Retracted] Combined GNSS/SLAM‐Based High Accuracy Indoor Dynamic Target Localization Method

This paper proposes a method for determining a pedestrian’s indoor location based on an UWB (ultra-wideband) and vison fusion algorithm. Firstly, an UWB localization algorithm based on EKF (extended Kalman filter) is proposed, which can achieve indoor positioning accuracy of 0.3 m. Secondly, a method to solve scale ambiguity and repositioning of the monocular ORB-SLAM (oriented fast and rotated brief-simultaneous localization and mapping) algorithm based on EKF is proposed, which can calculate the ambiguity in real time and can quickly reposition when the vision track fails. Lastly, two experiments were carried out, one in a corridor with sparse texture and the other with the light brightness changing frequently. The results show that the proposed scheme can reliably achieve positioning accuracy on the order of 0.2 m; with the combination of algorithms, the scale ambiguity of monocular ORB-Slam can be solved, with the failed vision trace repositioned by UWB, and the positioning accuracy of UWB can be improved, making it suitable for pedestrian location in indoor environments with sparse texture and frequent light brightness changes.

Received ultra wideband (UWB) signals in most indoor environments are stochastic in nature, typically composed of abundant multi-path components with random attenuation and arrival time. A commonly adopted detection strategy is to use a chosen deterministic template to correlate the received signal segment by segment, and only those segments having significant cross-correlation with the template are selected, temporally synchronized and co-phased in their projections for coherent combining. Correlating a deterministic template with a random signal usually results in only partial energy captured by the receiver since a random signal is of multidimensions. In this paper, we construct a receiver with multiple orthogonal branches to fully capture the signal energy, in much the same way as a quadrature receiver does in conventional digital communications. The local reference signals are derived from the orthogonal expansion of UWB signals. The random projections of a UWB signal onto different orthogonal basis functions are non-coherently combined, leading to the concept of dimension diversity. Dimension diversity can be either used alone, or alongside a Rake receiver to further exploit path diversity. The error performance of proposed receivers is analyzed, and their superiority over the conventional ones is examined by numerical results based on real UWB data.

Since there are many interferences in the indoor environment, it is difficult to achieve the precise positioning of the mobile robot using a single sensor. This paper presents a position estimation and positioning error correction method of mobile robots based on multisensor data. The robot’s positioning sensor includes ultra-wideband (UWB) components, inertial measurement unit (IMU), and encoders. UWB multipath interference causes more ranging errors, which can be reduced by the correction equation after data fitting. The real-time coordinates of the UWB robot tag can be calculated based on multiple UWB anchor data and the least squares method. The coordinate data x c , y c are acquired by UWB positioning subsystem, and the velocity data x ̇ c , y ̇ c are collected by IMU together with encoders. The multisensor data continuously update Kalman filter and estimate robot position. In the positioning process, the positioning data of different sensors can be mutually corrected and supplemented. The results of UWB ranging correction experiments indicate that data fitting can improve the UWB positioning accuracy. In the multisensor positioning experiments, compared with a single sensor, the positioning method based on data fusion of UWB, IMU, and encoders has higher accuracy and adaptability. When UWB signals are interfered or invalid, other sensors can still work normally and complete the robot positioning process. The multisensor positioning method not only improves the robot positioning accuracy but also has stronger environmental adaptability.

The Back-Projection Algorithm (BPA) is a time-domain-matched filtering technique to form synthetic aperture radar (SAR) images. To produce high-quality BPA images, precise navigation data for the radar platform must be known. Errors in position, velocity, or attitude result in improperly formed images that are corrupted by shifting and blurring. The contribution of this paper is the development of analytical expressions that characterise the relationship between navigation errors and image formation errors from an inertial navigation point of view, where trajectory estimation errors in position, velocity, and attitude propagate through time and cause compounding errors in the vehicle state vector. These analytical expressions are verified via simulated image formation and real-data image formation.

Ultra wide-band (UWB) radio uses pulses of very short duration, thereby spreading the energy of the radio signal very thinly over several Gigahertz bandwidth. The power spectral density (PSD) of UWB signals consists of a continuous component and a discrete component. The discrete component presents greater interference to narrow-band communication systems than the continuous component. Multi-band UWB, recently proposed to IEEE 802.15.3a, divides the working spectrum into sub-bands, in each of which one waveform is used for the impulse. In this paper, we devise an efficient mechanism to suppress the discrete PSD component of multi-band UWB.

