Passive Localization Research Articles

Passive localization of multiple sources using joint RSS and AOA measurements in spectrum sharing system

In spectrum sharing systems, locating multiple radiation sources can efficiently find out the intruders, which protects the shared spectrum from malicious jamming or other unauthorized usage. Compared to single-source localization, simultaneously locating multiple sources is more challenging in practice since the association between measurement parameters and source nodes are not known. Moreover, the number of possible measurements-source associations increases exponentially with the number of sensor nodes. It is crucial to discriminate which measurements correspond to the same source before localization. In this work, we propose a centralized localization scheme to estimate the positions of multiple sources. Firstly, we develop two computationally light methods to handle the unknown RSS-AOA measurements-source association problem. One method utilizes linear coordinate conversion to compute the minimum spatial Euclidean distance summation of measurements. Another method exploits the long-short-term memory (LSTM) network to classify the measurement sequences. Then, we propose a weighted least squares (WLS) approach to obtain the closed-form estimation of the positions by linearizing the non-convex localization problem. Numerical results demonstrate that the proposed scheme could gain sufficient localization accuracy under adversarial scenarios where the sources are in close proximity and the measurement noise is strong.

Optimal Passive Source Localization for Acoustic Emissions.

Acoustic emission is a non-destructive testing method where sensors monitor an area of a structure to detect and localize passive sources of elastic waves such as expanding cracks. Passive source localization methods based on times of arrival (TOAs) use TOAs estimated from the noisy signals received by the sensors to estimate the source position. In this work, we derive the probability distribution of TOAs assuming they were obtained by the fixed threshold technique—a popular low-complexity TOA estimation technique—and show that, if the sampling rate is high enough, TOAs can be approximated by a random variable distributed according to a mixture of Gaussian distributions, which reduces to a Gaussian in the low noise regime. The optimal source position estimator is derived assuming the parameters of the mixture are known, in which case its MSE matches the Cramér–Rao lower bound, and an algorithm to estimate the mixture parameters from noisy signals is presented. We also show that the fixed threshold technique produces biased time differences of arrival (TDOAs) and propose a modification of this method to remove the bias. The proposed source position estimator is validated in simulation using biased and unbiased TDOAs, performing better than other TOA-based passive source localization methods in most scenarios.

Bio-acoustic tracking and localization using heterogeneous, scalable microphone arrays

Microphone arrays are an essential tool in the field of bioacoustics as they provide a non-intrusive way to study animal vocalizations and monitor their movement and behavior. Microphone arrays can be used for passive localization and tracking of sound sources while analyzing beamforming or spatial filtering of the emitted sound. Studying free roaming animals usually requires setting up equipment over large areas and attaching a tracking device to the animal which may alter their behavior. However, monitoring vocalizing animals through arrays of microphones, spatially distributed over their habitat has the advantage that unrestricted/unmanipulated animals can be observed. Important insights have been achieved through the use of microphone arrays, such as the convergent acoustic field of view in echolocating bats or context-dependent functions of avian duets. Here we show the development and application of large flexible microphone arrays that can be used to localize and track any vocalizing animal and study their bio-acoustic behavior. In a first experiment with hunting pallid bats the acoustic data acquired from a dense array with 64 microphones revealed details of the bats’ echolocation beam in previously unseen resolution. We also demonstrate the flexibility of the proposed microphone array system in a second experiment, where we used a different array architecture allowing to simultaneously localize several species of vocalizing songbirds in a radius of 75 m. Our technology makes it possible to do longer measurement campaigns over larger areas studying changing habitats and providing new insights for habitat conservation. The flexible nature of the technology also makes it possible to create dense microphone arrays that can enhance our understanding in various fields of bioacoustics and can help to tackle the analytics of complex behaviors of vocalizing animals.

