated via numerical simulations.

Target detection using weather radars and electromagnetic vector sensors

We propose a method to detect a target in a bistatic passive polarimetric radar network, with weather surveillance radar as our illuminator of opportunity (IO). We build our signal model using electromagnetic vector sensors (EMVS) as the receiver, which captures the reflections from a point-like target present in the scene of interest, surrounded with strong clutter. We develop a generalized likelihood ratio test (GLRT) detector that is robust to inhomogeneous clutter. We also develop a maximum likelihood (ML) solution to extract the signal subspace from the received data contaminated by the clutter interference. We provide the exact distribution of the test statistic for the asymptotic case and evaluate its performance loss by considering a reduced set of data. The proposed GLRT method is a constant false alarm rate (CFAR) detector, which makes it robust against the inhomogeneous clutter. With the help of numerical results, we demonstrate the robustness and the limitations of our proposed method.

Modified Cramér‐Rao lower bounds for joint position and velocity estimation of a Rician target in OFDM‐based passive radar networks

AbstractOwing to the increased deployment and the favorable range and Doppler resolutions, orthogonal frequency‐division multiplexing (OFDM)‐based L band digital aeronautical communication system type 1 (LDACS1) stations have become attractive systems for target surveillance in passive radar applications. This paper investigates the problem of joint parameter (position and velocity) estimation of a Rician target in OFDM‐based passive radar network systems with multichannel receivers placed on moving platforms, which are composed of multiple OFDM‐based LDACS1 transmitters of opportunity and multiple radar receivers. The modified Cramér‐Rao lower bounds (MCRLBs) on the Cartesian coordinates of target position and velocity are computed, where the received signal from the target is composed of dominant scatterer (DS) component and weak isotropic scatterers (WIS) component. Simulation results are provided to demonstrate that the target parameter estimation accuracy can be improved by exploiting the DS component. It also shows that the joint MCRLB is not only a function of the transmitted waveform parameters, target radar cross section, and signal‐to‐noise ratio but also a function of the relative geometry between the target and the passive radar networks. The analytical expressions of MCRLB can be utilized as a performance metric to access the target parameter estimation in OFDM‐based passive radar networks in that they enable the selection of optimal transmitter‐receiver pairs for target estimation.

Cramér–Rao bound analysis for joint target location and velocity estimation in frequency modulation based passive radar networks

With the wide spread availability and the favourable Doppler resolution, the frequency modulation (FM) commercial radio signals have become attractive for passive radar applications. Passive radar networks using multiple illuminators of opportunity and multichannel receivers have been shown to offer significant performance improvement owing to their advantage of signal and spatial diversities. In this study, the authors compute the joint Cramer–Rao lower bound (CRLB) for the target parameter (delay and Doppler) estimation error utilising FM commercial radio signals as illuminators of opportunity for passive radar network systems, where the non-coherent and coherent processing scenarios are considered. The numerical simulations are provided to show that the joint CRLB is not only a function of the transmitted waveforms but also of the relative geometry between the target and the passive radar networks for both non-coherent and coherent cases. The expressions for joint CRLB are an important performance metric for target parameter estimation in FM-based passive radar networks.

Transmitter Subset Selection in FM-Based Passive Radar Networks for Joint Target Parameter Estimation

Passive radar network systems utilize multiple transmitters of opportunity and multichannel receivers to offer remarkable performance improvement due to the advantage of signal and spatial diversities. The frequency modulation (FM) commercial radio signals have become attractive for passive radar applications owing to their wide-spread availability and the favourable Doppler resolution. In this paper, two transmitter subset selection schemes, balancing the trade-off between target parameter estimation accuracy and infrastructure utilization, are proposed for FM-based passive radar networks. In the first, the subset size of selected transmitters employed in the estimation process is minimized by effectively selecting a subset of transmitters, such that the required target parameter estimation mean-square error (MSE) threshold is attained. In the second, an optimal subset of transmitters of a predetermined size $\kappa $ is selected, such that the estimation MSE is minimized. These problems are formulated as a knapsack problem, where the coherent Cramer–Rao lower bound (CRLB) is used as a performance metric. Both transmitter subset selection schemes are tackled with greedy selection algorithms by successively selecting transmitters so as to minimize the performance gap between the CRLB and a predetermined MSE threshold or a predetermined subset size. Numerical simulations demonstrate that the problem of transmitter subset selection is not only a function of the transmitted waveforms but also of the relative geometry between the target and the passive radar network systems, which leads to reductions in both computational load and signal processing costs.