Passive Localization Algorithm Research Articles

Passive Localization Algorithm for PRI-Staggered Radar Signal Based on RMD and NUFFT

Passive Localization Algorithm for PRI-Staggered Radar Signal Based on RMD and NUFFT

A Cooperative Target Localization Method Based on UAV Aerial Images

A passive localization algorithm based on UAV aerial images and Angle of Arrival (AOA) is proposed to solve the target passive localization problem. In this paper, the images are captured using fixed-focus shooting. A target localization factor is defined to eliminate the effect of focal length and simplify calculations. To synchronize the positions of multiple UAVs, a dynamic navigation coordinate system is defined with the leader at its center. The target positioning factor is calculated based on image information and azimuth elements within the UAV photoelectric reconnaissance device. The covariance equation is used to derive AOA, which is then used to obtain the target coordinate value by solving the joint UAV swarm positional information. The accuracy of the positioning algorithm is verified by actual aerial images. Based on this, an error model is established, the calculation method of the co-localization PDOP is given, and the correctness of the error model is verified through the simulation of the Monte Carlo statistical method. At the end of the article, the trackless Kalman filter algorithm is designed to improve positioning accuracy, and the simulation analysis is performed on the stationary and moving states of the target. The experimental results show that the algorithm can significantly improve the target positioning accuracy and ensure stable tracking of the target.

Autonomous Control System with Passive Positioning for Unmanned-Aerial-Vehicle-Assisted Edge Communication in 6G

UAVs can be deployed in many scenarios to provide various types of services via 6G edge communication. In these scenarios, it is necessary to obtain the position of the UAVs in a timely and accurate manner to avoid UAV collisions. In this paper, we consider improved passive localization algorithms aimed at reducing convergence time and adapting to extreme conditions. For the sake of reducing the complexity of signals and ensuring the reliability of receiving processes, we reconsidered the angle between arrival signals as the feature in positioning. Then, according to the characteristics of the positioning process, we draw on the cyclical process of the iterative greedy algorithm to construct the coding, destruction, and reorganization process to guide the movement of the UAV. Moreover, an improved Metropolis criterion is added to prevent falling into the local optimal solution. Finally, the proposed algorithm is verified in the simulation results. The results show that the algorithm can achieve precise positioning and excellent track planning within a small number of iterations, and it reduces the amount of information carried by the signal and convergence time compared with the traditional method.

An Efficient Hybrid Multi-Station TDOA and Single-Station AOA Localization Method

Multi-station passive localization algorithms based on hybrid Time Difference of Arrival (TDOA) and Angle of Arrival (AOA) have been thoroughly studied. But it usually requires each station to obtain the AOA of the source and relies on the precise station position. These conditions are generally not satisfied in practical applications. In addition, the geometry between the source and these stations also affects the localization accuracy. This paper studies the passive source localization problem based on multi-station TDOA and single-station AOA measurements. First, we propose a closed-form solution source localization method based on multi-station TDOA and single-station AOA measurements, which requires only the reference station used to calculate TDOA to be able to observe the AOA measurements of the source. It solves the problem of hybrid localization of different numbers of TDOA and AOA measurements. Theoretical analysis proves that the performance of the proposed method can reach the Cramer-Rao Lower Bound (CRLB) under small noise conditions. Then, we model the relationship between the position of each station relative to the source and the localization accuracy. Through the D-optimality criterion, we obtain the optimal geometry between multiple stations and the source Simulation results confirm the validity of these theories.

Passive Location for 5G OFDM Radiation Sources Based on Virtual Synthetic Aperture

Passive location technology has been greatly developed because of its low power consumption, long detection distance, good concealment, and strong anti-interference ability. Orthogonal frequency-division multiplexing (OFDM) is an efficient multi-carrier transmission technology, which is an important signal form of 5G communication. Researching passive locations for OFDM signals can realize the location of base stations, which is of great significance in the military. Space-borne passive location technology has a contradiction between wide coverage and high precision. Therefore, a single-satellite passive location algorithm for OFDM radiation sources based on the virtual synthetic aperture is proposed. The algorithm introduces virtual synthetic aperture technology, using antenna movement to accumulate data coherently over a long time period and synthesizing a long azimuth virtual aperture. In addition, it utilizes fast Fourier transform (FFT) to extract phase information at a specific frequency based on the multi-carrier modulation technology of the OFDM signal. Pilot technology of the communication system is used for phase compensation and noise reduction. Thus, the azimuth linear frequency modulation (LFM) signal containing the location information of the radiation source is obtained. The radiation source location can be obtained by range searching and azimuth focusing. Simulation results verify the effectiveness of the algorithm and show that the algorithm can realize high-precision and wide-coverage location for the OFDM radiation sources using a single antenna, turning the hardware structure into software to reduce the cost and complexity of the system.

