Direction of arrival (DOA) estimation is a fundamental problem in signal processing and has applications in various fields such as radar, sonar, and acoustics. In this article, we propose a method for DOA estimation using the received power at each sensor. The method is based on the directional sensitivity of the sensor elements at various frequencies. We model the directional sensitivity using a Fourier series (FS) model, where the parametric model enables Cramér-Rao lower-bound (CRLB) computations. The FS model is estimated from measurements of a wideband noise signal. To estimate the DOA, the measured power profile is compared to the FS model using the least-squares (LS) method. The proposed power-based method offers several advantages over classical time-delay methods, particularly in allowing arbitrarily small arrays and still handling broadband signals. Additionally, it enables low-rate sampling, which simplifies hardware requirements and significantly reduces processor load. In numerical evaluations with a microphone array and natural sound sources, we still benchmark our method against state-of-the-art time-delay methods. Real-world experiments show promising results, performing on par with the best of the other evaluated methods for all natural signals, despite relying on significantly less information. A key benefit is robustness against array size limitations. By utilizing the received signal power instead of time delays or phase information, the method enables small arrays with great DOA resolution. Furthermore, outdoor data collected a year after calibration confirms its robustness, demonstrating consistent performance over time.

Due to recent events that have demonstrated the vulnerabilities of global navigation satellite systems (GNSS) there has been an increased interest in alternative methods for localization. One traditional alternative method is terrain-aided navigation (TAN), where a platform localizes itself by measuring the terrain elevation and comparing it to a digital elevation map (DEM). While single-agent TAN has been extensively studied, multi-agent TAN remains less explored. This paper addresses the multi-agent TAN problem with a focus on its properties. We formulate a weighted least squares (WLS) estimator for computing a snapshot solution to the problem and formulate a CramérRao Lower Bound (CRLB) to evaluate it. Using the expressions for the estimator and the CRLB we are able to highlight some insightful properties of the problem. The findings are verified in a simulation study where we evaluate the performance with respect to the altitude sensor accuracy, the group formation accuracy, the number of agents and their formation. Notably, we observe that the solution is relatively insensitive to errors in agent position, suggesting that low-accuracy inertial navigation systems and distance sensors are sufficient for determining their positions. Increasing the number of agents beyond a few seems to have a large effect on both the efficiency and robustness of the estimator, which lessens as the number of agents increases. However, increasing the number of agents does not compensate for poor altitude sensor quality. Additionally, while spatial separation between agents is important for effective map utilization, further separation beyond a certain point does not enhance performance. These findings provide design guidelines for multi-agent TAN systems and identify areas for further research.

Accurate positioning, navigation and timing (PNT) is important for military and civilian applications alike. In recent years navigation using signals of opportunity (SOPs) has received increased interest as a complement or alternative to Global Navigation Satellite Systems (GNSS). We previously proposed a localization framework using opportunistic digital television signals. In it, a navigator localizes itself aided by a stationary base station at a known location, using two time difference of arrival (TDOA) measurements and one twoway ranging (TWR) measurement. In this work we implement and evaluate the proposed framework using real measurements, and based on this propose an extended model which includes the difference in clock bias between the navigator and the base station. The experimental results show that the framework can achieve a root mean square error (RMSE) in absolute position of less than 50 m provided that at least three TDOA measurements are used (with or without TWR), and sometimes if only two TDOA measurements are used in combination with a TWR measurement. We also show experimentally that the transmitter clocks are stable enough for the navigator to extract the TDOA measurements relying only on its own current measurements and previously collected data by the base station. Thus, by replacing the TWR measurement with a third TDOA measurement, the base station need not be active or communicate with the navigator during the localization. In scenarios where communication is undesirable or impossible, this can be advantageous.

This paper investigates time synchronization in distributed sensor networks using the primary synchronization signal (PSS) in Long-Term Evolution (LTE). Two LTE-based time synchronization methods with receiver-to-receiver characteristics have been evaluated in simulations, the passive Scalable Wireless Network Synchronization (SWINS) and the active Reference Broadcast Synchronization (RBS). In addition, small scale hardware experiments were conducted for SWINS. Time synchronization is crucial for many applications, such as power grid monitoring, communication systems, and sensor data fusion. Global Navigation Satellite Systems (GNSS) are currently the state of the art for time synchronization in distributed wireless sensor networks. However, GNSS is vulnerable to jamming and spoofing, which requires alternative methods, e.g., using signals of opportunity. Both evaluated methods achieve accuracy comparable to GNSS in Matlab simulations with high SNR. SWINS performs better in synchronized LTE networks while RBS is superior in unsynchronized networks, which means that the LTE base station transmissions are not synchronous. The disturbance and sensitivity analysis indicates that joint clock offset and position estimation is preferable to sole clock offset estimation when the receiver position uncertainty exceeds 5 and 7 meters for SWINS and RBS respectively. The hardware experiments, using real experimental data, verify the simulation results by showing promising results and potential for real-world application.

