Accurate speed and range estimation is critical in modern radar and sonar systems operating under challenging conditions such as high target velocities, low Signal-to-Noise Ratios (SNRs), and Doppler-induced distortions. Traditional linear frequency-modulated (LFM) signals suffer from range-Doppler coupling and reduced correlation performance when subjected to substantial Doppler shifts. This paper presents a comprehensive analytical and simulation-based study of Dual Hyperbolic Frequency-Modulated (DHFM) signals, which combine upward and downward frequency sweeps to enable simultaneous and decoupled estimation of target speed and range. We derive closed-form expressions for the matched filter response of DHFM waveforms under Doppler scaling and develop explicit formulas for speed and range extraction from the time separation of correlation peaks. Through extensive software simulations of an airborne sonar system, we systematically evaluate the impact of three key parameters: the sweep rate ( k ), Signal-to-Noise Ratio (SNR), and propagation-induced attenuation. Our results demonstrate that speed estimation errors drop below 0.05 m/s for k > 5000 across target speeds of up to 100 m/s, while range estimation remains below 0.2 m accuracy even at SNR levels as low as -15 dB. Distance attenuation analysis under ISO 9613-1 atmospheric absorption models shows that both estimators remain robust up to 200 m range with moderate k values. The findings establish DHFM signals as a superior alternative to conventional LFM waveforms for applications requiring high-accuracy parameter estimation in Doppler-sensitive environments, with particular relevance to airborne and underwater sonar systems.

This work establishes the theoretical foundations to optimally design Dual Linear Frequency Modulated (DLMF) Signals intended to measure range and speed. Expressions are derived to obtain the optimal DLFM signal bandwidth and sampling frequency to perform accurate estimations with a minimum computational load, for a given maximum signal frequency and expected speed. In addition, a software simulator has been developed to validate the theoretical predictions by recreating the conditions of emission and reception of DLFM signals with different bandwidths, subject to the Doppler effect at different speeds. Simulations are performed for speeds between 1 and 5 m/s and signal bandwidths ranging from 100 Hz to 2000 Hz. These results show that the relative errors obtained for range and speed are below 2% for the optimal bandwidth and sampling frequency values predicted in this work.

Fingerprinting using Bluetooth Low Energy (BLE) has emerged as a promising positioning approach due to its technological flexibility and lack of limitations. However, Received Signal Strength (RSS) measurements in BLE exhibit higher variability partly attributed to the protocol’s utilization of different frequency channels within its range. In this study, we leverage frequency information to enhance positioning accuracy. We compare various methods adapted from the existing literature and propose novel approaches. Two distinct BLE RSS databases are employed, including one specifically developed for this research. The results demonstrate that utilizing frequency information can effectively improve positioning accuracy by 10% to 20%, regardless of the selected fingerprinting algorithm. However, the optimal combination method depends on the specific environmental conditions.

It is undeniable that more and more tasks in which drones work autonomously are becoming essential. Of particular importance are indoor human-transited environments. These entail a challenge for drone flights as the safety of people must be ensured at all times while they are in the vicinity of flights. Millimetre-wave radar has proven to be a technology that provides accurate position and velocity measurements, making it ideal for monitoring spaces in search of moving targets. Thus, this work proposes a security system based on millimetre-wave radar, using a processing workflow based on machine learning techniques to detect humans and interrupt drone flights until people are in a safe place. The feasibility of the system is demonstrated experimentally, with accuracy, precision, recall and F1 score greater than 99% and a real-time system performance video.

Bluetooth Low Energy (BLE) fingerprinting has gained a lot of research effort in recent years due to flexibility in both beacons placement and configuration. Different works have addressed the effect of the configuration parameters, mainly the transmission power (Tx) and period (Ts), over positioning accuracy but not on the system lifespan and the trade-off between these two. In this work, different configurations of one, three and six slots have been tested over the same experimental setup. Positioning accuracy was obtained using different variations of the Weighted k-Nearest Neighbours (Wk-NN) algorithm, and the system lifespan was estimated using the actual current consumption and transmission mechanism for each configuration. Experimental results have shown that Tx and the number of slots can be adjusted to optimize this trade-off; meanwhile, changes in Ts worsen Wk-NN results more than in the other parameters, showing that the minimum Ts is always the best option.