A CLEAN-Based Synthetic Aperture Passive Localization Algorithm for Multiple Signal Sources

In passive localization, the received signal may come from multiple signal sources with different modulations. The modulations are usually resolved by high-order spectrum (HOS) processing. However, the processing causes multiple intersignal cross terms, resulting in a degradation of localization performance. To resolve the problem, this article proposes a CLEAN-based synthetic aperture passive positioning algorithm for multiple signal sources. The main idea is to locate the same modulated signal by focusing and then filtering out the located signal. Signals with the same modulation are located through the synthetic aperture passive localization method. Then, the located signals are removed and the remaining signals are recovered through inverse focusing. The multiple signals are focused, extracted, and separated according to the modulation. The effect of cross terms and multiplicative noise in the HOS is dramatically reduced. The simulation experiments show that the proposed algorithm can effectively improve localization accuracy.

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.

Estimation of sound source directions using a biological coupled sensor array with a multistage iteration method

Sound source localization (SSL) uses interaural time difference (ITD) or interaural intensity difference (IID) cues, and most of the methods based on ITD are limited by the aperture of array. However, a kind of parasitoid fly called Ormia ochracea with coupled ears has a remarkable ability to localize the calling of the host regardless of fly’s small body size. It is believed that the ITD is amplified by the auditory organ of Ormia. This paper proposes a biological coupled sensor array, which is generalized by the structure of Ormia’s ear. The array contains two pairs of microphones with a multistage iteration passive localization algorithm. Four microphones are symmetrically placed on the x- and y- axis of the array respectively. The proposed localization algorithm transforms the received signals to coupled signals by means of coupled calculator. The phase difference between two coupled signals is amplified. The ITD information is achieved based on the generalized cross-correlation function and amended by iteration of the coupled calculator and phase shift operation. To validate the effect of the proposed sensor array, experiments are conducted using sine waves from different angles of incidence. The ITDs between two microphones are amplified as expect. The localization results are achieved with localization error less than 0.5 degree with single-source cases in the experiment. The localization error is reduced by using this biological coupled sensor array with the multistage iteration method.

A Low SNR and Fast Passive Location Algorithm Based on Virtual Time Reversal

In order to achieve fast and accurate passive location of the target radiation source, this paper puts forward a Virtual Time Reversal Method Based on Uniform Linear Array (ULA-VTR) passive location algorithm. This framework employs the passive time reversal focusing theory, which does not to require the physical transmission of time reversal signals and does not require pre-estimation of the channel to realize passive direction finding. The above principle is utilized to perform the target radiation source signal through a uniform linear sensor array for detection. The article provides the signal model of linear time reversal array and the virtual retransmission process in the first place. Then utilizes ULA-VTR to establish a dual-station passive location framework and derives the coordinate transformation equation to find its observation angle, in addition, establishes the observation equation system for obtaining the site of the target radiation source. Ultimately, the paper analyses the computational complexity of the ULA-VTR algorithm and adopts Geometric Dilution of Precision(GDOP) to discuss the positioning performance. The experimental results indicate that the technique presented in this paper has higher positioning accuracy under the low SNR compared with other techniques.

SFM-MAC: Multichannel MAC Protocol for Underwater Sensor Networks Based on Underwater Passive Localization Mechanisms

Yang, Q.; Dong, W.; Xu, Z., and Huang, X., 2020. SFM-MAC: Multichannel MAC protocol for underwater sensor networks based on underwater passive localization mechanisms. Journal of Coastal Research, 36(6), 1332–1342. Coconut Creek (Florida), ISSN 0749-0208.With the continuous development of ocean research in recent years, underwater acoustic sensor networks, as the extension of terrestrial wireless sensor networks (WSNs), play an increasingly important role in ocean and coastal research. To solve the problem of spatial unfairness caused by spatiotemporal uncertainty of underwater acoustic signal propagation in underwater WSNs and improve the throughput of underwater networks, this paper proposes a slotted-floor multichannel medium access control (SFM-MAC) protocol based on a single transceiver. SFM-MAC uses an underwater passive localization algorithm to obtain location information and then transforms spatiotemporal information to avoid the synchronization of the whole network to reduce the spatiotemporal uncertainty of underwater networks; further, it uses request to send (RT)/channel announcement/channel listening/clear to send (CTS) handshaking mechanism to avoid multichannel hidden terminal problems and achieve fair access. In addition, the paper uses the Markov chain to construct the reservation model of control channels, and theoretically analyzes and calculates the theoretical throughput of the multichannel MAC protocol under the condition of reserved collision. Simulation experiments show that the network fairness index of SFM-MAC is 15% higher than that of traditional RTS/ CTS protocols. The effective network throughput of SFM-MAC in different business scenarios is 60–70% and 12–15% higher than the single-channel underwater MAC protocol SF acquisition multiple access and the multichannel underwater MAC protocol, respectively.

