Doppler Frequency Measurements Research Articles

Microwave photonics doppler speed measurement based on sagnac loops and four-wave mixing effect in a highly nonlinear fiber

Photonic radars are increasingly being developed and offer a promising replacement for traditional RF radars. They feature higher precision, and smaller size compared to the current microwave radars. One important part of a moving target indicating (MTI) radar is the Doppler shift measurement used to measure the radial velocity of a moving target. Therefore, for any photonic radar operating at MTI mode, it is necessary to have a Doppler measurement subsystem. In this paper, a microwave photonic Doppler frequency measurement system is conceived and implemented for this purpose specifically. The operation is based on making a Doppler shift-dependent yet low-frequency voltage component. It is all-optical and hence has the potential to be integrated into many electronic warfare systems. This feature not only makes the system independent of any sophisticated electrical device but also makes the measurement time lower than that of the electrical counterparts. The specific design presented here provides a much better stability compared to the recent works. An error as low as 0.012 Hz at a 10 GHz radar frequency was obtained, and the system performance was demonstrated up to 40 GHz, at which a 4.75 Hz error was recorded.

Fall Feature Enhancement and Fusion Using the Stockwell Transform With Dual mmWave Radars

Fall detection based on radar technology has recently attracted increasing attention. Human motions are often multidirectional and have complex trajectories. Numerous time–frequency representation methods have been used to extract micro-Doppler fall features. Peak-to-peak Doppler frequency measurements are limited due to the relatively weak energy in the high-Doppler-frequency area. Fall features are not obvious when using a single millimeter-wave (mmWave) radar if human motions are nearly perpendicular to the radar. A fall feature enhancement and fusion method using the Stockwell transform with dual mmWave radars is proposed in this article. Two mmWave radars synchronously collect human motions in different directions, and the micro-Doppler features of these human motions are obtained by using the Stockwell transform. To enhance the features obtained for recognition, the ridgelines extracted from the two radars are fused in the time–frequency domain. In addition, a model trained by a convolutional neural network (CNN) is used for fall recognition; 960 fall motions and 720 nonfall motions are collected from 60 volunteers. The experimental results show that the proposed method can produce a recognition accuracy of up to 94.14%, which is beneficial for the realization of an indoor fall detection system.

Fast Three‐Dimensional Search Algorithm to Improve the Carrier Tracking Performance During the Entry, Descent, and Landing (EDL) Phase of Mars Probes

AbstractCarrier tracking for highly dynamic and extremely weak signals is challenging, including during the entry, descent, and landing phase of Mars rovers and the soft landing of lunar probes. In this study, a novel frequency detector is designed based on the frequency maximum likelihood estimator, and a frequency‐locked loop algorithm is constructed. The frequency‐locked loop is suitable for tracking Doppler shifts with large first and second derivatives. Compared with the Jet Propulsion Laboratory frequency maximum likelihood estimator, the computation time is significantly reduced. The simulation results illustrate the effectiveness of the proposed algorithm. This method provides a good method for Doppler frequency measurement in deep space exploration, especially when tracking highly dynamic and extremely weak signals.

A Bayesian Network for the Classification of Human Motion as Observed by Distributed Radar

In this article, a statistical model of human motion as observed by a network of radar sensors is presented where knowledge on the position and heading of the target provides information on the observation conditions of each sensor node. Sequences of motions are estimated from measurements of instantaneous Doppler frequency, which captures informative micromotions exhibited by the human target. A closed-form Bayesian estimation algorithm is presented that jointly estimates the state of the target and its exhibited motion class which are described by a hidden Markov model. To correct errors in the estimated motion class distribution introduced by faulty modeling assumptions, calibration of the probability distribution and measurement likelihood is performed by isotonic regression. It is shown, by modeling sensor observation conditions and by isotonic calibration of the measurement likelihood that a cognitive resource management system is able to increase classification accuracy by 5 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\%}$</tex-math></inline-formula> –10 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\%}$</tex-math></inline-formula> while utilizing sensor resources in accordance with defined mission objectives.