An improving sparse coding algorithm for wireless passive target positioning

With the developments of smart city, wireless passive localization (WPL) technique that detects targets without carrying any devices draws a lot of research attention. Some machine learning methods, such as sparse coding and deep learning, have been developed to formulate the WPL as a classification problem. Despite these methods have been proved to be effective in WPL, it remains hot topics to design a precise objective function that could detect the position of the targets with high accuracy and robustness. In this paper, we exploited a sparsity regularizer, named log-regularizer, in the classification objective function. By virtue of the distinguished ability in measuring sparsity, our proposed improved sparse coding algorithm (ISCA) with log-regularizer could position targets accurately with robust performance even in the challenging environments Experimental results show that our proposal achieves better results compared with five other machine learning algorithms. Even the input data is severely polluted by noise (SNR = −10 dB), the proposed method could still obtain high localization accuracy of 99.4%.

Dublin à la Noir: Dermot Bolger’s "The Journey Home"

The article discusses the presentation of Dublin in the novel The Journey Home by Dermot Bolger (1990) with reference to both the novel’s historical and socio-economic setting in the 1980s and the literary tradition of urban representations, particularly Charles Dickens and the conventions of noir fiction. Drawing on the theoretical concepts of nonplace (Marc Augé) and site (Edward S. Casey), it argues that the modernization of the city centre and the sprawling of Dublin’s suburbs lead to the transformation of places, understood as locations of history, identity and community, into non-places/sites, i.e. non-differentiated, uniform spaces destructive of a sense of community and political responsibility. An analysis of the descriptions of the city centre and suburbs demonstrates that in the novel the urban setting becomes at once a cause and a reflection of the psychological and social problems of the protagonists. In this way, far from being a passive location of the action, the city becomes an active force, which shapes the lives of the protagonists with the inevitability characteristic of literary noir.

Probabilistic focalization for shallow water localization

We consider passive localization and tracking of an underwater acoustic source from measurements provided by a vertical line array. A focalization approach is introduced that probabilistically associates detected directions of arrival (DOAs) to modeled DOAs and jointly estimates the time-varying source location. Modelled DOAs are provided by an embedded ray tracing processor that makes use of environmental parameters to characterize the acoustic waveguide. Due to its statistical model, the proposed Bayesian method can provide robustness against inaccurate environmental parameters as well as an uncertainty characterization for estimated source locations. We demonstrate performance advantages compared to matched field processing using data collected during the SWellEx-96 experiment.

Real-Time Passive Localization of TDOA via Neural Networks

This letter proposes the use of neural networks to realize the passive localization by signal time difference of arrival (TDOA). In the face of multiple complex targets with radiation sources in a specific area, real-time localization is an urgent problem. In this letter, positions of the known targets from the prior data are obtained and their time difference is calculated, which will be connected as data pairs and input to the neural network trained to obtain a corresponding model. Subsequently, unknown targets can be localized by this network. It is verified that the localization accuracy of the algorithm is reliable and its robustness is higher than that of traditional algorithms. The proposed method also shows that great reduction of operation time depending on the previous network training can complete real-time goals.

Target localization in range and depth from a passive linear towed array

Stealthily estimating the range and depth of a sound source from a submarine passive sonar is of capital tactical importance: it allows to adopt the appropriate escape or attack strategy towards the threat. The main difficulty in passive localization is that the transmitted waveform is unknown. Nevertheless, array processing can reveal some features of the acoustic field allowing localization, such as the arrival angles of the multiple paths, and the time delays between them. In this paper, we have compared two methods. The first one is a direct method, which simulates the field features from a set of possible source locations, and compares them to the measured features. The second one, an indirect method, uses the measured features to backpropagate the acoustic field. The former method has already been tested on simulated and real data in (Bennaceur et al., in OCEANS’18 MTS/IEEE, Charleston) and (Bennaceur et al., in OCEANS 2021, Singapore-U.S. Gulf Coast). Here, both methods are applied on real signals from a linear towed array, using bearings only and both bearings and time delays. The main advantage of these methods is that they are automatic in the sense that no sonar operator is required to estimate the source location.

