In this study, we propose an accurate range-Doppler analysis algorithm for moving multiple objects in a short range using microwave (including millimeter wave) radars. As a promising Doppler analysis for the above model, we previously proposed a weighted kernel density (WKD) estimator algorithm, which overcomes several disadvantages in coherent integration based methods, such as a trade-off between temporal and frequency resolutions. However, in handling multiple objects like human body, it is difficult to maintain the accuracy of the Doppler velocity estimation, because there are multiple responses from multiple parts of object, like human body, incurring inaccuracies in range or Doppler velocity estimation. To address this issue, we propose an iterative algorithm by exploiting an output of the WKD algorithm. Three-dimensional numerical analysis, assuming a human body model in motion, and experimental tests demonstrate that the proposed algorithm provides more accurate, high-resolution range-Doppler velocity profiles than the original WKD algorithm, without increasing computational complexity. Particularly, the simulation results show that the cumulative probabilities of range errors within 10mm, and Doppler velocity error within 0.1m/s are enhanced from 34% (by the former method) to 63% (by the proposed method).

Recently, Doppler radars have been used in various applications from the detection and the classification of indoor human activities to the detection of airplanes. To improve both the degrees of freedom (DOF) and the estimation accuracy of the direction-of-arrival (DOA) of targets, multiple-input multiple-output (MIMO) radar has received much attention in recent years. The temporal spatial virtual array based on Doppler shift of a moving target has been one of methods to improve DOA estimation accuracy. However, the DOA estimation accuracy based on the method depends on the velocity and the direction of the target on which we focus. Also, the temporal spatial virtual array should be generated based on the information of the single target. Thus, it is difficult to implement the method if there are multiple targets. In this paper, we propose a new method that provides high accuracy of DOA estimation by using the temporal spatial virtual array without dependence on the velocity, the direction and the number of existing targets. We demonstrate the DOA estimation accuracy and the effectiveness of the proposed method via simulations.

Microwave systems have a number of promising applications in surveillance and monitoring systems. The main advantage of microwave systems is their ability to detect targets at distance under adverse conditions such as dim, smoky, and humid environments. Specifically, the wide bandwidth of ultra-wideband radar enables high range resolution. In a previous study, we proposed an accurate shape estimation algorithm for multiple targets using multiple ultra-wideband Doppler interferometers. However, this algorithm produces false image artifacts under conditions with severe interference. The present paper proposes a technique to suppress such false images by detecting inconsistent combinations of the radial velocity and time derivative of image positions. We study the performance of the proposed method through numerical simulations of a two-dimensional section of a moving human body, and demonstrate the remarkable performance of the proposed method in suppressing false image artifacts in many scenarios.

This paper presents a method of imaging a two-dimensional section of a walking person using multiple Doppler radar systems. Although each simple radar system consists of only two receivers, different radial speeds allow target positions to be separated and located. The signal received using each antenna is processed employing time-frequency analysis, which separates targets in the time-range-velocity space. This process is followed by a direction-of-arrival estimation employing interferometry. The data obtained using the multiple radar systems are integrated using a clustering algorithm and a target-tracking algorithm. Through realistic simulations, we demonstrate the remarkable performance of the proposed imaging method in generating a clear outline image of a human target in unknown motion.

Recent studies have focused on developing security systems using micro-Doppler radars to detect human bodies. However, the resolution of these conventional methods is unsuitable for identifying bodies and moreover, most of these conventional methods were designed for a solitary or sufficiently well-spaced targets. This paper proposes a solution to these problems with an image separation method for two closely spaced pedestrian targets. The proposed method first develops an image of the targets using ultra-wide-band (UWB) Doppler imaging radar. Next, the targets in the image are separated using a supervised learning-based separation method trained on a data set extracted using a range profile. We experimentally evaluated the performance of the image separation using some representative supervised separation methods and selected the most appropriate method. Finally, we reject false points caused by target interference based on the separation result. The experiment, assuming two pedestrians with a body separation of 0.44m, shows that our method accurately separates their images using a UWB Doppler radar with a nominal down-range resolution of 0.3m. We describe applications using various target positions, establish the performance, and derive optimal settings for our method.

