scholarly journals Direct Position Determination with Single Sensor Based on Signal Periodicity

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Cheng Wang ◽  
Ding Wang ◽  
Lu Gao ◽  
Bin Yang
Keyword(s):  
Sensor Array ◽  
Target Location ◽  
Iterative Solution ◽  
Joint Estimation ◽  
Target Localization ◽  
Position Determination ◽  
Wireless Signal ◽  
Single Sensor ◽  
Localization Performance ◽  
Oscillator Instability

Due to practical limitations on size and cost, aerial vehicles generally cannot equip complicated sensors to form sensor array for target localization. In this paper, we investigate the direct position determination (DPD) of stationary source via single moving sensor. First, we analyze artificial signal structure and construct the DPD model with the frame periodicity of artificial signal. The model incorporates Doppler information extracted from both transformation frames and adjacent samples into target localization. Secondly, we consider the effect of oscillator instability and present an iterative solution for joint estimation of target location and phase noise caused by oscillator imperfection. The proposed technique fully exploits periodic structure of artificial wireless signal, which leads to significant enhancement in localization performance. Both theoretical analysis and simulations are presented to confirm its effectiveness.

Antenna Placement Analysis for Coherent MIMO Radar with Phased Synchronization Errors

2011 ◽  
Vol 121-126 ◽  
pp. 3689-3693
Author(s):  
Peng Ma ◽  
Qing Song Zhou ◽  
Jian Yun Zhang
Keyword(s):  
Target Location ◽  
Phase Synchronization ◽  
Error Variance ◽  
Mimo Radar ◽  
Joint Estimation ◽  
Synchronization Errors ◽  
Localization Performance ◽  
Cramer Rao Bound ◽  
Antenna Placement

Phase synchronization errors are practically inevitable, so in this paper the quantitative tool to asses the effect of antenna placement for localization performance with phased synchronization errors is provided. The lower bound of mean-square error (MSE) is set by the hybrid Cramer-Rao bound (HCRB) for the joint estimation of the target location and phase synchronization errors at the receivers. It is shown that HCRB follow up to a lower limit, determined by the synchronization error variance and the number of transmit and receive sensors. For uniform antenna distributing, symmetrical placement is optimal, and all forms give exactly the same performance. The localization performance of nonuniform arrays when the antennas are spread out as much as possible is a little better than symmetrical placement at high SNR. Simulation results verify the correctness of conclusions.

THE COVARIANCE OF EXTERNAL SHOT NOISE AT A SENSOR ARRAY

Geophysics ◽  
1973 ◽  
Vol 38 (5) ◽  
pp. 957-958
Author(s):  
T. S. Edrington
Keyword(s):  
Power Spectrum ◽  
Bessel Function ◽  
Shot Noise ◽  
Sensor Array ◽  
Random Noise ◽  
Zero Order ◽  
Random Combination ◽  
Order Bessel Function ◽  
Single Sensor ◽  
Cross Power Spectrum

Noise (of seismic and other origins) is often modeled as a shot process, i.e., as a random combination of wavelets. Specifically, if X is the random noise process and g is the deterministic waveform of the wavelet, then [Formula: see text]where the [Formula: see text] are Poisson points in time. One useful result (derived by Backus et al., 1964) under this model is that if the wavelets are propagating across a sensor array, and if the wavelet origins are uniformly distributed in azimuth, then the cross‐power spectrum for a pair of sensors is the product of the power spectrum at a single sensor and a zero‐order Bessel function. In the notation of Backus et al., [Formula: see text]

scholarly journals Support Vector Regression for Mobile Target Localization in Indoor Environments

Sensors ◽  
2022 ◽  
Vol 22 (1) ◽  
pp. 358
Author(s):  
Satish R. Jondhale ◽  
Vijay Mohan ◽  
Bharat Bhushan Sharma ◽  
Jaime Lloret ◽  
Shashikant V. Athawale
Keyword(s):  
Support Vector Regression ◽  
Target Location ◽  
Indoor Localization ◽  
Target Localization ◽  
Superior Performance ◽  
Support Vector ◽  
Generalized Regression Neural Network ◽  
Indoor Environments ◽  
Localization Scheme ◽  
Mobile Target

