Data Fusion Center Research Articles

Investigation on position and attitude estimation and control of manipulator based on machine vision

With the development of science and technology, people have higher and higher requirements for robots. The application of robots in industrial production is also increasing, and there are more applications in people’s lives. Therefore, robots must have a better ability to receive and process the external environment. Therefore, visual servo system appears. Pose estimation is a major problem in the current vision system. It has great application value in positioning and navigation, target tracking and recognition, virtual reality and motion estimation. Therefore, this paper put forward the research of robot arm pose estimation and control based on machine vision. This paper first analyzed the technology of machine vision, and then carried out experiments. The accuracy and stability of the two methods for robot arm pose estimation were compared. The experimental results showed that when the noise of Kalman’s centralized data fusion method was 1 pixel, the maximum error of the X-axis angle was only 0.55, and the average error was 0.02. In Kalman’s distributed data fusion method, the average error of X-axis displacement was 0.06, and the maximum value was 17.66. In terms of accuracy, Kalman’s centralized data fusion method was better. In terms of stability, Kalman’s centralized data fusion method was also better. However, in general, these two methods had very good results, and could accurately control the position and posture of the manipulator.

The Impact of Measurement Passing in Sensor Network Measurement Selection

This article considers a set of sensors, which, as a group, are tasked with taking measurements of the environment and sending a small subset of the measurements to a centralized data fusion center, where the measurements will be used to estimate the overall state of the environment. The sensors’ goal is to send the most informative set of measurements so that the estimate is as accurate as possible. This problem is formulated as a submodular maximization problem, for which there exists a well-studied greedy algorithm, where each sensor sequentially chooses a set of measurements from its own local set and communicates its decision to the future sensors in the sequence. In this work, sensors can additionally share measurements with one another, in order to augment the decision set of each sensor. We explore how this increase in communication can be exploited to improve the results of the nominal greedy algorithm. Specifically, we show that this measurement passing can improve the quality of the resulting measurement set by up to a factor of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$n+1$</tex-math></inline-formula> , where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$n$</tex-math></inline-formula> is the number of sensors.

In-Field Measurement and Methodology for Modeling and Validation of Precipitation Effects on Solid-State LiDAR Sensors

There is a strong demand for high fidelity sensor models which are capable of simulating realistic automotive sensor perception of Radar, LiDAR and camera sensors in real time, in order to validate advanced driving assistance functions like lane change assist (LCA), automated emergency breaking (AEB), or even path planning virtually. For central data fusion the sensor models need to deliver realistic, artificial sensor raw data. In especially, optical sensors are heavily influenced by precipitation, fog and sun irradiance. However, most LiDAR models lack the capability of replicating the impact of specific weather characteristics. Furthermore, there is – in contrast to numerous publicly available LiDAR datasets – a strong lack of datasets which are annotated with quantitative weather data such as the precipitation rate or meteorological visibility in order to develop and validate such models. Hence, within this work, an automated infrastructure is setup to measure time-correlated LiDAR and weather data to develop and calibrate weather models. The effects of varying precipitation rates on an automotive Flash LiDAR system is demonstrated based on in-field measurements and a respective modeling methodology is developed. Based on the in-field measurement data, raw data LiDAR models can be developed which augment virtual LiDAR data obtained from raytracing capable driving simulation suits as well as real data, recorded under ideal weather conditions.

LDA 기반 분산 레이다 시스템을 활용한 손동작 분류 알고리즘 개발 및 성능 분석

In this study, we propose a linear discriminant analysis (LDA)-based classification algorithm for distributed radars that share their data at a data fusion center over wireless channels. Instead of sharing high-dimensional micro-Doppler signature (MDS) data over the resource-limited backhaul, each radar applies the LDA algorithm to reduce the dimension of the data. Because the dimension-reduced MDS data is a vector with complex values, it is expressed as a real-valued probability vector using the softmax processing method to reduce the quantization error. A probability vector with a sum of 1 is transmitted to the fusion center via a pyramid vector quantization (PVQ) scheme. To improve the classification performance at the fusion center, bit resources were adaptively allocated based on the separability of the distributed radars. The proposed algorithm was assessed using MNIST(modified national institute of standards and technology database) data as well as hand gesture data measured using a USRP(universal software radio peripheral)-based test bed.

