Kenshi Saho

Bio: Kenshi Saho is an academic researcher from Toyama Prefectural University. The author has contributed to research in topics: Doppler radar & Radar. The author has an hindex of 9, co-authored 56 publications receiving 296 citations. Previous affiliations of Kenshi Saho include Ritsumeikan University & Panasonic.

Kalman Filter for Moving Object Tracking: Performance Analysis and Filter Design

Abstract: This chapter presents Kalman filters for tracking moving objects and their efficient design strategy based on steady-state performance analysis. First, a dynamic/measurement model is defined for the tracking systems, assuming both position-only and position-velocity measurements. Then, problems with the Kalman filter design in tracking systems are summarized, and an efficient steady-state performance index proposed by the author [termed the root-mean-squared error index (the RMS index)] is introduced to resolve these concerns. The analytical relationship between the proposed RMS index and the covariance matrix of the process noise is shown, leading to a proposed design strategy that is based on this relationship. Theoretical performance analysis is conducted using the performance indices to show the optimality of the design strategy. Numerical simulations show the validity of the theoretical analyses and effectiveness of the proposed strategy in realistic situations. In addition, the optimal performance of the position-only-measured and position-velocity-measured systems is analyzed and compared. This comparison shows that the position-velocity-measured Kalman filter tracking is accurate when compared with the position-only-measured filter.

Using Micro-Doppler Radar to Measure Gait Features Associated With Cognitive Functions in Elderly Adults

Abstract: This paper used micro-Doppler radar (MDR) measurements to investigate the significance of associations between cognitive functions and gait features of elderly persons. The aim of this paper was to develop a system that would enable the risks of developing dementia and related diseases to be monitored remotely on a daily basis. Study participants were adults aged 75 years and older. Gait velocity parameters corresponding to the walking speed and leg and foot velocities were remotely extracted via a simple 24-GHz MDR system in real time. The relationships between the extracted gait velocity parameters and the global cognition and cognitive functions in various cognitive domains (processing speed, memory, executive function, and language domains) that were assessed by conventional paper- and question-based tests were statistically analyzed. Our results revealed that, apart from the walking speed, which was mainly considered in a previous study, other parameters reflecting the leg and foot velocities are effective for the detection and classification of elderly participants with lower cognitive functions in the various cognitive domains. In particular, the statistical significance of the association of the leg velocity in the swing phase with the results of all the cognitive function tests is larger than that of the walking speed. Another important finding is that different gait velocity parameters are associated with each cognitive domain and this means that the MDR-based gait measurement can be used to determine which cognitive domain has deteriorated.

Gait Classification of Young Adults, Elderly Non-Fallers, and Elderly Fallers Using Micro-Doppler Radar Signals: Simulation Study

Abstract: This letter presents a gait classification technique for the identification of individuals with different gait patterns using simulated micro-Doppler radar remote sensing data. Proposed feature parameters for the classification are principal components of velocities extracted via micro-Doppler radar signals generated using motion capture-based kinematic data. Distinct differences were found in the proposed parameters among three groups of subjects with different gait patterns: healthy young and elderly adults, and elderly adults with a history of falls (elderly fallers).

Radar imaging apparatus, imaging method, and program thereof

Abstract: A radar imaging apparatus includes: (i) a delay code generation unit which repeats, for M scan periods, scan processing of generating, using a transmission code, N delay codes in a scan period for scanning N range gates having mutually different distances from the radar imaging apparatus; (ii) a signal storage unit which stores, in association with a range gate and a scan period, a baseband signal; (iii) a memory control unit which repeatedly writes, in the signal storage unit, for the M scan periods, N demodulated signals corresponding to a single scan period, and reads out a group of M demodulated signals corresponding to mutually different scan periods; (iv) a Doppler frequency discrimination unit which performs frequency analysis on demodulated signals having the same range gate; and (v) a direction of arrival calculation unit which estimates a direction of a target

Three-Dimensional Imaging Method Incorporating Range Points Migration and Doppler Velocity Estimation for UWB Millimeter-Wave Radar

Abstract: High-resolution, short-range sensors that can be applied in optically challenging environments (e.g., in the presence of clouds, fog, and/or dark smog) are in high demand. Ultrawideband (UWB) millimeter-wave radars are one of the most promising devices for the above-mentioned applications. For target recognition using sensors, it is necessary to convert observational data into full 3-D images with both time efficiency and high accuracy. For such conversion algorithm, we have already proposed the range points migration (RPM) method. However, in the existence of multiple separated objects, this method suffers from inaccuracy and high computational cost due to dealing with many observed RPs. To address this issue, this letter introduces Doppler-based RPs clustering into the RPM method. The results from numerical simulations, assuming 140-GHz band millimeter radars, show that the addition of Doppler velocity into the RPM method results in more accurate 3-D images with reducing computational costs.