A Novel Carrier Tracking Approach for GPS Signals Based on Gauss–Hermite Kalman Filter

In a conventional GPS receiver, the carrier tracking system is the key stage that keeps the receiver locked to the radio navigation parameters (RNPs) of the received signal. The most commonly used approaches to the tracking system are phase lock loop (PLL), frequency lock loop (FLL), and FLL-assisted PLL. The main limitation of the above approaches is that their performance deteriorates when working with weak signals and in harsh environments. In recent years, Kalman filter (KF)-based tracking loop architectures have gained increased attention due to their robust and better performance compared with conventional architectures. In this paper, a novel Gauss–Hermite Kalman filtering-based carrier tracking algorithm is proposed for static and moving receivers with weak GPS signals. The performance of the proposed algorithm is compared with two other approaches: extended Kalman filter (EKF) and unscented Kalman filter (UKF). Simulations were conducted using a software-defined GPS simulator and software device radio (SDR) modules. A comparative analysis of the tracking methods demonstrated that the proposed tracking method shows a better performance and improves the tracking sensitivity and capability under weak signal conditions as compared with EKF- and UKF-based tracking methods. In addition, the results show that the proposed approach improves the Doppler frequency measurement accuracy under dynamic operation conditions.

Tracking an Underwater Object with Unknown Sensor Noise Covariance Using Orthogonal Polynomial Filters.

In this manuscript, an underwater target tracking problem with passive sensors is considered. The measurements used to track the target trajectories are (i) only bearing angles, and (ii) Doppler-shifted frequencies and bearing angles. Measurement noise is assumed to follow a zero mean Gaussian probability density function with unknown noise covariance. A method is developed which can estimate the position and velocity of the target along with the unknown measurement noise covariance at each time step. The proposed estimator linearises the nonlinear measurement using an orthogonal polynomial of first order, and the coefficients of the polynomial are evaluated using numerical integration. The unknown sensor noise covariance is estimated online from residual measurements. Compared to available adaptive sigma point filters, it is free from the Cholesky decomposition error. The developed method is applied to two underwater tracking scenarios which consider a nearly constant velocity target. The filter’s efficacy is evaluated using (i) root mean square error (RMSE), (ii) percentage of track loss, (iii) normalised (state) estimation error squared (NEES), (iv) bias norm, and (v) floating point operations (flops) count. From the simulation results, it is observed that the proposed method tracks the target in both scenarios, even for the unknown and time-varying measurement noise covariance case. Furthermore, the tracking accuracy increases with the incorporation of Doppler frequency measurements. The performance of the proposed method is comparable to the adaptive deterministic support point filters, with the advantage of a considerably reduced flops requirement.

Radial velocity of ocean surface current estimated from SAR Doppler frequency measurements—a case study of Kuroshio in the East China Sea

Radial velocity of ocean surface current estimated from SAR Doppler frequency measurements—a case study of Kuroshio in the East China Sea

A novel Doppler frequency measurement method based on the closed-loop signal correlation for deep space exploration

In deep space exploration, it is necessary to improve the accuracy of frequency measurement to meet the requirements of precise orbit determination and various scientific studies. A phase detector is one of the key modules that restricts the tracking performance of phase-locked loop (PLL). Based on the phase relationship between adjacent signals in the time domain, a novel phase detector is presented to replace the arctangent phase detector. The new PLL, which is a closed loop signal correlation algorithm, shows good performance in tracking signals with high precision and the tracking accuracy of frequency of 1 second integration is close to Cramer-Rao lower bound (CRLB) when setting proper parameters. Actual data processing results further illustrate the excellent performance of the novel PLL.