Trilateration-based target localization using received signal strength (RSS) in a wireless sensor network (WSN) generally yields inaccurate location estimates due to high fluctuations in RSS measurements in indoor environments. Improving the localization accuracy in RSS-based systems has long been the focus of a substantial amount of research. This paper proposes two range-free algorithms based on RSS measurements, namely support vector regression (SVR) and SVR + Kalman filter (KF). Unlike trilateration, the proposed SVR-based localization scheme can directly estimate target locations using field measurements without relying on the computation of distances. Unlike other state-of-the-art localization and tracking (L&T) schemes such as the generalized regression neural network (GRNN), SVR localization architecture needs only three RSS measurements to locate a mobile target. Furthermore, the SVR based localization scheme was fused with a KF in order to gain further refinement in target location estimates. Rigorous simulations were carried out to test the localization efficacy of the proposed algorithms for noisy radio frequency (RF) channels and a dynamic target motion model. Benefiting from the good generalization ability of SVR, simulation results showed that the presented SVR-based localization algorithms demonstrate superior performance compared to trilateration- and GRNN-based localization schemes in terms of indoor localization performance.

scholarly journals Bionic target localization method based on range-azimuth joint dictionary

2021 ◽  
Vol 39 (3) ◽  
pp. 471-476
Author(s):  
Changsheng Yang ◽  
Hangbo Li ◽  
Liping Hu ◽  
Hong Liang
Keyword(s):  
Joint Estimation ◽  
Target Localization ◽  
Sparse Signal ◽  
High Angle ◽  
Localization Method ◽  
Big Brown Bats ◽  
Sonar System ◽  
Random Array ◽  
Sparse Signal Representation ◽  
Array Aperture

The traditional underwater sonar system usually achieve high angle resolution by increasing array aperture and the number of array elements, but this method will inevitably lead to complex system and high cost. Given that big brown bats have obtained surprisingly high resolution using a simple system, this paper proposes a bionic target localization method. First, a range-azimuth joint dictionary was constructed based on the bionic system of multi-harmonic emission and double random array reception. Then, the coherence characteristic of the dictionary was analyzed and the range and azimuth of the target were estimated, and at last the experimental verification was completed. The results show that the bionic range-azimuth joint estimation based on sparse signal representation can achieve high-precision target localization under the condition of echo high aliasing.

Design and optimization of sensor array for magnetic gradient tensor system

Sensor Review ◽  
2019 ◽  
Vol 40 (1) ◽  
pp. 121-129
Author(s):  
Shengzhi Chen ◽  
Minghua Zhu ◽  
Qing Zhang ◽  
Xuesong Cai ◽  
Bo Xiao
Keyword(s):  
Sensor Array ◽  
Design Method ◽  
Optimization Strategy ◽  
Gradient Tensor ◽  
Content Type ◽  
Magnetic Gradient ◽  
Design And Optimization ◽  
Spatial Design ◽  
Magnetic Target ◽  
Localization Performance

Purpose The differential magnetic gradient tensor system is usually constructed from the three-axis magnetic sensor array. While the effects of measurement error, sensor performance and baseline distance on localization performance of such systems have been widely reported, the research about the effect of spatial design of sensor array is less presented. This paper aims to provide a spatial design method of sensor array and corresponding optimization strategy to localization based on magnetic tensor gradient to get the optimum design of the sensor array. Based on the results of simulation, magnetic localization systems constructed from the proposed array and the traditional array have been built to carry out a localization experiment. The results of experiment have verified the effectiveness of magnetic localization based on the proposed array. Design/methodology/approach The authors focus on the localization of the magnetic target based on magnetic gradient by using three-axis magnetic sensor array and combine a design method with corresponding optimization strategy to get the optimum design of the sensor array. Findings This paper provides an array design and optimization method for magnetic target localization based on magnetic gradient to improve the localization performance. Originality/value In this paper, the authors focus on the magnetic localization based on magnetic gradient by using three-axis magnetic sensors and study the effect of the spatial design of sensor array on localization performance.

scholarly journals Multitarget Parameter Estimation of Monopulse Radar Based on RJ-MCMC Algorithm

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Jian Gong ◽  
Yiduo Guo ◽  
Hui Yuan ◽  
Qun Wan
Keyword(s):  
Parameter Estimation ◽  
Target Location ◽  
Estimation Method ◽  
Observation Time ◽  
Joint Estimation ◽  
Estimation Methods ◽  
Mcmc Algorithm ◽  
Target Number ◽  
Real Time Processing ◽  
Location Parameters

A multiple parameter estimation method based on RJ-MCMC for multiple nondiscernible targets is proposed in this paper. Different from the traditional estimation methods, the proposed method can simultaneously complete the joint estimation of the target number and the target location parameters. More importantly, the method proposed in this chapter is applicable to many situations with different power and nondistinguishable target. The simulation results show that the method proposed in this chapter requires less observation time to obtain similar and even better estimation performance than the ML-MDL method, which is of great significance for real-time processing.

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