Digital Video And Hdtv Algorithms And Interfaces

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Separation of Overlapped Non-Stationary Signals by Ridge Path Regrouping and Intrinsic Chirp Component Decomposition

Abstract: In some applications, it is necessary to analyze multi-component non-stationary signals whose components severely overlap in the time-frequency (T-F) domain. Separating those signal components is desired but very challenging for existing methods. To address this issue, we propose a novel non-parametric algorithm called ridge path regrouping (RPRG) to extract the instantaneous frequencies (IFs) of the overlapped components from a T-F representation (TFR). The RPRG first detects the ridges of a multi-component signal from a TFR and then extracts the desired IFs by regrouping the ridge curves according to their variation rates at the intersections. After the IFs are obtained, component separation is achieved by using the intrinsic chirp component decomposition (ICCD) method. Different from traditional T-F filter-based methods, the ICCD can accurately reconstruct overlapped components by using a joint-estimation scheme. Finally, applications of separating some simulated and experimental micro-Doppler signals are presented to show the effectiveness of the method.

Scene Understanding With Automotive Radar

Abstract: Extracting semantic information solely from automotive radar data is a relatively new topic in the radar community. We present a complete pipeline to obtain semantic information for each target measured by a network of radar sensors. Static and dynamic objects are treated in two separate branches: In the first branch, a convolutional neural network performs semantic segmentation on radar grid maps of the static environment. In the second branch, a novel neural network architecture is used for recurrent instance segmentation on radar point clouds of moving objects. The class probabilities assigned to each cell in the grid map are mapped back to the radar targets in this spatial region so that in a merging step the results from the two classifiers can be combined into one point cloud. In addition to a novel network structure for recurrent instance segmentation of point clouds, we present a new set of layers for radar grid maps which are beneficial for semantic segmentation tasks and we also develop a weighting scheme for the network's loss function to account for the data integration process in grid maps. We evaluate our approaches on large data sets and we display that they outperform previously proposed methods.

DNN Transfer Learning From Diversified Micro-Doppler for Motion Classification

Abstract: Recently, deep neural networks (DNNs) have been the subject of intense research for the classification of radio frequency signals, such as synthetic aperture radar imagery or micro-Doppler signatures. However, a fundamental challenge is the typically small amount of data available due to the high costs and resources required for measurements. Small datasets limit the depth of DNNs implementable, and limit performance. In this work, a novel method for generating diversified radar micro-Doppler signatures using Kinect-based motion capture simulations is proposed as a training database for transfer learning with DNNs. In particular, it is shown that together with residual learning, the proposed DivNet approach allows for the construction of DNNs and offers improved performance in comparison to transfer learning from optical imagery. Furthermore, it is shown that initializing the network using diversified synthetic micro-Doppler signatures enables not only robust performance for previously unseen target profiles, but also class generalization. Results are presented for 7-class and 11-class human activity recognition scenarios using a 4-GHz continuous wave software-defined radar.

Sensor Radar for Object Tracking

Abstract: Precise localization and tracking of moving objects is of great interest for a variety of emerging applications including the Internet-of-Things (IoT). The localization and tracking tasks are challenging in harsh wireless environments, such as indoor ones, especially when objects are not equipped with dedicated tags (noncollaborative). The problem of detecting, localizing, and tracking noncollaborative objects within a limited area has often been undertaken by exploiting a network of radio sensors, scanning the zone of interest through wideband radio signals to create a radio image of the objects. This paper presents a sensor network for radio imaging (sensor radar) along with all of the signal processing steps necessary to achieve highaccuracy objects tracking in harsh propagation environments. The described sensor radar is based on the impulse radio (IR) ultrawideband (UWB) technology, entailing the transmission of very short duration pulses. Experimental results with actual UWB signals in indoor environments confirm the sensor radar’s potential in IoT applications.