Semi-Passive 3D Positioning of Multiple RIS-Enabled Users

Reconfigurable intelligent surfaces (RISs) are set to be a revolutionary technology in the 6th generation of wireless systems. In this work, we study the application of RIS in a multi-user passive localization scenario, where we have one transmitter (TX) and multiple asynchronous receivers (RXs) with known locations. Classical approaches fail in this scenario due to lack of synchronization and lack of data association between multi-static measurements and users. To resolve this, we consider each user to be equipped with an RIS, and show that we can avoid the data association problem and estimate users’ 3D position with submeter accuracy in a large area around the transmitter, using time-of-arrival measurements at the RXs. We develop a low-complexity estimator that attains the corresponding Cramér-Rao bound as well as a novel RIS phase profile design to remove inter-path interference.

Exploring LoRa for Sensing

Wireless sensing received a great amount of attention in recent years and various wireless technologies have been exploited for sensing, including WiFi [1], RFID [2], ultrasound [3], 60 GHz mmWave [4] and visible light [5]. The key advantage of wireless sensing over traditional sensing is that the target does not need to be equipped with any sensor(s) and the wireless signal itself is being used for sensing. Exciting new applications have been enabled, such as passive localization [6] and contactless human activity sensing [7]. While promising in many aspects, one key limitation of current wireless sensing techniques is the very small sensing range. This is because while both direct path and reflection path signals are used for communication, only the weak target-reflection signals can be used for sensing. Take Wi-Fi as an example: the communication range can reach 20 to 50 meters indoors but its sensing range is merely 4 to 8 meters. This small range further limits the through-wall sensing capability of Wi-Fi. On the other hand, many applications do require long-range and through-wall sensing capability. In a fire rescue scenario, the sensing device cannot be placed close to the building, and the long-range through-wall sensing capabilities are critical for detecting people deep inside the building. Table I summarizes the sensing range of existing wireless technologies. We can see that long-range through-wall sensing is still missing with wireless sensing.

A Novel Joint TDOA/FDOA Passive Localization Scheme Using Interval Intersection Algorithm

Due to the large measurement error in the practical non-cooperative scene, the passive localization algorithms based on traditional numerical calculation using time difference of arrival (TDOA) and frequency difference of arrival (FDOA) often have no solution, i.e., the estimated result cannot meet the localization background knowledge. In this context, this paper intends to introduce interval analysis theory into joint FDOA/TDOA-based localization algorithm. The proposed algorithm uses the dichotomy algorithm to fuse the interval measurement of TDOA and FDOA for estimating the velocity and position of a moving target. The estimation results are given in the form of an interval. The estimated interval must contain the true values of the position and velocity of the radiation target, and the size of the interval reflects the confidence of the estimation. The point estimation of the position and the velocity of the target is given by the midpoint of the estimation interval. Simulation analysis shows the efficacy of the algorithm.

A Virtualization Approach to Correct Systematic Errors in Measured TDOA and FDOA for Low Orbit Dual-Satellite Positioning Systems

The low-orbit dual-satellite passive location system provides a cost-efficient and easy implementation platform, by which positions of unknown emitters on the Earth can be determined through measuring both the time and the frequency differences by two low-orbit satellites in space. However, in reality, this dual-satellite location system has low positioning accuracy because of the existence of systematic errors. In this paper, in order to address the problem of low positioning accuracy in low-orbit dual-satellite systems, a virtualization approach, consisting of the establishment of the virtual reference station and virtual frequency conversion, is proposed to correct systematic errors in the system. Specifically, we first analyze the coming source of systematic errors in the dual-satellite location system, and then, a virtual reference station and virtual frequency are constructed to correct errors in the measured time difference of arrival and the frequency difference of arrival, respectively. Simulation results show that systematic errors caused by the measured time difference of arrival can be significantly reduced, and the correction efficiency, defined as a ratio between remaining errors after implementing the proposed method over uncorrected ones, for the measured frequency difference of arrival, largely relies on both the virtual frequency and the transmission frequency of reference stations.