The classification of human motion is an important aspect of monitoring pedestrian traffic. This requires the development of advanced surveillance and monitoring systems. Methods to achieve this have been proposed using micro-Doppler radars. However, reliable long-term data and/or complicated procedures are needed to classify motion accurately with these conventional methods because their accuracy and real-time capabilities are invariably inadequate. This paper proposes an accurate and real-time method for classifying the movements of pedestrians using ultra wide-band (UWB) Doppler radar to overcome these problems. The classification of various movements is achieved by extracting feature parameters based on UWB Doppler radar images and their radial velocity distributions. Experiments were carried out assuming six types of pedestrian movements (pedestrians swinging both arms, swinging only one arm, swinging no arms, on crutches, pushing wheelchairs, and seated in wheelchairs). We found they could be classified using the proposed feature parameters and a k-nearest neighbor algorithm. A classification accuracy of 96% was achieved with a mean calculation time of 0.55s. Moreover, the classification accuracy was 99% using our proposed method for classifying three groups of pedestrian movements (normal walkers, those on crutches, and those in wheelchairs).

We propose a new fine Doppler frequency estimator using two fast Fourier transform (FFT) samples for pulse Doppler radar that offers highly sensitive detection and a high resolution of velocity. The procedure of fine Doppler frequency estimation is completed through coarse frequency estimation (CFE) and fine frequency estimation (FFE) steps. During the CFE step, the integer part of the Doppler frequency is obtained by processing the FFT, after which, during the FFE step, the fractional part is estimated using the relationship between the FFT peak and its nearest resultant value. Our simulation results show that the proposed estimator has better accuracy than Candan's estimator in terms of bias. The root mean square error (RMSE) of the proposed estimator has more than 1.4 time better accuracy than Candan's estimator under a 1,024-point FFT and a signal-to-noise ratio (SNR) of 10 dB. In addition, when the FFT size is increased from 512 to 2,048, the RMSE characteristics of the proposed estimator improve by more than two-fold.

The imaging of humans using radar is promising for surveillance systems. Although conventional radar systems detect the presence or position of intruders, it is difficult to acquire shape and motion details because the resolution is insufficient. This paper presents a high-resolution human imaging algorithm for an ultra-wideband (UWB) Doppler radar. The proposed algorithm estimates three-dimensional human images using interferometry and, using velocity information, rejects false images created by the interference of body parts. Experiments verify that our proposed algorithm achieves adequate pedestrian imaging. In addition, accurate shape and motion parameters are extracted from the estimated images.

This letter proposes a novel technique for detecting a target signal buried in clutter using principal component analysis (PCA) for pulse-Doppler radar systems. The conventional detection algorithm is based on the fast Fourier transform-constant false alarm rate (FFT-CFAR) approaches. However, the detection task becomes extremely difficult when the Doppler spectrum of the target is completely buried in the spectrum of clutter. To enhance the detection probability in the above situations, the proposed method employs the PCA algorithm, which decomposes the target and clutter signals into uncorrelated components. The performances of the proposed method and the conventional FFT-CFAR based detection method are evaluated in terms of the receiver operating characteristics (ROC) for various signal-to-clutter ratio (SCR) cases. The results of numerical simulations show that the proposed method significantly enhances the detection probability compared with that obtained using the conventional FFT-CFAR method, especially for lower SCR situations.

This study demonstrates the ability of a portable X-band Doppler weather radar (XDR) to measure Doppler velocity (Vd). Existing portable X-band weather radars are housed in a container and hence have to be carried by a truck. Therefore they have limitations in their installation places. For installations at small areas where the existing X-band weather radars cannot be installed (e.g., rooftop area of small building), XDR is designed to be carried by a cart. Components of the outdoor unit (a parabolic antenna with a diameter of 1.2 m, magnetron transmitter, and radio frequency (RF) and intermediate frequency (IF) analog components) are housed in a compact body with a weight less than 300 kg. The radar operation, IF digital processing, and data storage are carried out by a desktop computer having a commercial IF digital receiver. In order to attain the required portability and reduced purchase and running costs, XDR uses a magnetron transmitter. Because XDR is the first that utilizes an IF digital receiver for the signal processing specific to magnetron transmitters (i.e., the phase correction of received signals due to the randomness of the transmitted pulse phase), Vd measured by XDR (hereafter VdXDR) was assessed. Using the dataset collected from 25 to 26 October 2009 at the Shigaraki MU Observatory (3451'N, 13606'E), the equivalent radar reflectivity factor (Ze) and VdXDR were assessed using Ze and Vd measured by a Micro Rain Radar and a L-band Doppler radar named LQ-7. The results using correlation coefficients and regression lines demonstrate that XDR measured Ze and Vd accurately. The results also show that IF digital receivers are useful for providing magnetron weather radars with the function of Vd measurement, and further suggest that a combination of IF digital receiver and magnetron transmitter contributes to future development of Doppler weather radars, because high cost performance is strongly required for a precipitation monitoring radar network.