Design of Remote Real-Time Monitoring and Control Management System for Smart Home Equipment Based on Wireless Multihop Sensor Network

With the intensification of the pace of modern urban life and the increase of work pressure, people have higher requirements for the safety and comfort of home. However, the rapid development of Internet of Things technology, wireless communication technology, embedded technology, and data fusion technology has made this desire possible, thus making intelligent and safe. Using wireless sensor network, intelligent home system can realize the real-time collection of object location and state information, remote operation of home equipment, as well as the monitoring of the information of personnel activities, etc. However, how to efficiently analyze and integrate these information to be applied to the intelligent home system is one of the problems that need to be solved; further study of the smart home system of personal behavior identifies fire monitoring and remote monitoring of mining applications. Such information collection and integration of technology and method are given based on wireless sensor network transfer of key technologies; intelligent home security monitoring system based on Bayesian networks is proposed and a card. According to the theoretical fire monitoring method, and based on the Bayesian belief network personal behavior identification model, this paper provides the theory and method for the remote safety monitoring and control in the intelligent home environment. In this paper, based on the multisensor data fusion of smart home security, real-time monitoring alarm platform is built. Through the multisensor detection module for the home fire gas human infrared monitoring, the detected data is transmitted to the data fusion center for processing; processed results are uploaded to the cloud through the Internet. When abnormal conditions occur, users can log in to the mobile phone client to view abnormal alarm results.

Optimized data rate allocation for dynamic sensor fusion over resource constrained communication networks

AbstractThis article presents a new method to solve a dynamic sensor fusion problem. We consider a large number of remote sensors which measure a common Gauss–Markov process. Each sensor encodes and transmits its measurement to a data fusion center through a resource restricted communication network. The communication cost incurred by a given sensor is quantified as the expected bitrate from the sensor to the fusion center. We propose an approach that attempts to minimize a weighted sum of these communication costs subject to a constraint on the state estimation error at the fusion center. We formulate the problem as a difference‐of‐convex program and apply the convex‐concave procedure (CCP) to obtain a heuristic solution. We consider a 1D heat transfer model and a model for 2D target tracking by a drone swarm for numerical studies. Through these simulations, we observe that our proposed approach has a tendency to assign zero data rate to unnecessary sensors indicating that our approach is sparsity‐promoting, and an effective sensor selection heuristic.

Nonlinear distributed state estimation on the Stiefel manifold using diffusion particle filters

Many relevant problems in engineering demand the estimation of dynamic system states that are constrained to non-Euclidean spaces. Furthermore, it is generally advantageous to implement estimation methods in a network of cooperating nodes instead of relying on a single data fusion center. With this in mind, we propose in this paper two new distributed particle filtering methods to track the states of a dynamic system that evolves on the Stiefel manifold, which arises naturally when the states are subject to nonlinear orthogonal constraints. The proposed algorithms are based on the Random Exchange and Adapt-then-Combine diffusion techniques, and perform parametric approximations using the matrix von Mises-Fisher distribution to compress information exchanged between network nodes. Estimates of the state are then determined via an empirical averaging method that approximates the centroid (Karcher mean) of the particle set. As we verify via numerical simulations, the proposed methods show improved performance compared to previous Particle and Extended Kalman filters designed for Euclidean state variables, and compared to a non-cooperative particle filtering algorithm.

Distributed Two-Dimensional MUSIC for Joint Range and Angle Estimation with Distributed FMCW MIMO Radars.