Ensuring sufficiently accurate models is crucial in target tracking systems. If the assumed models deviate too much from the truth, the tracking performance might be severely degraded. While the models are usually defined using multivariate conditions, the measures used to validate them are most often scalar-valued. In this article, we propose matrix-valued measures for both offline and online assessment of target tracking systems. Recent results from Wishart statistics, and approximations thereof, are adapted and it is shown how these can be incorporated to infer statistical properties for the eigenvalues of the proposed measures. In addition, we relate these results to the statistics of the baseline measures. Finally, the applicability of the proposed measures is demonstrated using two important problems in target tracking—distributed track fusion design and filter model mismatch detection.

Efficient localization in underwater sensor networks faces challenges due to limited bandwidth, energy constraints, and hardware complexity. Traditional systems separate sensing and communication, often resulting in inefficient resource usage. To address this, integrated sensing and communication (ISAC) has emerged, leveraging shared waveforms for both functions. This paper investigates the feasibility of using communication-centric waveforms for underwater localization. Specifically, we evaluate the performance of super-permutated frequency shift keying and multiple frequency shift keying signals using a Cramér-Rao lower bound framework in a simplified bistatic scenario. Simulations incorporate temporally correlated autoregressive AR(1) noise and varying signal-to-noise ratio levels to assess localization accuracy. A comparative analysis with a traditional sonar waveform, a linear frequency modulated signal, highlights the potential of communication signals for dual-purpose ISAC applications in underwater environments.

Road roughness significantly affects vehicle vibrations and ride quality. We introduce a Kalman filter (KF)-based method for estimating road roughness in terms of the international roughness index (IRI) by fusing inertial and speed measurements, offering a cost-effective solution for pavement monitoring. The method involves system identification on a physical vehicle to estimate realistic model parameters, followed by KF-based reconstruction of the longitudinal road profile to compute IRI values. It explores IRI estimation using vertical and lateral vibrations, the latter more common in modern vehicles. Validation on 230 km of real-world data shows promising results, with IRI estimation errors ranging from 1% to 10% of the reference values. However, accuracy deteriorates significantly when using only lateral vibrations, highlighting their limitations. These findings demonstrate the potential of KF-based estimation for efficient road roughness monitoring.

Over the past two and a half decades, covariance intersection (CI) has provided a means for robust estimation in scenarios where the uncertainty information is incomplete. Estimation in distributed and decentralized data fusion (DDF) settings is typically characterized by having nonzero cross-correlations between the estimates to be merged. Mean-square-error (MSE) optimal estimators, such as the Kalman filter (KF), are limited to data fusion problems where these cross-correlations are fully known. Keeping track of cross-correlations is unfortunately not always possible. To quantify confidence in the estimate's uncertainty, the concept of conservativeness has been introduced. A conservative estimator guarantees that the computed covariance matrix is not smaller than the actual covariance matrix. It turns out that CI guarantees conservativeness for any degree of unknown cross-correlations as long as the estimates to be fused are conservative. It should be noted that, in the CI literature, the notion of covariance consistency is often used to characterize conservativeness. In this work, we use the latter term.

High-definition map with accurate lane-level information is crucial for autonomous driving, but the creation of these maps is a resource-intensive process. To this end, we present a cost-effective solution to create lane-level roadmaps using only the global navigation satellite system (GNSS) and a camera on customer vehicles. Our proposed solution utilizes a prior standard-definition (SD) map, GNSS measurements, visual odometry, and lane marking edge detection points, to simultaneously estimate the vehicle's 6D pose, its position within a SD map, and also the 3D geometry of traffic lines. This is achieved using a Bayesian simultaneous localization and multi-object tracking filter, where the estimation of traffic lines is formulated as a multiple extended object tracking problem, solved using a trajectory Poisson multi-Bernoulli mixture (TPMBM) filter. In TPMBM filtering, traffic lines are modeled using B-spline trajectories, and each trajectory is parameterized by a sequence of control points. The proposed solution has been evaluated using experimental data collected by a test vehicle driving on highway. Preliminary results show that the traffic line estimates, overlaid on the satellite image, generally align with the lane markings up to some lateral offsets.