In this paper, we propose a memory-efficient structure for a pulse Doppler radar in order to reduce the hardware's complexity. The conventional pulse Doppler radar is computed by fast frequency transform (FFT) of all range cells in order to extract the velocity of targets. We observed that this method requires a huge amount of memory to perform the FFT processes for all of the range cells. Therefore, instead of detecting the velocity of all range cells, the proposed architecture extracts the velocity of the targets by using the cells related to the moving targets. According to our simulations and experiments, the detection performance of this proposed architecture is 93.5%, and the proposed structure can reduce the hardware's complexity by up to 66.2% compared with the conventional structure.

Recently, it has become important to rapidly detect human subjects buried under collapsed houses, rubble and soil due to earthquakes and avalanches to reduce the casualties in a disaster. Such detection systems have already been developed as one kind of microwave displacement sensors that are based on phase difference generated by the motion of the subject's breast. Because almost all the systems consist of single transmitter and receiver pair, it is difficult to rapidly scan a wide area. In this paper, we propose a single-frequency multistatic radar system to detect breathing human subjects which exist in the area surrounded by the transmitting and receiving array. The vibrating targets can be localized by the MUSIC algorithm with the complex amplitude in the Doppler frequency. This algorithm is validated by the simulated signals synthesized with a rigorous solution of a dielectric spherical target model. We show experimental 3D localization results using a developed multistatic Doppler radar system around 250 MHz.

Subcircuits designed for integrated silicon DCS1800/ PCS1900 base station receivers have been reconfigured into hybrid and single-chip Doppler radar transceivers. Radar chips have been fully integrated in 0.25 µm silicon CMOS and BiCMOS processes. These chips have been used to monitor heart and respiration activity without contact, and they have successfully detected heartbeat and respiration rate up to 1 m from the subject. This monitoring device may be useful in home monitoring, continuous monitoring, and physiological research.

A 5.3-GHz klystron has been recently designed and fabricated. In many countries, the transmitting frequency of 5.6 GHz (5,600 to 5,650 MHz) is commonly used for C-band meteorological radars. However, 5.3 GHz is generally used in Japan. To detect low-level wind shears by a Doppler radar, it is essential to use a MOPA (Master Oscillator and Power Amplifier) that generates stable coherent microwaves. The klystron is most suitable for this purpose. However, there are no commercially available klystrons in C-band that operate at 5.3 GHz. We developed a klystron for this band, making use of a simulation technique originally devised for S- and X-bands. The klystron operates at frequencies between 5,250 and 5,350 MHz. The typical operating parameters are a peak output power of 200 kW, a pulse width of 1 µs, and an RF duty cycle of 0.002. The klystron, including the electromagnet for focusing the magnetic field, is approximately 67 cm long with a diameter of 40 cm and a weight of 162 kg. Phase modulation is suppressed below 20% of the phase change required for the minimum resolution of Doppler velocity measurement by the radar for which this klystron is employed. The klystron shows favorable performance for Doppler radars operated in major airports in Japan.

A stabilized local oscillator is one of the key components for any radar system, especially for a Doppler radar in detecting slowly moving targets. Based on hybrid semiconductor/superconductor circuitry, the HTS local oscillator produces stable, low noise performance superior to that achieved with conventional technology. The device combines a high Q HTS sapphire cavity resonator (f=5.6 GHz) with a C-band low noise GsAs HEMT amplifier. The phase noise of the oscillator, measured by a HP 3048A noise measurement system, is -134 dBc/Hz at 10 kHz offset at 77 K.

A multi-beam airborne pulsed-Doppler radar (MBR) system is presented and its clutter rejection performance compared with conventional phased array radar (PAR)'s by PRF tuning is discussed. The pulsed-Doppler radar equations taking account of the multi-beam operation are introduced and some kinds of computer simulations for seeking the conditions to get maximum signal to clutter ratio are carried out. As a results of this, it is cleared that same order of signal to clutter ratio improvement gotten in high PRF operation by conventional PAR can be realized at lower PRF operation by MBR on clutter free area, and higher clutter rejection effect, which is proportional to beam numbers, is obtained under affection of both of mainlobe and sidelobe clutters with order of beam numbers. This also means observable numbers of range bin are increased in MBR operation.