To estimate range and angle information of multiple targets, FMCW MIMO radars have been exploited with 2D MUSIC algorithms. To improve estimation accuracy, received signals from multiple FMCW MIMO radars are collected at the data fusion center and processed coherently, which increases data communication overhead and implementation complexity. To resolve them, we propose the distributed 2D MUSIC algorithm with coordinate transformation, in which 2D MUSIC algorithm is operated with respect to the reference radar’s coordinate at each radar in a distributed way. Rather than forwarding the raw data of received signal to the fusion center, each radar performs 2D MUSIC with its own received signal in the transformed coordinates. Accordingly, the distributed radars do not need to report all their measured signals to the data fusion center, but they forward their local cost function values of 2D MUSIC for the radar image region of interest. The data fusion center can then estimate the range and angle information of targets jointly from the aggregated cost function. By applying the proposed scheme to the experimentally measured data, its performance is verified in the real environment test.

An Authentication Based Approach for Prevention of Spectrum Sensing Data Falsification Attacks in Cognitive Radio Network

This is the era of Intelligent Cognitive Radio Network (CRN) technology that provides the available spectrum with efficient utilization. Cognitive Radio (CR) technology must promise to allow interference-free spectrum access by users. The paper discusses the several attacks and motives of attacks. The authentication mechanism role to prevent the attacks for hassle-free spectrum utilization is demonstrated. In this paper, resolving the cognitive network security issues by the authentication mechanism and the methods and need of authentication is discussed. This paper addresses the research challenges in the way of securing the CRN and countermeasures in CRN security strategies. Spectrum sensing data falsification (SSDF) attack is most disruptive in which the malicious users degrade the decision-making process by sending the false sensing reports to data fusion center’s thus preventing honest users from utilizing the spectrum. In this paper, to counter the SSDF attack, the trust-based security mechanism is demonstrated to authenticate the honest users and it is observed that the proposed framework in the MATLAB environment is efficient and able to detect malicious users. CR technology is the strategy applied to the spectrum to make it efficient for wireless communication. The strategy is an intelligent way to access the spectrum as it can learn its environment and make decisions by easy adaptation of operating parameters. The initial step is to sense the spectrum available further steps are spectrum decision making, spectrum management, and spectrum mobility. The network is vulnerable to various attacks on spectrum sensing and policy protocols which lead to disturbing functionality of CR technology. The defence mechanism based on public-key cryptography is proposed in which the Primary User (PU) is authenticated by appending signature provided to PU signal. Authentication with a tag to the primary users is another perspective proposed. CRN technology should provide integrity, confidentiality and authenticity to the users.

An LSTM-based distributed scheme for data transmission reduction of IoT systems

The growth of the number of connected devices in Internet of Things (IoT) systems causes a huge increase in network traffic. Thus, there is a significant demand for systems that can predict the measurements of the distributed IoT-based applications to mitigate the increasing network traffic. Existing methods utilized a distributed scheme, i.e., dual prediction schemes (DPS), to achieve this task. The idea of this scheme is based on deploying a predictive model on the data sources (i.e., sensors) and the fusion center in a distributive manner. The state-of-the-art results can be simply reached using predictive models, e.g., adaptive filters. Deep learning-based approaches are not well utilized to address this problem. In this context, we proposed a distributed scheme utilizing fog and edge computing technologies. The proposed DPS includes an LSTM predictive model to reduce the data transmission instances of connected IoT devices; it is called LSTM-DPS. In addition, we proposed an updating mechanism that updates the LSTM model according to a set of tracking parameters that observes the model behavior during the deployment due to the changes of data properties through time. The proposed updating mechanism guarantees that the deployed LSTM model is identical at the data source and fusion center. The LSTM-DPS is evaluated using two real datasets. The obtained results show that the LSTM-DPS outperforms state-of-the-art methods in terms of communication reduction ratios.