Passive Localization Algorithm for Remote Multitarget Localization Information

The development of wireless communication has led to the wide application of passive localization. Although passive localization has strong antijamming and concealment, its accuracy will be reduced due to false location. In order to solve the above problem, particle swarm optimization–back‐propagation (PSO‐BP) algorithm was used to improve the accuracy of passive location model in this study, and it was compared with the traditional BP algorithm and extreme learning machine (ELM) algorithm by simulation. The results showed that the coordinate error calculated by the PSO‐BP neural network was smaller than that of the BP neural network and ELM algorithms, and the error fluctuation was smaller; with the increase of the number of multitarget localization, the average error and positioning time of the BP algorithm gradually increased, while the average positioning error and positioning time of the PSO‐BP algorithm basically remained stable, smaller than the BP algorithm. © 2020 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Robust mixed source localization in WSN using swarm intelligence algorithms

Passive localization and classification algorithms for mixed near-field and far-field sources have mainly been investigated for antenna arrays with regular or symmetrical geometry. However, these algorithms may not be applicable to wireless sensor networks, where spatially distributed sensor nodes form an array of random geometry. This paper proposes squared error norm of residual fitting error matrix (SRFEM) cost function for mixed source localization, which does not place any constraint on the geometry of antenna array. The proposed cost function is optimized using computationally simple swarm intelligence (SI) algorithms. Convergence performance of three SI algorithms, i.e., particle swarm optimization, Whale optimization, and Grey Wolf optimization is investigated to obtain estimates of the source location parameters with a minimal number of iterations or computational complexity. In order to provide robustness for the SRFEM cost function, this paper proposes an impulse denoising technique based on the long-short term memory recurrent neural network (LSTM-RNN). Simulation results confirm that, in the presence of AWGN, the proposed SRFEM cost function outperforms existing techniques based on the uniform linear array and approaches the theoretical Cramer-Rao lower bound even for a fewer number of snapshots. Further, simulation analysis also reveals that LSTM-RNN provides better mixed localization performance under impulse noise as compared to the bounded noise covariance approach at high signal-to-noise values.

Angle of Arrival Passive Location Algorithm Based on Proximal Policy Optimization

Location technology is playing an increasingly important role in urban life. Various active and passive wireless positioning technologies for mobile terminals have attracted research attention. However, positioning signals experience serious interference in high-density residential areas or in the interior of large buildings. The main type of interference is that caused by non-line-of-sight (NLOS) propagation. In this paper, we present a new method for optimizing the angle of arrival (AOA) measurement to obtain high accuracy location results based on proximal policy optimization (PPO). PPO is a new family of policy gradient methods for reinforcement learning, which can be used to adjust the sampling data under different environments using stochastic gradient ascent. Therefore, PPO can correct the NLOS propagation errors to produce a clear AOA measurement data set without building an offline fingerprinting database. Then, we used the least square method to calculate the location. The simulation result shows that the AOA passive location algorithm based on PPO produced more accurate location information.

Passive Localization Algorithm for Spaceborne SAR Using NYFR and Sparse Bayesian Learning

Passive Localization Algorithm for Spaceborne SAR Using NYFR and Sparse Bayesian Learning

Autonomous Passive Localization Algorithm for Shared Bikes

Opportune and accurate position information of the ubiquitous shared bikes can help users rapidly find the available bicycle around them. In order to remove the professional locating device of bicycles and reduce industrial costs, a passive positioning method for shared bikes named PSA is proposed in this paper with the support of activity recognition based on deep learning. First, an action recognition module based on LSTM algorithm is constructed through training three-axis accelerator data collected by commercial mobile phone to distinguish user's riding state from other actions like walking or running; then, the GPS module embedded in the user's mobile phone will record the location when the state changes from riding to non-riding, which is regarded as the bike's return location. Furthermore, considering the variety of state changing during riding procedure, a duration threshold mechanism is added for the sake of fault tolerance processing. Several simulations and test-bed experiments were conducted to verify the feasibility and practicality of the proposed method, where the accuracy of activity recognition in practical data set can reach more than 95% and the detection time in public data set is less than 0.2s. In addition, this method reduces phone operations and enhances the convenience of the experience.