In radio-frequency Indoor Positioning Systems, the fingerprinting technique, which involves a laborious database construction stage to achieve good positioning results, has become a standard solution. Several researchers have tried to find methods to reduce the number of reference points – which need to be manually collected – while maintaining the positioning results, but have focused on other technologies, such as XBee or WLAN, and more specifically Wi-Fi. In this work, a comparison between different fingerprint generation techniques is made in a Bluetooth Low-Energy (BLE) Indoor Positioning System using Received Signal Strength Indicator measurements. Various databases are collected using three Android phones. These data are employed to train different methods that will generate fingerprints to reconstruct the missing points in the databases. The methods used are Inverse Distance Weighting, Support Vector Regression, Gaussian Process Regression and Generative Adversarial Networks. After this comparison, it has been found out that all these techniques could reconstruct part of the missing points and improve the positioning results. However, Generative Adversarial Networks achieved the best reconstruction throughout all of the databases.

Fingerprint-based indoor positioning is widely used in many contexts, including pedestrian and autonomous vehicles navigation. Many approaches have used traditional Machine Learning models to deal with fingerprinting, being k-NN the most common used one. However, the reference data (or radio map) is generally limited, as data collection is a very demanding task, which degrades overall accuracy. In this work, we propose a novel approach to add random noise to the radio map which will be used in combination with an ensemble model. Instead of augmenting the radio map, we create n noisy versions of the same size, i.e. our proposed Indoor Positioning model will combine n estimations obtained by independent estimators built with the n noisy radio maps. The empirical results have shown that our proposed approach improves the baseline method results in around 10% on average.

Acoustic local positioning systems (ALPSs) are an interesting alternative for indoor positioning due to certain advantages over other approaches, including their relatively high accuracy, low cost, and room-level signal propagation. Centimeter-level or fine-grained indoor positioning can be an asset for robot navigation, guiding a person to, for instance, a particular piece in a museum or to a specific product in a shop, targeted advertising, or augmented reality. In airborne system applications, acoustic positioning can be based on using opportunistic signals or sounds produced by the person or object to be located (e.g., noise from appliances or the speech from a speaker) or from encoded emission beacons (or anchors) specifically designed for this purpose. This work presents a review of the different challenges that designers of systems based on encoded emission beacons must address in order to achieve suitable performance. At low-level processing, the waveform design (coding and modulation) and the processing of the received signal are key factors to address such drawbacks as multipath propagation, multiple-access interference, near-far effect, or Doppler shifting. With regards to high-level system design, the issues to be addressed are related to the distribution of beacons, ease of deployment, and calibration and positioning algorithms, including the possible fusion of information obtained from maps and onboard sensors. Apart from theoretical discussions, this work also includes the description of an ALPS that was implemented, installed in a large area and tested for mobile robot navigation. In addition to practical interest for real applications, airborne ALPSs can also be used as an excellent platform to test complex algorithms (taking advantage of the low sampling frequency required), which can be subsequently adapted for other positioning systems, such as underwater acoustic systems or ultrawideband radio-frequency (UWB RF) systems.

This paper proposes a Doppler resilient modulation approach that can be used in acoustic local positioning systems with Binary Phase-Shift Keying (BPSK) modulated signals for Time-of-Flight (ToF) estimation. The proposed approach is implemented in the receiver and can be performed in parallel with other classic techniques to improve the position estimation process. The technique is based on Differential Binary Phase-Shift Keying (DBPSK) modulation instead of the widely used BPSK modulation and presents a low computational complexity. The performance of the proposed technique is studied for BPSK-modulated Kasami codes with 63 and 255 bit length. Results have shown that the proposed technique is not only robust to Doppler effect but also to noise, multipath and multiple-access interference.

This paper proposes an algorithm to design band-limited Orthogonal Frequency Division Multiplexing (OFDM) pulses for accurate time-of-flight ranging in acoustic-based localization systems. The proposed algorithm is capable to generate OFDM pulses with higher energy when compared with other common pulse design approaches with similar characteristics. The results attained have shown that it is possible to design OFDM pulses with 17% more energy than a chirp or a BPSK pulse with the same characteristics. The proposed algorithm uses an iterative approach to increase the pulse energy while keeping the same amplitude, bandwidth and duration in each iteration. Two different OFDM pulses are provided and compared with two common pulses, namely chirp and BPSK pulses, with the same characteristics.