Proactive spectrum monitoring with spectrum monitoring data transmission in dynamic spectrum sharing network: Joint design of precoding and antenna selection

A proactive spectrum monitoring and spectrum monitoring data (SMD) transmission coexistence system is investigated in dynamic spectrum sharing network, where a spectrum monitor (SM) aims to monitor the spectrum information from the electromagnetic signal sent by the suspicious transmitter to the suspicious destination by using a portion of its antennas and transmit its SMD to the spectrum data fusion center by using the rest antennas. The SMD transmission of the SM can also be employed to jam the suspicious destination to realise the effective proactive spectrum monitoring. Thus, the precoding and antenna selection scheme are jointly designed at the SM to maximise the sum of the achievable spectrum monitoring rate and the SMD transmission rate. The scenario that the SM can assess the statistical channel state information of the suspicious links based on the spectrum information obtained from the spectrum database as the spectrum management node is then considered. The spectrum monitoring/SMD transmission success probability is also derived, and a trade-off between them is further revealed. Simulation results show that the proposed schemes can obtain higher sum rate of the spectrum monitoring and the SMD transmission, which also verifies its effectiveness and above “security-reliability” trade-off.

Linear Discriminant Analysis-Based Motion Classification Using Distributed Micro-Doppler Radars with Limited Backhaul.

In this paper, we propose a cooperative linear discriminant analysis (LDA)-based motion classification algorithm for distributed micro-Doppler (MD) radars which are connected to a data fusion center through the limited backhaul. Due to the limited backhaul, each radar cannot report the high-dimensional data of a multi-aspect angle MD signature to the fusion center. Instead, at each radar, the dimensionality of the MD signature is reduced by using the LDA algorithm and the dimensionally-reduced MD signature can be collected at the data fusion center. To further reduce the burden of backhaul, we also propose the softmax processing method in which the distances of the sensed MD signatures from the centers of clusters for all motion candidates are computed at each radar. The output of the softmax process at each radar is quantized through the pyramid vector quantization with a finite number of bits and is reported to the data fusion center. To improve the classification performance at the fusion center, the channel resources of the backhaul are adaptively allocated based on the classification separability at each radar. The proposed classification performance was assessed with synthetic simulation data as well as experimental data measured through the USRP-based MD radar.

Dependent Randomization in Parallel Binary Decision Fusion

We consider a parallel decentralized detection system employing a bank of local detectors (LDs) to access a commonly-observed phenomenon. The system makes a binary decision about the phenomenon, accepting one of two hypotheses (H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> (“absent” ) or H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> (“present” )). The kth LD uses a local decision rule to compress its local observations y <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</sub> into a binary local decision u <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</sub> ; u <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</sub> =0 if the kth LD accepts H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> and u <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</sub> = 1 accepts H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> . The kth LD sends its decision u <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</sub> over a noiseless dedicated channel to a Data Fusion Center (DFC). The DFC combines the local decisions it receives from n LDs (u <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> ,u <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> ,...,u <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sub> ) into a single binary global decision u <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> (u <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0=</sub> 0 for accepting H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> or u <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> =1 for accepting H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> ). If each LD uses a single deterministic local decision rule (calculating u <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</sub> from the local observations y <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</sub> ) and the DFC uses a single deterministic global decision rule (calculating u <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> from n local decisions), the team receiver operating characteristic (ROC) curve is in general non-concave. The system's performance under a Neyman-Pearson criterion may then be suboptimal in the sense that a mixed strategy may yield a higher probability of detection when the probability of false alarm is constrained not to exceed a certain value, α>0. Specifically, a “dependent randomization” detection scheme can be applied in certain circumstances to improve the system's performance by making the ROC curve concave. This scheme requires a coordinated and synchronized action between the DFC and the LDs. In this study, we specify when dependent randomization is needed, and discuss the proper response of the detection system if synchronization between the LDs and the DFC is temporarily lost.