An anti-outliers SRCKF algorithm for fixed single observer passive location

Outliers often appear in measurement noise of single observer passive location system, which has a negative effect on filtering accuracy and stability, and may even lead to filter divergence. Aiming at the effect of outliers, an anti-outliers square-root cubature Kalman filter algorithm is proposed based on Bayes theorem by using the normalized polluted normal model. In this method, the cubature rule is used to calculate the mean value and variance of the nonlinear function, the normalized contaminated normal model is used to deal with the measurement error, and the variance matrix of the measurement prediction residual is adjusted in real time according to the posterior probability of outliers. The simulation results in the fixed single observer passive location model show that the proposed algorithm is robust and can eliminate the adverse effects of discrete or continuous outliers in the measurement noise.

TODA Passive location for UAV based on chicken swarm algorithm

Accurate positioning of drones is one of the key technologies for drone management and security. Aiming at the characteristics of the active emission of electronic radiation signals during the operation of UAVs, the passive positioning method based on multi-station time difference of arrival (TDOA) is studied, and the problem of solving the nonlinear equation of time difference of arrival passive location is transformed into an optimization problem of seeking the extreme value of the time difference likelihood function. The chicken swarm algorithm is used to search for the best positioning coordinates. The simulation results show that the chicken swarm algorithm can quickly and stably converge to the target position in time difference of arrival passive location system.

Coherent Integration Algorithm for Frequency‐Agile and PRF‐Jittering Signals in Passive Localization

Increasing pulses Coherent processing interval (CPI) can effectively improve the location parameters estimation performance in passive localization. However, for a moving emitter transmitting pulses with Frequency agile and Pulse repetition frequency jittering (FA-PRFJ) in a CPI, there will exist random phase, uneven sampling and Range migration (RM), which deteriorates the estimation performance of location parameters. Aiming at long-time coherent localization parameters estimation for the above emitter, this paper proposes a joint Range difference (RD) and Range rate difference (RRD) estimation algorithm. Firstly, the signal model of a moving emitter transmitting FA-PRFJ signal is constructed, and the influence of the FA and PRFJ on coherent integration is analyzed. Secondly, the random phase induced by FA is eliminated by frequency symmetric autocorrelation function operation. Then, RD and RRD can be coherently estimated after RM correction via the modified scaled non-uniform fast Fourier transform. This method can be efficiently implemented by Fast Fourier transform (FFT), inverse FFT and FFT-based chirp-z transform without any searching operation. Simulation results demonstrate that the proposed method has a better antinoise capability with a much lower computational complexity compared with several representative methods.

The Direction of Arrival Location Deception Model Counter Duel Baseline Phase Interferometer Based on Frequency Diverse Array

With the emergence and development of the passive localization, the radiation source is more visible for the location system which endangers their survival. Therefore, there is an urgent demand for the radio frequency (RF) stealth technology. An effective method to realize RF stealth is location deception, therefore, for the passive localization system, this paper proposes a direction of arrival (DOA) location deception method using the frequency diverse array (FDA) against the dual baseline phase interferometer. Since the direction-finding of the dual baseline phase interferometer is based on the received signal with fixed frequency, the FDA signal has a deception effect on the interferometer owing to the introduction of the small frequency increment. Considering the influence of the frequency increment sequence on the deception effect, we derive the optimizations of the DOA location deception via the average location deviation for the sampling time in the case of no noise and noise, respectively. Besides, considering the time dependency of the beam, we investigate the average SNR (ASNR) and the corresponding CRLB to verify the proposed method. Numerical examples and simulations show that the proposed method can counter the interferometer by realizing location deception.

A Multiagent Reinforcement Learning Solution for Geometric Configuration Optimization in Passive Location Systems

Passive location systems receive electromagnetic waves at one or multiple base stations to locate the transmitters, which are widely used in security fields. However, the geometric configurations of stations can greatly affect the positioning precision. In the literature, the geometry of the passive location system is mainly designed based on empirical models. These empirical models, being hard to track the sophisticated electromagnetic environment in the real world, result in suboptimal geometric configurations and low positioning precision. In order to master the characteristics of complicated electromagnetic environments to improve positioning performance, this paper proposes a novel geometry optimization method based on multiagent reinforcement learning. In the proposed method, agents learn to optimize the geometry cooperatively by factorizing team value function into agentwise value functions. To facilitate cooperation and deal with data transmission challenges, a constraint is imposed on the data sent from the central station to vice stations to ensure conciseness and effectiveness of communications. According to the empirical results under direct position determination systems, agents can find better geometric configurations than the existing methods in complicated electromagnetic environments.

