Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userA Track-Before-Detect Algorithm for UWB Radar Sensor Networks
249
0
0.0
(
0
)
Added by
Enrico Paolini
Publication date
2021
fields
Electronic Engineering
and research's language is
English
Ask ChatGPT about the research
No Arabic abstract
Precise localization and tracking of moving non-collaborative persons and objects using a network of ultra-wideband (UWB) radar nodes has been shown to represent a practical and effective approach. In UWB radar sensor networks (RSNs), existence of strong clutter, weak target echoes, and closely spaced targets are obstacles to achieving a satisfactory tracking performance. Using a track-before-detect (TBD) approach, the waveform obtained by each node during a time period are jointly processed. Both spatial information and temporal relationship between measurements are exploited in generating all possible candidate trajectories and only the best trajectories are selected as the outcome. The effectiveness of the developed TBD technique for UWB RSNs is confirmed by numerical simulations and by two experimental results, both carried out with actual UWB signals. In the first experiment, a human target is tracked by a monostatic radar network with an average localization error of 41.9 cm with no false alarm trajectory in a cluttered outdoor environment. In the second experiment, two targets are detected by multistatic radar network with localization errors of 25.4 cm and 19.7 cm, and detection rate of the two targets is 88.75%, and no false alarm trajectory.
rate research
Read More
We propose a scalable track-before-detect (TBD) tracking method based on a Poisson/multi-Bernoulli model. To limit computational complexity, we approximate the exact multi-Bernoulli mixture posterior probability density function (pdf) by a multi-Bernoulli pdf. Data association based on the sum-product algorithm and recycling of Bernoulli components enable the detection and tracking of low-observable objects with limited computational resources. Our simulation results demonstrate a significantly improved tracking performance compared to a state-of-the-art TBD method.
This paper presents a novel algorithm to incorporate orbital parameters into radar ambiguity function expressions by extending the standard ambiguity function to match Keplerian two-body orbits. A coherent orbital matched-filter will maximise a radars sensitivity to objects in orbit, as well as provide rapid initial orbit determination from a single detection. This paper then shows how uncued detection searches can be practically achieved by incorporating radar parameters into the orbital search-space, especially for circular orbits. Simulated results are compared to results obtained from ephemeris data, showing that the orbital path determined by the proposed method, and the associated radar parameters that would be observed, match those derived from the ephemeris data.
Earth Trojan Asteroids are an important but elusive population that co-orbit with Earth at the L4 and L5 Lagrange points. There is only one known, but a large population is theoretically stable and could provide insight into our solar systems past and present as well as planetary defense. In this paper, we present the results of an Earth Trojan survey that uses a novel shift-and-stack detection method on two nights of data from the Dark Energy Camera. We find no new Earth Trojan Asteroids. We calculate an upper limit on the population that is consistent with previous searches despite much less sky coverage. Additionally, we elaborate on previous upper limit calculations using current asteroid population statistics and an extensive asteroid simulation to provide the most up to date population constraints. We find an L4 Earth Trojan population of NET < 1 for H = 13.93, NET < 7 for H = 16, and NET < 938 for H = 22.
This paper unveils the importance of intelligent reflecting surface (IRS) in a wireless powered sensor network (WPSN). Specifically, a multi-antenna power station (PS) employs energy beamforming to provide wireless charging for multiple Internet of Things (IoT) devices, which utilize the harvested energy to deliver their own messages to an access point (AP). Meanwhile, an IRS is deployed to enhance the performances of wireless energy transfer (WET) and wireless information transfer (WIT) by intelligently adjusting the phase shift of each reflecting element. To evaluate the performance of this IRS assisted WPSN, we are interested in maximizing its system sum throughput to jointly optimize the energy beamforming of the PS, the transmission time allocation, as well as the phase shifts of the WET and WIT phases. The formulated problem is not jointly convex due to the multiple coupled variables. To deal with its non-convexity, we first independently find the phase shifts of the WIT phase in closed-form. We further propose an alternating optimization (AO) algorithm to iteratively solve the sum throughput maximization problem. To be specific, a semidefinite programming (SDP) relaxation approach is adopted to design the energy beamforming and the time allocation for given phase shifts of WET phase, which is then optimized for given energy beamforming and time allocation. Moreover, we propose an AO low-complexity scheme to significantly reduce the computational complexity incurred by the SDP relaxation, where the optimal closed-form energy beamforming, time allocation, and phase shifts of the WET phase are derived. Finally, numerical results are demonstrated to validate the effectiveness of the proposed algorithm, and highlight the beneficial role of the IRS in comparison to the benchmark schemes.
Millimeter-wave radars are being increasingly integrated into commercial vehicles to support advanced driver-assistance system features. A key shortcoming for present-day vehicular radar imaging is poor azimuth resolution (for side-looking operation) due to the form factor limits on antenna size and placement. In this paper, we propose a solution via a new multiple-input and multiple-output synthetic aperture radar (MIMO-SAR) imaging technique, that applies coherent SAR principles to vehicular MIMO radar to improve the side-view (angular) resolution. The proposed 2-stage hierarchical MIMO-SAR processing workflow drastically reduces the computation load while preserving image resolution. To enable coherent processing over the synthetic aperture, we integrate a radar odometry algorithm that estimates the trajectory of ego-radar. The MIMO-SAR algorithm is validated by both simulations and real experiment data collected by a vehicle-mounted radar platform.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