A novel high precision Doppler frequency estimation method based on the third-order phase-locked loop

In deep space exploration, many engineering and scientific requirements require the accuracy of the measured Doppler frequency to be as high as possible. In our paper, we analyze the possible frequency measurement points of the third-order phase-locked loop (PLL) and find a new Doppler measurement strategy. Based on this finding, a Doppler frequency measurement algorithm with significantly higher measurement accuracy is obtained. In the actual data processing, compared with the existing engineering software, the accuracy of frequency of 1 second integration is about 5.5 times higher when using the new algorithm. The improved algorithm is simple and easy to implement. This improvement can be easily combined with other improvement methods of PLL, so that the performance of PLL can be further improved.

Distributed Multistatic Sky-Wave Over-The-Horizon Radar Based on the Doppler Frequency for Marine Target Positioning

Maritime safety issues have aroused great attention, and it has become a difficult problem to use the sky-wave over-the-horizon radar system to locate foreign targets or perform emergency rescue quickly and timely. In this paper, a distributed multi-point sky-wave over-the-horizon radar system is used to locate marine targets. A positioning algorithm based on the Doppler frequency is proposed, namely, the two-step weighted least squares (2WLS) method. This algorithm first converts the WGS-48 geodetic coordinates of the transceiver station to spatial rectangular coordinates; then, introduces intermediate variables to convert the nonlinear optimization problem into a linear problem. In the 2WLS method, four mobile transmitters and four mobile receivers are set up, and the Doppler frequency is calculated by transmitting and receiving signals at regular intervals; it is proven that the 2WLS algorithm has always maintained a better positioning accuracy than the WLS algorithm as the error continues to increase with a certain ionospheric height measurement error and the Doppler frequency measurement error. This paper provides an effective method for the sky-wave over-the-horizon radar to locate maritime targets.

Vehicle Length Estimation Using an LTE Transmitter Combined With a Software-Defined Receiver

Unobtrusive determination of an intruding vehicle's length is useful for threat assessments in perimeter surveillance, law enforcement, and antiterrorism applications. We describe a passive radar architecture wherein a broadcast signal from a cellular mobile communication system radio transmitter is bistatically scattered by a moving vehicle and received and processed in a software-defined radio platform. Measurement of the maximum Doppler frequency and the target viewing time combined with the known geometry of the measurement scenario permits the estimation of the target vehicle's length. Good agreement is observed between actual and estimated lengths for various vehicle types.

Development of an optimization method for measuring the Doppler frequency of a packet taking into account the fluctuations of the initial phases of its radio pulses

The necessity of estimating the decrease in the accuracy of measuring the informative parameters of a radar signal in real conditions of its propagation and reflection has been substantiated. The results of the estimation determine the requirements for optimizing this measurement to achieve the required efficiency. A numerical analysis of the decrease in the accuracy of measuring the Doppler frequency of a coherent packet is presented, depending on the statistical characteristics of fluctuations of the initial phases of its radio pulses. Expressions are given for calculating the fluctuation component of the measurement error of radio pulse packet frequency for various coefficients of interpulse correlation of phase fluctuations. An assessment is made of the possibility of increasing the accuracy of Doppler frequency measurement, which can be ensured by statistical optimization of the algorithm for time-frequency processing of a given radar signal by taking into account its phase fluctuations. The conditions for the multiplicative influence of phase fluctuations of radio pulses of the received packet are substantiated, which determine the efficiency of optimization of Doppler frequency measurement. Based on the results of the study, an optimization method for measuring the Doppler frequency of the packet taking into account fluctuations in the initial phases of its radio pulses is proposed. The accuracy of Doppler frequency measurement under the influence of both the internal noise of the radar receiver and the correlated phase fluctuations of its radio pulses is estimated. The efficiency of optimization of measuring the Doppler frequency of the packet is estimated taking into account fluctuations of the initial phases of its radio pulses by means of computer simulation. It is proved that, under the influence of phase fluctuations, the accuracy of Doppler frequency measurement can be increased due to the performed optimization from 1.86 to 6.29 times. This opens the way to improving the existing algorithms for measuring the higher time range derivatives to improve the quality of tracking complex maneuvering aerodynamic objects. This explains the importance and usefulness of the work for the radar theory.