As a core element of high-precision machining equipment, the CNC rotary table directly determines machining accuracy. A geometric error model for a five-axis rotary table is developed, and error-source sensitivity is assessed. Based on multibody system theory, error sources and their transmission paths are identified, and a mapping from component errors to end-point position and attitude errors is established. Using the Morris global sensitivity analysis method, the contributions of 34 error sources to endpoint position and attitude errors are evaluated, revealing the dominant factors. The findings provide a theoretical basis for improving rotary-table accuracy and implementing error compensation.

Abstract. Determination of the timing and duration of paleoclimatic events is a challenging task. Classical techniques for time-series analysis rely too strongly on having a constant sampling rate, which poorly adapts to the uneven time recording of paleoclimatic variables; new, more flexible methods issued from Non-Linear Physics are hence required. In this paper, we have used Huang's Empirical Mode Decomposition (EMD) for the analysis of paleoclimatic series. We have studied three different time series of temperature proxies, characterizing oscillation patterns by using EMD. To measure the degree of temporal correlation of two variables, we have developed a method that relates couples of modes from different series by calculating the instantaneous phase differences among the associated modes. We observed that when two modes exhibited a constant phase difference, their frequencies were nearly equal to that of Milankovich cycles. Our results show that EMD is a good methodology not only for synchronization of different records but also for determination of the different local frequencies in each time series. Some of the obtained modes may be interpreted as the result of global forcing mechanisms.

Ultra-wideband (UWB) has received significant attention for applications in target positioning and wireless communications recently. The extremely short pulses in turn generate a very wide bandwidth and offer several advantages, such as large throughput, covertness, robustness to jamming, lower power, and coexistence with current radio services. UWB not only can transmit a huge amount of data over a short distance at very low power, but also has the capability to pass through physical objects that tend to reflect signals with narrow bandwidth.

This paper presents a new cooperative active target-tracking strategy for a team of double-integrator aerial vehicles equipped with 3-D range-finding sensors. Our strategy is active because it moves the vehicles along paths that minimize the combined uncertainty about the target's position. We propose a gradient-based control approach that encompasses the three major optimum experimental-design criteria and relies on the Kalman filter for estimation fusion. We derive analytical lower and upper bounds on the target's position uncertainty by exploiting the monotonicity property of the Riccati differential equation arising from the Kalman-Bucy filter. These bounds allow us to study the impact of sensors' accuracy and target's dynamics on the steady-state performance of our coordination algorithm. Finally, in the case that the position of the vehicles is not perfectly known, we introduce a more challenging problem, termed Active Cooperative Localization and Multi-target Tracking (ACLMT). In this problem, the vehicles move in the 3-D space in order to maximize the accuracy of their own position estimate and that of multiple moving targets.

Experimental study of through-wall human detection using ultra wideband radar sensors

Ultra Wideband (UWB) communications are steadily gaining acceptance worldwide, as theregulatory bodies of several countries define its frequency bands and rules of operation. OutofthemanytechnologiesavailabletogenerateanUWBsignal,twoofthemhavebeenfavored,in recent standardization activities, namely Orthogonal Frequency Division Multiplexing(OFDM) and Impulse Radio (IR). On one hand the IEEE 802.15.3a standard defines an UWBsignal based on OFDM, which consists of a large number of digitally-generated narrow sub-carriers. On the other hand, the IEEE 802.15.4a standard defines an UWB signal based onpulses of short duration, technology known as IR. In addition to these technologies includedin standards, other modulation schemes have been proposed for UWB, such as frequency-modulated UWB, or IR transmitting even shorter, sub-nanosecond pulses. The latter has theadvantage of providing very accurate ranging and localization estimates based on the time ofarrival, which can be measured with high precision thanks to the large signal bandwidth.In this chapter we will address the challenges encountered in the transmission and detec-tion of an Impulse Radio Ultra Wideband (IR–UWB) signal in environments with multipathpropagation. The main problem in such systems is that multipath produces time dispersionresulting in a very large number of resolvable paths at the receiver. We will argue that it isconsiderably difficult to design receivers that have both high efficiency in capturing the re-ceived signal energy and low complexity. The traditional approaches, such as correlating thereceived signal with a local template, or the RAKE Receiver, suffer from poor performancesince they have to lock to a single propagation path, or a small subset of them, respectively.Non-coherent receivers also fail to deliver the full potential of UWB; in particular, they do notprovide sufficient ranging accuracy.Motivated by this challenge, in this chapter we develop a receiver architecture based on afrequency domain filter bank. The receiver consists mainly of a stage of bandpass filters fol-