In this work the performance of the Sucessive Interference Cancelation (SIC) architecture combined with Matching Pursuit (SIC/MP) and Least Squares (SIC/LS) algorithms is statistically analyzed when they are used to mitigate multipath effect in an Acoustic Local Positioning System (ALPS). The experiments are carried out with sets of two hundred real signals acquired in positions where there exists a strong multipath interference, showing that the proposed methods have a better performance than the classical correlation + thresholding-based detection method. Especially, in the SIC/MP method case, its corresponding Error Cummulative Distribution Function (ECDF) shows that practically 100% of the Time of Flight (TOF) measurements affected by multipath interference are recovered, obtaining positioning errors below 10 cm for 60% of the measurements.

This work benefits from the capability of the iPhone's or iPad's microphone to acquire high-frequency sound for accurate acoustic code identification. Although the maximum theoretical value for the frequency response of the built-in iOS device microphone is 20 kHz, emissions with frequencies close to 22 kHz have been experimentally detected. The frequencies used in this work are in the range from 18 to 22 kHz, which are high enough to be inaudible for almost every people but low enough to be generated by standard sound hardware. The aim of this work is to develop an inexpensive indoor positioning system where the user gets its location by using an iOS device's microphone. For this purpose a third-generation iPad is used for high-frequency sound data acquisition and neither external acquisition system nor ultrasonic microphone are required. The capability of iOS devices to acquire acoustic signals in the vicinity of 20 kHz has been successfully demonstrated. This fact allows the use of this kind of device for acoustic code detection and accurate positioning by means of multilateration.

Ultrasonic positioning systems presents performance problems when the object to be localized is in movement. The reasons are the degradation suffered by emitted sequences because of Doppler shifts, and the low level of orthogonality of the set of codes when they are being identified in different beacons. In this work, a search of a set of four 256-element polyphase sequences has been carried out, in order to improve the resilience to velocity possessed by other well-known codes, maintaining good orthogonality properties. As result, the obtained set can be used in an ultrasonic Local Positioning System (LPS), where the codes can be detected up to 1.5 m/s.

The need for monitoring underwater sensors, communications between submarines or sonar, makes underwater acoustic communications an important field of research. Electromagnetic waves are quickly attenuated in this medium and thus acoustic waves are the best option for communications. When two buoys are present, one acting as the emitter and the other as the receiver, it is crucial for the communication system to know the response of the channel. This paper presents a study of the underwater channel and proposes a model developed in Matlab where physical phenomena like attenuation, absorption and the swell effect are considered. This latter effect causes a Doppler spread in the signal, and a time-varying impulse response.

A method to locate reflectors, based on the spectral analysis of ultrasonic received echoes, is provided in this study. The main contribution of this work lies in the spectral analysis for bearing estimation. A single ultrasonic transducer, acting as emitter and receiver, is used. The transmitted signal is a 1023-bits Kasami code BPSK modulated by a carrier that ranges from 30 kHz to 55 kHz, implying a 6-component transmitted pulse. Since the ultrasonic transducer works as an angle-dependent filter, it is possible to estimate the bearing angle of the reflector based on the received spectrum. It is shown how the amplitude of each frequency contains information about the environment, particularly about the bearing angle of the reflector. Finally, a set of experimental results is included to validate the sensory system, showing how it can deal with realistic reflectors.

This work presents the performance analysis of an Ultrasonic Local Positioning System (ULPS). The system is composed of four beacons, forming a square structure in the ceiling of a rectangular room, that emit orthogonal Kasami codes BPSK modulated with a carrier frequency of 50 kHz. These emissions are detected by the receiver by pulse compression, giving the system high precision and robustness to noise. A complete model of the system has been built to conduct this study, considering effects such as the ultrasonic transducers response, signal attenuation in air, multipath propagation, reflection coefficient of walls and floor and receiver response. This model helps to identify critical zones where the self-induced noise generated in the cross-correlations masks the main peaks of the auto-correlations, making it difficult to obtain reliable Time-of-Flights from which the receiver's position is determined. Also, the dependence of these critical zones with different parameters integrated in the model is investigated.