Comparative study of Kalman filter-based target motion analysis by incorporating Doppler frequency measurements

Abstract Target motion analysis is a key requirement of autonomous and self-driving machines like drones and robots. However, with strict weight limits, the aerospace industry is always on the hunt for simpler and lighter sensing solutions. Continuous-wave Doppler radars are the simplest radars that can easily obtain a target’s relative velocity using the Doppler shift in the received wave. However, these radars cannot provide the target’s range. In this work, we address the problem of obtaining target’s range and velocity by incorporating Doppler frequency measurements from a simple continuous wave Doppler radar. To this end, we find out the movement patterns and maneuvers that an observer can make to converge to the target’s location. After presenting the observability requirements, we design and compare various non-linear Kalman filter-based target trackers. We experimented with different simulation scenarios to compare the tracking results with bearings-only, frequency-only, and bearings-frequency measurement sets. In our analysis, Unscented Kalman Filter with bearings-frequency measurements performed best. Experiments show that an observer can locate the target accurately within 10 cm by incorporating Doppler frequency measurements. Moreover, it also reduced the convergence time to a fraction of a second.

An Emergency Positioning System Fusing GEO Satellite Doppler Observation and INS for SOTM

This article presents a newly invented emergency positioning system deployed on a satellite communication on-the-move (SOTM) platform, which can extend the SOTM communication function to the positioning function. Inherent to the SOTM system's high reliability, this emergency positioning system can effectively estimate the vehicle's position by fusing a geosynchronous orbit (GEO) satellite Doppler frequency observation and inertial navigation system (INS) of a SOTM-mounted moving vehicle when the global navigation satellite system (GNSS) is unavailable. It is a great challenge to use GEO Doppler frequency measurement with weak observability to correct the INS's cumulative error to estimate the position of the vehicle. In this article, the traditional inertial measurement unit (IMU) pre-integration model is improved to meet the needs of large-scale and high-precision navigation. In addition, we use graph optimization (GO) to estimate all states in the trajectory, which can effectively improve the observability of the error and reduce the nonlinear influence. We evaluated the system on a Ku-band SOTM platform, and the experimental results indicate that the system can achieve a convergent positioning accuracy of tens of meters. This method can provide emergency positioning in the case of GNSS interference.

Адаптивный ансцентный фильтр Калмана для слежения за GPS-сигналами при неизвестной и изменяющейся во времени ковариации шума

A new adaptive unscented Kalman filter (AUKF) is proposed to estimate the radio navigation parameters of a GPS signal tracking system in noisy environments and on a highly dynamic object. The experimental results have shown that the proposed AUKFbased method improves the GPS tracking margin by approximately 8 dB and 3 dB as compared to the conventional algorithm and the KF-based tracking, respectively. At the same time, the accuracy of Doppler frequency measurements increases as well.

Новый подход в создании GPS-системы позиционирования в динамических условиях приема с высоким уровнем шумов

This paper discusses the possibility of creating an automated positioning system, including various search objects by means of engineering intelligence, based on the methodology of tracking GPS signals. The traditional tracking methodology is analyzed and a more efficient one is proposed based on a modification of the Kalman filter for environments with low signal-to-noise ratio and in high user dynamic conditions. To achieve tracking of the GPS signals, the data is processed using MATLAB program. A comparative analysis showed that the proposed tracking method improves the tracking performance by 7 dB compared to the traditional tracking and overcomes bit synchronization losses. In addition, the proposed method improves the accuracy of Doppler frequency measurements under dynamic conditions.