This paper introduces an active wave computing based algorithm for real-time robot navigation problem in dynamically changing environment and presents a real test setup with a roving robot. Travelling waves propagated on Cellular Neural Network (CNN) were employed by the system to find the shortest path as proposed in our previous work. The setup has a ceiling camera which captures an image of the top view of the platform at almost every one second. After preprocessing of this image, it is loaded to the network and an active wave which is generated by this network is propagated until the robot is covered by the wave. This method reduces the simulation time and speeds up the system. Although the robot is navigated in a dynamically changing environment, the system does not need to check the moving obstacles. The algorithm proposed here always generates a solution for the current state of the platform without the information about its past states. At every repetition of the algorithm, the target position is updated by the system if it is changed. Any change of the target position is immediately applied to the location of the wave source. Hence the system becomes working in dynamic environment with dynamic target.

Reverberation is an important interference factor that affects the active sonar detection effect generated by the sonar emission signal. Since the reverberation and the transmitted signal have almost the same frequency spectrum, suppressing this interference factor is the current research difficulty in the field of underwater acoustic target detection and position recognition. To solve this problem, this paper proposes a reverberation cancellation algorithm based on empirical mode decomposition (EMD). The design and verification ideas of the algorithm are as follows: First, the space-time-normal wave model is simulated and modeled. Secondly, the empirical modal decomposition of the echo data and the reverberation data is conducted. The difference between the various modes to obtain the characteristic information of the target signal. Finally, the experimental data verifies the effectiveness of the EMD reverberation cancellation algorithm proposed in this paper.

A novel nonlinear attitude error model based on square root cubature information filter (SR-CIF) is proposed aiming to speed up the convergence rate of DVL-aided strapdown inertial navigation system (SINS) in-motion initial alignment for autonomous underwater vehicle (AUV) when both large initial heading error and large initial level attitude errors exist The new nonlinear attitude error model and measurement model are applicable for initial alignment after a short-time coarse alignment with large attitude errors exist. The SR-CIF embeds the whole iterative process into the data structure framework of the information filtering, replacing the covariance matrix in the square root cubature Kalman filter (SR-CKF) with an information matrix which simplifies the filter initialization. The square root technology ensures the symmetry and positive definiteness of the information matrix with enhanced stability of the filter. The simulation and experimental results indicate that the proposed DVL-aided alignment filter is effective with large initial attitude errors. The rate of convergence and estimation accuracy of the SR-CIF is higher than that of the conventional SR-CKF with large attitude misalignments.

The non-contact monitoring of vital signs by radar has great prospects in clinical monitoring. However, the accuracy of separated respiratory and heartbeat signals has not satisfied the clinical limits of agreement. This paper presents a study for automated separation of respiratory and heartbeat signals based on empirical wavelet transform (EWT) for multiple people. The initial boundary of the EWT was set according to the limited prior information of vital signs. Using the initial boundary, empirical wavelets with a tight frame were constructed to adaptively separate the respiratory signal, the heartbeat signal and interference due to unconscious body movement. To verify the validity of the proposed method, the vital signs of three volunteers were simultaneously measured by a stepped-frequency continuous wave ultra-wideband (UWB) radar and contact physiological sensors. Compared with the vital signs from contact sensors, the proposed method can separate the respiratory and heartbeat signals among multiple people and obtain the precise rate that satisfies clinical monitoring requirements using a UWB radar. The detection errors of respiratory and heartbeat rates by the proposed method were within ±0.3 bpm and ±2 bpm, respectively, which are much smaller than those obtained by the bandpass filtering, empirical mode decomposition (EMD) and wavelet transform (WT) methods. The proposed method is unsupervised and does not require reference signals. Moreover, the proposed method can obtain accurate respiratory and heartbeat signal rates even when the persons unconsciously move their bodies.