A Novel Distributed Sensor Fusion Algorithm for RSSI-Based Location Estimation Using the Unscented Kalman Filter

We address the Bayesian sensor fusion approach for distributed location estimation in the wireless sensor network. Assume each sensor transmits local calculation of target position to a fusion center, which then generates under a Bayesian framework the final estimated trajectory. We study received signal strength indication-based approach using the unscented Kalman filter for each sensor to compute local estimation, and propose a novel distributed algorithm which combines the soft outputs sent from selected sensors and computes the approximated Bayesian estimates to the true position. Simulation results demonstrate that the proposed soft combining method can achieve similar tracking performance as the centralized data fusion approach. The computational cost of the proposed algorithm is less than the centralized method especially in large scale sensor networks. In addition, it is straightforward to incorporate the proposed soft combining strategy with other Bayesian filters for the general purpose of data fusion.

Centralized Cooperative Sensor Fusion for Dynamic Sensor Network With Limited Field-of-View via Labeled Multi-Bernoulli Filter

This paper presents a new solution for multi-target tracking over a network of sensors with limited spatial coverage. The proposed solution is based on the centralized data fusion architecture. The main contribution of the paper is the introduction of a new track-to-track fusion approach in which the posterior distributions of multi-target states, reported by various sensor nodes, are fused in a way that the redundant information are combined and the rest complement each other. The proposed solution is formulated within the labeled random finite set framework in which the fused posterior incorporates all the state and label information provided by multiple sensor nodes. The performance of the proposed method is evaluated via simulation experiments that involve challenging tracking scenarios. The proposed method is implemented using sequential Monte Carlo method and the results confirm its effectiveness.

Working Principle and Performance of a Scalable Gravimetric System for the Monitoring of Access to Public Places.

Here, we propose a novel application of a low-cost robust gravimetric system for public place access monitoring purposes. The proposed solution is intended to be exploited in a multi-sensor scenario, where heterogeneous information, coming from different sources (e.g., metal detectors and surveillance cameras), are collected in a central data fusion unit to obtain a more detailed and accurate evaluation of notable events. Specifically, the word “notable” refers essentially to two event categories: the first category is represented by irregular events, corresponding typically to multiple people passing together through a security gate; the second category includes some event subsets, whose notification can be interesting for assistance provision (in the case of people with disabilities), or for statistical analysis. The employed gravimetric sensor, compared to other devices existing in the literature, exhibits a simple scalable robust structure, made up of an array of rigid steel plates, each laid on four load cells. We developed a tailored hardware and software to individually acquire the load cell signals, and to post-process the data to formulate a classification of the notable events. The results are encouraging, showing a remarkable detectability of irregularities (95.3% of all the test cases) and a satisfactory identification of the other event types.

Variational Channel Estimation with Tempering: An Artificial Intelligence Algorithm for Wireless Intelligent Networks.

With the rapid development of wireless sensor networks (WSNs) technology, a growing number of applications and services need to acquire the states of channels or sensors, especially in order to use these states for monitoring, object tracking, motion detection, etc. A critical issue in WSNs is the ability to estimate the source parameters from the readings of a distributed sensor network. Although there are several studies on channel estimation (CE) algorithms, existing algorithms are all flawed with their high complexity, inability to scale, inability to ensure the convergence to a local optimum, low speed of convergence, etc. In this work, we turn to variational inference (VI) with tempering to solve the channel estimation problem due to its ability to reduce complexity, ability to generalize and scale, and guarantee of local optimum. To the best of our knowledge we are the first to use VI with tempering for advanced channel estimation. The parameters that we consider in the channel estimation problem include pilot signal and channel coefficients, assuming there is orthogonal access between different sensors (or users) and the data fusion center (or receiving center). By formulating the channel estimation problem into a probabilistic graphical model, the proposed Channel Estimation Variational Tempering Inference (CEVTI) approach can estimate the channel coefficient and the transmitted signal in a low-complexity manner while guaranteeing convergence. CEVTI can find out the optimal hyper-parameters of channels with fast convergence rate, and can be applied to the case of code division multiple access (CDMA) and uplink massive multi-input-multi-output (MIMO) easily. Simulations show that CEVTI has higher accuracy than state-of-the-art algorithms under different noise variance and signal-to-noise ratio. Furthermore, the results show that the more parameters are considered in each iteration, the faster the convergence rate and the lower the non-degenerate bit error rate with CEVTI. Analysis shows that CEVTI has satisfying computational complexity, and guarantees a better local optimum. Therefore, the main contribution of the paper is the development of a new efficient, simple and reliable algorithm for channel estimation in WSNs.