On current railway systems, it is becoming ever more necessary to install safety elements to avoid accidents. One of the causes that can provoke serious accidents is the existence of obstacles on the tracks, either fixed or mobile. In this paper, a multisensory system that can inform the monitoring system about the existence of obstacles is proposed. The system for obstacle detection consists of two emitting and receiving barriers, which are placed on opposing sides of the railway, respectively, and use infrared and ultrasonic sensors, thus establishing different optical and acoustic links between them. The interruption of one or several links should produce an alarm. However, even without the existence of objects, degradation of links could occur due to atmospheric attenuation, solar radiation, etc., also producing an activation of the alarm system. Since detection is based on the lack of radiation in the detectors, the use of complementary sensors for the same task is justified. Since the minimum size of an object for which an alarm is required to be generated is 50 × 50 × 50 cm, in some situations, several links are interrupted; however, alarms should not be generated. Typical cases are the flight of leaves or the movement of small animals in the scanned area. To avoid alarm activation in such situations, this paper proposes the combined use of diverse techniques of data fusion, based on fuzzy logic and the Dempster-Shafer theory of evidence, to validate the existence of objects, providing a highly reliable detection system.

Loosely Synchronous (LS) codes improves the performance of Local Positioning Systems (LPS) in terms of noise immunity, capability of simultaneous measurements and precision in the location, provided that the multipath spread and the relative time offset between the codes are within the zero correlation zone (ZCZ) that they achieve in their correlation functions. Furthermore, these codes can be very efficiently generated and correlated if compared to a straightforward implementation. This work presents the hardware implementation of the correlation tasks associated to an ultrasonic LPS based on LS sequences generated from Complementary Sets of Sequences (CSS). This implementation is able to carry out all the correlation processing tasks in a time shorter than the sampling period.

This work proposes a data fusion technique to validate the detection of objects in railway environments. The system for obstacle detection is based on an infrared emitting barrier and another of receivers placed on opposing side in the railway, establishing different optical links between them. The interruption of one or several links in the sensorial system should produce an alarm. Since the detection is based on the radiation lack in the detectors, channel degradation can produce a similar situation as if there was one obstacle. Since the interruption of the links is the necessary condition, but not sufficient, for the existence of an object, this work proposes the fusion of the sensors information by means of the Dempster-Shafer evidential theory to obtain a certainty value of the existence of objects supposing a risk for the railway.

Signal coding and pulse compression techniques have been widely used in the design of high performance airborne sonar as a mean to increase their robustness to noise and enhance their spatial resolution. These systems are still today in continuous development, with the appearance of new encoding schemes and processing algorithms which not only improve the previous features, but also provide the system with additional properties. All these improvements are achieved at the expense of increasing the computational complexity of the signal processing tasks, what put into risk the real-time operation capability of the system. This work presents the hardware implementation in a single FPGA of all the stages forming the detection module of an advanced sonar system which is based on the emission of complementary sets of sequences. This implementation is able to carry out all the data processing in the time interval between two successive firings, also obtaining a portable module that makes possible its incorporation into mobile robots

This paper presents a reflector recognition and localization technique in three-dimensional (3-D) environments, using only times-of-flight (TOFs) data obtained from ultrasonic transducers. The recognition and localization technique is based on the principal component analysis applied to the TOF vectors originating from a sensor that contains two emitting transducers and several receivers. The two emitters simultaneously transmit two coded pulses that are detected later on and discriminated by the receivers, after being reflected in the environment. The proposed technique allows for the possibility of not only recognizing the reflectors, but also estimating approximately its localization referred to the sensor. This technique has been tested with three types of reflectors in 3-D environments: planes, edges, and corners. The achieved results are very satisfactory for reflectors located in the range 50-350 cm.

Recent sonar modules applied to autonomous robots usually consist of several transducers, associated geometrically and electronically to improve performances and results, compared to systems based on only one isolated transducer. Nevertheless, in order to take advantage of these associations, it is necessary to develop new algorithms to consider suitably all the information given by times-of-flight (TOFs) from transducers. This work describes a new method to analyze these TOFs, so they can be used to obtain a heuristic map of the environment. A new operation mode is also proposed to allow the system adaptation to different environmental conditions in run-time.