Iterative Semidefinite Relaxation for Geolocation of Uncooperative Radars Using Doppler Frequency Measurements

In this article, we propose a novel method utilizing iterative semidefinite relaxation for geolocation of stationary uncooperative radars. In our scenario, the geolocation is to be performed in a receiver located on a moving platform. The proposed method uses frequency measurements that are Doppler-shifted due to the platform motion. First, constructed nonconvex maximum likelihood cost function for position estimation is relaxed to a convex optimization problem by applying linearization to range variables. Then, at each iteration of the method, carrier frequency and position of the radar are estimated jointly. Conducted experiments show that a few iterations are enough for convergence to accurate estimates. The proposed method is computationally less expensive compared to traditional techniques, which require extensive grid search procedures in either position or carrier frequency parameter space. In the experiments, the performance of the proposed method is compared to the state-of-the-art techniques and the Cramer-Rao lower bound (CRLB). It is observed that the proposed method attains the CRLB at low noise levels while still providing accurate solutions at high noise cases. Furthermore, it is also seen that platform-radar geometry has an impact on the proposed method's performance. If the radar lies in the convex hull of the receiver path, the proposed method performs significantly better due to improvement in the linearization of range values.

Underwater 3D Doppler-Angle Target Tracking with Signal Time Delay.

The traditional target tracking is a process of estimating the state of a moving target using measurement information obtained by sensors. However, underwater passive acoustic target tracking will confront further challenges, among which the system incomplete observability and time delay caused by the signal propagation create a great impact on tracking performance. Passive acoustic sensors cannot accurately obtain the target range information. The introduction of Doppler frequency measurement can improve the system observability performance; signal time delay cannot be ignored in underwater environments. It varies with time, which has a continuous negative impact on the tracking accuracy. In this paper, the Gauss–Helmert model is introduced to solve this problem by expanding the unknown signal emission time as an unknown variable to the state. This model allows the existence of the previous state and current state at the same time, while handling the implicit equations. To improve the algorithm accuracy, this paper further takes advantage of the estimated state and covariance for the second stage iteration and propose the Gauss–Helmert iterated Unscented Kalman filter under a three-dimensional environment. The simulation shows that the proposed method in this paper shows superior estimation accuracy and more stable performance compared with other filtering algorithms in underwater environments.

Underwater surveillance in non-Gaussian noisy environment

The aim of this paper is to evaluate the performance of different filtering algorithms in the presence of non-Gaussian noise environment for tracking underwater targets, using Doppler frequency and bearing measurements. The tracking using Doppler frequency and bearing measurements is popularly known as Doppler-bearing tracking. Here the measurements, that is, bearings and Doppler frequency, are considered to be corrupted with two types of non-Gaussian noises namely shot noise and Gaussian mixture noise. The non-Gaussian noise sampled measurements are assumed to be obtained (a) randomly throughout the process and (b) repeatedly at some particular time samples. The efficiency of these filters with the increase in non-Gaussian noise samples is discussed in this paper. The performance of filters is compared with that of Cramer-Rao Lower Bound. Doppler-bearing extended Kalman filter and Doppler-bearing unscented Kalman filter are chosen for this work.

Radar Doppler Frequency Measurements—Accuracy Versus SNR in Practical Processors

Radar performs Doppler frequency measurements to determine the range rate of detected targets. Doppler helps to resolve moving targets and stationary clutter located at the same spatial cell. Doppler is also used to reduce the azimuth resolution in SAR. Most often, the two-way nature of radar and the prevailing clutter, require reception and processing at low-signal-to-noise ratio (SNR). However, there are also high-SNR radar scenes, e.g., a single target in front of clear background. Doppler is also used in one-way applications such as navigation systems or transponder carrying radar targets. In those applications, the SNR can be relatively high. With a tutorial flavor, the article discusses and compares three frequency measuring methods that fit one or both scenes—low SNR and high SNR. The discussion revisits early applications of frequency measurements such as the TRANSIT satellite navigation system and the pulse-pair technique of spectral measurement used in weather radar.