Widely Distributed Photonics-Based Dual-Band MIMO Radar for Harbour Surveillance

A new architecture for a widely distributed dual-band coherent multiple-input multiple-output (MIMO) radar system is illustrated, and its implementation and testing are reported. The system consists in a central unit where radar signals are coherently generated and detected, which serves multiple remote sensors connected over transparent WDM optical network. Every remote node operates coherently both in the S- and X-band, and is displaced over distances of several kilometers, allowing to monitor a scene under different angles of view. All the remote sensors share the same oscillator and digital signal processing unit, both located in the central office, allowing to perform centralized raw data fusion on the acquired signals. By virtue of the system coherence, the system takes advantage of the coherent MIMO processing strategy to offer a superior spatial resolution, which is even magnified by the dual-band approach.

From Simultaneous Localization and Mapping to Collaborative Localization for Intelligent Vehicles

This paper describes a new method for real-time collaborative localization for intelligent vehicles based on a combination of an extension of simultaneous localization and mapping and track-to-track fusion methods. This strategy aims to overcome the shortcomings of the embedded proprioceptive sensors and solve the localization issues in a cluster of intelligent vehicles, which are equipped with low-cost navigation systems (global position systems, rangefinders, and vehicle to vehicle communication means) in an unknown environment. The strategy improves the vehicles’ localization accuracy and tracks a cluster of vehicles for advanced driver assistance systems and vehicles self-driving. For real-time purposes, the computational complexity of standard solutions, which are based on Kalman filter derivatives, is transformed, herein, from cubic to quadratic with respect to the state vector of the cluster. Furthermore, the correlated data is another challenge that needs to be solved in cooperative localization scenarios. To this end, two independent, local and global, fusion nodes are associated with each vehicle. The local node exploits multiple forms of the extended Kalman filter and its dual extended information filter to estimate the vehicles’ state, information vectors, covariance and information matrices in the moments and information forms, respectively. Then, the vehicles broadcast the result of their respective local fusion node to cluster. The information is used to solve the second problem, which is a track-to-track fusion on distributed architectures, with two methods: covariance intersection and information matrix fusion in the global fusion node. The simulation and the experimental results of the proposed methods, on distributed architectures, are compared to the optimal solution of centralized data fusion, and to the traditional SLAM method in terms of root mean square errors and consistency.

Coherent MIMO radar network enabled by photonics with unprecedented resolution.

The conventional concept of radar is based on stand-alone and independent apparatuses. Superior performance is possible, exploiting distributed points of view (i.e., distributed radars) and centralized data fusion, but systems based only on radio-frequency technology are not able to guarantee the requested degree of coherence and high capacity links among radars. In the current distributed systems, radars act almost independently from each other. Thus, data fusion, which must be performed on locally pre-processed information, can only exploit partial information content, harming the imaging capability of the distributed system. Here we present, to the best of our knowledge, the first extended analysis and experiment of a distributed coherent multiple input-multiple output radar system, enabled by photonics, which maximizes the information content extracted by a centralized data fusion, providing unprecedented resolution capabilities. Stepping from previous achievements, where photonics has been demonstrated in single radars, here photonics is used also for providing coherence and high capacity links among radars. The numerical analysis also demonstrated the benefits of coherent multi-band operation for sidelobe reduction, i.e., false alarms reduction.