Radio Tomographic Imaging Based On Quartile Outliers Filter and PCA

Device-free wireless localization (DFL) that does not need the target equipped with any device is a new technique which could estimate the location by analyzing its shadowing effect on surrounding wireless links. This technique neither requires the target to be equipped with any device nor involves privacy concerns, which makes it an attractive and promising technique for many emerging smart applications. Therefore, how to characterize the influence of human behaviors is the key question. However, the distance estimation based on received signal strength indicator (RSSI) is easily affected by the temporal and spatial variance due to the multipath effect, which contributes to most of the estimation errors in current radio tomographic imaging (RTI) system. This paper proposes a device-free passive localization algorithm based on Quartile Outliers Filter (QOF) and Principal Component Analysis (PCA). In this work, we explore and exploit lower quartile and upper quartile of the original RSSI to identify and remove the outliers caused by the multipath effect or hardware facilities, and apply PCA to extract the most useful features. Extensive experiments performed in a clutter indoor laboratory and a building courtyard with 20 wireless nodes demonstrate that the outstanding performance of the proposed scheme.

Robust Relative Fingerprinting-Based Passive Source Localization via Data Cleansing

Recently, source localization is becoming a major research focus. The majority of the existing studies focus on the design of received signal strength (RSS)-based localization methods. However, when in the face of complicated environments with severe fading, RSS-based localization methods achieve relatively inferior accuracy performance, compared with fingerprinting-based localization methods. Nevertheless, traditional fingerprinting-based localization methods are subject to the condition that the source transmit power is known, which cannot be directly used in passive localization cases where the sensing nodes do not have the prior information on the source. In addition, the received sensing data may contain errors and then affect the location precision due to various abnormal conditions, such as device failure and malicious cases. In this paper, we propose a novel robust relative fingerprinting-based passive localization algorithm via a data cleansing approach. First, we figure out the fingerprint correlations property and introduce a new relative fingerprint framework. The key idea is that by exploring the correlations between the source fingerprint and the reference fingerprint database, the correction factors can be achieved to apply the fingerprint idea into the passive localization case. Second, we formulate a generalized modeling of the abnormal data in localization problem and propose a data cleansing approach which utilizes the sparse property of the abnormal data. Based on this, the negative influence of abnormal data can be further eliminated. Third, considering the sparse property of the source position, we use the sparse Bayesian learning in the matching process for the purpose of achieving more precise estimated source position. Simulation results demonstrate that the proposed algorithm achieves higher accuracy performance in passive source localization in terms of eliminating the abnormal data impairment.

A passive location algorithm based on TDOA and FDOA of dual-satellite in the condition of unfiled rank

A passive location algorithm based on TDOA and FDOA of dual-satellite in the condition of unfiled rank

The Robust Passive Location Algorithm for Maneuvering Target Tracking

With the advantages such as high security and far responding distance, the passive location has a broad application in military and civil domains such as radar and aerospace. However, most of the current passive location methods are based on the framework of the probability theory and cannot be used to deal with fuzzy uncertainty in the passive location systems. Though the fuzzy Kalman filter can be used in the uncertainty systems, it could not deal with the abrupt change of state like the maneuvering target which will lead to the filter divergence. Therefore, in order to track the maneuvering target in the fuzzy passive system, we proposed a robust fuzzy extended Kalman filter based on the orthogonality principle and the fuzzy filter in the paper. Conclusion can be made based on the simulation result that this new approach is more precise and more robust than the fuzzy filter.

The Improvement of Passive Location Algorithm Based on Time Sequence Estimation

The multi station time difference sequence of arrival method is a kind of passive location method for pulse radiation source. It takes the advantage of mass time difference information obtained in a very short time to locate the radiation source. This paper models the time difference sequence of arrival process and estimates the time difference according to the characteristic of the time difference sequence. The simulation results show that, the localization after time sequence estimation algorithm can achieve higher positioning accuracy.