Research on Positioning Accuracy of Passive Acoustic Positioning System Based on Feature Matching in Air

Passive acoustic localization is an important interdisciplinary research branch which combines acoustics, communication and signal processing. Passive acoustic positioning system, as an important part of battlefield sensing and monitoring system, has been paid more and more attention. Due to the disturbance of battlefield and other environments and the existence of measurement errors, the positioning accuracy often cannot meet the requirements. Passive acoustic positioning system, as an important part of battlefield sensing and monitoring system, has been paid more and more attention. Due to the disturbance of battlefield and other environments and the existence of measurement errors, the positioning accuracy often cannot meet the requirements. In multi-sensor passive location, the measured data of each array element is not only related, but also compensatory and redundant, so some people use the optimal data fusion algorithm as post-processing algorithm to improve the positioning accuracy. In this paper, the positioning accuracy of ground passive acoustic positioning system is analyzed based on feature matching method, and some useful conclusions are drawn.

ОПРЕДЕЛЕНИЕ КООРДИНАТ ИСТОЧНИКОВ ИЗЛУЧЕНИЯ В ПАССИВНЫХ РЛС АМПЛИТУДНЫМ МЕТОДОМ

An amplitude method for determining the radiation sources coordinates in passive radar isconsidered. An algorithm for calculating the coordinates of radiation sources in the passive modeof radar stations based on the reception of electromagnetic oscillations regularly damped in spaceby antennas in a collinear arrangement is synthesized. The mathematical relationships necessaryto determine the coordinates of the target are obtained by solving the corresponding trianglesformed by the antenna bases and the target. This made it possible to determine the location of theradiation source (target) by the point of intersection of the hyperbolas with the foci at the locationsof the receiving antennas. Analytical expressions are given for determining the coordinatesof targets in Cartesian and polar coordinate systems. The error analysis of the proposed algorithmwas carried out taking into account the method of indirect measurements. The case of uniformdistribution of noise over space, additively related to the signal and uncorrelated with it, is investigated.The formula for the root-mean-square deviation of the target range is obtained. The resultsof calculations of estimates of the absolute errors in determining the location of the radiationsource, which depend on its location on the plane and show that the absolute error in determiningthe location of the radiation source, is minimal near the origin and increases with distance from it,are presented. Synthesized and investigated algorithm, due to the simplicity of the hardware implementation,can be used in passive location systems both independently and in addition to thewidely used differential rangefinder method based on measuring the mutual time delays of thereceived signals.

Indoor Passive Localization With Channel State Information Using a Single Access Point

With the development of multi-antenna technology, the localization technology based on Wi-Fi has made progress. However, the single station passive localization is still confronted with the problem of low accuracy. Towards this end, this paper proposes an indoor localization system with a single Access Point (AP). Firstly, we present a novel Channel State Information (CSI) phase error correction method to eliminate the phase error that caused by the Symbol Time Offset (STO) and the Sample Frequency Offset (SFO) due to the lack of rigid time synchronization between Wi-Fi transceivers. Then, the Time of Flight (ToF) of the reflected signal is obtained by the discrete correlation estimation algorithm. On this basis, we propose a Regionalized Orthogonal Matching Pursuit (R-OMP) algorithm to estimate the Angle of Arrival (AoA) of the reflected signal. The proposed R-OMP algorithm reduces the complexity of the traditional Orthogonal Matching Pursuit (OMP) algorithm via reducing the search area. Finally, we get the location of the target by building a single station localization model. Experiments show that the median localization error of the system can reach 0.75 meters in a typical indoor environment, which is significantly improved compared with the previous single station localization system.